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MicroPOTS Analysis of Barrett’s Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation StressClick to copy article linkArticle link copied!
- Kenneth WekeKenneth WekeUniversity of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, PolandMore by Kenneth Weke
- Ashita SinghAshita SinghInstitute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, U.K.Research Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, 656 53 Brno, Czech RepublicMore by Ashita Singh
- Naomi UwugiarenNaomi UwugiarenUniversity of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, PolandMore by Naomi Uwugiaren
- Javier A. AlfaroJavier A. AlfaroUniversity of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, PolandInstitute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, U.K.More by Javier A. Alfaro
- Tongjie WangTongjie WangInstitute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, U.K.More by Tongjie Wang
- Ted R. HuppTed R. HuppUniversity of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, PolandInstitute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, U.K.More by Ted R. Hupp
- J. Robert O’NeillJ. Robert O’NeillInstitute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland EH4 2XR, U.K.Cambridge Oesophagogastric Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, U.K.More by J. Robert O’Neill
- Borek VojtesekBorek VojtesekResearch Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, 656 53 Brno, Czech RepublicMore by Borek Vojtesek
- David R. Goodlett*David R. Goodlett*Email: [email protected]University of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, PolandUniversity of Victoria − Genome British Columbia Proteomics Centre, Victoria, BC V8Z 7X8, CanadaDepartment of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8P 5C2, CanadaMore by David R. Goodlett
- Sarah M. WilliamsSarah M. WilliamsEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Sarah M. Williams
- Mowei ZhouMowei ZhouEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Mowei Zhou
- Ryan T. KellyRyan T. KellyEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Ryan T. Kelly
- Ying ZhuYing ZhuEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Ying Zhu
- Irena Dapic*Irena Dapic*Email: [email protected]University of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, PolandMore by Irena Dapic
Abstract
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Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett’s esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson’s correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett’s cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.
Copyright © 2021 The Authors. Published by American Chemical Society
1. Introduction
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Mass spectrometry (MS) has emerged as the most powerful technology for analysis and discovery of proteins. (1) Since the term proteome was coined in 1994, (2) researchers have used MS to comprehensively define the molecular mechanisms that underpin cellular functions. To achieve this goal, there are two main approaches to proteomics: top-down and bottom-up, with the latter being more often applied today. (3,4) Significant gains have been made by applying these approaches in large-scale studies (5) to fully profile protein expression and their post-translational modifications. However, standard proteomic analysis demands substantial amounts of starting material to exhaustively characterize a proteome. For instance, about 105 to millions of cells have typically been used to achieve a high proteome coverage. (6) Historically, utilization of such large amounts of starting material often precluded the ability for proteomics to compete with genomics in the analysis of small numbers of cells. This is because genomics allows sample material to be amplified through polymerase chain reaction (PCR). (7) Proteomics on the other hand has had to pay special attention to sample preparation of small numbers of cells in order to avoid adsorptive losses of low abundance proteins. Therefore, this limitation has hampered the application of proteomics to study samples of limited availability such as human tissues from, e.g., biopsies.
Against the backdrop of the shortcomings of traditional macroscale sample preparation, mostly inherited from the field of protein chemistry, methods for working with limited numbers of cells have recently been reported, including laser capture microdissection, immobilized enzyme reactors, fluorescence-activated cell sorting (FACS), and microfluidics formats. (8−14) For example, using laser capture microdissection, Clair et al. (10) identified >3400 proteins from 4000 cells. However, none of these recent developments allowing characterization of proteomes from fewer than 1000 cells by MS-based proteomics can yet be said to be the method of choice for exploring proteomes. Thus, there is burgeoning interest in the community to develop and optimize highly sensitive and specific microscale proteomic workflows to interrogate protein changes in both health and disease. Consequently, reports of micro- and nanoscale MS-based proteomics have dramatically increased in number lately because they facilitate new opportunities to explore trace levels of samples previously out of reach to researchers. (15−18) For instance, an ultrasensitive nanoscale method, which used gold nanoparticles, identified 650 proteins from a proteomic analysis of 80 cells with a detection limit of proteins reaching 50 zmol. (19) Additionally, many integrated proteome methods optimized for single-cell analysis are increasingly becoming commonplace. (20,21) Some newly assembled proteome analysis devices have reported high numbers of identified proteins, e.g., 328 proteins identified from analysis of 10 single HeLa cells, and with a detection limit approximated to be between 1.7–170 zmol. (22)
One new microfluidics-based platform termed nanodroplet processing in one pot for trace samples (nanoPOTS) that was developed recently has demonstrated remarkable results from the proteomic analysis of small samples. (23) By applying a bottom-up proteomic approach, nanoPOTS proved to be capable of processing samples in nanowells with volumes of less than 200 nL. (23) This method was applied in the analysis of about 10 to 140 cells, and over 1500 proteins were confidently identified. Recently, nanoPOTS was also integrated with a top-down proteomic workflow, and ∼170 to ∼620 proteoforms from ∼70 to ∼770 HeLa cells were quantitatively identified with high confidence. (24) An adaptation of nanoPOTS that utilizes conventional micropipettes and operates in low-microliter range called microdroplet processing in one pot for trace samples (microPOTS) has also been developed to address a few bottlenecks such as the demands for nanoliter pipetting platform and highly skilled personnel to run nanoPOTS. Initially, microPOTS was applied to ∼25 cultured HeLa cells and 50 μm square mouse liver tissue thin sections, and about 1800 and 1200 unique proteins were generated from HeLa cells and mouse liver, respectively. (25) Additionally, high reproducibility was reported based on pairwise Pearson’s correlation coefficient values of 0.96–0.98, and with median CVs of ≤12.4% from the results of the previously mentioned analysis.
In this study, we applied the microPOTS to characterize proteomes of ∼200 cells used as a Barrett’s esophagus cell model following various perturbations. Barrett’s esophagus is a premalignant condition thought to arise in the lower esophagus due to chronic reflux of gastric acid and bile leading to genotoxic stress and mutation of the gatekeeper genes TP53 and SMAD4. (26) Barrett’s confers an approximately 100-fold greater risk of development of esophageal adenocarcinoma and understanding the molecular changes during carcinogenesis may be useful to guide preventative therapy. (27) It is known that gut bacteria modify bile acids derived from cholic acid and chenodeoxycholic acid to deoxycholic acid and lithocholic acid (LCA). (28) In turn, deoxycholic acid and lithocholic acid are conjugated to yield a variety of conjugated bile salts that can exist in wide-ranging concentrations in patients being monitored for the acid-reflux disease. It is not known whether there is a specific role for bile acids in the selection for specific genetic mutations in esophageal adenocarcinoma progression. Recent work has demonstrated a novel sponge-device (Cytosponge) can sample small numbers of surface cell populations from the esophagus without endoscopy and can determine the presence of Barrett’s esophagus. (29) This device can also be used to triage patients with Barrett’s for more intensive endoscopic surveillance according to the presence of markers of progression to esophageal adenocarcinoma. (30) As these devices become integrated into clinical practice the molecular changes during the progression from Barrett’s to esophageal adenocarcinoma need to be identified from the small numbers of cells retrieved during sampling. Identifying protein markers of progression that can be tested by immunohistochemistry will aid in improving the sensitivity and translation of this technology. The findings of this study reveal that microPOTS allowed for the identification of >1500 proteins from fewer than 200 cells, and radiologic and physiologic stress induce proteomic changes in cell models.
2. Experimental Section
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2.1. Materials
MicroPOTS chips were fabricated in-house as described previously. (25) The microwell chips were designed with a diameter of 2.2 mm and a well-to-well spacing of 4.5 mm. LC-MS grade water and acetonitrile, formic acid (FA), iodoacetamide (IAA), and dithiothreitol (DTT) were purchased from Thermo Fisher Scientific (Waltham, MA). N-Dodecyl β-d-maltose (DDM) was a product of Sigma-Aldrich (St. Louis MO). Both Lys-C and trypsin were purchased from Promega (Madison, WI).
2.2. Cell Culture
CP-A cells were cultured in keratinocyte media (ThermoFisher) supplemented with human recombinant epidermal growth factor (rEGF), bovine pituitary extract (BPE), 1% penicillin/streptomycin (Invitrogen) and incubated at 37 °C with 5% CO2. All other chemicals and reagents were obtained from Sigma unless otherwise mentioned. The guide RNAs targeting the p53 and smad4 genes to generate isogenic gene knockout cells are described in a separate manuscript. Briefly, the guide RNAs were either cloned into lentiCRISPRv2 transfer plasmid or procured as custom synthetic crRNAs from Integrated DNA Technologies (IDT), USA; tracrRNA was also manufactured by IDT. Cells were either transfected using attractene transfection reagent (Qiagen) or electroporated using Nucleofector Kit V (Amaxa, Lonza). After the electroporation, cells were transferred into a 6-well plate and allowed to recover for 3–5 days. The bulk population were single-cell isolated using flow cytometry and individual cells were deposited into 96-well plates using BD FACSJazz cell sorter. Individual colonies obtained were later replicated into 96-well plates and screened for successful gene deletion using immunocytochemistry against the p53 or Smad4. The clonal lines that stained negative for corresponding proteins were further expanded, and the loss of functional p53 and Smad4 protein was confirmed by immunoblotting against respective antibodies and Sanger sequencing for selected knockout clones using the primers flanking the gRNA cleavage site confirmed the genetic editing.
2.3. Cells Treatment with LCA and X-ray
CPA wildtype, p53 null, and p53 Smad4 double null cell lines were cultured in triplicate and treated with 10 μM lithocholic acid (LCA) or DMSO as a control for 24 h or irradiated with 2 Gy X-ray and cultured for further 24 h. Following the treatment, the cells were trypsinized, washed with PBS, counted and samples were prepared with 2 × 105 cells in 50 μL PBS for each condition.
2.4. Proteomic Sample Preparation in Microwells
To prepare samples for LC-MS analysis, 5 μL of 1% DDM and 0.5 μL of 500 mM DTT were added to 50 μL of sample, followed by incubation at 65 °C and 600 rpm for 1 h to lyse the cells and denature proteins. The cell lysates were diluted to 200 cells/500 nL with 50 mM ABC (pH 8.5) and 500 nL of cell suspension was pipetted into the microwells. Next, 500 nL of 10 mM IAA was added, and the samples were allowed to incubate in the dark at room temperature for 45 min. Two-step enzymatic digestion was applied by sequentially adding 500 nL of 10 ng/μL Lys-C and 500 nL of 20 ng/ μL trypsin in Ammonium bicarbonate buffer, followed by incubation at 37 °C for 3 and 10 h, respectively. Thereafter, 500 nL of 5% FA was added, followed by incubation at RT for 1 h. The chips were stored in the humidified box sealed in a Ziploc bag at 4 °C until analysis.
2.5. LC-MS/MS Analysis
A nanoPOTS autosampler was employed to introduce samples in microwells into LC-MS. (31) The samples in microwell chips were extracted and loaded into a solid-phase extraction (SPE) column (4 cm long, 150 μm i.d., packed with 3 μm, 300 Å C18 particles, Phenomenex, Torrance, CA, USA) using 100% buffer A (0.1% formic acid) delivered by a Dionex UltiMate NCP-3200RS pump. After sample loading, the concentrated peptides were separated using a 50 cm long, 50 μm i.d. nanoLC column with an integrated electrospray emitter (PicoFrit column, New Objective, Woburn, MA, USA). The LC column was packed in house with the same C18 particles used for the SPE column described above. The LC flow rate was 150 nL/min. A 50 min linear gradient from 8% to 22% buffer B (0.1% formic acid in ACN) was used for peptide elution, followed by raising the gradient to 35% buffer B in 10 min to elute hydrophobic peptides. The column was then washed by flushing the column with 80% buffer B for 5 min. Finally, the column was equilibrated using 2% buffer B for 15 min before the next injection.
An Orbitrap Fusion Lumos Tribrid MS (ThermoFisher, San Jose, USA) operating in data dependent acquisition mode was employed for peptide signal collection. To trigger electrospray, a high voltage of 2200 V was applied at the metal union (Valco, Houston, USA) between the SPE column and LC column. The ion transfer tube was set at 200 °C for desolvation and the radio frequency of the ion funnel was set at 30% for optimal peptide transmission. For MS1 acquisition, an Orbitrap resolution of 120 000, a MS scan range from 375 to 1600, an AGC level of 1 × 106, and a maximum injection time of 100 ms were used. Precursor ions with charges between +2 and +7, and intensity values over 1 × 104 were selected for HCD fragmentation and MS2 scanning. Precursors were isolated with an m/z window of 2 and fragmented by high energy dissociation (HCD) set at 30%. The fragment ions were transferred to Orbitrap for MS2 acquisition at a scan resolution of 60 000 and a maximum injection time of 118 ms. To reduce repeated sampling, an exclusion duration of 30 s and m/z tolerance of ±10 ppm were applied.
2.6. Data and Statistical Analysis
The .raw files from LC-MS/MS were loaded in the MaxQuant software (v1.6.7.0) for analysis. Identification of peptides was performed using the built-in Andromeda to search against the reviewed UniProt human proteome database (2019 release with a total of 42 427 entries, where 20 350 were reviewed and 22 077 were unreviewed). All of the search parameters were used in their default setting. The enzyme was set to trypsin, and maximum missed cleavages set to 2, while fixed modification was set to carbamidomethylation of cysteines and a false discovery rate (FDR) at peptide-to-spectrum matches (PSM) and protein levels set to 0.01. The resulting .txt output files from MaxQuant were loaded into R statistical environment (v4.0.2) and preprocessed before analyzing with DEP (differentially enrichment analysis of proteomic data) package. (32,33) The DEP package offers a robust and reproducible analysis workflow for MS-based proteomics data when determining differentially enriched/expressed proteins. DEP filters out contaminant and reverse protein sequences, logarithmically transforms the data, and then normalizes the data by variance stabilizing normalization (vsn) method. Subsequently, it imputes missing values and runs statistical tests to determine proteins with significantly altered expression levels. The latter step is made feasible by the test_diff function, which performs a differential enrichment test based on protein-wise linear models and empirical Bayes statistics using limma. (34,35) Data visualization was carried out using the BPG (v6.0.1) (36) and BioVenn (v1.0.2) (37) packages. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (38) partner repository with the data set identifier PXD020741.
2.7. Protein Annotation and Assessment of Physicochemical Aspects
3. Results and Discussion
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3.1. Analyzing Barrett’s Esophageal Cell Samples by MicroPOTS
The field of proteomics is rapidly changing with new technologies and methods advancing the capacity to examine a larger proportion of the proteome from small numbers of cells even down to the single-cell (41) level with a high degree of certainty. Over the past five years, the quest to design and develop highly sensitive and specific proteomic methodologies for this purpose has become intense, and many such methods have now been made available. (42−44) Moving away from macroscale preparative techniques in proteomics to micro- and nanotechnologies offers researchers the ability to characterize smaller numbers of cells that are more likely to be encountered in clinical settings. The recently unveiled microPOTS system is one of the methodologies that are promising and poised to widen the window of possibilities in proteomics research. (25) This study aimed at applying the microPOTS separation system to identify proteome signatures of either physiological stress or radiation in 200 Barrett’s esophageal cells. Initially, CP-A p53 single null (CP-A KO) and CP-A p53-SMAD4 double null cells (CP-A dKO) were generated, and together with the parental CP-A wild-type cells (CP-A WT), were subsequently subjected to either LCA or X-ray treatments. The microPOTS system was then applied for protein extraction and a nano-LC-MS workflow followed as depicted in Scheme 1.
Scheme 1
Scheme 1. Overview of the Workflow of MicroPOTS for the Identification of Proteomic Changes in ∼200 CP-A Cells after Treatment with Either LCA or X-raya
aFirst, cells were grown in keratinocyte media, and the generated CP-A null cells and wild-type were subjected to different stressors as previously described. (a) Samples were then processed for protein extraction and further digested into peptides using the microPOTS system. (b) The collected peptides were subsequently subjected to LC-MS/MS analysis. (c) A spectrum showing the relative intensity and mass to charge ratio (m/z) of the ions being analyzed. (d) The resulting files were loaded into MaxQuant for peptide identification, after which the output files of this step were next imported into R environment and analyzed using the DEP package. (e) The results of the analysis were then visualized using the previously mentioned R packages.
3.2. Protein Identification from Different CP-A Genotypes and Stress Conditions
All the samples reported a high number of identified protein groups, with each replicate having over 1500 protein groups from only 200 cells. Replicates from each sample type were averaged and the mean value of protein groups plotted in a bar graph (Figure 1a). The average number of confidently identified protein groups for each sample type was as follows with average number of proteins identified in parentheses: CP-A WT (2273), CP-A KO (1673), CP-A dKO (2008), CP-A LCA-WT (1685), CP-A LCA-KO (2004), CP-A LCA-dKO (1821), CP-A X-ray-WT (2300), CP-A X-ray-dKO (2345), and CP-A X-ray-KO (2367). The total number of proteins obtained from three replicates increased to over 2000 for all sample types when results were pooled from three wells for each sample type (Figure 1b). Next, an intersection analysis was performed to assess the number of shared and uniquely identified proteins. From this comparison, it was shown that over 1800 (60%) proteins could be identified in all sample types. Comparison of the total proteins from three replicates of CP-A LCA-WT, CP-A LCA-KO, and CP-A LCA-dKO produced a high protein overlap of 67% with 1867 proteins (Figure 1c). Out of these 1867 proteins, 93 (3.4%), 185 (6.7%), and 195 (7%) were unique to LCA-WT, LCA-KO, and LCA-dKO, respectively. The X-ray treatment equally reported a high overlap of 2354 (77%) proteins between the groups (Figure 1d). Also, the WT, KO, and dKO samples were overlapped with their corresponding treatment groups (LCA and X-ray) to determine the number of shared proteins and proteins that were unique to each sample. There was a high overlap of greater than 60% for the number of identified proteins for each sample type (Figure S1). The number of identified proteins in this work is comparable to a previous study that performed proteomic analysis on small numbers of cells (∼100) on an LTQ-Orbitrap system, and identified ∼1500 proteins. (45)
Figure 1
Figure 1. Barplots and Venn diagram plots respectively showing the number of identified protein groups and overlap for CP-A WT, CP-A KO, and CP-A dKO samples of ∼200 cells. (a) Average numbers of identified protein groups with each bar representing the mean and standard deviation (error bars) of replicates from individual sample type. (b) Total numbers of identified protein groups from three replicates for each sample type. (c) A 67% overlap of the identified proteins for samples treated with LCA. (d) Samples treated with X-ray shows a 77% overlap for the identified protein groups.
3.3. Evaluation of Protein Extraction Efficiency and Reproducibility
The results show that over 1500 proteins were confidently identified from 200 cells in all sample types. This high number of identified proteins from only 200 cells indicates the high extraction efficiency of the microPOTS system for microscale proteomic analysis. A look at the mean and standard deviations across the sample types reveals consistency in the number of identified proteins. As illustrated in Figure S2, almost all replicates had over 75% fully tryptic cleavage sites with fewer than 25% missed cleavages, indicating good tryptic digestion, which also translates into a high extraction efficiency.
The evaluation of microPOTS reproducibility in this study was predicated on two approaches—qualitative and quantitative analysis. Just like in the case of other method comparison studies (46−48) that often use this kind of approach, we decided to use the strategy to assess the performance of microPOTS. The reproducibility of the measurements is important in evaluation of the results and correct identification of proteins is critical to discovering new proteomic signatures with high certainty and specificity. (49−52) Qualitative reproducibility was achieved by comparing the overlap of the identified proteins between sample types and illustrating the shared proteins in an area-proportional Venn diagram. In Figure 2a, results for LCA treated CP-A dKO cells showed over 52% protein overlap between the replicates. Whereas for the X-ray treated CP-A dKO cells, an overlap of 73% was reported between replicates. Additionally, protein overlap between replicates for the rest of samples was assessed, and high protein overlap of over 50% was reported for almost all comparisons (Figure S3). Further, quantitative reproducibility between the replicates was assessed using the LFQ values of replicates to perform a pairwise Pearson’s correlation coefficient analysis. As shown in Figure 2b, the quantitative assessment of reproducibility demonstrated a high Pearson’s correlation coefficient value (R > 0.9). A high correlation coefficient (R > 0.9) was observed for almost all the pairwise comparisons that were conducted (Figure S4). CP-A KO replicates reported a lower correlation coefficient (R = 0.83) relative to the rest of the samples. Reproducibility of the MicroPOTS system was further assessed by computing the coefficient of variation (CV) for individual protein intensities in each sample condition (Figure 2c). All samples except CP-A KO showed little variation with a median CV that is less than 50% (Table S1).
Figure 2
Figure 2. Qualitative and quantitative comparison of the identified proteins between replicates. (a) The area-proportional Venn diagrams illustrate the proportion of shared and unique proteins between technical replicates. (b) Scatter plots showing Pearson’s correlation coefficient between logarithmically (log10) transformed quantile normalized LFQ values of the corresponding replicates. (c) A boxplot representing the distribution of coefficient of variation for quantile normalized protein intensities across different experimental conditions. Each dot represents the CV of an individual protein intensity within each sample type.
3.4. Comparison of MicroPOTS Data to Bulk Proteomics Data Set
The microPOTS data were compared to an existing data set of the same cell line (CP-A). The samples that generated the bulk data were prepared and analyzed according to standard proteomic workflows as described in Box S1. The resulting data were processed and analyzed using bioinformatics tools as stated in Box S2. Next, we correlated all the overlapping proteins that were confidently identified between the two data sets using Pearson correlation method. As illustrated in the scatter plot in Figure S5, there was a high positive correlation of R = 0.625.
3.5. Comparison of Physicochemical Aspects of Identified Proteins
It has been established that high molecular weight (MW) and basic proteins are often challenging to extract due to their propensity to undergo intra- and intermolecular interactions. (53,54) As such, we were interested in exploring the MW, and GRAVY distribution pattern of the identified proteins. The GRAVY distribution scores for LCA treated CP-A dKO cells indicated that a high number of hydrophobic proteins were detected with microPOTS (Figure 3a). A similar distribution trend of GRAVY was noticed from all the sample types with the majority of identified protein IDs within 0.4 to −0.2 range (Figure S6). LCA treated CP-A WT cells showed that the most abundant proteins had a MW between 20 and 30 kDa in all sample types (Figure 3b). Additionally, a similar MW distribution pattern was observed across all sample types, which demonstrates that the proteins show no major difference regarding their physicochemical characteristics (Figure S7). Next, the distribution of identified proteins according to their subcellular localization was explored, which showed that over 50% of the identified proteins resided within the cytosolic region, and this was observed across all sample types. Nearly 20% of confidently identified proteins for all the sample types were located within the cytosol (Figure 3c). All samples revealed that few ribosomal proteins could be detected by the microPOTS system, and this finding is consistent with Zhu et al. (23) reported in their study. Ribosomal and cytoskeleton-derived proteins were the least likely to be identified with a percentage of less than 5%. The distribution pattern for subcellular proteins was almost the same across all sample types (Figure S8).
Figure 3
Figure 3. Assessment of the physicochemical features and subcellular localization of the identified proteins. Distribution of (A) GRAVY in CP-A LCA-dKO, (B) molecular weight in CP-A LCA-WT, and (C) subcellular localization of identified proteins in CP-A X-ray-KO.
3.6. Effect of Stress on Protein Expression
Differential expression levels of the identified proteins were determined between CP-A WT, CP-A KO, and CP-A dKO as well as between their corresponding treatment set (LCA and X-ray treated groups). A pairwise comparison for all sample types was carried out to evaluate differentially expressed proteins between cells treated with different stresses. The findings from the present study indicate that LCA and X-ray induced changes in the proteome of CP-A cells, which is also consistent with the findings of Proungvitaya et al. that reported bile acids-induced alteration of protein expression in model cells system. (55) Significant alterations were observed in CPA-dKO cells following LCA and X-ray treatment. Specifically, a pairwise comparison between CP-A LCA-dKO and CP-A dKo revealed that 21 proteins were upregulated, and 13 proteins were downregulated, some of which include ASNS, RALY, CSRP1, CTSD, FAM120A, ESTD, and GSR (Figure 4a). Also, CP-A X-ray-dKO and CP-A dKO comparison reported 32 upregulated proteins with 14 downregulated proteins, including NONO, SAR1A, HNRL2, PLEC.1, FARSA, S100P, FKBP3, and AGR2 (Figure 4b). Table S2 and Table S3 represent the complete list of proteins that were significantly differentially expressed for LCA and X-ray treated CP-A dKO cells, respectively. Anterior Gradient 2 (AGR2) is a member of the protein disulfide isomerase family, and its overexpression has been associated with many human cancers including neoplasia of esophagus. (56,57) This evidence is consistent with the present study, which has shown that AGR2 is overexpressed in Barrett’s esophageal cells (CP-A X-ray-dKO). Having detected ∼1500 proteins from fewer than 200 cells, and capturing differential expression, signify a potential use of the microPOTS-LC-MS method to explore subcellular populations within a tissue/tumor microenvironment including T-cells, fibroblasts and macrophages.
Figure 4
Figure 4. Volcano plots illustrating the fold difference in the expression levels of proteins in (a) CP-A LCA-dKO vs CP-A dKO and (b) CP-A X-ray-dKO vs CP-A dKO. The horizontal coordinate represents the log2 fold change difference, while the vertical coordinate represents the −log10p-value. Black color indicates proteins with nonsignificant changes in abundance, blue color represents significantly downregulated proteins, and red color represents significantly upregulated proteins.
Conclusion
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In this study, a recently developed proteomic method called microPOTS was applied to identify proteins and determine the changes in the proteome of ∼200 cells (including an isogenic cell panel being used for the Barrett’s esophageal studies) following radiation and physiological stress treatment. The results show that the microPOTS method is applicable for use in qualitative and quantitative proteomic studies where only low cell numbers are available. Ionizing radiation was one stress used since it is DNA damaging and can activate p53 function. LCA was used since it is a component of bile acids that can impact on acid reflux disease and cancer progression in this tissue. The results were highly reproducible (R > 0.9) between replicates, allowing us to investigate confidently the effect of stress on the cells important in biological applications. With the microPOTS method ∼1500 unique proteins were quantified in all the samples. Moreover, results for the cells treated with LCA revealed differential expression analysis of 21 upregulated proteins and 13 downregulated proteins (CP-A LCA-dKO vs CP-A dKO), some of which include RALY, CSRP1, ASNS, ESTD, and FAM120A. Also, a comparison set between CP-A X-ray-dKO and CP-A dKO reported 33 significantly overexpressed proteins and 15 underexpressed proteins, including NONO, SKP1, HNRL2, PLEC.1, S100P, FKBP3, and AGR2. The results of the present study offer a basis for further studies to deeply interrogate in the future the molecular mechanisms that underpin LCA induction of proteome changes using clinical biopsies, which could aid in uncoupling the distinct role of bile acids in the selection for specific genetic mutations in esophageal adenocarcinoma progression. Importantly, the use of microPOTS, while not as sensitive as its companion technique nanoPOTS, was implemented and will be used in future studies.
Supporting Information
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00629.
Figure S1: Venn diagram illustrating the shared number of identified proteins among different sample types, as well as proteins that are unique to each sample type; Figure S2: Number of missed cleavages are shown for all replicates and are expressed in percentage; Figure S3: Qualitative assessment of reproducibility of the microPOTS system; Figure S4: Quantitative assessment of reproducibility of the microPOTS system; Table S1: Median coefficient of variation (CV) for quantile normalized protein LFQ values for each sample type; Box S1: Sample preparation and LC-MS analysis for bulk proteomics data set; Box S2: Bulk data analysis and comparison to microPOTS data; Figure S5: Scatter plot with associated Pearson’s correlation coefficient (R = 0.625) between microPOTS and bulk proteomics for all overlapping 1066 proteins that were identified; Figure S6: Assessment of physicochemical characteristics; Figure S7: Assessment of physicochemical characteristics; Figure S8: Subcellular localization; Table S2: List of differentially expressed proteins between CP-A dKO cells with or without LCA treatment; Table S3: List of differentially expressed proteins between CP-A dKO cells with or without X-ray treatment (PDF)
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Author Information
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- Irena Dapic - University of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, Poland;
http://orcid.org/0000-0002-0716-6500;
- Kenneth Weke - University of Gdansk, International Centre for Cancer Vaccine Science, ul. Kładki 24, 80-822 Gdansk, Poland;http://orcid.org/0000-0001-8947-7793
- Mowei Zhou - Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States;http://orcid.org/0000-0003-3575-3224
- Ying Zhu - Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States;http://orcid.org/0000-0002-5416-0566
Acknowledgments
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This work was funded by the International Research Agenda’s Program of the Foundation for Polish Science (MAB/2017/03). The “International Centre for Cancer Vaccine Science” project is carried out within the International Research Agendas programme of the Foundation for Polish Science cofinanced by the European Union under the European Regional Development Fund. The authors would also like to thank the CI-TASK, Gdansk and the PL-Grid Infrastructure, Poland for providing their hardware and software resources. The University of Victoria-Genome BC Proteomics Centre is grateful to Genome Canada and Genome British Columbia for financial support for Genomics Technology Platforms (GTP) funding for operations and technology development (264PRO). The experiment was performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research and located at PNNL. The Brno CP-A study was supported by the European Regional Development Fund – Project ENOCH (No. CZ.02.1.01/0.0/0.0/16_019/0000868) and by the Ministry of Health Development of Research Organization, MH CZ – DRO (MMCI, 00209805).
References
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This article references 57 other publications.
- 1Mann, M.; Hendrickson, R. C.; Pandey, A. Analysis of Proteins and Proteomes by Mass Spectrometry. Annu. Rev. Biochem. 2001, 70, 437– 473, DOI: 10.1146/annurev.biochem.70.1.437Google Scholar1https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXlsVehtL4%253D&md5=1bd749a47bfc1ec30226dce9a820aef6Analysis of proteins and proteomes by mass spectrometryMann, Matthias; Hendrickson, Ronald C.; Pandey, AkhileshAnnual Review of Biochemistry (2001), 70 (), 437-473CODEN: ARBOAW; ISSN:0066-4154. (Annual Reviews Inc.)A review with 91 refs. A decade after the discovery of electrospray and matrix-assisted laser desorption ionization (MALDI), methods that finally allowed gentle ionization of large biomols., mass spectrometry has become a powerful tool in protein anal. and the key technol. in the emerging field of proteomics. The success of mass spectrometry is driven both by innovative instrumentation designs, esp. those operating on the time-of-flight or ion-trapping principles, and by large-scale biochem. strategies, which use mass spectrometry to detect the isolated proteins. Any human protein can now be identified directly from genome databases on the basis of minimal data derived by mass spectrometry. As has already happened in genomics, increased automation of sample handling, anal., and the interpretation of results will generate an avalanche of qual. and quant. proteomic data. Protein-protein interactions can be analyzed directly by pptn. of a tagged bait followed by mass spectrometric identification of its binding partners. By these and similar strategies, entire protein complexes, signaling pathways, and whole organelles are being characterized. Posttranslational modifications remain difficult to analyze but are starting to yield to generic strategies.
- 2Wasinger, V. C.; Cordwell, S. J.; Cerpa-Poljak, A.; Yan, J. X.; Gooley, A. A.; Wilkins, M. R.; Duncan, M. W.; Harris, R.; Williams, K. L.; Humphery-Smith, I. Progress with Gene-Product Mapping of the Mollicutes: Mycoplasma Genitalium. Electrophoresis 1995, 16 (7), 1090– 1094, DOI: 10.1002/elps.11501601185Google Scholar2https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXntF2lsLg%253D&md5=11e7acd04dc0eaf17ec7f99996a0c654Progress with gene-product mapping of the Mollicutes: Mycoplasma genitaliumWasinger, Valerie C.; Cordwell, Stuart J.; Cerpa-Poljak, Anne; Yan, Jun X.; Gooley, Andrew A.; Wilkins, Marc R.; Duncan, Mark W.; Harris, Ray; Williams, Keith L.; Humphery-Smith, IanElectrophoresis (1995), 16 (7), 1090-4CODEN: ELCTDN; ISSN:0173-0835. (VCH)A protein map of the smallest known self-replicating organism Mycoplasma genitalium (Class: Mollicutes) revealed a high proportion of acidic proteins. Amino acid compn. was used to putatively identify or provide unique parameters for 50 gene products sepd. by two-dimensional gel electrophoresis. A further 19 proteins were subjected to peptide-mass fingerprinting using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and 4 were subjected to N-terminal Edman degrdn. The majority of M. genitalium proteins remain uncharacterized. However, the combined approach of amino acid anal. and peptide-mass fingerprinting allowed gene products to be linked to homologous genes in a variety of organisms. This allowed proteins to be identified prior to detection of their resp. genes via the M. genitalium sequencing initiative. The principle of 'hierarchical' anal. for the mass screening of proteins and the anal. of microbial genomes via their protein complement or 'proteome' is detailed. Here, characterization of gene products depends upon the quickest and most economical technols. being employed initially to det. if a large no. of proteins are already present in both homologous and heterologous species databases. Initial screening, which lends itself to automation and robotics, can then be followed by more time and cost intensive procedures, when necessary.
- 3Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M.-C.; Yates, J. R. Protein Analysis by Shotgun/Bottom-up Proteomics. Chem. Rev. 2013, 113 (4), 2343– 2394, DOI: 10.1021/cr3003533Google Scholar3https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjtVClu7g%253D&md5=a27c72f72bc6fa2f827725d2e71df241Protein Analysis by Shotgun/Bottom-up ProteomicsZhang, Yaoyang; Fonslow, Bryan R.; Shan, Bing; Baek, Moon-Chang; Yates, John R.Chemical Reviews (Washington, DC, United States) (2013), 113 (4), 2343-2394CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)A review.
- 4Mayne, J.; Ning, Z.; Zhang, X.; Starr, A. E.; Chen, R.; Deeke, S.; Chiang, C.-K.; Xu, B.; Wen, M.; Cheng, K.; Seebun, D.; Star, A.; Moore, J. I.; Figeys, D. Bottom-Up Proteomics (2013–2015): Keeping up in the Era of Systems Biology. Anal. Chem. 2016, 88 (1), 95– 121, DOI: 10.1021/acs.analchem.5b04230Google Scholar4https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvVSlsLvJ&md5=291844fae9e91fc1b1d219948ef7d3c4Bottom-Up Proteomics (2013-2015): Keeping up in the Era of Systems BiologyMayne, Janice; Ning, Zhibin; Zhang, Xu; Starr, Amanda E.; Chen, Rui; Deeke, Shelley; Chiang, Cheng-Kang; Xu, Bo; Wen, Ming; Cheng, Kai; Seebun, Deeptee; Star, Alexandra; Moore, Jasmine I.; Figeys, DanielAnalytical Chemistry (Washington, DC, United States) (2016), 88 (1), 95-121CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. Over 17,000 papers related to proteomics have been published since the authors' 2011-2013 review. A subset are true gems reporting amazing applications of proteomics to better understand protein interactions, pathways, dynamic signaling and the human proteome, to name a few. While the authors have clearly seen more attention paid to data quality, and the proper exptl. design in manuscripts reporting biol. applications of proteomics, more improvements are needed. Technol. development still remains a strong driving force of proteomics, making up a large portion of these papers. As in any maturing field, some areas of technol. development in proteomics are satg. However, many new areas in proteomics are burgeoning. Examples include the development of enrichment materials to study different protein modifications, approaches for protein quantification, data independent acquisition (DIA) methods, bioinformatics and the rapid developments in metaproteomics. The acceptance of proteomics in the biol. community really depends on its applications. For instance, protein interaction mapping, post-translational modification (PTM) analyses and protein Atlas are routinely used by the biol. community. However, other aspects of proteomics are still under appreciated including biomarker discovery and studies of diseases from tissues. In part, this is due to previous and, to some extent, still ongoing overstatements from underpowered studies that reported unsubstantiated biol. conclusions. Very large-scale, good quality, quant. proteomic studies are now tech. feasible and ongoing in a few labs. around the world. However, few bioinformatics tools are capable of handling these large studies. Moreover, proteomics is increasingly integrated in expts. involving multiple -omics datasets obtained across large nos. of biol. replicates. "Big data" challenges are clearly appearing in proteomics. This biannual review is focused on technol. trends, which the authors believe are important in proteomics. The authors continue to foresee that technol. and bioinformatics development will remain important areas of research in proteomics.
- 5Fagerberg, L.; Strömberg, S.; El-Obeid, A.; Gry, M.; Nilsson, K.; Uhlen, M.; Ponten, F.; Asplund, A. Large-Scale Protein Profiling in Human Cell Lines Using Antibody-Based Proteomics. J. Proteome Res. 2011, 10 (9), 4066– 4075, DOI: 10.1021/pr200259vGoogle Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXptFKlsr4%253D&md5=38f0615cccc16390ae4f6be6fd806c85Large-Scale Protein Profiling in Human Cell Lines Using Antibody-Based ProteomicsFagerberg, Linn; Stromberg, Sara; El-Obeid, Adila; Gry, Marcus; Nilsson, Kenneth; Uhlen, Mathias; Ponten, Fredrik; Asplund, AnnaJournal of Proteome Research (2011), 10 (9), 4066-4075CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)Human cancer cell lines grown in vitro are frequently used to decipher basic cell biol. phenomena and to also specifically study different forms of cancer. Here the authors present the first large-scale study of protein expression patterns in cell lines using an antibody-based proteomics approach. The authors analyzed the expression pattern of 5436 proteins in 45 different cell lines using hierarchical clustering, principal component anal., and two-group comparisons for the identification of differentially expressed proteins. The authors' results show that immunohistochem. detd. protein profiles can categorize cell lines into groups that overall reflect the tumor tissue of origin and that hematol. cell lines appear to retain their protein profiles to a higher degree than cell lines established from solid tumors. The two-group comparisons reveal well-characterized proteins as well as previously unstudied proteins that could be of potential interest for further investigations. Moreover, multiple myeloma cells and cells of myeloid origin were found to share a protein profile, relative to the protein profile of lymphoid leukemia and lymphoma cells, possibly reflecting their common dependency of bone marrow microenvironment. This work also provides an extensive list of antibodies, for which high-resoln. images as well as validation data are available on the Human Protein Atlas (www.proteinatlas.org), that are of potential use in cell line studies.
- 6Branca, R. M. M.; Orre, L. M.; Johansson, H. J.; Granholm, V.; Huss, M.; Pérez-Bercoff, Å.; Forshed, J.; Käll, L.; Lehtiö, J. HiRIEF LC-MS Enables Deep Proteome Coverage and Unbiased Proteogenomics. Nat. Methods 2014, 11 (1), 59– 62, DOI: 10.1038/nmeth.2732Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhslygsLnE&md5=acdad94163f52313a336a41ccfed44c4HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomicsBranca, Rui M. M.; Orre, Lukas M.; Johansson, Henrik J.; Granholm, Viktor; Huss, Mikael; Perez-Bercoff, Aasa; Forshed, Jenny; Kaell, Lukas; Lehtioe, JanneNature Methods (2014), 11 (1), 59-62CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)We present a liq. chromatog.-mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resoln. isoelec. focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelec. point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, resp. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.
- 7Wang, G.; Brennan, C.; Rook, M.; Wolfe, J. L.; Leo, C.; Chin, L.; Pan, H.; Liu, W.-H.; Price, B.; Makrigiorgos, G. M. Balanced-PCR Amplification Allows Unbiased Identification of Genomic Copy Changes in Minute Cell and Tissue Samples. Nucleic Acids Res. 2004, 32 (9), e76, DOI: 10.1093/nar/gnh070Google Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXksVahsLk%253D&md5=e87e9779bb0d6186644d4f7b394d1448Balanced-PCR amplification allows unbiased identification of genomic copy changes in minute cell and tissue samplesWang, Gang; Brennan, Cameron; Rook, Martha; Wolfe, Jia Liu; Leo, Christopher; Chin, Lynda; Pan, Hongjie; Liu, Wei-Hua; Price, Brendan; Makrigiorgos, G. MikeNucleic Acids Research (2004), 32 (9), e76/1-e76/10CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Anal. of genomic DNA derived from cells and fresh or fixed tissues often requires whole genome amplification prior to microarray screening. Tech. hurdles to this process are the introduction of amplification bias and/or the inhibitory effects of formalin fixation on DNA amplification. Here we demonstrate a balanced-PCR procedure that allows unbiased amplification of genomic DNA from fresh or modestly degraded paraffin-embedded DNA samples. Following digestion and ligation of a target and a control genome with distinct linkers, the two are mixed and amplified in a single PCR, thereby avoiding biases assocd. with PCR satn. and impurities. We demonstrate genome-wide retention of allelic differences following balanced-PCR amplification of DNA from breast cancer and normal human cells and genomic profiling by array-CGH (cDNA arrays, 100 kb resoln.) and by real-time PCR (single gene resoln.). Comparison of balanced-PCR with multiple displacement amplification (MDA) demonstrates equiv. performance between the two when intact genomic DNA is used. When DNA from paraffin-embedded samples is used, balanced PCR overcomes problems assocd. with modest DNA degrdn. and produces unbiased amplification whereas MDA does not. Balanced-PCR allows amplification and recovery of modestly degraded genomic DNA for subsequent retrospective anal. of human tumors with known outcomes.
- 8Wang, N.; Xu, M.; Wang, P.; Li, L. Development of Mass Spectrometry-Based Shotgun Method for Proteome Analysis of 500 to 5000 Cancer Cells. Anal. Chem. 2010, 82 (6), 2262– 2271, DOI: 10.1021/ac9023022Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptlGrtA%253D%253D&md5=89ac83bbe62a4e164961925b870a13e4Development of Mass Spectrometry-Based Shotgun Method for Proteome Analysis of 500 to 5000 Cancer CellsWang, Nan; Xu, Mingguo; Wang, Peng; Li, LiangAnalytical Chemistry (Washington, DC, United States) (2010), 82 (6), 2262-2271CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A shotgun proteome anal. method and its performance for protein identification from 500 to 5000 cells are described. Sample prepn., which was done in one tube, involved the use of a surfactant (NP-40) for cell lysis, followed by acetone pptn. of the proteins. The resulting protein pellet was washed with cold acetone to remove remaining surfactant, and the pellet was then solubilized in NH4HCO3. After trypsin digestion of the proteins, the digest was analyzed by the use of nanoflow liq. chromatog. (LC) quadrupole time-of-flight mass spectrometry (QTOF MS). Sample injection and gradient speed in running LC QTOF MS were optimized. It was shown that this method could identify an av. (n = 3) of 167±21, 237±30, 491±63, and 619±59 proteins from 500, 1000, 2500, and 5000 MCF-7 breast cancer cells, resp. To demonstrate the potential use of this method for generating proteome profile from circulating tumor cells (CTCs) isolated from human blood, a healthy human blood sample was spiked with MCF-7 cells, and this mixt. was processed and then subjected to antibody tagging of the MCF-7 cells. The tagged cells were sorted and collected using flow cytometry. The proteome profiles of small nos. of cells isolated in this way were similar to those of the original MCF-7 cells, suggesting the possibility of the use of this method for cell typing of CTCs.
- 9Parapatics, K.; Huber, M. L.; Lehmann, D.; Knoll, C.; Superti-Furga, G.; Bennett, K. L.; Rudashevskaya, E. L. Proteomic Analysis of Low Quantities of Cellular Material in the Range Obtainable from Scarce Patient Samples. J. Integr. OMICS 2015, 5 (1), 30– 43–43, DOI: 10.5584/jiomics.v5i1.172Google ScholarThere is no corresponding record for this reference.
- 10Clair, G.; Piehowski, P. D.; Nicola, T.; Kitzmiller, J. A.; Huang, E. L.; Zink, E. M.; Sontag, R. L.; Orton, D. J.; Moore, R. J.; Carson, J. P.; Smith, R. D.; Whitsett, J. A.; Corley, R. A.; Ambalavanan, N.; Ansong, C. Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples. Sci. Rep. 2016, 6, 6, DOI: 10.1038/srep39223Google ScholarThere is no corresponding record for this reference.
- 11Coscia, F.; Doll, S.; Bech, J. M.; Mund, A.; Lengyel, E.; Lindebjerg, J.; Madsen, G. I.; Moreira, J. M. A.; Mann, M. A Streamlined Mass Spectrometry-Based Proteomics Workflow for Large Scale FFPE Tissue Analysis. bioRxiv , Sep 23, 2019, 779009. DOI: 10.1101/779009 .Google ScholarThere is no corresponding record for this reference.
- 12Martin, J. G.; Rejtar, T.; Martin, S. A. Integrated Microscale Analysis System for Targeted Liquid Chromatography Mass Spectrometry Proteomics on Limited Amounts of Enriched Cell Populations. Anal. Chem. 2013, 85 (22), 10680– 10685, DOI: 10.1021/ac401937cGoogle Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFGkurbN&md5=7fd20d18e145d970a56b1aadfe651f89Integrated Microscale Analysis System for Targeted Liquid Chromatography Mass Spectrometry Proteomics on Limited Amounts of Enriched Cell PopulationsMartin, Jeffrey G.; Rejtar, Tomas; Martin, Stephen A.Analytical Chemistry (Washington, DC, United States) (2013), 85 (22), 10680-10685CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Limited samples, such as those that are in vivo sourced via biopsy, are closely representative of biol. systems and contain valuable information for drug discovery. However, these precious samples are often heterogeneous and require cellular prefractionation prior to proteomic anal. to isolate specific subpopulations of interest. Enriched cells from in vivo samples are often very limited (<104 cells) and pose a significant challenge to proteomic nanoliquid chromatog. mass spectrometry (nanoLCMS) sample prepn. To enable the streamlined anal. of these limited samples, the authors have developed an online cell enrichment, microscale sample prepn., nanoLCMS proteomics workflow by integrating fluorescence activated cell sorting (FACS), focused ultrasonication, microfluidics, immobilized trypsin digestion, and nanoLCMS. To assess the performance of the online FACS-Chip-LCMS workflow, 5000 fluorescent labeled cells were enriched from a 5% heterogeneous cell population and processed for LCMS proteomics in <2 h. Within these 5000 enriched cells, 30 peptides corresponding to 17 proteins spanning >4 orders of magnitude of cellular abundance were quantified using a QExactive MS. The results from the online FACS-Chip-LCMS workflow starting from 5000 enriched cells were directly compared to results from a traditional macroscale sample prepn. workflow starting from 2.0 × 106 cells. The microscale FACS-Chip-LCMS workflow demonstrated high cellular enrichment efficiency and high peptide recovery across the wide dynamic range of targeted peptides. Overall the microscale FACS-Chip-LCMS workflow showed effectiveness in efficiently prepg. limited amts. of FACS enriched cells in an online manner for proteomic LCMS.
- 13Yamaguchi, H.; Miyazaki, M.; Honda, T.; Briones-Nagata, M. P.; Arima, K.; Maeda, H. Rapid and Efficient Proteolysis for Proteomic Analysis by Protease-Immobilized Microreactor. Electrophoresis 2009, 30 (18), 3257– 3264, DOI: 10.1002/elps.200900134Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtFGqsLjK&md5=9870da1a12d77e6e302a53e76019fd79Rapid and efficient proteolysis for proteomic analysis by protease-immobilized microreactorYamaguchi, Hiroshi; Miyazaki, Masaya; Honda, Takeshi; Briones-Nagata, Maria Portia; Arima, Kazunari; Maeda, HideakiElectrophoresis (2009), 30 (18), 3257-3264CODEN: ELCTDN; ISSN:0173-0835. (Wiley-VCH Verlag GmbH & Co. KGaA)Proteolysis is an important part of protein identification in proteomics anal. The conventional method of in-soln. digestion of proteins is time-consuming and has limited sensitivity. In this study, trypsin- or α-chymotrypsin-immobilized microreactors prepd. by a microfluidics-based enzyme-immobilization technique were studied for rapid sample prepn. in proteomic anal. The kinetic studies for hydrolysis of substrate by microreactors revealed that immobilized proteases had higher hydrolytic efficiency than those performed by in-soln. digestion. The performance of the microreactors was evaluated by digesting cytochrome c and bovine serum albumin (BSA). Protein digestion was achieved within a short period of time (∼5 min) at 30° without any complicated redn. and alkylation procedures. The efficiency of digestion by trypsin-immobilized reactor was evaluated by analyzing the sequence coverage, which was 47 and 12% for cytochrome c and BSA, resp. These values were higher than those performed by in-soln. digestion. Besides, because of higher stability against high concn. of denaturant, the microreactors can be useful for immediate digestion of the denatured protein. In the present study, the authors propose a protease-immobilized microreactor digestion method, which can utilize as a proteome technique for biol. and clin. research.
- 14Myers, S. A.; Rhoads, A.; Cocco, A. R.; Peckner, R.; Haber, A. L.; Schweitzer, L. D.; Krug, K.; Mani, D. R.; Clauser, K. R.; Rozenblatt-Rosen, O.; Hacohen, N.; Regev, A.; Carr, S. A. Streamlined Protocol for Deep Proteomic Profiling of FAC-Sorted Cells and Its Application to Freshly Isolated Murine Immune Cells. Mol. Cell. Proteomics 2019, 18 (5), 995– 1009, DOI: 10.1074/mcp.RA118.001259Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXht1OhsbbP&md5=c4a2707f8e65bcc5fd9462e4952b5e06Streamlined protocol for deep proteomic profiling of FAC-sorted cells and its application to freshly isolated murine immune cellsMyers, Samuel A.; Rhoads, Andrew; Cocco, Alexandra R.; Peckner, Ryan; Haber, Adam L.; Schweitzer, Lawrence D.; Krug, Karsten; Mani, D. R.; Clauser, Karl R.; Rozenblatt-Rosen, Orit; Hacohen, Nir; Regev, Aviv; Carr, Steven A.Molecular & Cellular Proteomics (2019), 18 (5), 995-1009CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)Proteomic profiling describes the mol. landscape of proteins in cells immediately available to sense, transduce, and enact the appropriate responses to extracellular queues. Transcriptional profiling has proven invaluable to our understanding of cellular responses; however, insights may be lost as mounting evidence suggests transcript levels only moderately correlate with protein levels in steady state cells. Mass spectrometry-based quant. proteomics is a well-suited and widely used anal. tool for studying global protein abundances. Typical proteomic workflows are often limited by the amt. of sample input that is required for deep and quant. proteome profiling. This is esp. true if the cells of interest need to be purified by fluorescence-activated cell sorting (FACS) and one wants to avoid ex vivo culturing. To address this need, we developed an easy to implement, streamlined workflow that enables quant. proteome profiling from roughly 2μg of protein input per exptl. condition. Utilizing a combination of facile cell collection from cell sorting, solid-state isobaric labeling and multiplexing of peptides, and small-scale fractionation, we profiled the proteomes of 12 freshly isolated, primary murine immune cell types. Analyzing half of the 3e5 cells collected per cell type, we quantified over 7000 proteins across 12 key immune cell populations directly from their resident tissues. We show that low input proteomics is precise, and the data generated accurately reflects many aspects of known immunol., while expanding the list of cell-type specific proteins across the cell types profiled. The low input proteomics methods we developed are readily adaptable and broadly applicable to any cell or sample types and should enable proteome profiling in systems previously unattainable.
- 15Gao, W.; Li, H.; Liu, L.; Huang, P.; Wang, Z.; Chen, W.; Ye, M.; Yu, X.; Tian, R. An Integrated Strategy for High-Sensitive and Multi-Level Glycoproteome Analysis from Low Micrograms of Protein Samples. J. Chromatogr. A 2019, 1600, 46– 54, DOI: 10.1016/j.chroma.2019.04.041Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosVyjsrg%253D&md5=d483a4c2b76420673a2b34063171c9b4An integrated strategy for high-sensitive and multi-level glycoproteome analysis from low micrograms of protein samplesGao, Weina; Li, Hongjie; Liu, Liping; Huang, Peiwu; Wang, Zhikun; Chen, Wendong; Ye, Mingliang; Yu, Xiaofang; Tian, RuijunJournal of Chromatography A (2019), 1600 (), 46-54CODEN: JCRAEY; ISSN:0021-9673. (Elsevier B.V.)Glycosylation, as a biol. important protein post-translational modification, often alters on both glycosites and glycans, simultaneously. However, most of current approaches focused on biased profiling of either glycosites or glycans, and limited by time-consuming process and milligrams of starting protein material. We describe here a simple and integrated spintip-based glycoproteomics technol. (termed Glyco-SISPROT) for achieving a comprehensive view of glycoproteome with shorter sample processing time and low microgram starting material. By carefully integrating and optimizing SCX, C18 and Con A (Con A) packing material and their combination in spintip format, both predigested peptides and protein lysates could be processed by Glyco-SISPROT with high efficiency. More importantly, deglycopeptide, intact glycopeptide and glycans released by multiple glycosidases could be readily collected from the same Glyco-SISPROT workflow for LC-MS anal. In total, above 1850 glycosites in ∼1770 unique deglycopeptides were characterized from mouse liver by using either 100 μg of predigested peptides or directly using 100 μg of protein lysates, in which about 30% of glycosites were released by both PNGase F and Endos. To the best of our knowledge, this approach should be one of the most comprehensive glycoproteomic approaches by using limited protein starting material. One significant benefit of Glyco-SISPROT is that whole processing time is dramatically reduced from a few days to less than 6 h with good reproducibility when protein lysates were directly processed by Glyco-SISPROT. We expect that this method will be suitable for multi-level glycoproteome anal. of rare biol. samples with high sensitivity.
- 16Chen, W.; Wang, S.; Adhikari, S.; Deng, Z.; Wang, L.; Chen, L.; Ke, M.; Yang, P.; Tian, R. Simple and Integrated Spintip-Based Technology Applied for Deep Proteome Profiling. Anal. Chem. 2016, 88 (9), 4864– 4871, DOI: 10.1021/acs.analchem.6b00631Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xls1Kis7w%253D&md5=8d60eba33022626272064cf060d9fa1eSimple and Integrated Spintip-Based Technology Applied for Deep Proteome ProfilingChen, Wendong; Wang, Shuai; Adhikari, Subash; Deng, Zuhui; Wang, Lingjue; Chen, Lan; Ke, Mi; Yang, Pengyuan; Tian, RuijunAnalytical Chemistry (Washington, DC, United States) (2016), 88 (9), 4864-4871CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Great efforts have been taken for developing high-sensitive mass spectrometry (MS)-based proteomic technologies, among which sample prepn. is one of the major focus. Here, a simple and integrated spintip-based proteomics technol. (SISPROT) consisting of strong cation exchange beads and C18 disk in one pipet tip was developed. Both proteomics sample prepn. steps, including protein preconcn., redn., alkylation, and digestion, and reversed phase (RP)-based desalting and high-pH RP-based peptide fractionation can be achieved in a fully integrated manner for the first time. This easy-to-use technol. achieved high sensitivity with negligible sample loss. Proteomic anal. of 2000 HEK 293 cells readily identified 1270 proteins within 1.4 h of MS time, while 7826 proteins were identified when 100000 cells were processed and analyzed within only 22 h of MS time. More importantly, the SISPROT can be easily multiplexed on a std. centrifuge with good reproducibility (Pearson correlation coeff. > 0.98) for both single-shot anal. and deep proteome profiling with five-step high-pH RP fractionation. The SISPROT was exemplified by the triplicate anal. of 100000 stem cells from human exfoliated deciduous teeth (SHED). This led to the identification of 9078 proteins contg. 3771 annotated membrane proteins, which was the largest proteome data set for dental stem cells reported to date. We expect that the SISPROT will be well suited for deep proteome profiling for fewer than 100000 cells and applied for translational studies where multiplexed technol. with good label-free quantification precision is required.
- 17Dou, M.; Zhu, Y.; Liyu, A.; Liang, Y.; Chen, J.; Piehowski, P. D.; Xu, K.; Zhao, R.; Moore, R. J.; Atkinson, M. A.; Mathews, C. E.; Qian, W.-J.; Kelly, R. T. Nanowell-Mediated Two-Dimensional Liquid Chromatography Enables Deep Proteome Profiling of < 1000 Mammalian Cells. Chem. Sci. 2018, 9 (34), 6944– 6951, DOI: 10.1039/C8SC02680GGoogle Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtlCmtbbF&md5=1ec9556a474d130acc404d70f7a6a838Nanowell-mediated two-dimensional liquid chromatography enables deep proteome profiling of <1000 mammalian cellsDou, Maowei; Zhu, Ying; Liyu, Andrey; Liang, Yiran; Chen, Jing; Piehowski, Paul D.; Xu, Kerui; Zhao, Rui; Moore, Ronald J.; Atkinson, Mark A.; Mathews, Clayton E.; Qian, Wei-Jun; Kelly, Ryan T.Chemical Science (2018), 9 (34), 6944-6951CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)Multidimensional peptide sepns. can greatly increase the depth of coverage in proteome profiling. However, a major challenge for multidimensional sepns. is the requirement of large biol. samples, often contg. milligram amts. of protein. We have developed nanowell-mediated two-dimensional (2D) reversed-phase nanoflow liq. chromatog. (LC) sepns. for in-depth proteome profiling of low-nanogram samples. Peptides are first sepd. using high-pH LC and the effluent is concatenated into 4 or 12 nanowells. The contents of each nanowell are reconstituted in LC buffer and collected for subsequent sepn. and anal. by low-pH nanoLC-MS/MS. The nanowell platform minimizes peptide losses to surfaces in offline 2D LC fractionation, enabling >5800 proteins to be confidently identified from just 50 ng of HeLa digest. Furthermore, in combination with a recently developed nanowell-based sample prepn. workflow, we demonstrated deep proteome profiling of >6000 protein groups from small populations of cells, including ∼650 HeLa cells and 10 single human pancreatic islet thin sections (∼1000 cells) from a pre-symptomatic type 1 diabetic donor.
- 18Zhu, Y.; Scheibinger, M.; Ellwanger, D. C.; Krey, J. F.; Choi, D.; Kelly, R. T.; Heller, S.; Barr-Gillespie, P. G. Single-Cell Proteomics Reveals Changes in Expression during Hair-Cell Development. eLife 2019, 8, e50777, DOI: 10.7554/eLife.50777Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtlamurbN&md5=66e9a7e7876b6b48af6f2fb378ccd1d1Single-cell proteomics reveals changes in expression during hair-cell developmentZhu, Ying; Scheibinger, Mirko; Ellwanger, Daniel Christian; Krey, Jocelyn F.; Choi, Dongseok; Kelly, Ryan T.; Heller, Stefan; Barr-Gillespie, Peter G.eLife (2019), 8 (), e50777CODEN: ELIFA8; ISSN:2050-084X. (eLife Sciences Publications Ltd.)Hearing and balance rely on small sensory hair cells that reside in the inner ear. To explore dynamic changes in the abundant proteins present in differentiating hair cells, we used nanoliter-scale shotgun mass spectrometry of single cells, each ~ 1 pL, from utricles of embryonic day 15 chickens. We identified unique constellations of proteins or protein groups from presumptive hair cells and from progenitor cells. The single-cell proteomes enabled the de novo reconstruction of a developmental trajectory using protein expression levels, revealing proteins that greatly increased in expression during differentiation of hair cells (e.g., OCM, CRABP1, GPX2, AK1, GSTO1) and those that decreased during differentiation (e.g., TMSB4X, AGR3). Complementary single-cell transcriptome profiling showed corresponding changes in mRNA during maturation of hair cells. Single-cell proteomics data thus can be mined to reveal features of cellular development that may be missed with transcriptomics.
- 19Shao, X.; Zhang, X. Design of Five-Layer Gold Nanoparticles Self-Assembled in a Liquid Open Tubular Column for Ultrasensitive Nano-LC-MS/MS Proteomic Analysis of 80 Living Cells. Proteomics 2017, 17 (8), 1600463, DOI: 10.1002/pmic.201600463Google ScholarThere is no corresponding record for this reference.
- 20Chen, Q.; Yan, G.; Gao, M.; Zhang, X. Ultrasensitive Proteome Profiling for 100 Living Cells by Direct Cell Injection, Online Digestion and Nano-LC-MS/MS Analysis. Anal. Chem. 2015, 87 (13), 6674– 6680, DOI: 10.1021/acs.analchem.5b00808Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVaku77P&md5=1b3f40134e4afd0f68bbec6483dd7455Ultrasensitive Proteome Profiling for 100 Living Cells by Direct Cell Injection, Online Digestion and Nano-LC-MS/MS AnalysisChen, Qi; Yan, Guoquan; Gao, Mingxia; Zhang, XiangminAnalytical Chemistry (Washington, DC, United States) (2015), 87 (13), 6674-6680CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Single-cell proteome anal. has always been an exciting goal because it provides crucial information about cellular heterogeneity and dynamic change. Here the authors presented an integrated proteome anal. device (iPAD) for 100 living cells (iPAD-100) that might be suitable for single-cell anal. Once cells were cultured, the iPAD-100 could be applied to inject 100 living cells, to transform the living cells into peptides, and to produce protein identification results with total automation. Due to the major obstacle for detection limit of mass spectrometry, the authors applied the iPAD-100 to analyze the proteome of 100 cells. In total, 813 proteins were identified in a DLD-cell proteome by three duplicate runs. Gene Ontol. anal. revealed that proteins from different cellular compartments were well-represented, including membrane proteins. The iPAD-100 greatly simplified the sampling process, reduced sample loss, and prevented contamination. As a result, proteins whose copy nos. were <1000 were identified from 100-cell samples with the iPAD-100, showing that a detection limit of 200 zmol was achieved. With increased sensitivity of mass spectrometry, the iPAD-100 may be able to reveal bountiful proteome information from a single cell in the near future.
- 21Zhu, Y.; Clair, G.; Chrisler, W. B.; Shen, Y.; Zhao, R.; Shukla, A. K.; Moore, R. J.; Misra, R. S.; Pryhuber, G. S.; Smith, R. D.; Ansong, C.; Kelly, R. T. Proteomic Analysis of Single Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLC-MS. Angew. Chem., Int. Ed. 2018, 57 (38), 12370– 12374, DOI: 10.1002/anie.201802843Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtFemur7N&md5=1c6276ba46c3866bfaeb163aee051663Proteomic Analysis of Single Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLC-MSZhu, Ying; Clair, Geremy; Chrisler, William B.; Shen, Yufeng; Zhao, Rui; Shukla, Anil K.; Moore, Ronald J.; Misra, Ravi S.; Pryhuber, Gloria S.; Smith, Richard D.; Ansong, Charles; Kelly, Ryan T.Angewandte Chemie, International Edition (2018), 57 (38), 12370-12374CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)The authors report on the quant. proteomic anal. of single mammalian cells. Fluorescence-activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing chip, after which samples were analyzed by ultrasensitive nanoLC-MS. An av. of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. The single-cell proteomics platform can be used to differentiate cell types from enzyme-dissocd. human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.
- 22Shao, X.; Wang, X.; Guan, S.; Lin, H.; Yan, G.; Gao, M.; Deng, C.; Zhang, X. Integrated Proteome Analysis Device for Fast Single-Cell Protein Profiling. Anal. Chem. 2018, 90 (23), 14003– 14010, DOI: 10.1021/acs.analchem.8b03692Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitVart7%252FO&md5=f7f84b752d5638b2765d395b56eeecf6Integrated Proteome Analysis Device for Fast Single-Cell Protein ProfilingShao, Xi; Wang, Xuantang; Guan, Sheng; Lin, Haizhu; Yan, Guoquan; Gao, Mingxia; Deng, Chunhui; Zhang, XiangminAnalytical Chemistry (Washington, DC, United States) (2018), 90 (23), 14003-14010CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)In the authors' previous work, the authors demonstrated an integrated proteome anal. device (iPAD-100) to analyze proteomes from 100 cells. For the first time, a novel integrated device for single-cell anal. (iPAD-1) was developed to profile proteins in a single cell within 1 h. In the iPAD-1, a selected single cell was directly sucked into a 22 μm internal diam. capillary. Then the cell lysis and protein digestion were simultaneously accomplished in the capillary in a 2 nL vol., which could prevent protein loss and excessive diln. Digestion was accelerated by using elevated temp. with ultrasonication. The whole time of cell treatment was 30 min. After that, single-cell digest peptides were transferred into an LC column directly through a true zero dead vol. union, to minimize protein transfer loss. A homemade 22 μm internal diam. nano-LC packing column with 3 μm internal diam. ESI tip was used in the device to achieve ultrasensitive detection. A 30 min elution program was applied to anal. of the single-cell proteome. Therefore, the total time needed for a single-cell anal. was only 1 h. In an anal. of 10 single HeLa cells, a max. of 328 proteins were identified in one cell by using an Orbitrap Fusion Tribrid MS instrument, and the detection limit was estd. at ∼1.7-170 zmol. Such a sensitivity of the iPAD-1 was ∼120-fold higher than that of the authors' previously developed iPAD-100 system. Prominent cellular heterogeneity in protein expressive profiling was obsd. Furthermore, the authors roughly estd. the phases of the cell cycle of tested HeLa cells by the amt. of core histone proteins.
- 23Zhu, Y.; Piehowski, P. D.; Zhao, R.; Chen, J.; Shen, Y.; Moore, R. J.; Shukla, A. K.; Petyuk, V. A.; Campbell-Thompson, M.; Mathews, C. E.; Smith, R. D.; Qian, W.-J.; Kelly, R. T. Nanodroplet Processing Platform for Deep and Quantitative Proteome Profiling of 10–100 Mammalian Cells. Nat. Commun. 2018, 9 (1), 882, DOI: 10.1038/s41467-018-03367-wGoogle Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1MrmtlyntQ%253D%253D&md5=ed52404cccbcdc4f8b9081ede1d092cbNanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cellsZhu Ying; Zhao Rui; Kelly Ryan T; Piehowski Paul D; Shen Yufeng; Moore Ronald J; Shukla Anil K; Petyuk Vladislav A; Smith Richard D; Qian Wei-Jun; Chen Jing; Campbell-Thompson Martha; Mathews Clayton ENature communications (2018), 9 (1), 882 ISSN:.Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.
- 24Zhou, M.; Uwugiaren, N.; Williams, S. M.; Moore, R. J.; Zhao, R.; Goodlett, D.; Dapic, I.; Paša-Tolić, L.; Zhu, Y. Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform. Anal. Chem. 2020, 92 (10), 7087– 7095, DOI: 10.1021/acs.analchem.0c00467Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXosFWnsrg%253D&md5=2472ebd1f191b4f3419913f0d5999fcaSensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing PlatformZhou, Mowei; Uwugiaren, Naomi; Williams, Sarah M.; Moore, Ronald J.; Zhao, Rui; Goodlett, David; Dapic, Irena; Pasa-Tolic, Ljiljana; Zhu, YingAnalytical Chemistry (Washington, DC, United States) (2020), 92 (10), 7087-7095CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. However, one significant tech. gap of top-down proteomics is the inability to analyze a low amt. of biol. samples, which limits its access to isolated rare cells, fine needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample prepn. system, termed nanoPOTS (nanodroplet processing in one pot for trace samples), into a top-down proteomic workflow. A unique combination of a nonionic detergent dodecyl-β-D-maltopyranoside (DDM) with urea as protein extn. buffer significantly improved both protein extn. efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing nonspecific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream anal. The nanoPOTS-based top-down platform reproducibly and quant. identified ~ 170 to ~ 620 proteoforms from ~ 70 to ~ 770 HeLa cells contg. ~ 10 to ~ 115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical research, particularly where clin. specimens are not available in amts. amenable to std. workflows.
- 25Xu, K.; Liang, Y.; Piehowski, P. D.; Dou, M.; Schwarz, K. C.; Zhao, R.; Sontag, R. L.; Moore, R. J.; Zhu, Y.; Kelly, R. T. Benchtop-Compatible Sample Processing Workflow for Proteome Profiling of < 100 Mammalian Cells. Anal. Bioanal. Chem. 2019, 411 (19), 4587– 4596, DOI: 10.1007/s00216-018-1493-9Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXit12ls7nJ&md5=5f57393c6315af49f67303e080dfe016Benchtop-compatible sample processing workflow for proteome profiling of < 100 mammalian cellsXu, Kerui; Liang, Yiran; Piehowski, Paul D.; Dou, Maowei; Schwarz, Kaitlynn C.; Zhao, Rui; Sontag, Ryan L.; Moore, Ronald J.; Zhu, Ying; Kelly, Ryan T.Analytical and Bioanalytical Chemistry (2019), 411 (19), 4587-4596CODEN: ABCNBP; ISSN:1618-2642. (Springer)Extending proteomics to smaller samples can enable the mapping of protein expression across tissues with high spatial resoln. and can reveal sub-group heterogeneity. However, despite the continually improving sensitivity of LC-MS instrumentation, in-depth profiling of samples contg. low-nanogram amts. of protein has remained challenging due to analyte losses incurred during prepn. and anal. To address this, the authors recently developed nanodroplet processing in one pot for trace samples (nanoPOTS), a robotic/microfluidic platform that generates ready-to-analyze peptides from cellular material in ∼200 nL droplets with greatly reduced sample losses. In combination with ultrasensitive LC-MS, nanoPOTS has enabled >3000 proteins to be confidently identified from as few as 10 cultured human cells and ∼700 proteins from single cells. However, the nanoPOTS platform requires a highly skilled operator and a costly inhouse-built robotic nanopipetting instrument. The authors sought to evaluate the extent to which the benefits of nanodroplet processing could be preserved when upscaling reagent dispensing vols. by a factor of 10 to those addressable by com. micropipette. The authors characterized the resulting platform, termed microdroplet processing in one pot for trace samples (μPOTS), for the anal. of as few as ∼25 cultured HeLa cells (4 ng total protein) or 50 μm square mouse liver tissue thin sections and found that ∼1800 and ∼1200 unique proteins were resp. identified with high reproducibility. The reduced equipment requirements should facilitate broad dissemination of nanoproteomics workflows by obviating the need for a capital-intensive custom liq. handling system.
- 26Ross-Innes, C. S.; Becq, J.; Warren, A.; Cheetham, R. K.; Northen, H.; O’Donovan, M.; Malhotra, S.; di Pietro, M.; Ivakhno, S.; He, M.; Weaver, J. M. J.; Lynch, A. G.; Kingsbury, Z.; Ross, M.; Humphray, S.; Bentley, D.; Fitzgerald, R. C. Whole-Genome Sequencing Provides New Insights into the Clonal Architecture of Barrett’s Esophagus and Esophageal Adenocarcinoma. Nat. Genet. 2015, 47 (9), 1038– 1046, DOI: 10.1038/ng.3357Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1WltLjN&md5=a60d7a9bfd4221c3838bdfdffc50ee2aWhole-genome sequencing provides new insights into the clonal architecture of Barrett's esophagus and esophageal adenocarcinomaRoss-Innes, Caryn S.; Becq, Jennifer; Warren, Andrew; Cheetham, R. Keira; Northen, Helen; O'Donovan, Maria; Malhotra, Shalini; di Pietro, Massimiliano; Ivakhno, Sergii; He, Miao; Weaver, Jamie M. J.; Lynch, Andy G.; Kingsbury, Zoya; Ross, Mark; Humphray, Sean; Bentley, David; Fitzgerald, Rebecca C.; Hayes, Stephen J.; Ang, Yeng; Welch, Ian; Preston, Shaun; Oakes, Sarah; Save, Vicki; Skipworth, Richard; Tucker, Olga; Davies, Jim; Crichton, Charles; Schusterreiter, Christian; Underwood, Tim; Noble, Fergus; Stacey, Bernard; Kelly, Jamie; Byrne, James; Haydon, Annette; Sharland, Donna; Owsley, Jack; Barr, Hugh; Lagergren, Jesper; Gossage, James; Davies, Andrew; Mason, Robert; Chang, Fuju; Zylstra, Janine; Sanders, Grant; Wheatley, Tim; Berrisford, Richard; Bracey, Tim; Harden, Catherine; Bunting, David; Roques, Tom; Nobes, Jenny; Loo, Suat; Lewis, Mike; Cheong, Ed; Priest, Oliver; Parsons, Simon L.; Soomro, Irshad; Kaye, Philip; Saunders, John; Pang, Vincent; Welch, Neil T.; Catton, James A.; Duffy, John P.; Ragunath, Krish; Lovat, Laurence; Haidry, Rehan; Miah, Haroon; Kerr, Sarah; Eneh, Victor; Butawan, Rommel; Roques, Tom; Lewis, Michael; Cheong, Edward; Kumar, Bhasker; Igali, Laszlo; Walton, Sharon; Dann, Adela; Safranek, Peter; Hindmarsh, Andy; Sudjendran, Vijayendran; Scott, Michael; Cluroe, Alison; Miremadi, Ahmad; Mahler-Araujo, Betania; Nutzinger, Barbara; Peters, Chris; Abdullahi, Zarah; Crawte, Jason; MacRae, Shona; Noorani, Ayesha; Elliott, Rachael Fels; Bower, Lawrence; Edwards, Paul; Tavare, Simon; Eldridge, Matthew; Bornschein, Jan; Secrier, Maria; Yang, Tsun-Po; O'Neill, J. Robert; Adamczuk, Kasia; Lao-Sirieix, Pierre; Grehan, Nicola; Smith, Laura; Lishman, Suzy; Beardsmore, Duncan; Dawson, SarahNature Genetics (2015), 47 (9), 1038-1046CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)The mol. genetic relationship between esophageal adenocarcinoma (EAC) and its precursor lesion, Barrett's esophagus, is poorly understood. Using whole-genome sequencing on 23 paired Barrett's esophagus and EAC samples, together with one in-depth Barrett's esophagus case study sampled over time and space, we have provided the following new insights: (i) Barrett's esophagus is polyclonal and highly mutated even in the absence of dysplasia; (ii) when cancer develops, copy no. increases and heterogeneity persists such that the spectrum of mutations often shows surprisingly little overlap between EAC and adjacent Barrett's esophagus; and (iii) despite differences in specific coding mutations, the mutational context suggests a common causative insult underlying these two conditions. From a clin. perspective, the histopathol. assessment of dysplasia appears to be a poor reflection of the mol. disarray within the Barrett's epithelium, and a mol. Cytosponge technique overcomes sampling bias and has the capacity to reflect the entire clonal architecture.
- 27Peters, Y.; Al-Kaabi, A.; Shaheen, N. J.; Chak, A.; Blum, A.; Souza, R. F.; Di Pietro, M.; Iyer, P. G.; Pech, O.; Fitzgerald, R. C.; Siersema, P. D. Barrett Oesophagus. Nat. Rev. Dis. Primer 2019, 5 (1), 35, DOI: 10.1038/s41572-019-0086-zGoogle ScholarThere is no corresponding record for this reference.
- 28Hofmann, A. F. The Continuing Importance of Bile Acids in Liver and Intestinal Disease. Arch. Intern. Med. 1999, 159 (22), 2647– 2658, DOI: 10.1001/archinte.159.22.2647Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3c%252FmsVKhtw%253D%253D&md5=4f017c841da85f0723c292fb31e70037The continuing importance of bile acids in liver and intestinal diseaseHofmann A FArchives of internal medicine (1999), 159 (22), 2647-58 ISSN:0003-9926.Bile acids, the water-soluble, amphipathic end products of cholesterol metabolism, are involved in liver, biliary, and intestinal disease. Formed in the liver, bile acids are absorbed actively from the small intestine, with each molecule undergoing multiple enterohepatic circulations before being excreted. After their synthesis from cholesterol, bile acids are conjugated with glycine or taurine, a process that makes them impermeable to cell membranes and permits high concentrations to persist in bile and intestinal content. The relation between the chemical structure and the multiple physiological functions of bile acids is reviewed. Bile acids induce biliary lipid secretion and solubilize cholesterol in bile, promoting its elimination. In the small intestine, bile acids solubilize dietary lipids promoting their absorption. Bile acids are cytotoxic when present in abnormally high concentrations. This may occur intracellularly, as occurs in the hepatocyte in cholestasis, or extracellularly, as occurs in the colon in patients with bile acid malabsorption. Disturbances in bile acid metabolism can be caused by (1) defective biosynthesis from cholesterol or defective conjugation, (2) defective membrane transport in the hepatocyte or ileal enterocyte, (3) defective transport between organs or biliary diversion, and (4) increased bacterial degradation during enterohepatic cycling. Bile acid therapy involves bile acid replacement in deficiency states or bile acid displacement by ursodeoxycholic acid, a noncytotoxic bile acid. In cholestatic liver disease, administration of ursodeoxycholic acid decreases hepatocyte injury by retained bile acids, improving liver tests, and slowing disease progression. Bile acid malabsorption may lead to high concentrations of bile acids in the colon and impaired colonic mucosal function; bile acid sequestrants provide symptomatic benefit for diarrhea. A knowledge of bile acid physiology and the perturbations of bile acid metabolism in liver and digestive disease should be useful for the internist.
- 29Fitzgerald, R. C.; di Pietro, M.; O’Donovan, M.; Maroni, R.; Muldrew, B.; Debiram-Beecham, I.; Gehrung, M.; Offman, J.; Tripathi, M.; Smith, S. G.; Aigret, B.; Walter, F. M.; Rubin, G.; Bagewadi, A.; Patrick, A.; Shenoy, A.; Redmond, A.; Muddu, A.; Northrop, A.; Groves, A.; Shiner, A.; Heer, A.; Takhar, A.; Bowles, A.; Jarman, A.; Wong, A.; Lucas, A.; Gibbons, A.; Dhar, A.; Curry, A.; Lalonde, A.; Swinburn, A.; Turner, A.; Lydon, A.-M.; Gunstone, A.; Lee, A.; Nambi, A.; Ariyarathenam, A.; Elden, A.; Wilson, A.; Donepudi, B.; Campbell, B.; Uszycka, B.; Bowers, B.; Coghill, B.; de Quadros, B.; Cheah, C.; Bratten, C.; Brown, C.; Moorbey, C.; Clisby, C.; Gordon, C.; Schramm, C.; Castle, C.; Newark, C.; Norris, C.; A’Court, C.; Graham, C.; Fletcher, C.; Grocott, C.; Rees, C.; Bakker, C.; Paschalides, C.; Vickery, C.; Schembri, D.; Morris, D.; Hagan, D.; Cronk, D.; Goddard, D.; Graham, D.; Phillips, D.; Prabhu, D.; Kejariwal, D.; Garg, D.; Lonsdale, D.; Butterworth, D.; Clements, D.; Bradman, D.; Blake, D.; Mather, E.; O’Farrell, E.; Markowetz, F.; Adams, F.; Pesola, F.; Forbes, G.; Taylor, G.; Collins, G.; Irvine, G.; Fourie, G.; Doyle, H.; Barnes, H.; Bowyer, H.; Whiting, H.; Beales, I.; Binnian, I.; Bremner, I.; Jennings, I.; Troiceanu, I.; Modelell, I.; Emmerson, I.; Ortiz, J.; Lilley, J.; Harvey, J.; Vicars, J.; Takhar, J.; Larcombe, J.; Bornschein, J.; Aldegather, J.; Johnson, J.; Ducker, J.; Skinner, J.; Dash, J.; Walsh, J.; Miralles, J.; Ridgway, J.; Ince, J.; Kennedy, J.; Hampson, K.; Milne, K.; Ellerby, K.; Priddis, K.; Rainsbury, K.; Powell, K.; Gunner, K.; Ragunath, K.; Knox, K.; Baseley, L.; White, L.; Lovat, L.; Berney, L.; Crockett, L.; Murray, L.; Westwood, L.; Chalkley, L.; Leggett, L.; Dale, L.; Scovell, L.; Brooks, L.; Saunders, L.; Owen, L.; Dilwershah, M.; Baldry, M.; Corcoran, M.; Roy, M.; Macedo, M.; Attah, M.; Anson, M.-J.; Rutter, M.; Wallard, M.; Gaw, M.; Hunt, M.; Lea-Hagerty, M.; Penacerrada, M.; Bianchi, M.; Baker-Moffatt, M.; Czajkowski, M.; Sleeth, M.; Brewer, N.; Wooding, N.; Todd, N.; Millen, N.; Zolle, O.; Whitehead, O.; Ojechi, P.; Moore, P.; Banim, P.; Spellar, P.; Bhandari, P.; Kant, P.; Nixon, R.; Russell, R.; Roberts, R.; Skule, R.; West, R.; Fox, R.; Beesley, R.; Gibbins, R.; Osborne, R.; Thiagarajan, S.; Bastiman, S.; Warburton, S.; Pai, S.; Leith-Russell, S.; Utting, S.; Watson, S.; Wytrykowski, S.; Singh, S.; Malhotra, S.; Woods, S.; Conway, S.; Mateer, S.; Macrae, S.; Singh, S.; Fourie, S.; Campbell, S.; Parslow-Williams, S.; Goel, S.; Dellar, S.; Jones, S.; Knight, S.; Mackay-Thomas, S.; Mukherjee, S.; Allen, S.; Henry, S.; Evans, T.; Leighton, T.; Bray, T.; Shackleton, T.; Santosh, V.; Glover, V.; Chandraraj, V.; Elson, W.; Briggs, W.; Barron, Z.; Khan, Z.; Sasieni, P. Cytosponge-Trefoil Factor 3 versus Usual Care to Identify Barrett’s Oesophagus in a Primary Care Setting: A Multicentre, Pragmatic, Randomised Controlled Trial. Lancet 2020, 396 (10247), 333– 344, DOI: 10.1016/S0140-6736(20)31099-0Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38jpvVGrtQ%253D%253D&md5=b0b2f86fdf8f90356abf375b51307abeCytosponge-trefoil factor 3 versus usual care to identify Barrett's oesophagus in a primary care setting: a multicentre, pragmatic, randomised controlled trialFitzgerald Rebecca C; di Pietro Massimiliano; O'Donovan Maria; Tripathi Monika; Maroni Roberta; Muldrew Beth; Aigret Benoit; Sasieni Peter; Debiram-Beecham Irene; Gehrung Marcel; Offman Judith; Smith Samuel G; Walter Fiona M; Rubin GregLancet (London, England) (2020), 396 (10247), 333-344 ISSN:.BACKGROUND: Treatment of dysplastic Barrett's oesophagus prevents progression to adenocarcinoma; however, the optimal diagnostic strategy for Barrett's oesophagus is unclear. The Cytosponge-trefoil factor 3 (TFF3) is a non-endoscopic test for Barrett's oesophagus. The aim of this study was to investigate whether offering this test to patients on medication for gastro-oesophageal reflux would increase the detection of Barrett's oesophagus compared with standard management. METHODS: This multicentre, pragmatic, randomised controlled trial was done in 109 socio-demographically diverse general practice clinics in England. Randomisation was done both at the general practice clinic level (cluster randomisation) and at the individual patient level, and the results for each type of randomisation were analysed separately before being combined. Patients were eligible if they were aged 50 years or older, had been taking acid-suppressants for symptoms of gastro-oesophageal reflux for more than 6 months, and had not undergone an endoscopy procedure within the past 5 years. General practice clinics were selected by the local clinical research network and invited to participate in the trial. For cluster randomisation, clinics were randomly assigned (1:1) by the trial statistician using a computer-generated randomisation sequence; for individual patient-level randomisation, patients were randomly assigned (1:1) by the general practice clinics using a centrally prepared computer-generated randomisation sequence. After randomisation, participants received either standard management of gastro-oesophageal reflux (usual care group), in which participants only received an endoscopy if required by their general practitioner, or usual care plus an offer of the Cytosponge-TFF3 procedure, with a subsequent endoscopy if the procedure identified TFF3-positive cells (intervention group). The primary outcome was the diagnosis of Barrett's oesophagus at 12 months after enrolment, expressed as a rate per 1000 person-years, in all participants in the intervention group (regardless of whether they had accepted the offer of the Cytosponge-TFF3 procedure) compared with all participants in the usual care group. Analyses were intention-to-treat. The trial is registered with the ISRCTN registry, ISRCTN68382401, and is completed. FINDINGS: Between March 20, 2017, and March 21, 2019, 113 general practice clinics were enrolled, but four clinics dropped out shortly after randomisation. Using an automated search of the electronic prescribing records of the remaining 109 clinics, we identified 13 657 eligible patients who were sent an introductory letter with 14 days to opt out. 13 514 of these patients were randomly assigned (per practice or at the individual patient level) to the usual care group (n=6531) or the intervention group (n=6983). Following randomisation, 149 (2%) of 6983 participants in the intervention group and 143 (2%) of 6531 participants in the usual care group, on further scrutiny, did not meet all eligibility criteria or withdrew from the study. Of the remaining 6834 participants in the intervention group, 2679 (39%) expressed an interest in undergoing the Cytosponge-TFF3 procedure. Of these, 1750 (65%) met all of the eligibility criteria on telephone screening and underwent the procedure. Most of these participants (1654 [95%]; median age 69 years) swallowed the Cytosponge successfully and produced a sample. 231 (3%) of 6834 participants had a positive Cytosponge-TFF3 result and were referred for an endoscopy. Patients who declined the offer of the Cytosponge-TFF3 procedure and all participants in the usual care group only had an endoscopy if deemed necessary by their general practitioner. During an average of 12 months of follow-up, 140 (2%) of 6834 participants in the intervention group and 13 (<1%) of 6388 participants in the usual care group were diagnosed with Barrett's oesophagus (absolute difference 18·3 per 1000 person-years [95% CI 14·8-21·8]; rate ratio adjusted for cluster randomisation 10·6 [95% CI 6·0-18·8], p<0·0001). Nine (<1%) of 6834 participants were diagnosed with dysplastic Barrett's oesophagus (n=4) or stage I oesophago-gastric cancer (n=5) in the intervention group, whereas no participants were diagnosed with dysplastic Barrett's oesophagus or stage I gastro-oesophageal junction cancer in the usual care group. Among 1654 participants in the intervention group who swallowed the Cytosponge device successfully, 221 (13%) underwent endoscopy after testing positive for TFF3 and 131 (8%, corresponding to 59% of those having an endoscopy) were diagnosed with Barrett's oesophagus or cancer. One patient had a detachment of the Cytosponge from the thread requiring endoscopic removal, and the most common side-effect was a sore throat in 63 (4%) of 1654 participants. INTERPRETATION: In patients with gastro-oesophageal reflux, the offer of Cytosponge-TFF3 testing results in improved detection of Barrett's oesophagus. Cytosponge-TFF3 testing could also lead to the diagnosis of treatable dysplasia and early cancer. This strategy will lead to additional endoscopies with some false positive results. FUNDING: Cancer Research UK, National Institute for Health Research, the UK National Health Service, Medtronic, and the Medical Research Council.
- 30Ross-Innes, C. S.; Chettouh, H.; Achilleos, A.; Galeano-Dalmau, N.; Debiram-Beecham, I.; MacRae, S.; Fessas, P.; Walker, E.; Varghese, S.; Evan, T.; Lao-Sirieix, P. S.; O’Donovan, M.; Malhotra, S.; Novelli, M.; Disep, B.; Kaye, P. V.; Lovat, L. B.; Haidry, R.; Griffin, M.; Ragunath, K.; Bhandari, P.; Haycock, A.; Morris, D.; Attwood, S.; Dhar, A.; Rees, C.; Rutter, M. D.; Ostler, R.; Aigret, B.; Sasieni, P. D.; Fitzgerald, R. C. Risk Stratification of Barrett’s Oesophagus Using a Non-Endoscopic Sampling Method Coupled with a Biomarker Panel: A Cohort Study. Lancet Gastroenterol. Hepatol. 2017, 2 (1), 23– 31, DOI: 10.1016/S2468-1253(16)30118-2Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cvnt1OmtQ%253D%253D&md5=e51955fc3160fbf1f2d64146831713ddRisk stratification of Barrett's oesophagus using a non-endoscopic sampling method coupled with a biomarker panel: a cohort studyRoss-Innes Caryn S; Chettouh Hamza; Achilleos Achilleas; Galeano-Dalmau Nuria; Debiram-Beecham Irene; MacRae Shona; Fessas Petros; Walker Elaine; Varghese Sibu; Evan Theodore; Lao-Sirieix Pierre S; O'Donovan Maria; Malhotra Shalini; Novelli Marco; Lovat Laurence B; Haidry Rehan; Disep Babett; Griffin Michael; Kaye Phillip V; Ragunath Krish; Bhandari Pradeep; Haycock Adam; Morris Danielle; Attwood Stephen; Dhar Anjan; Rees Colin; Rutter Matt D; Ostler Richard; Aigret Benoit; Sasieni Peter D; Fitzgerald Rebecca CThe lancet. Gastroenterology & hepatology (2017), 2 (1), 23-31 ISSN:.BACKGROUND: Barrett's oesophagus predisposes to adenocarcinoma. However, most patients with Barrett's oesophagus will not progress and endoscopic surveillance is invasive, expensive, and fraught by issues of sampling bias and the subjective assessment of dysplasia. We investigated whether a non-endoscopic device, the Cytosponge, could be coupled with clinical and molecular biomarkers to identify a group of patients with low risk of progression suitable for non-endoscopic follow-up. METHODS: In this multicentre cohort study (BEST2), patients with Barrett's oesophagus underwent the Cytosponge test before their surveillance endoscopy. We collected clinical and demographic data and tested Cytosponge samples for a molecular biomarker panel including three protein biomarkers (P53, c-Myc, and Aurora kinase A), two methylation markers (MYOD1 and RUNX3), glandular atypia, and TP53 mutation status. We used a multivariable logistic regression model to compute the conditional probability of dysplasia status. We selected a simple model with high classification accuracy and applied it to an independent validation cohort. The BEST2 study is registered with ISRCTN, number 12730505. FINDINGS: The discovery cohort consisted of 468 patients with Barrett's oesophagus and intestinal metaplasia. Of these, 376 had no dysplasia and 22 had high-grade dysplasia or intramucosal adenocarcinoma. In the discovery cohort, a model with high classification accuracy consisted of glandular atypia, P53 abnormality, and Aurora kinase A positivity, and the interaction of age, waist-to-hip ratio, and length of the Barrett's oesophagus segment. 162 (35%) of 468 of patients fell into the low-risk category and the probability of being a true non-dysplastic patient was 100% (99% CI 96-100) and the probability of having high-grade dysplasia or intramucosal adenocarcinoma was 0% (0-4). 238 (51%) of participants were classified as of moderate risk; the probability of having high-grade dysplasia was 14% (9-21). 58 (12%) of participants were classified as high-risk; the probability of having non-dysplastic endoscopic biopsies was 13% (5-27), whereas the probability of having high-grade dysplasia or intramucosal adenocarcinoma was 87% (73-95). In the validation cohort (65 patients), 51 were non-dysplastic and 14 had high-grade dysplasia. In this cohort, 25 (38%) of 65 patients were classified as being low-risk, and the probability of being non-dysplastic was 96·0% (99% CI 73·80-99·99). The moderate-risk group comprised 27 non-dysplastic and eight high-grade dysplasia cases, whereas the high-risk group (8% of the cohort) had no non-dysplastic cases and five patients with high-grade dysplasia. INTERPRETATION: A combination of biomarker assays from a single Cytosponge sample can be used to determine a group of patients at low risk of progression, for whom endoscopy could be avoided. This strategy could help to avoid overdiagnosis and overtreatment in patients with Barrett's oesophagus. FUNDING: Cancer Research UK.
- 31Williams, S. M.; Liyu, A. V.; Tsai, C.-F.; Moore, R. J.; Orton, D. J.; Chrisler, W. B.; Gaffrey, M. J.; Liu, T.; Smith, R. D.; Kelly, R. T.; Pasa-Tolic, L.; Zhu, Y. Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics. Anal. Chem. 2020, 92 (15), 10588– 10596, DOI: 10.1021/acs.analchem.0c01551Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtlCmtL3O&md5=11a723a01a532b3fd5dbb8f43fb00134Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell ProteomicsWilliams, Sarah M.; Liyu, Andrey V.; Tsai, Chia-Feng; Moore, Ronald J.; Orton, Daniel J.; Chrisler, William B.; Gaffrey, Matthew J.; Liu, Tao; Smith, Richard D.; Kelly, Ryan T.; Pasa-Tolic, Ljiljana; Zhu, YingAnalytical Chemistry (Washington, DC, United States) (2020), 92 (15), 10588-10596CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Single-cell proteomics can provide crit. biol. insight into the cellular heterogeneity that is masked by bulk-scale anal. The authors have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample vols., the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, the authors have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. The authors also developed a sample drying, extn., and loading workflow to enable reproducible and reliable sample injection. The sequential anal. of 20 samples contg. 10 ng tryptic peptides demonstrated high reproducibility with correlation coeffs. of >0.995 between any two samples. The nanoPOTS autosampler can provide anal. throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, resp. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an av. of 256 proteins was identified from each cell and the no. was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~ 77 single cells could be analyzed per day. The authors analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quant. single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.
- 32DEP: Differential Enrichment analysis of Proteomics data version 1.10.0 from Bioconductor, https://rdrr.io/bioc/DEP/ (accessed Jul 4, 2020).Google ScholarThere is no corresponding record for this reference.
- 33Zhang, X.; Smits, A. H.; van Tilburg, G. B.; Ovaa, H.; Huber, W.; Vermeulen, M. Proteome-Wide Identification of Ubiquitin Interactions Using UbIA-MS. Nat. Protoc. 2018, 13 (3), 530– 550, DOI: 10.1038/nprot.2017.147Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisl2ku74%253D&md5=88719f04dffb8fffe078df606dd6f5d7Proteome-wide identification of ubiquitin interactions using UbIA-MSZhang, Xiaofei; Smits, Arne H.; van Tilburg, Gabrielle B. A.; Ovaa, Huib; Huber, Wolfgang; Vermeulen, MichielNature Protocols (2018), 13 (3), 530-550CODEN: NPARDW; ISSN:1750-2799. (Nature Research)Ubiquitin-binding proteins play an important role in eukaryotes by translating differently linked polyubiquitin chains into proper cellular responses. Current knowledge about ubiquitin-binding proteins and ubiquitin linkage-selective interactions is mostly based on case-by-case studies. We have recently reported a method called ubiquitin interactor affinity enrichment-mass spectrometry (UbIA-MS), which enables comprehensive identification of ubiquitin interactors for all ubiquitin linkages from crude cell lysates. One major strength of UbIA-MS is the fact that ubiquitin interactors are enriched from crude cell lysates, in which proteins are present at endogenous levels, contain biol. relevant post-translational modifications (PTMs) and are assembled in native protein complexes. In addn., UbIA-MS uses chem. synthesized nonhydrolyzable diubiquitin, which mimics native diubiquitin and is inert to cleavage by endogenous deubiquitinases (DUBs). Here, we present a detailed protocol for UbIA-MS that proceeds in five stages: (i) chem. synthesis of ubiquitin precursors and click chem. for the generation of biotinylated nonhydrolyzable diubiquitin baits, (ii) in vitro affinity purifn. of ubiquitin interactors, (iii) on-bead interactor digestion, (iv) liq. chromatog. (LC)-MS/MS anal. and (v) data anal. to identify differentially enriched proteins. The computational anal. tools are freely available as an open-source R software package, including a graphical interface. Typically, UbIA-MS allows the identification of dozens to hundreds of ubiquitin interactors from any type of cell lysate, and can be used to study cell type or stimulus-dependent ubiquitin interactions. The nonhydrolyzable diubiquitin synthesis can be completed in 3 wk, followed by ubiquitin interactor enrichment and identification, which can be completed within another 2 wk.
- 34test_diff: Differential enrichment test in DEP: Differential Enrichment analysis of Proteomics data, https://rdrr.io/bioc/DEP/man/test_diff.html (accessed Jul 5, 2020).Google ScholarThere is no corresponding record for this reference.
- 35Ritchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K. Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res. 2015, 43 (7), e47– e47, DOI: 10.1093/nar/gkv007Google Scholar35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsFaiu7%252FN&md5=e8a5f44160c650ad89adc48a9912bd96limma powers differential expression analyses for RNA-sequencing and microarray studiesRitchie, Matthew E.; Phipson, Belinda; Wu, Di; Hu, Yifang; Law, Charity W.; Shi, Wei; Smyth, Gordon K.Nucleic Acids Research (2015), 43 (7), e47/1-e47/13CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Limma is an R/Bioconductor software package that provides an integrated soln. for analyzing data from gene expression expts. It contains rich features for handling complex exptl. designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream anal. tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyze both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analyzing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biol. interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
- 36P’ng, C.; Green, J.; Chong, L. C.; Waggott, D.; Prokopec, S. D.; Shamsi, M.; Nguyen, F.; Mak, D. Y. F.; Lam, F.; Albuquerque, M. A.; Wu, Y.; Jung, E. H.; Starmans, M. H. W.; Chan-Seng-Yue, M. A.; Yao, C. Q.; Liang, B.; Lalonde, E.; Haider, S.; Simone, N. A.; Sendorek, D.; Chu, K. C.; Moon, N. C.; Fox, N. S.; Grzadkowski, M. R.; Harding, N. J.; Fung, C.; Murdoch, A. R.; Houlahan, K. E.; Wang, J.; Garcia, D. R.; de Borja, R.; Sun, R. X.; Lin, X.; Chen, G. M.; Lu, A.; Shiah, Y.-J.; Zia, A.; Kearns, R.; Boutros, P. C. BPG: Seamless, Automated and Interactive Visualization of Scientific Data. BMC Bioinf. 2019, 20 (1), 42, DOI: 10.1186/s12859-019-2610-2Google Scholar36https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3cjjs1altA%253D%253D&md5=61ec34f7005b4dfc1819b7e76300d80fBPG: Seamless, automated and interactive visualization of scientific dataP'ng Christine; Green Jeffrey; Chong Lauren C; Waggott Daryl; Prokopec Stephenie D; Shamsi Mehrdad; Nguyen Francis; Mak Denise Y F; Lam Felix; Albuquerque Marco A; Wu Ying; Jung Esther H; Starmans Maud H W; Chan-Seng-Yue Michelle A; Yao Cindy Q; Liang Bianca; Lalonde Emilie; Haider Syed; Simone Nicole A; Sendorek Dorota; Chu Kenneth C; Moon Nathalie C; Fox Natalie S; Grzadkowski Michal R; Harding Nicholas J; Fung Clement; Murdoch Amanda R; Houlahan Kathleen E; Wang Jianxin; Garcia David R; de Borja Richard; Sun Ren X; Lin Xihui; Chen Gregory M; Lu Aileen; Shiah Yu-Jia; Zia Amin; Kearns Ryan; Boutros Paul C; Yao Cindy Q; Lalonde Emilie; Fox Natalie S; Houlahan Kathleen E; Shiah Yu-Jia; Boutros Paul C; Wang Jianxin; Sun Ren X; Lu Aileen; Boutros Paul C; Boutros Paul C; Boutros Paul C; Boutros Paul C; Boutros Paul CBMC bioinformatics (2019), 20 (1), 42 ISSN:.BACKGROUND: We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. RESULTS: This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. CONCLUSION: BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.
- 37Hulsen, T.; de Vlieg, J.; Alkema, W. BioVenn – a Web Application for the Comparison and Visualization of Biological Lists Using Area-Proportional Venn Diagrams. BMC Genomics 2008, 9 (1), 488, DOI: 10.1186/1471-2164-9-488Google Scholar37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjksF2ltQ%253D%253D&md5=1c79e2fa6accc5ff3c4cd77bab9aca8cBioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagramsHulsen Tim; de Vlieg Jacob; Alkema WynandBMC genomics (2008), 9 (), 488 ISSN:.BACKGROUND: In many genomics projects, numerous lists containing biological identifiers are produced. Often it is useful to see the overlap between different lists, enabling researchers to quickly observe similarities and differences between the data sets they are analyzing. One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram: a diagram consisting of two or more circles in which each circle corresponds to a data set, and the overlap between the circles corresponds to the overlap between the data sets. Venn diagrams are especially useful when they are 'area-proportional' i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. Currently there are no programs available that can create area-proportional Venn diagrams connected to a wide range of biological databases. RESULTS: We designed a web application named BioVenn to summarize the overlap between two or three lists of identifiers, using area-proportional Venn diagrams. The user only needs to input these lists of identifiers in the textboxes and push the submit button. Parameters like colors and text size can be adjusted easily through the web interface. The position of the text can be adjusted by 'drag-and-drop' principle. The output Venn diagram can be shown as an SVG or PNG image embedded in the web application, or as a standalone SVG or PNG image. The latter option is useful for batch queries. Besides the Venn diagram, BioVenn outputs lists of identifiers for each of the resulting subsets. If an identifier is recognized as belonging to one of the supported biological databases, the output is linked to that database. Finally, BioVenn can map Affymetrix and EntrezGene identifiers to Ensembl genes. CONCLUSION: BioVenn is an easy-to-use web application to generate area-proportional Venn diagrams from lists of biological identifiers. It supports a wide range of identifiers from the most used biological databases currently available. Its implementation on the World Wide Web makes it available for use on any computer with internet connection, independent of operating system and without the need to install programs locally. BioVenn is freely accessible at http://www.cmbi.ru.nl/cdd/biovenn/.
- 38Perez-Riverol, Y.; Csordas, A.; Bai, J.; Bernal-Llinares, M.; Hewapathirana, S.; Kundu, D. J.; Inuganti, A.; Griss, J.; Mayer, G.; Eisenacher, M.; Pérez, E.; Uszkoreit, J.; Pfeuffer, J.; Sachsenberg, T.; Yılmaz, Ş.; Tiwary, S.; Cox, J.; Audain, E.; Walzer, M.; Jarnuczak, A. F.; Ternent, T.; Brazma, A.; Vizcaíno, J. A. The PRIDE Database and Related Tools and Resources in 2019: Improving Support for Quantification Data. Nucleic Acids Res. 2019, 47 (D1), D442– D450, DOI: 10.1093/nar/gky1106Google Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhs1GqtrzK&md5=4cd0f929c2df840a5cc0631d0d708d6cThe PRIDE database and related tools and resources in 2019: improving support for quantification dataPerez-Riverol, Yasset; Csordas, Attila; Bai, Jingwen; Bernal-Llinare, Manuel; Hewapathirana, Suresh; Kundu, Deepti J.; Iuganti, Avinash; Griss, Johannes; Mayer, Gerhard; Eisenacher, Martin; Perez, Enrique; Uszkoreit, Julian; Pfeuffer, Julianus; Sachsenberg, Timo; Yilmaz, Sule; Tiwary, Shivani; Cox, Jurgen; Audain, Enrique; Walzer, Mathias; Jarnuczak, Andrew F.; Ternent, Tobias; Brazma, Alvis; Vizcaino, Juan AntonioNucleic Acids Research (2019), 47 (D1), D442-D450CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)A review. The PRoteomics IDEntifications (PRIDE) database is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data vols. and addnl. data types. A new scalable and fault tolerant storage backed, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Addnl., we emphasize the improved support for quant. proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and vol. of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
- 39UniProt, https://www.uniprot.org/ (accessed Jun 25, 2020).Google ScholarThere is no corresponding record for this reference.
- 40Protein GRAVY, https://www.bioinformatics.org/sms2/protein_gravy.html (accessed Jun 25, 2020).Google ScholarThere is no corresponding record for this reference.
- 41Budnik, B.; Levy, E.; Harmange, G.; Slavov, N. SCoPE-MS: Mass Spectrometry of Single Mammalian Cells Quantifies Proteome Heterogeneity during Cell Differentiation. Genome Biol. 2018, 19 (1), 161, DOI: 10.1186/s13059-018-1547-5Google Scholar41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisFKls7nM&md5=bfdeda4d526356689afaa4c6d97a20d5SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiationBudnik, Bogdan; Levy, Ezra; Harmange, Guillaume; Slavov, NikolaiGenome Biology (2018), 19 (), 161/1-161/12CODEN: GNBLFW; ISSN:1474-760X. (BioMed Central Ltd.)Some exciting biol. questions require quantifying thousands of proteins in single cells. To achieve this goal, we develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and validate its ability to identify distinct human cancer cell types based on their proteomes. We use SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem cells. The single-cell proteomes enable us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicates coordinated mRNA and protein covariation, yet many genes exhibit functionally concerted and distinct regulatory patterns at the mRNA and the protein level.
- 42Dou, M.; Clair, G.; Tsai, C.-F.; Xu, K.; Chrisler, W. B.; Sontag, R. L.; Zhao, R.; Moore, R. J.; Liu, T.; Pasa-Tolic, L.; Smith, R. D.; Shi, T.; Adkins, J. N.; Qian, W.-J.; Kelly, R. T.; Ansong, C.; Zhu, Y. High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labelling in a Nanodroplet Sample Preparation Platform. Anal. Chem. 2019, 91 (20), 13119– 13127, DOI: 10.1021/acs.analchem.9b03349Google Scholar42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhslKisbbJ&md5=800d1bf514934f3fe80ae23fa44729d7High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labeling in a Nanodroplet Sample Preparation PlatformDou, Maowei; Clair, Geremy; Tsai, Chia-Feng; Xu, Kerui; Chrisler, William B.; Sontag, Ryan L.; Zhao, Rui; Moore, Ronald J.; Liu, Tao; Pasa-Tolic, Ljiljana; Smith, Richard D.; Shi, Tujin; Adkins, Joshua N.; Qian, Wei-Jun; Kelly, Ryan T.; Ansong, Charles; Zhu, YingAnalytical Chemistry (Washington, DC, United States) (2019), 91 (20), 13119-13127CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein the authors combined microfluidic nanodroplet technol. with tandem mass tag (TMT) isobaric labeling to significantly improve anal. throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex anal. of single cell-sized protein quantities to a depth of ∼1600 proteins with median CV of 10.9% and correlation coeff. of 0.98. To demonstrate in-depth high throughput single cell anal., the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2300 proteins identified. Principal component anal. grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. The platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.
- 43Weng, S. S. H.; Demir, F.; Ergin, E. K.; Dirnberger, S.; Uzozie, A.; Tuscher, D.; Nierves, L.; Tsui, J.; Huesgen, P. F.; Lange, P. F. Sensitive Determination of Proteolytic Proteoforms in Limited Microscale Proteome Samples. Mol. Cell. Proteomics 2019, 18 (11), 2335– 2347, DOI: 10.1074/mcp.TIR119.001560Google Scholar43https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVSjsrjN&md5=5f4d95ae406fe34978aa8860add4fa9cSensitive determination of proteolytic proteoforms in limited microscale proteome samplesWeng, Samuel S. H.; Demir, Fatih; Ergin, Enes K.; Dirnberger, Sabrina; Uzozie, Anuli; Tuscher, Domenic; Nierves, Lorenz; Tsui, Janice; Huesgen, Pitter F.; Lange, Philipp F.Molecular & Cellular Proteomics (2019), 18 (11), 2335-2347CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)Protein N termini unambiguously identify truncated, alternatively translated or modified proteoforms with distinct functions and reveal perturbations in disease. Selective enrichment of N-terminal peptides is necessary to achieve proteome-wide coverage for unbiased identification of site-specific regulatory proteolytic processing and protease substrates. However, many proteolytic processes are strictly confined in time and space and therefore can only be analyzed in minute samples that provide insufficient starting material for current enrichment protocols. Here we present High-efficiency Undecanal-based N Termini EnRichment (HUNTER), a robust, sensitive and scalable method for the anal. of previously inaccessible microscale samples. HUNTER achieved identification of >1000 N termini from as little as 2 mug raw HeLa cell lysate. Broad applicability is demonstrated by the first N-terminome anal. of sorted human primary immune cells and enriched mitochondrial fractions from pediatric cancer patients, as well as protease substrate identification from individual Arabidopsis thaliana wild type and Vacuolar Processing Enzyme-deficient mutant seedlings. We further implemented the workflow on a liq. handling system and demonstrate the feasibility of clin. degradomics by automated processing of liq. biopsies from pediatric cancer patients.
- 44Kasuga, K.; Katoh, Y.; Nagase, K.; Igarashi, K. Microproteomics with Microfluidic-Based Cell Sorting: Application to 1000 and 100 Immune Cells. Proteomics 2017, 17 (13–14), 1600420, DOI: 10.1002/pmic.201600420Google ScholarThere is no corresponding record for this reference.
- 45Sun, J.; Zhang, G. L.; Li, S.; Ivanov, A. R.; Fenyo, D.; Lisacek, F.; Murthy, S. K.; Karger, B. L.; Brusic, V. Pathway Analysis and Transcriptomics Improve Protein Identification by Shotgun Proteomics from Samples Comprising Small Number of Cells - a Benchmarking Study. BMC Genomics 2014, 15 (Suppl 9), S1, DOI: 10.1186/1471-2164-15-S9-S1Google ScholarThere is no corresponding record for this reference.
- 46Tanca, A.; Abbondio, M.; Pisanu, S.; Pagnozzi, D.; Uzzau, S.; Addis, M. F. Critical Comparison of Sample Preparation Strategies for Shotgun Proteomic Analysis of Formalin-Fixed, Paraffin-Embedded Samples: Insights from Liver Tissue. Clin. Proteomics 2014, 11 (1), 28, DOI: 10.1186/1559-0275-11-28Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhslShsLjJ&md5=407da191800181ff40883e9c36fef0c3Critical comparison of sample preparation strategies for shotgun proteomic analysis of formalin-fixed, paraffin-embedded samples: insights from liver tissueTanca, Alessandro; Abbondio, Marcello; Pisanu, Salvatore; Pagnozzi, Daniela; Uzzau, Sergio; Addis, Maria FilippaClinical Proteomics (2014), 11 (), 28/1-28/12, 12 pp.CODEN: CPLRCX; ISSN:1542-6416. (BioMed Central Ltd.)Background: The growing field of formalin-fixed paraffin-embedded (FFPE) tissue proteomics holds promise for improving translational research. Direct tissue trypsinization (DT) and protein extn. followed by in soln. digestion (ISD) or filter-aided sample prepn. (FASP) are the most common workflows for shotgun anal. of FFPE samples, but a crit. comparison of the different methods is currently lacking. Exptl. Design: DT, FASP and ISD workflows were compared by subjecting to the same label-free quant. approach three independent tech. replicates of each method applied to FFPE liver tissue. Data were evaluated in terms of method reproducibility and protein/peptide distribution according to localization, MW, pI and hydrophobicity. Results: DT showed lower reproducibility, good preservation of high-MW proteins, a general bias towards hydrophilic and acidic proteins, much lower keratin contamination, as well as higher abundance of non-tryptic peptides. Conversely, FASP and ISD proteomes were depleted in high-MW proteins and enriched in hydrophobic and membrane proteins; FASP provided higher identification yields, while ISD exhibited higher reproducibility. Conclusions: These results highlight that diverse sample prepn. strategies provide significantly different proteomic information, and present typical biases that should be taken into account when dealing with FFPE samples. When a sufficient amt. of tissue is available, the complementary use of different methods is suggested to increase proteome coverage and depth.
- 47Föll, M. C.; Fahrner, M.; Oria, V. O.; Kühs, M.; Biniossek, M. L.; Werner, M.; Bronsert, P.; Schilling, O. Reproducible Proteomics Sample Preparation for Single FFPE Tissue Slices Using Acid-Labile Surfactant and Direct Trypsinization. Clin. Proteomics 2018, 15 (1), 11, DOI: 10.1186/s12014-018-9188-yGoogle Scholar47https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1MrpvFarsg%253D%253D&md5=48eb2283b6a607be9332be0d1ddf983bReproducible proteomics sample preparation for single FFPE tissue slices using acid-labile surfactant and direct trypsinizationFoll Melanie Christine; Fahrner Matthias; Oria Victor Oginga; Biniossek Martin Lothar; Schilling Oliver; Foll Melanie Christine; Fahrner Matthias; Oria Victor Oginga; Fahrner Matthias; Oria Victor Oginga; Kuhs Markus; Werner Martin; Bronsert Peter; Kuhs Markus; Werner Martin; Bronsert Peter; Kuhs Markus; Werner Martin; Bronsert Peter; Werner Martin; Bronsert Peter; Schilling Oliver; Schilling OliverClinical proteomics (2018), 15 (), 11 ISSN:1542-6416.BACKGROUND: Proteomic analyses of clinical specimens often rely on human tissues preserved through formalin-fixation and paraffin embedding (FFPE). Minimal sample consumption is the key to preserve the integrity of pathological archives but also to deal with minimal invasive core biopsies. This has been achieved by using the acid-labile surfactant RapiGest in combination with a direct trypsinization (DTR) strategy. A critical comparison of the DTR protocol with the most commonly used filter aided sample preparation (FASP) protocol is lacking. Furthermore, it is unknown how common histological stainings influence the outcome of the DTR protocol. METHODS: Four single consecutive murine kidney tissue specimens were prepared with the DTR approach or with the FASP protocol using both 10 and 30 k filter devices and analyzed by label-free, quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS). We compared the different protocols in terms of proteome coverage, relative label-free quantitation, missed cleavages, physicochemical properties and gene ontology term annotations of the proteins. Additionally, we probed compatibility of the DTR protocol for the analysis of common used histological stainings, namely hematoxylin & eosin (H&E), hematoxylin and hemalaun. These were proteomically compared to an unstained control by analyzing four human tonsil FFPE tissue specimens per condition. RESULTS: On average, the DTR protocol identified 1841 ± 22 proteins in a single, non-fractionated LC-MS/MS analysis, whereas these numbers were 1857 ± 120 and 1970 ± 28 proteins for the FASP 10 and 30 k protocol. The DTR protocol showed 15% more missed cleavages, which did not adversely affect quantitation and intersample comparability. Hematoxylin or hemalaun staining did not adversely impact the performance of the DTR protocol. A minor perturbation was observed for H&E staining, decreasing overall protein identification by 13%. CONCLUSIONS: In essence, the DTR protocol can keep up with the FASP protocol in terms of qualitative and quantitative reproducibility and performed almost as well in terms of proteome coverage and missed cleavages. We highlight the suitability of the DTR protocol as a viable and straightforward alternative to the FASP protocol for proteomics-based clinical research.
- 48Broeckx, V.; Boonen, K.; Pringels, L.; Sagaert, X.; Prenen, H.; Landuyt, B.; Schoofs, L.; Maes, E. Comparison of Multiple Protein Extraction Buffers for GeLC-MS/MS Proteomic Analysis of Liver and Colon Formalin-Fixed, Paraffin-Embedded Tissues. Mol. BioSyst. 2016, 12 (2), 553– 565, DOI: 10.1039/C5MB00670HGoogle Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvF2hsrvK&md5=1e245f198339d7405f0c8709bbc4e44aComparison of multiple protein extraction buffers for GeLC-MS/MS proteomic analysis of liver and colon formalin-fixed, paraffin-embedded tissuesBroeckx, Valerie; Boonen, Kurt; Pringels, Lentel; Sagaert, Xavier; Prenen, Hans; Landuyt, Bart; Schoofs, Liliane; Maes, EvelyneMolecular BioSystems (2016), 12 (2), 553-565CODEN: MBOIBW; ISSN:1742-2051. (Royal Society of Chemistry)Formalin-fixed paraffin-embedded (FFPE) tissue specimens represent a potential valuable source of samples for clin. research. Since these specimens are banked in hospital archives, large cohorts of samples can be collected in short periods of time which can all be linked with a patients' clin. history. Therefore, the use of FFPE tissue in protein biomarker discovery studies gains interest. However, despite the growing no. of FFPE proteome studies in the literature, there is a lack of a FFPE proteomics std. operating procedure (SOP). One of the challenging steps in the development of such a SOP is the ability to obtain an efficient and repeatable extn. of full length FFPE proteins. In this study, the protein extn. efficiency of eight protein extn. buffers is critically compared with GeLC-MS/MS (1D gel electrophoresis followed by in-gel digestion and LC-MS/MS). The data variation caused by using these extn. buffers was investigated since the variation is a very important aspect when using FFPE tissue as a source for biomarker detection. In addn., a qual. comparison was made between the protein extn. efficiency and repeatability for FFPE tissue and fresh frozen tissue.
- 49Tabb, D. L.; Vega-Montoto, L.; Rudnick, P. A.; Variyath, A. M.; Ham, A.-J. L.; Bunk, D. M.; Kilpatrick, L. E.; Billheimer, D. D.; Blackman, R. K.; Cardasis, H. L.; Carr, S. A.; Clauser, K. R.; Jaffe, J. D.; Kowalski, K. A.; Neubert, T. A.; Regnier, F. E.; Schilling, B.; Tegeler, T. J.; Wang, M.; Wang, P.; Whiteaker, J. R.; Zimmerman, L. J.; Fisher, S. J.; Gibson, B. W.; Kinsinger, C. R.; Mesri, M.; Rodriguez, H.; Stein, S. E.; Tempst, P.; Paulovich, A. G.; Liebler, D. C.; Spiegelman, C. Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography–Tandem Mass Spectrometry. J. Proteome Res. 2010, 9 (2), 761– 776, DOI: 10.1021/pr9006365Google Scholar49https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhsFGnt7bP&md5=77c1052918728af58b3b5c4a5bc250a9Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography-Tandem Mass SpectrometryTabb, David L.; Vega-Montoto, Lorenzo; Rudnick, Paul A.; Variyath, Asokan Mulayath; Ham, Amy-Joan L.; Bunk, David M.; Kilpatrick, Lisa E.; Billheimer, Dean D.; Blackman, Ronald K.; Cardasis, Helene L.; Carr, Steven A.; Clauser, Karl R.; Jaffe, Jacob D.; Kowalski, Kevin A.; Neubert, Thomas A.; Regnier, Fred E.; Schilling, Birgit; Tegeler, Tony J.; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Fisher, Susan J.; Gibson, Bradford W.; Kinsinger, Christopher R.; Mesri, Mehdi; Rodriguez, Henry; Stein, Stephen E.; Tempst, Paul; Paulovich, Amanda G.; Liebler, Daniel C.; Spiegelman, CliffJournal of Proteome Research (2010), 9 (2), 761-776CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)The complexity of proteomic instrumentation for LC-MS/MS introduces many possible sources of variability. Data-dependent sampling of peptides constitutes a stochastic element at the heart of discovery proteomics. Although this variation impacts the identification of peptides, proteomic identifications are far from completely random. In this study, the authors analyzed interlab. data sets from the NCI Clin. Proteomic Technol. Assessment for Cancer to examine repeatability and reproducibility in peptide and protein identifications. Included data spanned 144 LC-MS/MS expts. on four Thermo LTQ and four Orbitrap instruments. Samples included yeast lysate, the NCI-20 defined dynamic range protein mix, and the Sigma UPS 1 defined equimolar protein mix. Some of the authors' findings reinforced conventional wisdom, such as repeatability and reproducibility being higher for proteins than for peptides. Most lessons from the data, however, were more subtle. Orbitrap proved capable of higher repeatability and reproducibility, but aberrant performance occasionally erased these gains. Even the simplest protein digestions yielded more peptide ions than LC-MS/MS could identify during a single expt. The authors obsd. that peptide lists from pairs of tech. replicates overlapped by 35-60%, giving a range for peptide-level repeatability in these expts. Sample complexity did not appear to affect peptide identification repeatability, even as nos. of identified spectra changed by an order of magnitude. Statistical anal. of protein spectral counts revealed greater stability across tech. replicates for Orbitrap, making them superior to LTQ instruments for biomarker candidate discovery. The most repeatable peptides were those corresponding to conventional tryptic cleavage sites, those that produced intense MS signals, and those that resulted from proteins generating many distinct peptides. Reproducibility among different instruments of the same type lagged behind repeatability of tech. replicates on a single instrument by several percent. These findings reinforce the importance of evaluating repeatability as a fundamental characteristic of anal. technologies.
- 50Delmotte, N.; Lasaosa, M.; Tholey, A.; Heinzle, E.; van Dorsselaer, A.; Huber, C. G. Repeatability of Peptide Identifications in Shotgun Proteome Analysis Employing Off-Line Two-Dimensional Chromatographic Separations and Ion-Trap MS. J. Sep. Sci. 2009, 32 (8), 1156– 1164, DOI: 10.1002/jssc.200800615Google Scholar50https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXlvVCltr8%253D&md5=81ccb5ce2f00a4a8fc0eec13bf94fd05Repeatability of peptide identifications in shotgun proteome analysis employing off-line two-dimensional chromatographic separations and ion-trap MSDelmotte, Nathanael; Lasaosa, Maria; Tholey, Andreas; Heinzle, Elmar; van Dorsselaer, Alain; Huber, Christian G.Journal of Separation Science (2009), 32 (8), 1156-1164CODEN: JSSCCJ; ISSN:1615-9306. (Wiley-VCH Verlag GmbH & Co. KGaA)The repeatability of peptide identifications in shotgun proteome analyses employing strong cation-exchange- × ion-pair RP HPLC hyphenated to ESI MS/MS was compared to an alternative scheme, comprising high-pH RP chromatog. combined with low-pH ion-pair RP chromatog. Equivalent results were obtained with both methods in proteome anal. of Corynebacterium glutamicum. From a total no. of 1350 to 1850 peptides identified in triplicate analyses of five consecutive fractions chosen from the first-dimension sepn., 41-45% of the peptides were identified three times, whereas 16-22 and 37-39% of the peptides were identified only twice or once, resp. A comparison of the repeatability of peptide identifications from complex samples upon 1- or 2-D chromatog. sepn. revealed that an addnl. sepn. dimension decreases the repeatability by approx. 25%.
- 51Resing, K. A.; Meyer-Arendt, K.; Mendoza, A. M.; Aveline-Wolf, L. D.; Jonscher, K. R.; Pierce, K. G.; Old, W. M.; Cheung, H. T.; Russell, S.; Wattawa, J. L.; Goehle, G. R.; Knight, R. D.; Ahn, N. G. Improving Reproducibility and Sensitivity in Identifying Human Proteins by Shotgun Proteomics. Anal. Chem. 2004, 76 (13), 3556– 3568, DOI: 10.1021/ac035229mGoogle Scholar51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXktlWmsbs%253D&md5=056551e9a48b0bdb1d0f5668bb9625a9Improving Reproducibility and Sensitivity in Identifying Human Proteins by Shotgun ProteomicsResing, Katheryn A.; Meyer-Arendt, Karen; Mendoza, Alex M.; Aveline-Wolf, Lauren D.; Jonscher, Karen R.; Pierce, Kevin G.; Old, William M.; Cheung, Hiu T.; Russell, Steven; Wattawa, Joy L.; Goehle, Geoff R.; Knight, Robin D.; Ahn, Natalie G.Analytical Chemistry (2004), 76 (13), 3556-3568CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Identifying proteins in cell exts. by shotgun proteomics involves digesting the proteins, sequencing the resulting peptides by data-dependent mass spectrometry (MS/MS), and searching protein databases to identify the proteins from which the peptides are derived. Manual anal. and direct spectral comparison reveal that scores from two commonly used search programs (Sequest and Mascot) validate less than half of potentially identifiable MS/MS spectra (class pos.) from shotgun analyses of the human erythroleukemia K562 cell line. Here we demonstrate increased sensitivity and accuracy using a focused search strategy along with a peptide sequence validation script that does not rely exclusively on XCorr or Mowse scores generated by Sequest or Mascot, but uses consensus between the search programs, along with chem. properties and scores describing the nature of the fragmentation spectrum (ion score and RSP). The approach yielded 4.2% false pos. and 8% false neg. frequencies in peptide assignments. The protein profile is then assembled from peptide assignments using a novel peptide-centric protein nomenclature that more accurately reports protein variants that contain identical peptide sequences. An Isoform Resolver algorithm ensures that the protein count is not inflated by variants in the protein database, eliminating ∼25% of redundant proteins. Anal. of sol. proteins from a human K562 cells identified 5130 unique proteins, with ∼100 false pos. protein assignments.
- 52Washburn, M. P.; Ulaszek, R. R.; Yates, J. R. Reproducibility of Quantitative Proteomic Analyses of Complex Biological Mixtures by Multidimensional Protein Identification Technology. Anal. Chem. 2003, 75 (19), 5054– 5061, DOI: 10.1021/ac034120bGoogle Scholar52https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXmsVegsLs%253D&md5=93f083bea4d279e1cf92ea0f6248a8c3Reproducibility of quantitative proteomic analyses of complex biological mixtures by multidimensional protein identification technologyWashburn, Michael P.; Ulaszek, Ryan R.; Yates, John R., IIIAnalytical Chemistry (2003), 75 (19), 5054-5061CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)If quant. proteomic technologies are to be of widespread use to the biol. community, the reproducibility of each method must be investigated and detd. We have analyzed the reproducibility of complex quant. proteomic analyses of metabolically labeled S. cerevisiae analyzed via multidimensional protein identification technol. (MudPIT). Three independent cell growths of S. cerevisiae grown in rich and minimal media and independent MudPIT analyses of each were compared and contrasted. Quant. MudPIT was found to be intra- and interexperimentally reproducible at both the peptide and protein levels. Proteins of potential low abundance were detected, identified, and quantified by identical peptides from three independent samples. In addn., when multiple peptides were matched to a protein, the relative abundance of each peptide was in agreement across the three samples. Despite the reproducibility, errors in the exptl. detn. of protein expression levels occurred, but the impact of the variation was minimized by replicate expts. Last, quant. MudPIT analyses will likely be improved by increasing the no. of peptide hits per protein in a given anal., which will provide for greater intraexperimental reproducibility.
- 53Magdeldin, S.; Yamamoto, T. Toward Deciphering Proteomes of Formalin-Fixed Paraffin-Embedded (FFPE) Tissues. Proteomics 2012, 12 (7), 1045– 1058, DOI: 10.1002/pmic.201100550Google Scholar53https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XlvVKms78%253D&md5=7cd5165b42a902e467226a114103f3c1Toward deciphering proteomes of formalin-fixed paraffin-embedded (FFPE) tissuesMagdeldin, Sameh; Yamamoto, TadashiProteomics (2012), 12 (7), 1045-1058CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)A review. Formalin-fixed paraffin-embedded (FFPE) tissue specimens comprise a potentially valuable resource for both prospective and retrospective biomarker discovery. Unlocking the proteomic profile of clinicopathol. FFPE tissues is a critically essential step for annotating clin. findings and predicting biomarkers for ultimate disease prognosis and therapeutic follow-up.
- 54Maes, E.; Broeckx, V.; Mertens, I.; Sagaert, X.; Prenen, H.; Landuyt, B.; Schoofs, L. Analysis of the Formalin-Fixed Paraffin-Embedded Tissue Proteome: Pitfalls, Challenges, and Future Prospectives. Amino Acids 2013, 45 (2), 205– 218, DOI: 10.1007/s00726-013-1494-0Google Scholar54https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFemtbvN&md5=cfb25a4dc12c7a6a9b76472d8c4da015Analysis of the formalin-fixed paraffin-embedded tissue proteome: pitfalls, challenges, and future prospectivesMaes, Evelyne; Broeckx, Valerie; Mertens, Inge; Sagaert, Xavier; Prenen, Hans; Landuyt, Bart; Schoofs, LilianeAmino Acids (2013), 45 (2), 205-218CODEN: AACIE6; ISSN:0939-4451. (Springer-Verlag GmbH)A review. Formalin-fixed paraffin-embedded (FFPE) tissues are a real treasure for retrospective anal. considering the amt. of samples present in hospital archives, combined with pathol., clin., and outcome information available for every sample. Although unlocking the proteome of these tissues is still a challenge, new approaches are being developed. In this review, we summarize the different mass spectrometry platforms that are used in human clin. studies to unravel the FFPE proteome. The different ways of extg. crosslinked proteins and the anal. strategies are pointed out. Also, the pitfalls and challenges concerning the quality of FFPE proteomic approaches are depicted. We also evaluated the potential of these anal. methods for future clin. FFPE proteomics applications.
- 55Proungvitaya, S.; Klinthong, W.; Proungvitaya, T.; Limpaiboon, T.; Jearanaikoon, P.; Roytrakul, S.; Wongkham, C.; Nimboriboonporn, A.; Wongkham, S. High Expression of CCDC25 in Cholangiocarcinoma Tissue Samples. Oncol. Lett. 2017, 14 (2), 2566– 2572, DOI: 10.3892/ol.2017.6446Google Scholar55https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cflslWhsg%253D%253D&md5=dbbf13668447ea01c723e4bf965dca50High expression of CCDC25 in cholangiocarcinoma tissue samplesProungvitaya Siriporn; Klinthong Wachiraya; Proungvitaya Tanakorn; Limpaiboon Temduang; Jearanaikoon Patcharee; Proungvitaya Siriporn; Proungvitaya Tanakorn; Limpaiboon Temduang; Jearanaikoon Patcharee; Wongkham Chaisiri; Nimboriboonporn Anongporn; Wongkham Sopit; Roytrakul Sittiruk; Nimboriboonporn AnongpornOncology letters (2017), 14 (2), 2566-2572 ISSN:1792-1074.Cholangiocarcinoma (CCA) is a malignant transformation of biliary epithelial cells. It is a slow growing tumor, but is also highly metastatic with a poor prognosis. Bile acids are known to transactivate the epidermal growth factor receptor (EGFR) in cholangiocytes and induce cyclooxygenase-2 expression. The protein expression profiles of bile acid-treated CCA cells were studied using a proteomic approach. To elucidate the possible mechanisms involved in the bile acid-mediated enhancement of CCA cell migration, the effects of six bile acids, including cholic, deoxycholic, taurocholic, taurodeoxycholic, glycocholic and glycodeoxycholic acid, on the migration of CCA cells were examined in vitro using wound healing assays. Subsequently, the possible proteins involved in enhanced CCA cell migration were investigated using a proteomic approach. Changes to the protein expression profiles of CCA cells following bile acid treatment was examined using two-dimensional electrophoresis and mass spectrometry. The results demonstrated that cholic and deoxycholic acid significantly enhanced the migration of CCA cells, compared with the treated MMNK-1 control cells. CCA cells had 77 overexpressed protein spots following cholic acid treatment, and 50 protein spots following deoxycholic acid treatment, compared with the treated MMNK-1 control cells. Liquid chromatography tandem-mass spectrometry analysis revealed that coiled-coil domain containing 25 (CCDC25) was significantly overexpressed in cholic acid-treated CCA cells compared with in cholic acid-treated control cells. When the expression levels of CCDC25 were investigated using western blot analysis, CCDC25 was demonstrated to be highly expressed in CCA tissues, but not in the adjacent non-cancerous tissue samples. The identified proteins were further analyzed for protein-chemical interactions using STITCH version 3.1 software. CCDC25 protein was identified to be associated with Son of sevenless homolog 1 and growth factor receptor-bound protein 2, which are involved in EGFR signaling. The results of the present study demonstrated that following cholic acid treatment, CCDC25 is overexpressed in CCA cells, which is associated with significantly enhanced cell migration. This suggests that CCDC25 is a potential therapeutic target for the treatment of patients with CCA.
- 56Brychtova, V.; Vojtesek, B.; Hrstka, R. Anterior Gradient 2: A Novel Player in Tumor Cell Biology. Cancer Lett. 2011, 304 (1), 1– 7, DOI: 10.1016/j.canlet.2010.12.023Google Scholar56https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXjsFWru7g%253D&md5=6baccba91e15dfacd80ed874ae409574Anterior gradient 2: A novel player in tumor cell biologyBrychtova, Veronika; Vojtesek, Borivoj; Hrstka, RomanCancer Letters (New York, NY, United States) (2011), 304 (1), 1-7CODEN: CALEDQ; ISSN:0304-3835. (Elsevier)A review. AGR2 has evolutionarily conserved roles in development and tissue regeneration and is linked with several human cancers. The exact functions and regulation of AGR2 are poorly understood, but current data identify AGR2 as a clin. relevant factor that modulates the behavior and response of hormone-dependent cancers (breast, prostate) and hormone-independent cancers (colorectal, pancreatic, esophageal and other common cancers). AGR2 protein expression induces metastasis, acts as a p53 tumor suppressor inhibitor and survival factor, participates directly in neoplastic transformation and is involved in drug resistance. Thus, AGR2 is an important tumor biomarker and neg. prognostic factor potentially exploitable in clin. practice.
- 57Suwanmanee, G.; Yosudjai, J.; Phimsen, S.; Wongkham, S.; Jirawatnotai, S.; Kaewkong, W. Upregulation of AGR2vH Facilitates Cholangiocarcinoma Cell Survival under Endoplasmic Reticulum Stress via the Activation of the Unfolded Protein Response Pathway. Int. J. Mol. Med. 2019, 45 (2), 669– 677, DOI: 10.3892/ijmm.2019.4432Google ScholarThere is no corresponding record for this reference.
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Abstract
Scheme 1
Scheme 1. Overview of the Workflow of MicroPOTS for the Identification of Proteomic Changes in ∼200 CP-A Cells after Treatment with Either LCA or X-rayaaFirst, cells were grown in keratinocyte media, and the generated CP-A null cells and wild-type were subjected to different stressors as previously described. (a) Samples were then processed for protein extraction and further digested into peptides using the microPOTS system. (b) The collected peptides were subsequently subjected to LC-MS/MS analysis. (c) A spectrum showing the relative intensity and mass to charge ratio (m/z) of the ions being analyzed. (d) The resulting files were loaded into MaxQuant for peptide identification, after which the output files of this step were next imported into R environment and analyzed using the DEP package. (e) The results of the analysis were then visualized using the previously mentioned R packages.
Figure 1
Figure 1. Barplots and Venn diagram plots respectively showing the number of identified protein groups and overlap for CP-A WT, CP-A KO, and CP-A dKO samples of ∼200 cells. (a) Average numbers of identified protein groups with each bar representing the mean and standard deviation (error bars) of replicates from individual sample type. (b) Total numbers of identified protein groups from three replicates for each sample type. (c) A 67% overlap of the identified proteins for samples treated with LCA. (d) Samples treated with X-ray shows a 77% overlap for the identified protein groups.
Figure 2
Figure 2. Qualitative and quantitative comparison of the identified proteins between replicates. (a) The area-proportional Venn diagrams illustrate the proportion of shared and unique proteins between technical replicates. (b) Scatter plots showing Pearson’s correlation coefficient between logarithmically (log10) transformed quantile normalized LFQ values of the corresponding replicates. (c) A boxplot representing the distribution of coefficient of variation for quantile normalized protein intensities across different experimental conditions. Each dot represents the CV of an individual protein intensity within each sample type.
Figure 3
Figure 3. Assessment of the physicochemical features and subcellular localization of the identified proteins. Distribution of (A) GRAVY in CP-A LCA-dKO, (B) molecular weight in CP-A LCA-WT, and (C) subcellular localization of identified proteins in CP-A X-ray-KO.
Figure 4
Figure 4. Volcano plots illustrating the fold difference in the expression levels of proteins in (a) CP-A LCA-dKO vs CP-A dKO and (b) CP-A X-ray-dKO vs CP-A dKO. The horizontal coordinate represents the log2 fold change difference, while the vertical coordinate represents the −log10p-value. Black color indicates proteins with nonsignificant changes in abundance, blue color represents significantly downregulated proteins, and red color represents significantly upregulated proteins.
References
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- 1Mann, M.; Hendrickson, R. C.; Pandey, A. Analysis of Proteins and Proteomes by Mass Spectrometry. Annu. Rev. Biochem. 2001, 70, 437– 473, DOI: 10.1146/annurev.biochem.70.1.4371https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXlsVehtL4%253D&md5=1bd749a47bfc1ec30226dce9a820aef6Analysis of proteins and proteomes by mass spectrometryMann, Matthias; Hendrickson, Ronald C.; Pandey, AkhileshAnnual Review of Biochemistry (2001), 70 (), 437-473CODEN: ARBOAW; ISSN:0066-4154. (Annual Reviews Inc.)A review with 91 refs. A decade after the discovery of electrospray and matrix-assisted laser desorption ionization (MALDI), methods that finally allowed gentle ionization of large biomols., mass spectrometry has become a powerful tool in protein anal. and the key technol. in the emerging field of proteomics. The success of mass spectrometry is driven both by innovative instrumentation designs, esp. those operating on the time-of-flight or ion-trapping principles, and by large-scale biochem. strategies, which use mass spectrometry to detect the isolated proteins. Any human protein can now be identified directly from genome databases on the basis of minimal data derived by mass spectrometry. As has already happened in genomics, increased automation of sample handling, anal., and the interpretation of results will generate an avalanche of qual. and quant. proteomic data. Protein-protein interactions can be analyzed directly by pptn. of a tagged bait followed by mass spectrometric identification of its binding partners. By these and similar strategies, entire protein complexes, signaling pathways, and whole organelles are being characterized. Posttranslational modifications remain difficult to analyze but are starting to yield to generic strategies.
- 2Wasinger, V. C.; Cordwell, S. J.; Cerpa-Poljak, A.; Yan, J. X.; Gooley, A. A.; Wilkins, M. R.; Duncan, M. W.; Harris, R.; Williams, K. L.; Humphery-Smith, I. Progress with Gene-Product Mapping of the Mollicutes: Mycoplasma Genitalium. Electrophoresis 1995, 16 (7), 1090– 1094, DOI: 10.1002/elps.115016011852https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXntF2lsLg%253D&md5=11e7acd04dc0eaf17ec7f99996a0c654Progress with gene-product mapping of the Mollicutes: Mycoplasma genitaliumWasinger, Valerie C.; Cordwell, Stuart J.; Cerpa-Poljak, Anne; Yan, Jun X.; Gooley, Andrew A.; Wilkins, Marc R.; Duncan, Mark W.; Harris, Ray; Williams, Keith L.; Humphery-Smith, IanElectrophoresis (1995), 16 (7), 1090-4CODEN: ELCTDN; ISSN:0173-0835. (VCH)A protein map of the smallest known self-replicating organism Mycoplasma genitalium (Class: Mollicutes) revealed a high proportion of acidic proteins. Amino acid compn. was used to putatively identify or provide unique parameters for 50 gene products sepd. by two-dimensional gel electrophoresis. A further 19 proteins were subjected to peptide-mass fingerprinting using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and 4 were subjected to N-terminal Edman degrdn. The majority of M. genitalium proteins remain uncharacterized. However, the combined approach of amino acid anal. and peptide-mass fingerprinting allowed gene products to be linked to homologous genes in a variety of organisms. This allowed proteins to be identified prior to detection of their resp. genes via the M. genitalium sequencing initiative. The principle of 'hierarchical' anal. for the mass screening of proteins and the anal. of microbial genomes via their protein complement or 'proteome' is detailed. Here, characterization of gene products depends upon the quickest and most economical technols. being employed initially to det. if a large no. of proteins are already present in both homologous and heterologous species databases. Initial screening, which lends itself to automation and robotics, can then be followed by more time and cost intensive procedures, when necessary.
- 3Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M.-C.; Yates, J. R. Protein Analysis by Shotgun/Bottom-up Proteomics. Chem. Rev. 2013, 113 (4), 2343– 2394, DOI: 10.1021/cr30035333https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjtVClu7g%253D&md5=a27c72f72bc6fa2f827725d2e71df241Protein Analysis by Shotgun/Bottom-up ProteomicsZhang, Yaoyang; Fonslow, Bryan R.; Shan, Bing; Baek, Moon-Chang; Yates, John R.Chemical Reviews (Washington, DC, United States) (2013), 113 (4), 2343-2394CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)A review.
- 4Mayne, J.; Ning, Z.; Zhang, X.; Starr, A. E.; Chen, R.; Deeke, S.; Chiang, C.-K.; Xu, B.; Wen, M.; Cheng, K.; Seebun, D.; Star, A.; Moore, J. I.; Figeys, D. Bottom-Up Proteomics (2013–2015): Keeping up in the Era of Systems Biology. Anal. Chem. 2016, 88 (1), 95– 121, DOI: 10.1021/acs.analchem.5b042304https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvVSlsLvJ&md5=291844fae9e91fc1b1d219948ef7d3c4Bottom-Up Proteomics (2013-2015): Keeping up in the Era of Systems BiologyMayne, Janice; Ning, Zhibin; Zhang, Xu; Starr, Amanda E.; Chen, Rui; Deeke, Shelley; Chiang, Cheng-Kang; Xu, Bo; Wen, Ming; Cheng, Kai; Seebun, Deeptee; Star, Alexandra; Moore, Jasmine I.; Figeys, DanielAnalytical Chemistry (Washington, DC, United States) (2016), 88 (1), 95-121CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. Over 17,000 papers related to proteomics have been published since the authors' 2011-2013 review. A subset are true gems reporting amazing applications of proteomics to better understand protein interactions, pathways, dynamic signaling and the human proteome, to name a few. While the authors have clearly seen more attention paid to data quality, and the proper exptl. design in manuscripts reporting biol. applications of proteomics, more improvements are needed. Technol. development still remains a strong driving force of proteomics, making up a large portion of these papers. As in any maturing field, some areas of technol. development in proteomics are satg. However, many new areas in proteomics are burgeoning. Examples include the development of enrichment materials to study different protein modifications, approaches for protein quantification, data independent acquisition (DIA) methods, bioinformatics and the rapid developments in metaproteomics. The acceptance of proteomics in the biol. community really depends on its applications. For instance, protein interaction mapping, post-translational modification (PTM) analyses and protein Atlas are routinely used by the biol. community. However, other aspects of proteomics are still under appreciated including biomarker discovery and studies of diseases from tissues. In part, this is due to previous and, to some extent, still ongoing overstatements from underpowered studies that reported unsubstantiated biol. conclusions. Very large-scale, good quality, quant. proteomic studies are now tech. feasible and ongoing in a few labs. around the world. However, few bioinformatics tools are capable of handling these large studies. Moreover, proteomics is increasingly integrated in expts. involving multiple -omics datasets obtained across large nos. of biol. replicates. "Big data" challenges are clearly appearing in proteomics. This biannual review is focused on technol. trends, which the authors believe are important in proteomics. The authors continue to foresee that technol. and bioinformatics development will remain important areas of research in proteomics.
- 5Fagerberg, L.; Strömberg, S.; El-Obeid, A.; Gry, M.; Nilsson, K.; Uhlen, M.; Ponten, F.; Asplund, A. Large-Scale Protein Profiling in Human Cell Lines Using Antibody-Based Proteomics. J. Proteome Res. 2011, 10 (9), 4066– 4075, DOI: 10.1021/pr200259v5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXptFKlsr4%253D&md5=38f0615cccc16390ae4f6be6fd806c85Large-Scale Protein Profiling in Human Cell Lines Using Antibody-Based ProteomicsFagerberg, Linn; Stromberg, Sara; El-Obeid, Adila; Gry, Marcus; Nilsson, Kenneth; Uhlen, Mathias; Ponten, Fredrik; Asplund, AnnaJournal of Proteome Research (2011), 10 (9), 4066-4075CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)Human cancer cell lines grown in vitro are frequently used to decipher basic cell biol. phenomena and to also specifically study different forms of cancer. Here the authors present the first large-scale study of protein expression patterns in cell lines using an antibody-based proteomics approach. The authors analyzed the expression pattern of 5436 proteins in 45 different cell lines using hierarchical clustering, principal component anal., and two-group comparisons for the identification of differentially expressed proteins. The authors' results show that immunohistochem. detd. protein profiles can categorize cell lines into groups that overall reflect the tumor tissue of origin and that hematol. cell lines appear to retain their protein profiles to a higher degree than cell lines established from solid tumors. The two-group comparisons reveal well-characterized proteins as well as previously unstudied proteins that could be of potential interest for further investigations. Moreover, multiple myeloma cells and cells of myeloid origin were found to share a protein profile, relative to the protein profile of lymphoid leukemia and lymphoma cells, possibly reflecting their common dependency of bone marrow microenvironment. This work also provides an extensive list of antibodies, for which high-resoln. images as well as validation data are available on the Human Protein Atlas (www.proteinatlas.org), that are of potential use in cell line studies.
- 6Branca, R. M. M.; Orre, L. M.; Johansson, H. J.; Granholm, V.; Huss, M.; Pérez-Bercoff, Å.; Forshed, J.; Käll, L.; Lehtiö, J. HiRIEF LC-MS Enables Deep Proteome Coverage and Unbiased Proteogenomics. Nat. Methods 2014, 11 (1), 59– 62, DOI: 10.1038/nmeth.27326https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhslygsLnE&md5=acdad94163f52313a336a41ccfed44c4HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomicsBranca, Rui M. M.; Orre, Lukas M.; Johansson, Henrik J.; Granholm, Viktor; Huss, Mikael; Perez-Bercoff, Aasa; Forshed, Jenny; Kaell, Lukas; Lehtioe, JanneNature Methods (2014), 11 (1), 59-62CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)We present a liq. chromatog.-mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resoln. isoelec. focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelec. point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, resp. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.
- 7Wang, G.; Brennan, C.; Rook, M.; Wolfe, J. L.; Leo, C.; Chin, L.; Pan, H.; Liu, W.-H.; Price, B.; Makrigiorgos, G. M. Balanced-PCR Amplification Allows Unbiased Identification of Genomic Copy Changes in Minute Cell and Tissue Samples. Nucleic Acids Res. 2004, 32 (9), e76, DOI: 10.1093/nar/gnh0707https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXksVahsLk%253D&md5=e87e9779bb0d6186644d4f7b394d1448Balanced-PCR amplification allows unbiased identification of genomic copy changes in minute cell and tissue samplesWang, Gang; Brennan, Cameron; Rook, Martha; Wolfe, Jia Liu; Leo, Christopher; Chin, Lynda; Pan, Hongjie; Liu, Wei-Hua; Price, Brendan; Makrigiorgos, G. MikeNucleic Acids Research (2004), 32 (9), e76/1-e76/10CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Anal. of genomic DNA derived from cells and fresh or fixed tissues often requires whole genome amplification prior to microarray screening. Tech. hurdles to this process are the introduction of amplification bias and/or the inhibitory effects of formalin fixation on DNA amplification. Here we demonstrate a balanced-PCR procedure that allows unbiased amplification of genomic DNA from fresh or modestly degraded paraffin-embedded DNA samples. Following digestion and ligation of a target and a control genome with distinct linkers, the two are mixed and amplified in a single PCR, thereby avoiding biases assocd. with PCR satn. and impurities. We demonstrate genome-wide retention of allelic differences following balanced-PCR amplification of DNA from breast cancer and normal human cells and genomic profiling by array-CGH (cDNA arrays, 100 kb resoln.) and by real-time PCR (single gene resoln.). Comparison of balanced-PCR with multiple displacement amplification (MDA) demonstrates equiv. performance between the two when intact genomic DNA is used. When DNA from paraffin-embedded samples is used, balanced PCR overcomes problems assocd. with modest DNA degrdn. and produces unbiased amplification whereas MDA does not. Balanced-PCR allows amplification and recovery of modestly degraded genomic DNA for subsequent retrospective anal. of human tumors with known outcomes.
- 8Wang, N.; Xu, M.; Wang, P.; Li, L. Development of Mass Spectrometry-Based Shotgun Method for Proteome Analysis of 500 to 5000 Cancer Cells. Anal. Chem. 2010, 82 (6), 2262– 2271, DOI: 10.1021/ac90230228https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXptlGrtA%253D%253D&md5=89ac83bbe62a4e164961925b870a13e4Development of Mass Spectrometry-Based Shotgun Method for Proteome Analysis of 500 to 5000 Cancer CellsWang, Nan; Xu, Mingguo; Wang, Peng; Li, LiangAnalytical Chemistry (Washington, DC, United States) (2010), 82 (6), 2262-2271CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A shotgun proteome anal. method and its performance for protein identification from 500 to 5000 cells are described. Sample prepn., which was done in one tube, involved the use of a surfactant (NP-40) for cell lysis, followed by acetone pptn. of the proteins. The resulting protein pellet was washed with cold acetone to remove remaining surfactant, and the pellet was then solubilized in NH4HCO3. After trypsin digestion of the proteins, the digest was analyzed by the use of nanoflow liq. chromatog. (LC) quadrupole time-of-flight mass spectrometry (QTOF MS). Sample injection and gradient speed in running LC QTOF MS were optimized. It was shown that this method could identify an av. (n = 3) of 167±21, 237±30, 491±63, and 619±59 proteins from 500, 1000, 2500, and 5000 MCF-7 breast cancer cells, resp. To demonstrate the potential use of this method for generating proteome profile from circulating tumor cells (CTCs) isolated from human blood, a healthy human blood sample was spiked with MCF-7 cells, and this mixt. was processed and then subjected to antibody tagging of the MCF-7 cells. The tagged cells were sorted and collected using flow cytometry. The proteome profiles of small nos. of cells isolated in this way were similar to those of the original MCF-7 cells, suggesting the possibility of the use of this method for cell typing of CTCs.
- 9Parapatics, K.; Huber, M. L.; Lehmann, D.; Knoll, C.; Superti-Furga, G.; Bennett, K. L.; Rudashevskaya, E. L. Proteomic Analysis of Low Quantities of Cellular Material in the Range Obtainable from Scarce Patient Samples. J. Integr. OMICS 2015, 5 (1), 30– 43–43, DOI: 10.5584/jiomics.v5i1.172There is no corresponding record for this reference.
- 10Clair, G.; Piehowski, P. D.; Nicola, T.; Kitzmiller, J. A.; Huang, E. L.; Zink, E. M.; Sontag, R. L.; Orton, D. J.; Moore, R. J.; Carson, J. P.; Smith, R. D.; Whitsett, J. A.; Corley, R. A.; Ambalavanan, N.; Ansong, C. Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples. Sci. Rep. 2016, 6, 6, DOI: 10.1038/srep39223There is no corresponding record for this reference.
- 11Coscia, F.; Doll, S.; Bech, J. M.; Mund, A.; Lengyel, E.; Lindebjerg, J.; Madsen, G. I.; Moreira, J. M. A.; Mann, M. A Streamlined Mass Spectrometry-Based Proteomics Workflow for Large Scale FFPE Tissue Analysis. bioRxiv , Sep 23, 2019, 779009. DOI: 10.1101/779009 .There is no corresponding record for this reference.
- 12Martin, J. G.; Rejtar, T.; Martin, S. A. Integrated Microscale Analysis System for Targeted Liquid Chromatography Mass Spectrometry Proteomics on Limited Amounts of Enriched Cell Populations. Anal. Chem. 2013, 85 (22), 10680– 10685, DOI: 10.1021/ac401937c12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFGkurbN&md5=7fd20d18e145d970a56b1aadfe651f89Integrated Microscale Analysis System for Targeted Liquid Chromatography Mass Spectrometry Proteomics on Limited Amounts of Enriched Cell PopulationsMartin, Jeffrey G.; Rejtar, Tomas; Martin, Stephen A.Analytical Chemistry (Washington, DC, United States) (2013), 85 (22), 10680-10685CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Limited samples, such as those that are in vivo sourced via biopsy, are closely representative of biol. systems and contain valuable information for drug discovery. However, these precious samples are often heterogeneous and require cellular prefractionation prior to proteomic anal. to isolate specific subpopulations of interest. Enriched cells from in vivo samples are often very limited (<104 cells) and pose a significant challenge to proteomic nanoliquid chromatog. mass spectrometry (nanoLCMS) sample prepn. To enable the streamlined anal. of these limited samples, the authors have developed an online cell enrichment, microscale sample prepn., nanoLCMS proteomics workflow by integrating fluorescence activated cell sorting (FACS), focused ultrasonication, microfluidics, immobilized trypsin digestion, and nanoLCMS. To assess the performance of the online FACS-Chip-LCMS workflow, 5000 fluorescent labeled cells were enriched from a 5% heterogeneous cell population and processed for LCMS proteomics in <2 h. Within these 5000 enriched cells, 30 peptides corresponding to 17 proteins spanning >4 orders of magnitude of cellular abundance were quantified using a QExactive MS. The results from the online FACS-Chip-LCMS workflow starting from 5000 enriched cells were directly compared to results from a traditional macroscale sample prepn. workflow starting from 2.0 × 106 cells. The microscale FACS-Chip-LCMS workflow demonstrated high cellular enrichment efficiency and high peptide recovery across the wide dynamic range of targeted peptides. Overall the microscale FACS-Chip-LCMS workflow showed effectiveness in efficiently prepg. limited amts. of FACS enriched cells in an online manner for proteomic LCMS.
- 13Yamaguchi, H.; Miyazaki, M.; Honda, T.; Briones-Nagata, M. P.; Arima, K.; Maeda, H. Rapid and Efficient Proteolysis for Proteomic Analysis by Protease-Immobilized Microreactor. Electrophoresis 2009, 30 (18), 3257– 3264, DOI: 10.1002/elps.20090013413https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtFGqsLjK&md5=9870da1a12d77e6e302a53e76019fd79Rapid and efficient proteolysis for proteomic analysis by protease-immobilized microreactorYamaguchi, Hiroshi; Miyazaki, Masaya; Honda, Takeshi; Briones-Nagata, Maria Portia; Arima, Kazunari; Maeda, HideakiElectrophoresis (2009), 30 (18), 3257-3264CODEN: ELCTDN; ISSN:0173-0835. (Wiley-VCH Verlag GmbH & Co. KGaA)Proteolysis is an important part of protein identification in proteomics anal. The conventional method of in-soln. digestion of proteins is time-consuming and has limited sensitivity. In this study, trypsin- or α-chymotrypsin-immobilized microreactors prepd. by a microfluidics-based enzyme-immobilization technique were studied for rapid sample prepn. in proteomic anal. The kinetic studies for hydrolysis of substrate by microreactors revealed that immobilized proteases had higher hydrolytic efficiency than those performed by in-soln. digestion. The performance of the microreactors was evaluated by digesting cytochrome c and bovine serum albumin (BSA). Protein digestion was achieved within a short period of time (∼5 min) at 30° without any complicated redn. and alkylation procedures. The efficiency of digestion by trypsin-immobilized reactor was evaluated by analyzing the sequence coverage, which was 47 and 12% for cytochrome c and BSA, resp. These values were higher than those performed by in-soln. digestion. Besides, because of higher stability against high concn. of denaturant, the microreactors can be useful for immediate digestion of the denatured protein. In the present study, the authors propose a protease-immobilized microreactor digestion method, which can utilize as a proteome technique for biol. and clin. research.
- 14Myers, S. A.; Rhoads, A.; Cocco, A. R.; Peckner, R.; Haber, A. L.; Schweitzer, L. D.; Krug, K.; Mani, D. R.; Clauser, K. R.; Rozenblatt-Rosen, O.; Hacohen, N.; Regev, A.; Carr, S. A. Streamlined Protocol for Deep Proteomic Profiling of FAC-Sorted Cells and Its Application to Freshly Isolated Murine Immune Cells. Mol. Cell. Proteomics 2019, 18 (5), 995– 1009, DOI: 10.1074/mcp.RA118.00125914https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXht1OhsbbP&md5=c4a2707f8e65bcc5fd9462e4952b5e06Streamlined protocol for deep proteomic profiling of FAC-sorted cells and its application to freshly isolated murine immune cellsMyers, Samuel A.; Rhoads, Andrew; Cocco, Alexandra R.; Peckner, Ryan; Haber, Adam L.; Schweitzer, Lawrence D.; Krug, Karsten; Mani, D. R.; Clauser, Karl R.; Rozenblatt-Rosen, Orit; Hacohen, Nir; Regev, Aviv; Carr, Steven A.Molecular & Cellular Proteomics (2019), 18 (5), 995-1009CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)Proteomic profiling describes the mol. landscape of proteins in cells immediately available to sense, transduce, and enact the appropriate responses to extracellular queues. Transcriptional profiling has proven invaluable to our understanding of cellular responses; however, insights may be lost as mounting evidence suggests transcript levels only moderately correlate with protein levels in steady state cells. Mass spectrometry-based quant. proteomics is a well-suited and widely used anal. tool for studying global protein abundances. Typical proteomic workflows are often limited by the amt. of sample input that is required for deep and quant. proteome profiling. This is esp. true if the cells of interest need to be purified by fluorescence-activated cell sorting (FACS) and one wants to avoid ex vivo culturing. To address this need, we developed an easy to implement, streamlined workflow that enables quant. proteome profiling from roughly 2μg of protein input per exptl. condition. Utilizing a combination of facile cell collection from cell sorting, solid-state isobaric labeling and multiplexing of peptides, and small-scale fractionation, we profiled the proteomes of 12 freshly isolated, primary murine immune cell types. Analyzing half of the 3e5 cells collected per cell type, we quantified over 7000 proteins across 12 key immune cell populations directly from their resident tissues. We show that low input proteomics is precise, and the data generated accurately reflects many aspects of known immunol., while expanding the list of cell-type specific proteins across the cell types profiled. The low input proteomics methods we developed are readily adaptable and broadly applicable to any cell or sample types and should enable proteome profiling in systems previously unattainable.
- 15Gao, W.; Li, H.; Liu, L.; Huang, P.; Wang, Z.; Chen, W.; Ye, M.; Yu, X.; Tian, R. An Integrated Strategy for High-Sensitive and Multi-Level Glycoproteome Analysis from Low Micrograms of Protein Samples. J. Chromatogr. A 2019, 1600, 46– 54, DOI: 10.1016/j.chroma.2019.04.04115https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosVyjsrg%253D&md5=d483a4c2b76420673a2b34063171c9b4An integrated strategy for high-sensitive and multi-level glycoproteome analysis from low micrograms of protein samplesGao, Weina; Li, Hongjie; Liu, Liping; Huang, Peiwu; Wang, Zhikun; Chen, Wendong; Ye, Mingliang; Yu, Xiaofang; Tian, RuijunJournal of Chromatography A (2019), 1600 (), 46-54CODEN: JCRAEY; ISSN:0021-9673. (Elsevier B.V.)Glycosylation, as a biol. important protein post-translational modification, often alters on both glycosites and glycans, simultaneously. However, most of current approaches focused on biased profiling of either glycosites or glycans, and limited by time-consuming process and milligrams of starting protein material. We describe here a simple and integrated spintip-based glycoproteomics technol. (termed Glyco-SISPROT) for achieving a comprehensive view of glycoproteome with shorter sample processing time and low microgram starting material. By carefully integrating and optimizing SCX, C18 and Con A (Con A) packing material and their combination in spintip format, both predigested peptides and protein lysates could be processed by Glyco-SISPROT with high efficiency. More importantly, deglycopeptide, intact glycopeptide and glycans released by multiple glycosidases could be readily collected from the same Glyco-SISPROT workflow for LC-MS anal. In total, above 1850 glycosites in ∼1770 unique deglycopeptides were characterized from mouse liver by using either 100 μg of predigested peptides or directly using 100 μg of protein lysates, in which about 30% of glycosites were released by both PNGase F and Endos. To the best of our knowledge, this approach should be one of the most comprehensive glycoproteomic approaches by using limited protein starting material. One significant benefit of Glyco-SISPROT is that whole processing time is dramatically reduced from a few days to less than 6 h with good reproducibility when protein lysates were directly processed by Glyco-SISPROT. We expect that this method will be suitable for multi-level glycoproteome anal. of rare biol. samples with high sensitivity.
- 16Chen, W.; Wang, S.; Adhikari, S.; Deng, Z.; Wang, L.; Chen, L.; Ke, M.; Yang, P.; Tian, R. Simple and Integrated Spintip-Based Technology Applied for Deep Proteome Profiling. Anal. Chem. 2016, 88 (9), 4864– 4871, DOI: 10.1021/acs.analchem.6b0063116https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xls1Kis7w%253D&md5=8d60eba33022626272064cf060d9fa1eSimple and Integrated Spintip-Based Technology Applied for Deep Proteome ProfilingChen, Wendong; Wang, Shuai; Adhikari, Subash; Deng, Zuhui; Wang, Lingjue; Chen, Lan; Ke, Mi; Yang, Pengyuan; Tian, RuijunAnalytical Chemistry (Washington, DC, United States) (2016), 88 (9), 4864-4871CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Great efforts have been taken for developing high-sensitive mass spectrometry (MS)-based proteomic technologies, among which sample prepn. is one of the major focus. Here, a simple and integrated spintip-based proteomics technol. (SISPROT) consisting of strong cation exchange beads and C18 disk in one pipet tip was developed. Both proteomics sample prepn. steps, including protein preconcn., redn., alkylation, and digestion, and reversed phase (RP)-based desalting and high-pH RP-based peptide fractionation can be achieved in a fully integrated manner for the first time. This easy-to-use technol. achieved high sensitivity with negligible sample loss. Proteomic anal. of 2000 HEK 293 cells readily identified 1270 proteins within 1.4 h of MS time, while 7826 proteins were identified when 100000 cells were processed and analyzed within only 22 h of MS time. More importantly, the SISPROT can be easily multiplexed on a std. centrifuge with good reproducibility (Pearson correlation coeff. > 0.98) for both single-shot anal. and deep proteome profiling with five-step high-pH RP fractionation. The SISPROT was exemplified by the triplicate anal. of 100000 stem cells from human exfoliated deciduous teeth (SHED). This led to the identification of 9078 proteins contg. 3771 annotated membrane proteins, which was the largest proteome data set for dental stem cells reported to date. We expect that the SISPROT will be well suited for deep proteome profiling for fewer than 100000 cells and applied for translational studies where multiplexed technol. with good label-free quantification precision is required.
- 17Dou, M.; Zhu, Y.; Liyu, A.; Liang, Y.; Chen, J.; Piehowski, P. D.; Xu, K.; Zhao, R.; Moore, R. J.; Atkinson, M. A.; Mathews, C. E.; Qian, W.-J.; Kelly, R. T. Nanowell-Mediated Two-Dimensional Liquid Chromatography Enables Deep Proteome Profiling of < 1000 Mammalian Cells. Chem. Sci. 2018, 9 (34), 6944– 6951, DOI: 10.1039/C8SC02680G17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtlCmtbbF&md5=1ec9556a474d130acc404d70f7a6a838Nanowell-mediated two-dimensional liquid chromatography enables deep proteome profiling of <1000 mammalian cellsDou, Maowei; Zhu, Ying; Liyu, Andrey; Liang, Yiran; Chen, Jing; Piehowski, Paul D.; Xu, Kerui; Zhao, Rui; Moore, Ronald J.; Atkinson, Mark A.; Mathews, Clayton E.; Qian, Wei-Jun; Kelly, Ryan T.Chemical Science (2018), 9 (34), 6944-6951CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)Multidimensional peptide sepns. can greatly increase the depth of coverage in proteome profiling. However, a major challenge for multidimensional sepns. is the requirement of large biol. samples, often contg. milligram amts. of protein. We have developed nanowell-mediated two-dimensional (2D) reversed-phase nanoflow liq. chromatog. (LC) sepns. for in-depth proteome profiling of low-nanogram samples. Peptides are first sepd. using high-pH LC and the effluent is concatenated into 4 or 12 nanowells. The contents of each nanowell are reconstituted in LC buffer and collected for subsequent sepn. and anal. by low-pH nanoLC-MS/MS. The nanowell platform minimizes peptide losses to surfaces in offline 2D LC fractionation, enabling >5800 proteins to be confidently identified from just 50 ng of HeLa digest. Furthermore, in combination with a recently developed nanowell-based sample prepn. workflow, we demonstrated deep proteome profiling of >6000 protein groups from small populations of cells, including ∼650 HeLa cells and 10 single human pancreatic islet thin sections (∼1000 cells) from a pre-symptomatic type 1 diabetic donor.
- 18Zhu, Y.; Scheibinger, M.; Ellwanger, D. C.; Krey, J. F.; Choi, D.; Kelly, R. T.; Heller, S.; Barr-Gillespie, P. G. Single-Cell Proteomics Reveals Changes in Expression during Hair-Cell Development. eLife 2019, 8, e50777, DOI: 10.7554/eLife.5077718https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtlamurbN&md5=66e9a7e7876b6b48af6f2fb378ccd1d1Single-cell proteomics reveals changes in expression during hair-cell developmentZhu, Ying; Scheibinger, Mirko; Ellwanger, Daniel Christian; Krey, Jocelyn F.; Choi, Dongseok; Kelly, Ryan T.; Heller, Stefan; Barr-Gillespie, Peter G.eLife (2019), 8 (), e50777CODEN: ELIFA8; ISSN:2050-084X. (eLife Sciences Publications Ltd.)Hearing and balance rely on small sensory hair cells that reside in the inner ear. To explore dynamic changes in the abundant proteins present in differentiating hair cells, we used nanoliter-scale shotgun mass spectrometry of single cells, each ~ 1 pL, from utricles of embryonic day 15 chickens. We identified unique constellations of proteins or protein groups from presumptive hair cells and from progenitor cells. The single-cell proteomes enabled the de novo reconstruction of a developmental trajectory using protein expression levels, revealing proteins that greatly increased in expression during differentiation of hair cells (e.g., OCM, CRABP1, GPX2, AK1, GSTO1) and those that decreased during differentiation (e.g., TMSB4X, AGR3). Complementary single-cell transcriptome profiling showed corresponding changes in mRNA during maturation of hair cells. Single-cell proteomics data thus can be mined to reveal features of cellular development that may be missed with transcriptomics.
- 19Shao, X.; Zhang, X. Design of Five-Layer Gold Nanoparticles Self-Assembled in a Liquid Open Tubular Column for Ultrasensitive Nano-LC-MS/MS Proteomic Analysis of 80 Living Cells. Proteomics 2017, 17 (8), 1600463, DOI: 10.1002/pmic.201600463There is no corresponding record for this reference.
- 20Chen, Q.; Yan, G.; Gao, M.; Zhang, X. Ultrasensitive Proteome Profiling for 100 Living Cells by Direct Cell Injection, Online Digestion and Nano-LC-MS/MS Analysis. Anal. Chem. 2015, 87 (13), 6674– 6680, DOI: 10.1021/acs.analchem.5b0080820https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVaku77P&md5=1b3f40134e4afd0f68bbec6483dd7455Ultrasensitive Proteome Profiling for 100 Living Cells by Direct Cell Injection, Online Digestion and Nano-LC-MS/MS AnalysisChen, Qi; Yan, Guoquan; Gao, Mingxia; Zhang, XiangminAnalytical Chemistry (Washington, DC, United States) (2015), 87 (13), 6674-6680CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Single-cell proteome anal. has always been an exciting goal because it provides crucial information about cellular heterogeneity and dynamic change. Here the authors presented an integrated proteome anal. device (iPAD) for 100 living cells (iPAD-100) that might be suitable for single-cell anal. Once cells were cultured, the iPAD-100 could be applied to inject 100 living cells, to transform the living cells into peptides, and to produce protein identification results with total automation. Due to the major obstacle for detection limit of mass spectrometry, the authors applied the iPAD-100 to analyze the proteome of 100 cells. In total, 813 proteins were identified in a DLD-cell proteome by three duplicate runs. Gene Ontol. anal. revealed that proteins from different cellular compartments were well-represented, including membrane proteins. The iPAD-100 greatly simplified the sampling process, reduced sample loss, and prevented contamination. As a result, proteins whose copy nos. were <1000 were identified from 100-cell samples with the iPAD-100, showing that a detection limit of 200 zmol was achieved. With increased sensitivity of mass spectrometry, the iPAD-100 may be able to reveal bountiful proteome information from a single cell in the near future.
- 21Zhu, Y.; Clair, G.; Chrisler, W. B.; Shen, Y.; Zhao, R.; Shukla, A. K.; Moore, R. J.; Misra, R. S.; Pryhuber, G. S.; Smith, R. D.; Ansong, C.; Kelly, R. T. Proteomic Analysis of Single Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLC-MS. Angew. Chem., Int. Ed. 2018, 57 (38), 12370– 12374, DOI: 10.1002/anie.20180284321https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtFemur7N&md5=1c6276ba46c3866bfaeb163aee051663Proteomic Analysis of Single Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLC-MSZhu, Ying; Clair, Geremy; Chrisler, William B.; Shen, Yufeng; Zhao, Rui; Shukla, Anil K.; Moore, Ronald J.; Misra, Ravi S.; Pryhuber, Gloria S.; Smith, Richard D.; Ansong, Charles; Kelly, Ryan T.Angewandte Chemie, International Edition (2018), 57 (38), 12370-12374CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)The authors report on the quant. proteomic anal. of single mammalian cells. Fluorescence-activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing chip, after which samples were analyzed by ultrasensitive nanoLC-MS. An av. of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. The single-cell proteomics platform can be used to differentiate cell types from enzyme-dissocd. human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.
- 22Shao, X.; Wang, X.; Guan, S.; Lin, H.; Yan, G.; Gao, M.; Deng, C.; Zhang, X. Integrated Proteome Analysis Device for Fast Single-Cell Protein Profiling. Anal. Chem. 2018, 90 (23), 14003– 14010, DOI: 10.1021/acs.analchem.8b0369222https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitVart7%252FO&md5=f7f84b752d5638b2765d395b56eeecf6Integrated Proteome Analysis Device for Fast Single-Cell Protein ProfilingShao, Xi; Wang, Xuantang; Guan, Sheng; Lin, Haizhu; Yan, Guoquan; Gao, Mingxia; Deng, Chunhui; Zhang, XiangminAnalytical Chemistry (Washington, DC, United States) (2018), 90 (23), 14003-14010CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)In the authors' previous work, the authors demonstrated an integrated proteome anal. device (iPAD-100) to analyze proteomes from 100 cells. For the first time, a novel integrated device for single-cell anal. (iPAD-1) was developed to profile proteins in a single cell within 1 h. In the iPAD-1, a selected single cell was directly sucked into a 22 μm internal diam. capillary. Then the cell lysis and protein digestion were simultaneously accomplished in the capillary in a 2 nL vol., which could prevent protein loss and excessive diln. Digestion was accelerated by using elevated temp. with ultrasonication. The whole time of cell treatment was 30 min. After that, single-cell digest peptides were transferred into an LC column directly through a true zero dead vol. union, to minimize protein transfer loss. A homemade 22 μm internal diam. nano-LC packing column with 3 μm internal diam. ESI tip was used in the device to achieve ultrasensitive detection. A 30 min elution program was applied to anal. of the single-cell proteome. Therefore, the total time needed for a single-cell anal. was only 1 h. In an anal. of 10 single HeLa cells, a max. of 328 proteins were identified in one cell by using an Orbitrap Fusion Tribrid MS instrument, and the detection limit was estd. at ∼1.7-170 zmol. Such a sensitivity of the iPAD-1 was ∼120-fold higher than that of the authors' previously developed iPAD-100 system. Prominent cellular heterogeneity in protein expressive profiling was obsd. Furthermore, the authors roughly estd. the phases of the cell cycle of tested HeLa cells by the amt. of core histone proteins.
- 23Zhu, Y.; Piehowski, P. D.; Zhao, R.; Chen, J.; Shen, Y.; Moore, R. J.; Shukla, A. K.; Petyuk, V. A.; Campbell-Thompson, M.; Mathews, C. E.; Smith, R. D.; Qian, W.-J.; Kelly, R. T. Nanodroplet Processing Platform for Deep and Quantitative Proteome Profiling of 10–100 Mammalian Cells. Nat. Commun. 2018, 9 (1), 882, DOI: 10.1038/s41467-018-03367-w23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1MrmtlyntQ%253D%253D&md5=ed52404cccbcdc4f8b9081ede1d092cbNanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cellsZhu Ying; Zhao Rui; Kelly Ryan T; Piehowski Paul D; Shen Yufeng; Moore Ronald J; Shukla Anil K; Petyuk Vladislav A; Smith Richard D; Qian Wei-Jun; Chen Jing; Campbell-Thompson Martha; Mathews Clayton ENature communications (2018), 9 (1), 882 ISSN:.Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.
- 24Zhou, M.; Uwugiaren, N.; Williams, S. M.; Moore, R. J.; Zhao, R.; Goodlett, D.; Dapic, I.; Paša-Tolić, L.; Zhu, Y. Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform. Anal. Chem. 2020, 92 (10), 7087– 7095, DOI: 10.1021/acs.analchem.0c0046724https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXosFWnsrg%253D&md5=2472ebd1f191b4f3419913f0d5999fcaSensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing PlatformZhou, Mowei; Uwugiaren, Naomi; Williams, Sarah M.; Moore, Ronald J.; Zhao, Rui; Goodlett, David; Dapic, Irena; Pasa-Tolic, Ljiljana; Zhu, YingAnalytical Chemistry (Washington, DC, United States) (2020), 92 (10), 7087-7095CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Top-down proteomics is a powerful tool for characterizing genetic variations and post-translational modifications at intact protein level. However, one significant tech. gap of top-down proteomics is the inability to analyze a low amt. of biol. samples, which limits its access to isolated rare cells, fine needle aspiration biopsies, and tissue substructures. Herein, we developed an ultrasensitive top-down platform by incorporating a microfluidic sample prepn. system, termed nanoPOTS (nanodroplet processing in one pot for trace samples), into a top-down proteomic workflow. A unique combination of a nonionic detergent dodecyl-β-D-maltopyranoside (DDM) with urea as protein extn. buffer significantly improved both protein extn. efficiency and sample recovery. We hypothesize that the DDM detergent improves protein recovery by efficiently reducing nonspecific adsorption of intact proteins on container surfaces, while urea serves as a strong denaturant to disrupt noncovalent complexes and release intact proteins for downstream anal. The nanoPOTS-based top-down platform reproducibly and quant. identified ~ 170 to ~ 620 proteoforms from ~ 70 to ~ 770 HeLa cells contg. ~ 10 to ~ 115 ng of total protein. A variety of post-translational modifications including acetylation, myristoylation, and iron binding were identified using only less than 800 cells. We anticipate the nanoPOTS top-down proteomics platform will be broadly applicable in biomedical research, particularly where clin. specimens are not available in amts. amenable to std. workflows.
- 25Xu, K.; Liang, Y.; Piehowski, P. D.; Dou, M.; Schwarz, K. C.; Zhao, R.; Sontag, R. L.; Moore, R. J.; Zhu, Y.; Kelly, R. T. Benchtop-Compatible Sample Processing Workflow for Proteome Profiling of < 100 Mammalian Cells. Anal. Bioanal. Chem. 2019, 411 (19), 4587– 4596, DOI: 10.1007/s00216-018-1493-925https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXit12ls7nJ&md5=5f57393c6315af49f67303e080dfe016Benchtop-compatible sample processing workflow for proteome profiling of < 100 mammalian cellsXu, Kerui; Liang, Yiran; Piehowski, Paul D.; Dou, Maowei; Schwarz, Kaitlynn C.; Zhao, Rui; Sontag, Ryan L.; Moore, Ronald J.; Zhu, Ying; Kelly, Ryan T.Analytical and Bioanalytical Chemistry (2019), 411 (19), 4587-4596CODEN: ABCNBP; ISSN:1618-2642. (Springer)Extending proteomics to smaller samples can enable the mapping of protein expression across tissues with high spatial resoln. and can reveal sub-group heterogeneity. However, despite the continually improving sensitivity of LC-MS instrumentation, in-depth profiling of samples contg. low-nanogram amts. of protein has remained challenging due to analyte losses incurred during prepn. and anal. To address this, the authors recently developed nanodroplet processing in one pot for trace samples (nanoPOTS), a robotic/microfluidic platform that generates ready-to-analyze peptides from cellular material in ∼200 nL droplets with greatly reduced sample losses. In combination with ultrasensitive LC-MS, nanoPOTS has enabled >3000 proteins to be confidently identified from as few as 10 cultured human cells and ∼700 proteins from single cells. However, the nanoPOTS platform requires a highly skilled operator and a costly inhouse-built robotic nanopipetting instrument. The authors sought to evaluate the extent to which the benefits of nanodroplet processing could be preserved when upscaling reagent dispensing vols. by a factor of 10 to those addressable by com. micropipette. The authors characterized the resulting platform, termed microdroplet processing in one pot for trace samples (μPOTS), for the anal. of as few as ∼25 cultured HeLa cells (4 ng total protein) or 50 μm square mouse liver tissue thin sections and found that ∼1800 and ∼1200 unique proteins were resp. identified with high reproducibility. The reduced equipment requirements should facilitate broad dissemination of nanoproteomics workflows by obviating the need for a capital-intensive custom liq. handling system.
- 26Ross-Innes, C. S.; Becq, J.; Warren, A.; Cheetham, R. K.; Northen, H.; O’Donovan, M.; Malhotra, S.; di Pietro, M.; Ivakhno, S.; He, M.; Weaver, J. M. J.; Lynch, A. G.; Kingsbury, Z.; Ross, M.; Humphray, S.; Bentley, D.; Fitzgerald, R. C. Whole-Genome Sequencing Provides New Insights into the Clonal Architecture of Barrett’s Esophagus and Esophageal Adenocarcinoma. Nat. Genet. 2015, 47 (9), 1038– 1046, DOI: 10.1038/ng.335726https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1WltLjN&md5=a60d7a9bfd4221c3838bdfdffc50ee2aWhole-genome sequencing provides new insights into the clonal architecture of Barrett's esophagus and esophageal adenocarcinomaRoss-Innes, Caryn S.; Becq, Jennifer; Warren, Andrew; Cheetham, R. Keira; Northen, Helen; O'Donovan, Maria; Malhotra, Shalini; di Pietro, Massimiliano; Ivakhno, Sergii; He, Miao; Weaver, Jamie M. J.; Lynch, Andy G.; Kingsbury, Zoya; Ross, Mark; Humphray, Sean; Bentley, David; Fitzgerald, Rebecca C.; Hayes, Stephen J.; Ang, Yeng; Welch, Ian; Preston, Shaun; Oakes, Sarah; Save, Vicki; Skipworth, Richard; Tucker, Olga; Davies, Jim; Crichton, Charles; Schusterreiter, Christian; Underwood, Tim; Noble, Fergus; Stacey, Bernard; Kelly, Jamie; Byrne, James; Haydon, Annette; Sharland, Donna; Owsley, Jack; Barr, Hugh; Lagergren, Jesper; Gossage, James; Davies, Andrew; Mason, Robert; Chang, Fuju; Zylstra, Janine; Sanders, Grant; Wheatley, Tim; Berrisford, Richard; Bracey, Tim; Harden, Catherine; Bunting, David; Roques, Tom; Nobes, Jenny; Loo, Suat; Lewis, Mike; Cheong, Ed; Priest, Oliver; Parsons, Simon L.; Soomro, Irshad; Kaye, Philip; Saunders, John; Pang, Vincent; Welch, Neil T.; Catton, James A.; Duffy, John P.; Ragunath, Krish; Lovat, Laurence; Haidry, Rehan; Miah, Haroon; Kerr, Sarah; Eneh, Victor; Butawan, Rommel; Roques, Tom; Lewis, Michael; Cheong, Edward; Kumar, Bhasker; Igali, Laszlo; Walton, Sharon; Dann, Adela; Safranek, Peter; Hindmarsh, Andy; Sudjendran, Vijayendran; Scott, Michael; Cluroe, Alison; Miremadi, Ahmad; Mahler-Araujo, Betania; Nutzinger, Barbara; Peters, Chris; Abdullahi, Zarah; Crawte, Jason; MacRae, Shona; Noorani, Ayesha; Elliott, Rachael Fels; Bower, Lawrence; Edwards, Paul; Tavare, Simon; Eldridge, Matthew; Bornschein, Jan; Secrier, Maria; Yang, Tsun-Po; O'Neill, J. Robert; Adamczuk, Kasia; Lao-Sirieix, Pierre; Grehan, Nicola; Smith, Laura; Lishman, Suzy; Beardsmore, Duncan; Dawson, SarahNature Genetics (2015), 47 (9), 1038-1046CODEN: NGENEC; ISSN:1061-4036. (Nature Publishing Group)The mol. genetic relationship between esophageal adenocarcinoma (EAC) and its precursor lesion, Barrett's esophagus, is poorly understood. Using whole-genome sequencing on 23 paired Barrett's esophagus and EAC samples, together with one in-depth Barrett's esophagus case study sampled over time and space, we have provided the following new insights: (i) Barrett's esophagus is polyclonal and highly mutated even in the absence of dysplasia; (ii) when cancer develops, copy no. increases and heterogeneity persists such that the spectrum of mutations often shows surprisingly little overlap between EAC and adjacent Barrett's esophagus; and (iii) despite differences in specific coding mutations, the mutational context suggests a common causative insult underlying these two conditions. From a clin. perspective, the histopathol. assessment of dysplasia appears to be a poor reflection of the mol. disarray within the Barrett's epithelium, and a mol. Cytosponge technique overcomes sampling bias and has the capacity to reflect the entire clonal architecture.
- 27Peters, Y.; Al-Kaabi, A.; Shaheen, N. J.; Chak, A.; Blum, A.; Souza, R. F.; Di Pietro, M.; Iyer, P. G.; Pech, O.; Fitzgerald, R. C.; Siersema, P. D. Barrett Oesophagus. Nat. Rev. Dis. Primer 2019, 5 (1), 35, DOI: 10.1038/s41572-019-0086-zThere is no corresponding record for this reference.
- 28Hofmann, A. F. The Continuing Importance of Bile Acids in Liver and Intestinal Disease. Arch. Intern. Med. 1999, 159 (22), 2647– 2658, DOI: 10.1001/archinte.159.22.264728https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3c%252FmsVKhtw%253D%253D&md5=4f017c841da85f0723c292fb31e70037The continuing importance of bile acids in liver and intestinal diseaseHofmann A FArchives of internal medicine (1999), 159 (22), 2647-58 ISSN:0003-9926.Bile acids, the water-soluble, amphipathic end products of cholesterol metabolism, are involved in liver, biliary, and intestinal disease. Formed in the liver, bile acids are absorbed actively from the small intestine, with each molecule undergoing multiple enterohepatic circulations before being excreted. After their synthesis from cholesterol, bile acids are conjugated with glycine or taurine, a process that makes them impermeable to cell membranes and permits high concentrations to persist in bile and intestinal content. The relation between the chemical structure and the multiple physiological functions of bile acids is reviewed. Bile acids induce biliary lipid secretion and solubilize cholesterol in bile, promoting its elimination. In the small intestine, bile acids solubilize dietary lipids promoting their absorption. Bile acids are cytotoxic when present in abnormally high concentrations. This may occur intracellularly, as occurs in the hepatocyte in cholestasis, or extracellularly, as occurs in the colon in patients with bile acid malabsorption. Disturbances in bile acid metabolism can be caused by (1) defective biosynthesis from cholesterol or defective conjugation, (2) defective membrane transport in the hepatocyte or ileal enterocyte, (3) defective transport between organs or biliary diversion, and (4) increased bacterial degradation during enterohepatic cycling. Bile acid therapy involves bile acid replacement in deficiency states or bile acid displacement by ursodeoxycholic acid, a noncytotoxic bile acid. In cholestatic liver disease, administration of ursodeoxycholic acid decreases hepatocyte injury by retained bile acids, improving liver tests, and slowing disease progression. Bile acid malabsorption may lead to high concentrations of bile acids in the colon and impaired colonic mucosal function; bile acid sequestrants provide symptomatic benefit for diarrhea. A knowledge of bile acid physiology and the perturbations of bile acid metabolism in liver and digestive disease should be useful for the internist.
- 29Fitzgerald, R. C.; di Pietro, M.; O’Donovan, M.; Maroni, R.; Muldrew, B.; Debiram-Beecham, I.; Gehrung, M.; Offman, J.; Tripathi, M.; Smith, S. G.; Aigret, B.; Walter, F. M.; Rubin, G.; Bagewadi, A.; Patrick, A.; Shenoy, A.; Redmond, A.; Muddu, A.; Northrop, A.; Groves, A.; Shiner, A.; Heer, A.; Takhar, A.; Bowles, A.; Jarman, A.; Wong, A.; Lucas, A.; Gibbons, A.; Dhar, A.; Curry, A.; Lalonde, A.; Swinburn, A.; Turner, A.; Lydon, A.-M.; Gunstone, A.; Lee, A.; Nambi, A.; Ariyarathenam, A.; Elden, A.; Wilson, A.; Donepudi, B.; Campbell, B.; Uszycka, B.; Bowers, B.; Coghill, B.; de Quadros, B.; Cheah, C.; Bratten, C.; Brown, C.; Moorbey, C.; Clisby, C.; Gordon, C.; Schramm, C.; Castle, C.; Newark, C.; Norris, C.; A’Court, C.; Graham, C.; Fletcher, C.; Grocott, C.; Rees, C.; Bakker, C.; Paschalides, C.; Vickery, C.; Schembri, D.; Morris, D.; Hagan, D.; Cronk, D.; Goddard, D.; Graham, D.; Phillips, D.; Prabhu, D.; Kejariwal, D.; Garg, D.; Lonsdale, D.; Butterworth, D.; Clements, D.; Bradman, D.; Blake, D.; Mather, E.; O’Farrell, E.; Markowetz, F.; Adams, F.; Pesola, F.; Forbes, G.; Taylor, G.; Collins, G.; Irvine, G.; Fourie, G.; Doyle, H.; Barnes, H.; Bowyer, H.; Whiting, H.; Beales, I.; Binnian, I.; Bremner, I.; Jennings, I.; Troiceanu, I.; Modelell, I.; Emmerson, I.; Ortiz, J.; Lilley, J.; Harvey, J.; Vicars, J.; Takhar, J.; Larcombe, J.; Bornschein, J.; Aldegather, J.; Johnson, J.; Ducker, J.; Skinner, J.; Dash, J.; Walsh, J.; Miralles, J.; Ridgway, J.; Ince, J.; Kennedy, J.; Hampson, K.; Milne, K.; Ellerby, K.; Priddis, K.; Rainsbury, K.; Powell, K.; Gunner, K.; Ragunath, K.; Knox, K.; Baseley, L.; White, L.; Lovat, L.; Berney, L.; Crockett, L.; Murray, L.; Westwood, L.; Chalkley, L.; Leggett, L.; Dale, L.; Scovell, L.; Brooks, L.; Saunders, L.; Owen, L.; Dilwershah, M.; Baldry, M.; Corcoran, M.; Roy, M.; Macedo, M.; Attah, M.; Anson, M.-J.; Rutter, M.; Wallard, M.; Gaw, M.; Hunt, M.; Lea-Hagerty, M.; Penacerrada, M.; Bianchi, M.; Baker-Moffatt, M.; Czajkowski, M.; Sleeth, M.; Brewer, N.; Wooding, N.; Todd, N.; Millen, N.; Zolle, O.; Whitehead, O.; Ojechi, P.; Moore, P.; Banim, P.; Spellar, P.; Bhandari, P.; Kant, P.; Nixon, R.; Russell, R.; Roberts, R.; Skule, R.; West, R.; Fox, R.; Beesley, R.; Gibbins, R.; Osborne, R.; Thiagarajan, S.; Bastiman, S.; Warburton, S.; Pai, S.; Leith-Russell, S.; Utting, S.; Watson, S.; Wytrykowski, S.; Singh, S.; Malhotra, S.; Woods, S.; Conway, S.; Mateer, S.; Macrae, S.; Singh, S.; Fourie, S.; Campbell, S.; Parslow-Williams, S.; Goel, S.; Dellar, S.; Jones, S.; Knight, S.; Mackay-Thomas, S.; Mukherjee, S.; Allen, S.; Henry, S.; Evans, T.; Leighton, T.; Bray, T.; Shackleton, T.; Santosh, V.; Glover, V.; Chandraraj, V.; Elson, W.; Briggs, W.; Barron, Z.; Khan, Z.; Sasieni, P. Cytosponge-Trefoil Factor 3 versus Usual Care to Identify Barrett’s Oesophagus in a Primary Care Setting: A Multicentre, Pragmatic, Randomised Controlled Trial. Lancet 2020, 396 (10247), 333– 344, DOI: 10.1016/S0140-6736(20)31099-029https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB38jpvVGrtQ%253D%253D&md5=b0b2f86fdf8f90356abf375b51307abeCytosponge-trefoil factor 3 versus usual care to identify Barrett's oesophagus in a primary care setting: a multicentre, pragmatic, randomised controlled trialFitzgerald Rebecca C; di Pietro Massimiliano; O'Donovan Maria; Tripathi Monika; Maroni Roberta; Muldrew Beth; Aigret Benoit; Sasieni Peter; Debiram-Beecham Irene; Gehrung Marcel; Offman Judith; Smith Samuel G; Walter Fiona M; Rubin GregLancet (London, England) (2020), 396 (10247), 333-344 ISSN:.BACKGROUND: Treatment of dysplastic Barrett's oesophagus prevents progression to adenocarcinoma; however, the optimal diagnostic strategy for Barrett's oesophagus is unclear. The Cytosponge-trefoil factor 3 (TFF3) is a non-endoscopic test for Barrett's oesophagus. The aim of this study was to investigate whether offering this test to patients on medication for gastro-oesophageal reflux would increase the detection of Barrett's oesophagus compared with standard management. METHODS: This multicentre, pragmatic, randomised controlled trial was done in 109 socio-demographically diverse general practice clinics in England. Randomisation was done both at the general practice clinic level (cluster randomisation) and at the individual patient level, and the results for each type of randomisation were analysed separately before being combined. Patients were eligible if they were aged 50 years or older, had been taking acid-suppressants for symptoms of gastro-oesophageal reflux for more than 6 months, and had not undergone an endoscopy procedure within the past 5 years. General practice clinics were selected by the local clinical research network and invited to participate in the trial. For cluster randomisation, clinics were randomly assigned (1:1) by the trial statistician using a computer-generated randomisation sequence; for individual patient-level randomisation, patients were randomly assigned (1:1) by the general practice clinics using a centrally prepared computer-generated randomisation sequence. After randomisation, participants received either standard management of gastro-oesophageal reflux (usual care group), in which participants only received an endoscopy if required by their general practitioner, or usual care plus an offer of the Cytosponge-TFF3 procedure, with a subsequent endoscopy if the procedure identified TFF3-positive cells (intervention group). The primary outcome was the diagnosis of Barrett's oesophagus at 12 months after enrolment, expressed as a rate per 1000 person-years, in all participants in the intervention group (regardless of whether they had accepted the offer of the Cytosponge-TFF3 procedure) compared with all participants in the usual care group. Analyses were intention-to-treat. The trial is registered with the ISRCTN registry, ISRCTN68382401, and is completed. FINDINGS: Between March 20, 2017, and March 21, 2019, 113 general practice clinics were enrolled, but four clinics dropped out shortly after randomisation. Using an automated search of the electronic prescribing records of the remaining 109 clinics, we identified 13 657 eligible patients who were sent an introductory letter with 14 days to opt out. 13 514 of these patients were randomly assigned (per practice or at the individual patient level) to the usual care group (n=6531) or the intervention group (n=6983). Following randomisation, 149 (2%) of 6983 participants in the intervention group and 143 (2%) of 6531 participants in the usual care group, on further scrutiny, did not meet all eligibility criteria or withdrew from the study. Of the remaining 6834 participants in the intervention group, 2679 (39%) expressed an interest in undergoing the Cytosponge-TFF3 procedure. Of these, 1750 (65%) met all of the eligibility criteria on telephone screening and underwent the procedure. Most of these participants (1654 [95%]; median age 69 years) swallowed the Cytosponge successfully and produced a sample. 231 (3%) of 6834 participants had a positive Cytosponge-TFF3 result and were referred for an endoscopy. Patients who declined the offer of the Cytosponge-TFF3 procedure and all participants in the usual care group only had an endoscopy if deemed necessary by their general practitioner. During an average of 12 months of follow-up, 140 (2%) of 6834 participants in the intervention group and 13 (<1%) of 6388 participants in the usual care group were diagnosed with Barrett's oesophagus (absolute difference 18·3 per 1000 person-years [95% CI 14·8-21·8]; rate ratio adjusted for cluster randomisation 10·6 [95% CI 6·0-18·8], p<0·0001). Nine (<1%) of 6834 participants were diagnosed with dysplastic Barrett's oesophagus (n=4) or stage I oesophago-gastric cancer (n=5) in the intervention group, whereas no participants were diagnosed with dysplastic Barrett's oesophagus or stage I gastro-oesophageal junction cancer in the usual care group. Among 1654 participants in the intervention group who swallowed the Cytosponge device successfully, 221 (13%) underwent endoscopy after testing positive for TFF3 and 131 (8%, corresponding to 59% of those having an endoscopy) were diagnosed with Barrett's oesophagus or cancer. One patient had a detachment of the Cytosponge from the thread requiring endoscopic removal, and the most common side-effect was a sore throat in 63 (4%) of 1654 participants. INTERPRETATION: In patients with gastro-oesophageal reflux, the offer of Cytosponge-TFF3 testing results in improved detection of Barrett's oesophagus. Cytosponge-TFF3 testing could also lead to the diagnosis of treatable dysplasia and early cancer. This strategy will lead to additional endoscopies with some false positive results. FUNDING: Cancer Research UK, National Institute for Health Research, the UK National Health Service, Medtronic, and the Medical Research Council.
- 30Ross-Innes, C. S.; Chettouh, H.; Achilleos, A.; Galeano-Dalmau, N.; Debiram-Beecham, I.; MacRae, S.; Fessas, P.; Walker, E.; Varghese, S.; Evan, T.; Lao-Sirieix, P. S.; O’Donovan, M.; Malhotra, S.; Novelli, M.; Disep, B.; Kaye, P. V.; Lovat, L. B.; Haidry, R.; Griffin, M.; Ragunath, K.; Bhandari, P.; Haycock, A.; Morris, D.; Attwood, S.; Dhar, A.; Rees, C.; Rutter, M. D.; Ostler, R.; Aigret, B.; Sasieni, P. D.; Fitzgerald, R. C. Risk Stratification of Barrett’s Oesophagus Using a Non-Endoscopic Sampling Method Coupled with a Biomarker Panel: A Cohort Study. Lancet Gastroenterol. Hepatol. 2017, 2 (1), 23– 31, DOI: 10.1016/S2468-1253(16)30118-230https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cvnt1OmtQ%253D%253D&md5=e51955fc3160fbf1f2d64146831713ddRisk stratification of Barrett's oesophagus using a non-endoscopic sampling method coupled with a biomarker panel: a cohort studyRoss-Innes Caryn S; Chettouh Hamza; Achilleos Achilleas; Galeano-Dalmau Nuria; Debiram-Beecham Irene; MacRae Shona; Fessas Petros; Walker Elaine; Varghese Sibu; Evan Theodore; Lao-Sirieix Pierre S; O'Donovan Maria; Malhotra Shalini; Novelli Marco; Lovat Laurence B; Haidry Rehan; Disep Babett; Griffin Michael; Kaye Phillip V; Ragunath Krish; Bhandari Pradeep; Haycock Adam; Morris Danielle; Attwood Stephen; Dhar Anjan; Rees Colin; Rutter Matt D; Ostler Richard; Aigret Benoit; Sasieni Peter D; Fitzgerald Rebecca CThe lancet. Gastroenterology & hepatology (2017), 2 (1), 23-31 ISSN:.BACKGROUND: Barrett's oesophagus predisposes to adenocarcinoma. However, most patients with Barrett's oesophagus will not progress and endoscopic surveillance is invasive, expensive, and fraught by issues of sampling bias and the subjective assessment of dysplasia. We investigated whether a non-endoscopic device, the Cytosponge, could be coupled with clinical and molecular biomarkers to identify a group of patients with low risk of progression suitable for non-endoscopic follow-up. METHODS: In this multicentre cohort study (BEST2), patients with Barrett's oesophagus underwent the Cytosponge test before their surveillance endoscopy. We collected clinical and demographic data and tested Cytosponge samples for a molecular biomarker panel including three protein biomarkers (P53, c-Myc, and Aurora kinase A), two methylation markers (MYOD1 and RUNX3), glandular atypia, and TP53 mutation status. We used a multivariable logistic regression model to compute the conditional probability of dysplasia status. We selected a simple model with high classification accuracy and applied it to an independent validation cohort. The BEST2 study is registered with ISRCTN, number 12730505. FINDINGS: The discovery cohort consisted of 468 patients with Barrett's oesophagus and intestinal metaplasia. Of these, 376 had no dysplasia and 22 had high-grade dysplasia or intramucosal adenocarcinoma. In the discovery cohort, a model with high classification accuracy consisted of glandular atypia, P53 abnormality, and Aurora kinase A positivity, and the interaction of age, waist-to-hip ratio, and length of the Barrett's oesophagus segment. 162 (35%) of 468 of patients fell into the low-risk category and the probability of being a true non-dysplastic patient was 100% (99% CI 96-100) and the probability of having high-grade dysplasia or intramucosal adenocarcinoma was 0% (0-4). 238 (51%) of participants were classified as of moderate risk; the probability of having high-grade dysplasia was 14% (9-21). 58 (12%) of participants were classified as high-risk; the probability of having non-dysplastic endoscopic biopsies was 13% (5-27), whereas the probability of having high-grade dysplasia or intramucosal adenocarcinoma was 87% (73-95). In the validation cohort (65 patients), 51 were non-dysplastic and 14 had high-grade dysplasia. In this cohort, 25 (38%) of 65 patients were classified as being low-risk, and the probability of being non-dysplastic was 96·0% (99% CI 73·80-99·99). The moderate-risk group comprised 27 non-dysplastic and eight high-grade dysplasia cases, whereas the high-risk group (8% of the cohort) had no non-dysplastic cases and five patients with high-grade dysplasia. INTERPRETATION: A combination of biomarker assays from a single Cytosponge sample can be used to determine a group of patients at low risk of progression, for whom endoscopy could be avoided. This strategy could help to avoid overdiagnosis and overtreatment in patients with Barrett's oesophagus. FUNDING: Cancer Research UK.
- 31Williams, S. M.; Liyu, A. V.; Tsai, C.-F.; Moore, R. J.; Orton, D. J.; Chrisler, W. B.; Gaffrey, M. J.; Liu, T.; Smith, R. D.; Kelly, R. T.; Pasa-Tolic, L.; Zhu, Y. Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics. Anal. Chem. 2020, 92 (15), 10588– 10596, DOI: 10.1021/acs.analchem.0c0155131https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhtlCmtL3O&md5=11a723a01a532b3fd5dbb8f43fb00134Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell ProteomicsWilliams, Sarah M.; Liyu, Andrey V.; Tsai, Chia-Feng; Moore, Ronald J.; Orton, Daniel J.; Chrisler, William B.; Gaffrey, Matthew J.; Liu, Tao; Smith, Richard D.; Kelly, Ryan T.; Pasa-Tolic, Ljiljana; Zhu, YingAnalytical Chemistry (Washington, DC, United States) (2020), 92 (15), 10588-10596CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Single-cell proteomics can provide crit. biol. insight into the cellular heterogeneity that is masked by bulk-scale anal. The authors have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample vols., the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, the authors have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. The authors also developed a sample drying, extn., and loading workflow to enable reproducible and reliable sample injection. The sequential anal. of 20 samples contg. 10 ng tryptic peptides demonstrated high reproducibility with correlation coeffs. of >0.995 between any two samples. The nanoPOTS autosampler can provide anal. throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, resp. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an av. of 256 proteins was identified from each cell and the no. was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~ 77 single cells could be analyzed per day. The authors analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quant. single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.
- 32DEP: Differential Enrichment analysis of Proteomics data version 1.10.0 from Bioconductor, https://rdrr.io/bioc/DEP/ (accessed Jul 4, 2020).There is no corresponding record for this reference.
- 33Zhang, X.; Smits, A. H.; van Tilburg, G. B.; Ovaa, H.; Huber, W.; Vermeulen, M. Proteome-Wide Identification of Ubiquitin Interactions Using UbIA-MS. Nat. Protoc. 2018, 13 (3), 530– 550, DOI: 10.1038/nprot.2017.14733https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisl2ku74%253D&md5=88719f04dffb8fffe078df606dd6f5d7Proteome-wide identification of ubiquitin interactions using UbIA-MSZhang, Xiaofei; Smits, Arne H.; van Tilburg, Gabrielle B. A.; Ovaa, Huib; Huber, Wolfgang; Vermeulen, MichielNature Protocols (2018), 13 (3), 530-550CODEN: NPARDW; ISSN:1750-2799. (Nature Research)Ubiquitin-binding proteins play an important role in eukaryotes by translating differently linked polyubiquitin chains into proper cellular responses. Current knowledge about ubiquitin-binding proteins and ubiquitin linkage-selective interactions is mostly based on case-by-case studies. We have recently reported a method called ubiquitin interactor affinity enrichment-mass spectrometry (UbIA-MS), which enables comprehensive identification of ubiquitin interactors for all ubiquitin linkages from crude cell lysates. One major strength of UbIA-MS is the fact that ubiquitin interactors are enriched from crude cell lysates, in which proteins are present at endogenous levels, contain biol. relevant post-translational modifications (PTMs) and are assembled in native protein complexes. In addn., UbIA-MS uses chem. synthesized nonhydrolyzable diubiquitin, which mimics native diubiquitin and is inert to cleavage by endogenous deubiquitinases (DUBs). Here, we present a detailed protocol for UbIA-MS that proceeds in five stages: (i) chem. synthesis of ubiquitin precursors and click chem. for the generation of biotinylated nonhydrolyzable diubiquitin baits, (ii) in vitro affinity purifn. of ubiquitin interactors, (iii) on-bead interactor digestion, (iv) liq. chromatog. (LC)-MS/MS anal. and (v) data anal. to identify differentially enriched proteins. The computational anal. tools are freely available as an open-source R software package, including a graphical interface. Typically, UbIA-MS allows the identification of dozens to hundreds of ubiquitin interactors from any type of cell lysate, and can be used to study cell type or stimulus-dependent ubiquitin interactions. The nonhydrolyzable diubiquitin synthesis can be completed in 3 wk, followed by ubiquitin interactor enrichment and identification, which can be completed within another 2 wk.
- 34test_diff: Differential enrichment test in DEP: Differential Enrichment analysis of Proteomics data, https://rdrr.io/bioc/DEP/man/test_diff.html (accessed Jul 5, 2020).There is no corresponding record for this reference.
- 35Ritchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K. Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res. 2015, 43 (7), e47– e47, DOI: 10.1093/nar/gkv00735https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsFaiu7%252FN&md5=e8a5f44160c650ad89adc48a9912bd96limma powers differential expression analyses for RNA-sequencing and microarray studiesRitchie, Matthew E.; Phipson, Belinda; Wu, Di; Hu, Yifang; Law, Charity W.; Shi, Wei; Smyth, Gordon K.Nucleic Acids Research (2015), 43 (7), e47/1-e47/13CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Limma is an R/Bioconductor software package that provides an integrated soln. for analyzing data from gene expression expts. It contains rich features for handling complex exptl. designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream anal. tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyze both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analyzing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biol. interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
- 36P’ng, C.; Green, J.; Chong, L. C.; Waggott, D.; Prokopec, S. D.; Shamsi, M.; Nguyen, F.; Mak, D. Y. F.; Lam, F.; Albuquerque, M. A.; Wu, Y.; Jung, E. H.; Starmans, M. H. W.; Chan-Seng-Yue, M. A.; Yao, C. Q.; Liang, B.; Lalonde, E.; Haider, S.; Simone, N. A.; Sendorek, D.; Chu, K. C.; Moon, N. C.; Fox, N. S.; Grzadkowski, M. R.; Harding, N. J.; Fung, C.; Murdoch, A. R.; Houlahan, K. E.; Wang, J.; Garcia, D. R.; de Borja, R.; Sun, R. X.; Lin, X.; Chen, G. M.; Lu, A.; Shiah, Y.-J.; Zia, A.; Kearns, R.; Boutros, P. C. BPG: Seamless, Automated and Interactive Visualization of Scientific Data. BMC Bioinf. 2019, 20 (1), 42, DOI: 10.1186/s12859-019-2610-236https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3cjjs1altA%253D%253D&md5=61ec34f7005b4dfc1819b7e76300d80fBPG: Seamless, automated and interactive visualization of scientific dataP'ng Christine; Green Jeffrey; Chong Lauren C; Waggott Daryl; Prokopec Stephenie D; Shamsi Mehrdad; Nguyen Francis; Mak Denise Y F; Lam Felix; Albuquerque Marco A; Wu Ying; Jung Esther H; Starmans Maud H W; Chan-Seng-Yue Michelle A; Yao Cindy Q; Liang Bianca; Lalonde Emilie; Haider Syed; Simone Nicole A; Sendorek Dorota; Chu Kenneth C; Moon Nathalie C; Fox Natalie S; Grzadkowski Michal R; Harding Nicholas J; Fung Clement; Murdoch Amanda R; Houlahan Kathleen E; Wang Jianxin; Garcia David R; de Borja Richard; Sun Ren X; Lin Xihui; Chen Gregory M; Lu Aileen; Shiah Yu-Jia; Zia Amin; Kearns Ryan; Boutros Paul C; Yao Cindy Q; Lalonde Emilie; Fox Natalie S; Houlahan Kathleen E; Shiah Yu-Jia; Boutros Paul C; Wang Jianxin; Sun Ren X; Lu Aileen; Boutros Paul C; Boutros Paul C; Boutros Paul C; Boutros Paul C; Boutros Paul CBMC bioinformatics (2019), 20 (1), 42 ISSN:.BACKGROUND: We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. RESULTS: This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. CONCLUSION: BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.
- 37Hulsen, T.; de Vlieg, J.; Alkema, W. BioVenn – a Web Application for the Comparison and Visualization of Biological Lists Using Area-Proportional Venn Diagrams. BMC Genomics 2008, 9 (1), 488, DOI: 10.1186/1471-2164-9-48837https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjksF2ltQ%253D%253D&md5=1c79e2fa6accc5ff3c4cd77bab9aca8cBioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagramsHulsen Tim; de Vlieg Jacob; Alkema WynandBMC genomics (2008), 9 (), 488 ISSN:.BACKGROUND: In many genomics projects, numerous lists containing biological identifiers are produced. Often it is useful to see the overlap between different lists, enabling researchers to quickly observe similarities and differences between the data sets they are analyzing. One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram: a diagram consisting of two or more circles in which each circle corresponds to a data set, and the overlap between the circles corresponds to the overlap between the data sets. Venn diagrams are especially useful when they are 'area-proportional' i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. Currently there are no programs available that can create area-proportional Venn diagrams connected to a wide range of biological databases. RESULTS: We designed a web application named BioVenn to summarize the overlap between two or three lists of identifiers, using area-proportional Venn diagrams. The user only needs to input these lists of identifiers in the textboxes and push the submit button. Parameters like colors and text size can be adjusted easily through the web interface. The position of the text can be adjusted by 'drag-and-drop' principle. The output Venn diagram can be shown as an SVG or PNG image embedded in the web application, or as a standalone SVG or PNG image. The latter option is useful for batch queries. Besides the Venn diagram, BioVenn outputs lists of identifiers for each of the resulting subsets. If an identifier is recognized as belonging to one of the supported biological databases, the output is linked to that database. Finally, BioVenn can map Affymetrix and EntrezGene identifiers to Ensembl genes. CONCLUSION: BioVenn is an easy-to-use web application to generate area-proportional Venn diagrams from lists of biological identifiers. It supports a wide range of identifiers from the most used biological databases currently available. Its implementation on the World Wide Web makes it available for use on any computer with internet connection, independent of operating system and without the need to install programs locally. BioVenn is freely accessible at http://www.cmbi.ru.nl/cdd/biovenn/.
- 38Perez-Riverol, Y.; Csordas, A.; Bai, J.; Bernal-Llinares, M.; Hewapathirana, S.; Kundu, D. J.; Inuganti, A.; Griss, J.; Mayer, G.; Eisenacher, M.; Pérez, E.; Uszkoreit, J.; Pfeuffer, J.; Sachsenberg, T.; Yılmaz, Ş.; Tiwary, S.; Cox, J.; Audain, E.; Walzer, M.; Jarnuczak, A. F.; Ternent, T.; Brazma, A.; Vizcaíno, J. A. The PRIDE Database and Related Tools and Resources in 2019: Improving Support for Quantification Data. Nucleic Acids Res. 2019, 47 (D1), D442– D450, DOI: 10.1093/nar/gky110638https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhs1GqtrzK&md5=4cd0f929c2df840a5cc0631d0d708d6cThe PRIDE database and related tools and resources in 2019: improving support for quantification dataPerez-Riverol, Yasset; Csordas, Attila; Bai, Jingwen; Bernal-Llinare, Manuel; Hewapathirana, Suresh; Kundu, Deepti J.; Iuganti, Avinash; Griss, Johannes; Mayer, Gerhard; Eisenacher, Martin; Perez, Enrique; Uszkoreit, Julian; Pfeuffer, Julianus; Sachsenberg, Timo; Yilmaz, Sule; Tiwary, Shivani; Cox, Jurgen; Audain, Enrique; Walzer, Mathias; Jarnuczak, Andrew F.; Ternent, Tobias; Brazma, Alvis; Vizcaino, Juan AntonioNucleic Acids Research (2019), 47 (D1), D442-D450CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)A review. The PRoteomics IDEntifications (PRIDE) database is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data vols. and addnl. data types. A new scalable and fault tolerant storage backed, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Addnl., we emphasize the improved support for quant. proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and vol. of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
- 39UniProt, https://www.uniprot.org/ (accessed Jun 25, 2020).There is no corresponding record for this reference.
- 40Protein GRAVY, https://www.bioinformatics.org/sms2/protein_gravy.html (accessed Jun 25, 2020).There is no corresponding record for this reference.
- 41Budnik, B.; Levy, E.; Harmange, G.; Slavov, N. SCoPE-MS: Mass Spectrometry of Single Mammalian Cells Quantifies Proteome Heterogeneity during Cell Differentiation. Genome Biol. 2018, 19 (1), 161, DOI: 10.1186/s13059-018-1547-541https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXisFKls7nM&md5=bfdeda4d526356689afaa4c6d97a20d5SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiationBudnik, Bogdan; Levy, Ezra; Harmange, Guillaume; Slavov, NikolaiGenome Biology (2018), 19 (), 161/1-161/12CODEN: GNBLFW; ISSN:1474-760X. (BioMed Central Ltd.)Some exciting biol. questions require quantifying thousands of proteins in single cells. To achieve this goal, we develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and validate its ability to identify distinct human cancer cell types based on their proteomes. We use SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem cells. The single-cell proteomes enable us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicates coordinated mRNA and protein covariation, yet many genes exhibit functionally concerted and distinct regulatory patterns at the mRNA and the protein level.
- 42Dou, M.; Clair, G.; Tsai, C.-F.; Xu, K.; Chrisler, W. B.; Sontag, R. L.; Zhao, R.; Moore, R. J.; Liu, T.; Pasa-Tolic, L.; Smith, R. D.; Shi, T.; Adkins, J. N.; Qian, W.-J.; Kelly, R. T.; Ansong, C.; Zhu, Y. High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labelling in a Nanodroplet Sample Preparation Platform. Anal. Chem. 2019, 91 (20), 13119– 13127, DOI: 10.1021/acs.analchem.9b0334942https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhslKisbbJ&md5=800d1bf514934f3fe80ae23fa44729d7High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labeling in a Nanodroplet Sample Preparation PlatformDou, Maowei; Clair, Geremy; Tsai, Chia-Feng; Xu, Kerui; Chrisler, William B.; Sontag, Ryan L.; Zhao, Rui; Moore, Ronald J.; Liu, Tao; Pasa-Tolic, Ljiljana; Smith, Richard D.; Shi, Tujin; Adkins, Joshua N.; Qian, Wei-Jun; Kelly, Ryan T.; Ansong, Charles; Zhu, YingAnalytical Chemistry (Washington, DC, United States) (2019), 91 (20), 13119-13127CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein the authors combined microfluidic nanodroplet technol. with tandem mass tag (TMT) isobaric labeling to significantly improve anal. throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex anal. of single cell-sized protein quantities to a depth of ∼1600 proteins with median CV of 10.9% and correlation coeff. of 0.98. To demonstrate in-depth high throughput single cell anal., the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2300 proteins identified. Principal component anal. grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. The platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.
- 43Weng, S. S. H.; Demir, F.; Ergin, E. K.; Dirnberger, S.; Uzozie, A.; Tuscher, D.; Nierves, L.; Tsui, J.; Huesgen, P. F.; Lange, P. F. Sensitive Determination of Proteolytic Proteoforms in Limited Microscale Proteome Samples. Mol. Cell. Proteomics 2019, 18 (11), 2335– 2347, DOI: 10.1074/mcp.TIR119.00156043https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVSjsrjN&md5=5f4d95ae406fe34978aa8860add4fa9cSensitive determination of proteolytic proteoforms in limited microscale proteome samplesWeng, Samuel S. H.; Demir, Fatih; Ergin, Enes K.; Dirnberger, Sabrina; Uzozie, Anuli; Tuscher, Domenic; Nierves, Lorenz; Tsui, Janice; Huesgen, Pitter F.; Lange, Philipp F.Molecular & Cellular Proteomics (2019), 18 (11), 2335-2347CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)Protein N termini unambiguously identify truncated, alternatively translated or modified proteoforms with distinct functions and reveal perturbations in disease. Selective enrichment of N-terminal peptides is necessary to achieve proteome-wide coverage for unbiased identification of site-specific regulatory proteolytic processing and protease substrates. However, many proteolytic processes are strictly confined in time and space and therefore can only be analyzed in minute samples that provide insufficient starting material for current enrichment protocols. Here we present High-efficiency Undecanal-based N Termini EnRichment (HUNTER), a robust, sensitive and scalable method for the anal. of previously inaccessible microscale samples. HUNTER achieved identification of >1000 N termini from as little as 2 mug raw HeLa cell lysate. Broad applicability is demonstrated by the first N-terminome anal. of sorted human primary immune cells and enriched mitochondrial fractions from pediatric cancer patients, as well as protease substrate identification from individual Arabidopsis thaliana wild type and Vacuolar Processing Enzyme-deficient mutant seedlings. We further implemented the workflow on a liq. handling system and demonstrate the feasibility of clin. degradomics by automated processing of liq. biopsies from pediatric cancer patients.
- 44Kasuga, K.; Katoh, Y.; Nagase, K.; Igarashi, K. Microproteomics with Microfluidic-Based Cell Sorting: Application to 1000 and 100 Immune Cells. Proteomics 2017, 17 (13–14), 1600420, DOI: 10.1002/pmic.201600420There is no corresponding record for this reference.
- 45Sun, J.; Zhang, G. L.; Li, S.; Ivanov, A. R.; Fenyo, D.; Lisacek, F.; Murthy, S. K.; Karger, B. L.; Brusic, V. Pathway Analysis and Transcriptomics Improve Protein Identification by Shotgun Proteomics from Samples Comprising Small Number of Cells - a Benchmarking Study. BMC Genomics 2014, 15 (Suppl 9), S1, DOI: 10.1186/1471-2164-15-S9-S1There is no corresponding record for this reference.
- 46Tanca, A.; Abbondio, M.; Pisanu, S.; Pagnozzi, D.; Uzzau, S.; Addis, M. F. Critical Comparison of Sample Preparation Strategies for Shotgun Proteomic Analysis of Formalin-Fixed, Paraffin-Embedded Samples: Insights from Liver Tissue. Clin. Proteomics 2014, 11 (1), 28, DOI: 10.1186/1559-0275-11-2846https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhslShsLjJ&md5=407da191800181ff40883e9c36fef0c3Critical comparison of sample preparation strategies for shotgun proteomic analysis of formalin-fixed, paraffin-embedded samples: insights from liver tissueTanca, Alessandro; Abbondio, Marcello; Pisanu, Salvatore; Pagnozzi, Daniela; Uzzau, Sergio; Addis, Maria FilippaClinical Proteomics (2014), 11 (), 28/1-28/12, 12 pp.CODEN: CPLRCX; ISSN:1542-6416. (BioMed Central Ltd.)Background: The growing field of formalin-fixed paraffin-embedded (FFPE) tissue proteomics holds promise for improving translational research. Direct tissue trypsinization (DT) and protein extn. followed by in soln. digestion (ISD) or filter-aided sample prepn. (FASP) are the most common workflows for shotgun anal. of FFPE samples, but a crit. comparison of the different methods is currently lacking. Exptl. Design: DT, FASP and ISD workflows were compared by subjecting to the same label-free quant. approach three independent tech. replicates of each method applied to FFPE liver tissue. Data were evaluated in terms of method reproducibility and protein/peptide distribution according to localization, MW, pI and hydrophobicity. Results: DT showed lower reproducibility, good preservation of high-MW proteins, a general bias towards hydrophilic and acidic proteins, much lower keratin contamination, as well as higher abundance of non-tryptic peptides. Conversely, FASP and ISD proteomes were depleted in high-MW proteins and enriched in hydrophobic and membrane proteins; FASP provided higher identification yields, while ISD exhibited higher reproducibility. Conclusions: These results highlight that diverse sample prepn. strategies provide significantly different proteomic information, and present typical biases that should be taken into account when dealing with FFPE samples. When a sufficient amt. of tissue is available, the complementary use of different methods is suggested to increase proteome coverage and depth.
- 47Föll, M. C.; Fahrner, M.; Oria, V. O.; Kühs, M.; Biniossek, M. L.; Werner, M.; Bronsert, P.; Schilling, O. Reproducible Proteomics Sample Preparation for Single FFPE Tissue Slices Using Acid-Labile Surfactant and Direct Trypsinization. Clin. Proteomics 2018, 15 (1), 11, DOI: 10.1186/s12014-018-9188-y47https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1MrpvFarsg%253D%253D&md5=48eb2283b6a607be9332be0d1ddf983bReproducible proteomics sample preparation for single FFPE tissue slices using acid-labile surfactant and direct trypsinizationFoll Melanie Christine; Fahrner Matthias; Oria Victor Oginga; Biniossek Martin Lothar; Schilling Oliver; Foll Melanie Christine; Fahrner Matthias; Oria Victor Oginga; Fahrner Matthias; Oria Victor Oginga; Kuhs Markus; Werner Martin; Bronsert Peter; Kuhs Markus; Werner Martin; Bronsert Peter; Kuhs Markus; Werner Martin; Bronsert Peter; Werner Martin; Bronsert Peter; Schilling Oliver; Schilling OliverClinical proteomics (2018), 15 (), 11 ISSN:1542-6416.BACKGROUND: Proteomic analyses of clinical specimens often rely on human tissues preserved through formalin-fixation and paraffin embedding (FFPE). Minimal sample consumption is the key to preserve the integrity of pathological archives but also to deal with minimal invasive core biopsies. This has been achieved by using the acid-labile surfactant RapiGest in combination with a direct trypsinization (DTR) strategy. A critical comparison of the DTR protocol with the most commonly used filter aided sample preparation (FASP) protocol is lacking. Furthermore, it is unknown how common histological stainings influence the outcome of the DTR protocol. METHODS: Four single consecutive murine kidney tissue specimens were prepared with the DTR approach or with the FASP protocol using both 10 and 30 k filter devices and analyzed by label-free, quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS). We compared the different protocols in terms of proteome coverage, relative label-free quantitation, missed cleavages, physicochemical properties and gene ontology term annotations of the proteins. Additionally, we probed compatibility of the DTR protocol for the analysis of common used histological stainings, namely hematoxylin & eosin (H&E), hematoxylin and hemalaun. These were proteomically compared to an unstained control by analyzing four human tonsil FFPE tissue specimens per condition. RESULTS: On average, the DTR protocol identified 1841 ± 22 proteins in a single, non-fractionated LC-MS/MS analysis, whereas these numbers were 1857 ± 120 and 1970 ± 28 proteins for the FASP 10 and 30 k protocol. The DTR protocol showed 15% more missed cleavages, which did not adversely affect quantitation and intersample comparability. Hematoxylin or hemalaun staining did not adversely impact the performance of the DTR protocol. A minor perturbation was observed for H&E staining, decreasing overall protein identification by 13%. CONCLUSIONS: In essence, the DTR protocol can keep up with the FASP protocol in terms of qualitative and quantitative reproducibility and performed almost as well in terms of proteome coverage and missed cleavages. We highlight the suitability of the DTR protocol as a viable and straightforward alternative to the FASP protocol for proteomics-based clinical research.
- 48Broeckx, V.; Boonen, K.; Pringels, L.; Sagaert, X.; Prenen, H.; Landuyt, B.; Schoofs, L.; Maes, E. Comparison of Multiple Protein Extraction Buffers for GeLC-MS/MS Proteomic Analysis of Liver and Colon Formalin-Fixed, Paraffin-Embedded Tissues. Mol. BioSyst. 2016, 12 (2), 553– 565, DOI: 10.1039/C5MB00670H48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvF2hsrvK&md5=1e245f198339d7405f0c8709bbc4e44aComparison of multiple protein extraction buffers for GeLC-MS/MS proteomic analysis of liver and colon formalin-fixed, paraffin-embedded tissuesBroeckx, Valerie; Boonen, Kurt; Pringels, Lentel; Sagaert, Xavier; Prenen, Hans; Landuyt, Bart; Schoofs, Liliane; Maes, EvelyneMolecular BioSystems (2016), 12 (2), 553-565CODEN: MBOIBW; ISSN:1742-2051. (Royal Society of Chemistry)Formalin-fixed paraffin-embedded (FFPE) tissue specimens represent a potential valuable source of samples for clin. research. Since these specimens are banked in hospital archives, large cohorts of samples can be collected in short periods of time which can all be linked with a patients' clin. history. Therefore, the use of FFPE tissue in protein biomarker discovery studies gains interest. However, despite the growing no. of FFPE proteome studies in the literature, there is a lack of a FFPE proteomics std. operating procedure (SOP). One of the challenging steps in the development of such a SOP is the ability to obtain an efficient and repeatable extn. of full length FFPE proteins. In this study, the protein extn. efficiency of eight protein extn. buffers is critically compared with GeLC-MS/MS (1D gel electrophoresis followed by in-gel digestion and LC-MS/MS). The data variation caused by using these extn. buffers was investigated since the variation is a very important aspect when using FFPE tissue as a source for biomarker detection. In addn., a qual. comparison was made between the protein extn. efficiency and repeatability for FFPE tissue and fresh frozen tissue.
- 49Tabb, D. L.; Vega-Montoto, L.; Rudnick, P. A.; Variyath, A. M.; Ham, A.-J. L.; Bunk, D. M.; Kilpatrick, L. E.; Billheimer, D. D.; Blackman, R. K.; Cardasis, H. L.; Carr, S. A.; Clauser, K. R.; Jaffe, J. D.; Kowalski, K. A.; Neubert, T. A.; Regnier, F. E.; Schilling, B.; Tegeler, T. J.; Wang, M.; Wang, P.; Whiteaker, J. R.; Zimmerman, L. J.; Fisher, S. J.; Gibson, B. W.; Kinsinger, C. R.; Mesri, M.; Rodriguez, H.; Stein, S. E.; Tempst, P.; Paulovich, A. G.; Liebler, D. C.; Spiegelman, C. Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography–Tandem Mass Spectrometry. J. Proteome Res. 2010, 9 (2), 761– 776, DOI: 10.1021/pr900636549https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhsFGnt7bP&md5=77c1052918728af58b3b5c4a5bc250a9Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography-Tandem Mass SpectrometryTabb, David L.; Vega-Montoto, Lorenzo; Rudnick, Paul A.; Variyath, Asokan Mulayath; Ham, Amy-Joan L.; Bunk, David M.; Kilpatrick, Lisa E.; Billheimer, Dean D.; Blackman, Ronald K.; Cardasis, Helene L.; Carr, Steven A.; Clauser, Karl R.; Jaffe, Jacob D.; Kowalski, Kevin A.; Neubert, Thomas A.; Regnier, Fred E.; Schilling, Birgit; Tegeler, Tony J.; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Fisher, Susan J.; Gibson, Bradford W.; Kinsinger, Christopher R.; Mesri, Mehdi; Rodriguez, Henry; Stein, Stephen E.; Tempst, Paul; Paulovich, Amanda G.; Liebler, Daniel C.; Spiegelman, CliffJournal of Proteome Research (2010), 9 (2), 761-776CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)The complexity of proteomic instrumentation for LC-MS/MS introduces many possible sources of variability. Data-dependent sampling of peptides constitutes a stochastic element at the heart of discovery proteomics. Although this variation impacts the identification of peptides, proteomic identifications are far from completely random. In this study, the authors analyzed interlab. data sets from the NCI Clin. Proteomic Technol. Assessment for Cancer to examine repeatability and reproducibility in peptide and protein identifications. Included data spanned 144 LC-MS/MS expts. on four Thermo LTQ and four Orbitrap instruments. Samples included yeast lysate, the NCI-20 defined dynamic range protein mix, and the Sigma UPS 1 defined equimolar protein mix. Some of the authors' findings reinforced conventional wisdom, such as repeatability and reproducibility being higher for proteins than for peptides. Most lessons from the data, however, were more subtle. Orbitrap proved capable of higher repeatability and reproducibility, but aberrant performance occasionally erased these gains. Even the simplest protein digestions yielded more peptide ions than LC-MS/MS could identify during a single expt. The authors obsd. that peptide lists from pairs of tech. replicates overlapped by 35-60%, giving a range for peptide-level repeatability in these expts. Sample complexity did not appear to affect peptide identification repeatability, even as nos. of identified spectra changed by an order of magnitude. Statistical anal. of protein spectral counts revealed greater stability across tech. replicates for Orbitrap, making them superior to LTQ instruments for biomarker candidate discovery. The most repeatable peptides were those corresponding to conventional tryptic cleavage sites, those that produced intense MS signals, and those that resulted from proteins generating many distinct peptides. Reproducibility among different instruments of the same type lagged behind repeatability of tech. replicates on a single instrument by several percent. These findings reinforce the importance of evaluating repeatability as a fundamental characteristic of anal. technologies.
- 50Delmotte, N.; Lasaosa, M.; Tholey, A.; Heinzle, E.; van Dorsselaer, A.; Huber, C. G. Repeatability of Peptide Identifications in Shotgun Proteome Analysis Employing Off-Line Two-Dimensional Chromatographic Separations and Ion-Trap MS. J. Sep. Sci. 2009, 32 (8), 1156– 1164, DOI: 10.1002/jssc.20080061550https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXlvVCltr8%253D&md5=81ccb5ce2f00a4a8fc0eec13bf94fd05Repeatability of peptide identifications in shotgun proteome analysis employing off-line two-dimensional chromatographic separations and ion-trap MSDelmotte, Nathanael; Lasaosa, Maria; Tholey, Andreas; Heinzle, Elmar; van Dorsselaer, Alain; Huber, Christian G.Journal of Separation Science (2009), 32 (8), 1156-1164CODEN: JSSCCJ; ISSN:1615-9306. (Wiley-VCH Verlag GmbH & Co. KGaA)The repeatability of peptide identifications in shotgun proteome analyses employing strong cation-exchange- × ion-pair RP HPLC hyphenated to ESI MS/MS was compared to an alternative scheme, comprising high-pH RP chromatog. combined with low-pH ion-pair RP chromatog. Equivalent results were obtained with both methods in proteome anal. of Corynebacterium glutamicum. From a total no. of 1350 to 1850 peptides identified in triplicate analyses of five consecutive fractions chosen from the first-dimension sepn., 41-45% of the peptides were identified three times, whereas 16-22 and 37-39% of the peptides were identified only twice or once, resp. A comparison of the repeatability of peptide identifications from complex samples upon 1- or 2-D chromatog. sepn. revealed that an addnl. sepn. dimension decreases the repeatability by approx. 25%.
- 51Resing, K. A.; Meyer-Arendt, K.; Mendoza, A. M.; Aveline-Wolf, L. D.; Jonscher, K. R.; Pierce, K. G.; Old, W. M.; Cheung, H. T.; Russell, S.; Wattawa, J. L.; Goehle, G. R.; Knight, R. D.; Ahn, N. G. Improving Reproducibility and Sensitivity in Identifying Human Proteins by Shotgun Proteomics. Anal. Chem. 2004, 76 (13), 3556– 3568, DOI: 10.1021/ac035229m51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXktlWmsbs%253D&md5=056551e9a48b0bdb1d0f5668bb9625a9Improving Reproducibility and Sensitivity in Identifying Human Proteins by Shotgun ProteomicsResing, Katheryn A.; Meyer-Arendt, Karen; Mendoza, Alex M.; Aveline-Wolf, Lauren D.; Jonscher, Karen R.; Pierce, Kevin G.; Old, William M.; Cheung, Hiu T.; Russell, Steven; Wattawa, Joy L.; Goehle, Geoff R.; Knight, Robin D.; Ahn, Natalie G.Analytical Chemistry (2004), 76 (13), 3556-3568CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Identifying proteins in cell exts. by shotgun proteomics involves digesting the proteins, sequencing the resulting peptides by data-dependent mass spectrometry (MS/MS), and searching protein databases to identify the proteins from which the peptides are derived. Manual anal. and direct spectral comparison reveal that scores from two commonly used search programs (Sequest and Mascot) validate less than half of potentially identifiable MS/MS spectra (class pos.) from shotgun analyses of the human erythroleukemia K562 cell line. Here we demonstrate increased sensitivity and accuracy using a focused search strategy along with a peptide sequence validation script that does not rely exclusively on XCorr or Mowse scores generated by Sequest or Mascot, but uses consensus between the search programs, along with chem. properties and scores describing the nature of the fragmentation spectrum (ion score and RSP). The approach yielded 4.2% false pos. and 8% false neg. frequencies in peptide assignments. The protein profile is then assembled from peptide assignments using a novel peptide-centric protein nomenclature that more accurately reports protein variants that contain identical peptide sequences. An Isoform Resolver algorithm ensures that the protein count is not inflated by variants in the protein database, eliminating ∼25% of redundant proteins. Anal. of sol. proteins from a human K562 cells identified 5130 unique proteins, with ∼100 false pos. protein assignments.
- 52Washburn, M. P.; Ulaszek, R. R.; Yates, J. R. Reproducibility of Quantitative Proteomic Analyses of Complex Biological Mixtures by Multidimensional Protein Identification Technology. Anal. Chem. 2003, 75 (19), 5054– 5061, DOI: 10.1021/ac034120b52https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXmsVegsLs%253D&md5=93f083bea4d279e1cf92ea0f6248a8c3Reproducibility of quantitative proteomic analyses of complex biological mixtures by multidimensional protein identification technologyWashburn, Michael P.; Ulaszek, Ryan R.; Yates, John R., IIIAnalytical Chemistry (2003), 75 (19), 5054-5061CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)If quant. proteomic technologies are to be of widespread use to the biol. community, the reproducibility of each method must be investigated and detd. We have analyzed the reproducibility of complex quant. proteomic analyses of metabolically labeled S. cerevisiae analyzed via multidimensional protein identification technol. (MudPIT). Three independent cell growths of S. cerevisiae grown in rich and minimal media and independent MudPIT analyses of each were compared and contrasted. Quant. MudPIT was found to be intra- and interexperimentally reproducible at both the peptide and protein levels. Proteins of potential low abundance were detected, identified, and quantified by identical peptides from three independent samples. In addn., when multiple peptides were matched to a protein, the relative abundance of each peptide was in agreement across the three samples. Despite the reproducibility, errors in the exptl. detn. of protein expression levels occurred, but the impact of the variation was minimized by replicate expts. Last, quant. MudPIT analyses will likely be improved by increasing the no. of peptide hits per protein in a given anal., which will provide for greater intraexperimental reproducibility.
- 53Magdeldin, S.; Yamamoto, T. Toward Deciphering Proteomes of Formalin-Fixed Paraffin-Embedded (FFPE) Tissues. Proteomics 2012, 12 (7), 1045– 1058, DOI: 10.1002/pmic.20110055053https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XlvVKms78%253D&md5=7cd5165b42a902e467226a114103f3c1Toward deciphering proteomes of formalin-fixed paraffin-embedded (FFPE) tissuesMagdeldin, Sameh; Yamamoto, TadashiProteomics (2012), 12 (7), 1045-1058CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)A review. Formalin-fixed paraffin-embedded (FFPE) tissue specimens comprise a potentially valuable resource for both prospective and retrospective biomarker discovery. Unlocking the proteomic profile of clinicopathol. FFPE tissues is a critically essential step for annotating clin. findings and predicting biomarkers for ultimate disease prognosis and therapeutic follow-up.
- 54Maes, E.; Broeckx, V.; Mertens, I.; Sagaert, X.; Prenen, H.; Landuyt, B.; Schoofs, L. Analysis of the Formalin-Fixed Paraffin-Embedded Tissue Proteome: Pitfalls, Challenges, and Future Prospectives. Amino Acids 2013, 45 (2), 205– 218, DOI: 10.1007/s00726-013-1494-054https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtFemtbvN&md5=cfb25a4dc12c7a6a9b76472d8c4da015Analysis of the formalin-fixed paraffin-embedded tissue proteome: pitfalls, challenges, and future prospectivesMaes, Evelyne; Broeckx, Valerie; Mertens, Inge; Sagaert, Xavier; Prenen, Hans; Landuyt, Bart; Schoofs, LilianeAmino Acids (2013), 45 (2), 205-218CODEN: AACIE6; ISSN:0939-4451. (Springer-Verlag GmbH)A review. Formalin-fixed paraffin-embedded (FFPE) tissues are a real treasure for retrospective anal. considering the amt. of samples present in hospital archives, combined with pathol., clin., and outcome information available for every sample. Although unlocking the proteome of these tissues is still a challenge, new approaches are being developed. In this review, we summarize the different mass spectrometry platforms that are used in human clin. studies to unravel the FFPE proteome. The different ways of extg. crosslinked proteins and the anal. strategies are pointed out. Also, the pitfalls and challenges concerning the quality of FFPE proteomic approaches are depicted. We also evaluated the potential of these anal. methods for future clin. FFPE proteomics applications.
- 55Proungvitaya, S.; Klinthong, W.; Proungvitaya, T.; Limpaiboon, T.; Jearanaikoon, P.; Roytrakul, S.; Wongkham, C.; Nimboriboonporn, A.; Wongkham, S. High Expression of CCDC25 in Cholangiocarcinoma Tissue Samples. Oncol. Lett. 2017, 14 (2), 2566– 2572, DOI: 10.3892/ol.2017.644655https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cflslWhsg%253D%253D&md5=dbbf13668447ea01c723e4bf965dca50High expression of CCDC25 in cholangiocarcinoma tissue samplesProungvitaya Siriporn; Klinthong Wachiraya; Proungvitaya Tanakorn; Limpaiboon Temduang; Jearanaikoon Patcharee; Proungvitaya Siriporn; Proungvitaya Tanakorn; Limpaiboon Temduang; Jearanaikoon Patcharee; Wongkham Chaisiri; Nimboriboonporn Anongporn; Wongkham Sopit; Roytrakul Sittiruk; Nimboriboonporn AnongpornOncology letters (2017), 14 (2), 2566-2572 ISSN:1792-1074.Cholangiocarcinoma (CCA) is a malignant transformation of biliary epithelial cells. It is a slow growing tumor, but is also highly metastatic with a poor prognosis. Bile acids are known to transactivate the epidermal growth factor receptor (EGFR) in cholangiocytes and induce cyclooxygenase-2 expression. The protein expression profiles of bile acid-treated CCA cells were studied using a proteomic approach. To elucidate the possible mechanisms involved in the bile acid-mediated enhancement of CCA cell migration, the effects of six bile acids, including cholic, deoxycholic, taurocholic, taurodeoxycholic, glycocholic and glycodeoxycholic acid, on the migration of CCA cells were examined in vitro using wound healing assays. Subsequently, the possible proteins involved in enhanced CCA cell migration were investigated using a proteomic approach. Changes to the protein expression profiles of CCA cells following bile acid treatment was examined using two-dimensional electrophoresis and mass spectrometry. The results demonstrated that cholic and deoxycholic acid significantly enhanced the migration of CCA cells, compared with the treated MMNK-1 control cells. CCA cells had 77 overexpressed protein spots following cholic acid treatment, and 50 protein spots following deoxycholic acid treatment, compared with the treated MMNK-1 control cells. Liquid chromatography tandem-mass spectrometry analysis revealed that coiled-coil domain containing 25 (CCDC25) was significantly overexpressed in cholic acid-treated CCA cells compared with in cholic acid-treated control cells. When the expression levels of CCDC25 were investigated using western blot analysis, CCDC25 was demonstrated to be highly expressed in CCA tissues, but not in the adjacent non-cancerous tissue samples. The identified proteins were further analyzed for protein-chemical interactions using STITCH version 3.1 software. CCDC25 protein was identified to be associated with Son of sevenless homolog 1 and growth factor receptor-bound protein 2, which are involved in EGFR signaling. The results of the present study demonstrated that following cholic acid treatment, CCDC25 is overexpressed in CCA cells, which is associated with significantly enhanced cell migration. This suggests that CCDC25 is a potential therapeutic target for the treatment of patients with CCA.
- 56Brychtova, V.; Vojtesek, B.; Hrstka, R. Anterior Gradient 2: A Novel Player in Tumor Cell Biology. Cancer Lett. 2011, 304 (1), 1– 7, DOI: 10.1016/j.canlet.2010.12.02356https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXjsFWru7g%253D&md5=6baccba91e15dfacd80ed874ae409574Anterior gradient 2: A novel player in tumor cell biologyBrychtova, Veronika; Vojtesek, Borivoj; Hrstka, RomanCancer Letters (New York, NY, United States) (2011), 304 (1), 1-7CODEN: CALEDQ; ISSN:0304-3835. (Elsevier)A review. AGR2 has evolutionarily conserved roles in development and tissue regeneration and is linked with several human cancers. The exact functions and regulation of AGR2 are poorly understood, but current data identify AGR2 as a clin. relevant factor that modulates the behavior and response of hormone-dependent cancers (breast, prostate) and hormone-independent cancers (colorectal, pancreatic, esophageal and other common cancers). AGR2 protein expression induces metastasis, acts as a p53 tumor suppressor inhibitor and survival factor, participates directly in neoplastic transformation and is involved in drug resistance. Thus, AGR2 is an important tumor biomarker and neg. prognostic factor potentially exploitable in clin. practice.
- 57Suwanmanee, G.; Yosudjai, J.; Phimsen, S.; Wongkham, S.; Jirawatnotai, S.; Kaewkong, W. Upregulation of AGR2vH Facilitates Cholangiocarcinoma Cell Survival under Endoplasmic Reticulum Stress via the Activation of the Unfolded Protein Response Pathway. Int. J. Mol. Med. 2019, 45 (2), 669– 677, DOI: 10.3892/ijmm.2019.4432There is no corresponding record for this reference.
Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00629.
Figure S1: Venn diagram illustrating the shared number of identified proteins among different sample types, as well as proteins that are unique to each sample type; Figure S2: Number of missed cleavages are shown for all replicates and are expressed in percentage; Figure S3: Qualitative assessment of reproducibility of the microPOTS system; Figure S4: Quantitative assessment of reproducibility of the microPOTS system; Table S1: Median coefficient of variation (CV) for quantile normalized protein LFQ values for each sample type; Box S1: Sample preparation and LC-MS analysis for bulk proteomics data set; Box S2: Bulk data analysis and comparison to microPOTS data; Figure S5: Scatter plot with associated Pearson’s correlation coefficient (R = 0.625) between microPOTS and bulk proteomics for all overlapping 1066 proteins that were identified; Figure S6: Assessment of physicochemical characteristics; Figure S7: Assessment of physicochemical characteristics; Figure S8: Subcellular localization; Table S2: List of differentially expressed proteins between CP-A dKO cells with or without LCA treatment; Table S3: List of differentially expressed proteins between CP-A dKO cells with or without X-ray treatment (PDF)
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