timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker DiscoveryClick to copy article linkArticle link copied!
- Dijana VitkoDijana VitkoPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Dijana Vitko
- Wan-Fang ChouWan-Fang ChouPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Wan-Fang Chou
- Sara Nouri GolmaeiSara Nouri GolmaeiPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Sara Nouri Golmaei
- Joon-Yong LeeJoon-Yong LeePrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Joon-Yong Lee
- Chinmay BelthangadyChinmay BelthangadyPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Chinmay Belthangady
- John BlumeJohn BlumePrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by John Blume
- Jessica K. ChanJessica K. ChanPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Jessica K. Chan
- Guillermo Flores-CampuzanoGuillermo Flores-CampuzanoPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Guillermo Flores-Campuzano
- Yuntao HuYuntao HuPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Yuntao Hu
- Manway LiuManway LiuPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Manway Liu
- Mark A. MarispiniMark A. MarispiniPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Mark A. Marispini
- Megan G. MoraMegan G. MoraPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Megan G. Mora
- Saividya RamaswamySaividya RamaswamyPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Saividya Ramaswamy
- Purva RanjanPurva RanjanPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Purva Ranjan
- Preston B. WilliamsPreston B. WilliamsPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Preston B. Williams
- Robert J. X. ZawadaRobert J. X. ZawadaPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Robert J. X. Zawada
- Philip MaPhilip MaPrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Philip Ma
- Bruce E. Wilcox*Bruce E. Wilcox*Email: [email protected]PrognomiQ Inc., 1900 Alameda de las Pulgas, San Mateo, California 94403, United StatesMore by Bruce E. Wilcox
Abstract
Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography–mass spectrometry (LC–MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).
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Attribution (BY): Credit must be given to the creator.
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Attribution (BY): Credit must be given to the creator.
Non-Commercial (NC): Only non-commercial uses of the work are permitted.
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Note Added after ASAP Publication
This paper was published ASAP on January 15, 2024, with corrections throughout the paper. The corrected version was reposted on January 30, 2024.
Introduction
Materials and Methods
Neat and Proteograph Plasma Sample Preparation
LC–MS Data Acquisition
Data Search and Postanalysis
Results
Qualitative Performance of timsTOF HT Exceeds timsTOF Pro 2 across a Wide Range of Plasma Peptide Loading Masses and LC Gradients
Figure 1
Figure 1. Qualitative performance of timsTOF HT exceeded that of timsTOF Pro 2 across a wide range of plasma peptide loading masses and LC gradients. Compared to timsTOF Pro 2, HT identified up to 76 and 46% more peptide precursors in (A) neat and (B) Proteograph plasma, respectively, with up to 4.5-fold more precursors detected in Proteograph plasma than neat plasma. Red (timsTOF HT) and blue (timsTOF Pro 2) lines represent the number of peptide precursors uniquely detected in all replicate injections (n = 3) of neat and Proteograph plasma samples (NP1–3,5). Proteograph plasma NP4 was not considered for plotting due to insufficient yield for 1200 ng peptide mass load. Abbreviations: LC, liquid chromatography; NP, nanoparticle, SPD, samples per day.
timsTOF HT Allows for a Superior Quantitative Linear Range Compared to timsTOF Pro 2
Figure 2
Figure 2. timsTOF HT had a greater quantitative linear range compared to timsTOF Pro 2. (A) Distribution of peptide precursor MS2 peak area (triplicate average) ratios quantified with timsTOF HT vs Pro 2. For visualization purposes, the MS2 peak area ratio cutoff was set to 5. (B) Extracted ion chromatogram of MS2 fragment ion (y9) for a randomly selected precursor, FLVGPDGIPIMR (2+), from within the 3rd quartile of the total precursor intensity range. (C) R-squared distribution for the precursors quantified in all three replicate measurements of each peptide loading mass for Proteograph plasma NP2 at 60 SPD gradient with timsTOF HT (n = 4256) and timsTOF Pro 2 (n = 3331). Abbreviations: NP, nanoparticle; SPD, samples per day.
timsTOF HT Has Enhanced Reproducibility Compared to timsTOF Pro 2
Figure 3
Figure 3. timsTOF HT had greater reproducibility compared to timsTOF Pro 2. The number of reproducibly quantified peptide precursors (CV < 20% of triplicate measurements) was increased by up to 127% in (A) neat and 81% in (B) Proteograph plasma (NP2) with timsTOF HT relative to Pro 2 across different LC gradients. As the loading mass increased, the quantitative reproducibility of timsTOF HT improved significantly compared to timsTOF Pro 2. Abbreviations: CV, coefficient of variation; LC, liquid chromatography; NP, nanoparticle; SPD, samples per day.
Qualitative and Quantitative Improvements of timsTOF HT Allow for Higher Sensitivity and Reproducibility of Putative Lung Cancer Biomarkers Detected in Plasma
Figure 4
Figure 4. Qualitative and quantitative improvements of timsTOF HT allowed for higher sensitivity and reproducibility of lung cancer biomarkers detected in plasma. (A) Volcano plot comparison of precursors quantified in the control (n = 20) vs. lung cancer (n = 20) samples for representative Proteograph plasma NP2 when data were analyzed by timsTOF HT or Pro 2. (B) Improved sensitivity and reproducibility of timsTOF HT resulted in 48% more identified peptide precursors compared to timsTOF Pro 2, which translated to 52% more statistically significant precursors (q < 0.05) across 5 NPs. Abbreviations: NP, nanoparticle.
Discussion
Conclusions
Data Availability
MS raw files (.d folders) and processed DIA-NN v18.1 outputs are deposited in a public repository PRIDE/ProteomeXchange under the identifiers PXD047854 and PXD047839.
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00646.
Plasma sample processing and data acquisition; plasma peptide precursors identified in neat and Proteograph plasma (NP1–5) by timsTOF HT and Pro 2 across a wide range of plasma peptide loading masses and LC gradients (Figure S1); qualitative and quantitative assessment of spike-in PQ500 QC peptides (Figure S2); density distribution of MS1 fold-change quantities for neat and Proteograph plasma (NP1–5) precursors measured in timsTOF HT vs. Pro 2 (Figure S3); density distribution of MS2 fold-change quantities for neat and Proteograph plasma (NP1–5) precursors measured in timsTOF HT vs. Pro 2 (Figure S4); density distribution of MS2 peak area quantities for neat and Proteograph plasma (NP1–5) peptide precursors measured in timsTOF HT vs. Pro 2 (Figure S5); timsTOF HT showed increased sensitivity compared to timsTOF Pro 2 (Figure S6); timsTOF HT demonstrated a superior linear response compared to timsTOF Pro 2 for neat and Proteograph plasma (NP1-NP5) across a wide range of peptide loading masses and LC gradients (Figure S7) timsTOF HT resulted in a higher number of reproducibly quantified precursors (CV < 10% and CV < 20%) in neat and Proteograph plasma (NP1-NP5) compared to timsTOF Pro 2 across a wide range of peptide loading masses and LC gradients (Figure S8); comparison of reproducibly quantified precursors (CV < 20%) in neat and Proteograph plasma (NP1–5) analyzed with timsTOF HT and Pro 2 (Figure S9); demographic plots for lung cancer and control cohorts (Figure S10); timsTOF HT increased total and statistically significant peptide precursors identified in lung cancer and control cohorts (Figure S11); overlap of statistically significant precursors, peptides, and proteins in a cohort of 20 lung cancer and 20 control samples analyzed with timsTOF HT and Pro 2 (Figure S12); peptide dilution schema for neat and Proteograph plasma (Table S1); patient demographic information for the cohorts of 20 lung cancer and 20 control patient samples (Table S2); DIA m/z and IM window ranges within the MS2 fragmentation cycle (Table S3); peptide precursors identified in timsTOF HT and Pro 2 with CV < 10% (Table S4A); peptide precursors identified in timsTOF HT and Pro 2 with CV < 20% (Table S4B); total peptide precursor identified in timsTOF HT vs. Pro 2 in neat and Proteograph plasma; total peptide precursors identified in timsTOF HT vs. Pro 2 in Proteograph plasma NP1–5 (Table S5) (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Acknowledgments
We want to thank the clinical operation, research & development, and data science departments at PrognomiQ Inc. for collaborative work and fruitful data discussions, particularly Ghristine Boundalian for clinical quality assurance, Brian Koh for clinical documentation review, and Rabab Karimjee for providing technical details on filter-based plasma protein digestion. We extend thanks to Jimmy Zeng, Hao Qian, and Rabab Karimjee for assistance with generating proprietary spectral libraries created independently of this project and Ehdieh Khaledian and Jin Choi for help in performing univariate analysis. We acknowledge Seer, Inc. (Redwood City, CA) for Proteograph sample processing of cancer and control plasma samples. We would also like to thank Bruker Daltonics Inc. (Fremont, CA) for early access to the timsTOF HT technology and directions regarding timsTOF Pro 2 to timsTOF HT upgrade, particularly Oliver Raether, Eduardo Carrascosa, and Narayanaganesh Balasubramanian. We appreciate guidance from Monika Puchalska (Biognosys AG) regarding the PQ500 data search. Editorial and graphical assistance was provided by the Prescott Medical Communication Group (Chicago, IL).
References
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- 12Blume, J. E.; Manning, W. C.; Troiano, G. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat. Commun. 2020, 11 (1), 3662 DOI: 10.1038/s41467-020-17033-7Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVKmur%252FK&md5=5825436c998c601d196e52744ab662aeRapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein coronaBlume, John E.; Manning, William C.; Troiano, Gregory; Hornburg, Daniel; Figa, Michael; Hesterberg, Lyndal; Platt, Theodore L.; Zhao, Xiaoyan; Cuaresma, Rea A.; Everley, Patrick A.; Ko, Marwin; Liou, Hope; Mahoney, Max; Ferdosi, Shadi; Elgierari, Eltaher M.; Stolarczyk, Craig; Tangeysh, Behzad; Xia, Hongwei; Benz, Ryan; Siddiqui, Asim; Carr, Steven A.; Ma, Philip; Langer, Robert; Farias, Vivek; Farokhzad, Omid C.Nature Communications (2020), 11 (1), 3662CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liq. chromatog.-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochem. properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biol. samples, including deep sampling of the plasma proteome. Spike expts. confirm a linear signal response. The median coeff. of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quant. proteomic studies.
- 13Huang, T.; Wang, J.; Stukalov, A. Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics. bioRxiv 2023, DOI: 10.1101/2023.08.555225Google ScholarThere is no corresponding record for this reference.
- 14Ferdosi, S.; Tangeysh, B.; Brown, T. R.; Everley, P. A.; Figa, M.; McLean, M.; Elgierari, E. M.; Zhao, X.; Garcia, V. J.; Wang, T.; Chang, M. E. K.; Riedesel, K.; Chu, J.; Mahoney, M.; Xia, H.; O’Brien, E. S.; Stolarczyk, C.; Harris, D.; Platt, T. L.; Ma, P.; Goldberg, M.; Langer, R.; Flory, M. R.; Benz, R.; Tao, W.; Cuevas, J. C.; Batzoglou, S.; Blume, J. E.; Siddiqui, A.; Hornburg, D.; Farokhzad, O. C. Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions. Proc Natl Acad Sci U S A 2022, 119, e2106053119 DOI: 10.1073/pnas.2106053119Google Scholar201https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XotVSmt7g%253D&md5=833a5fde158da3aceabc1529d7e0d8faEngineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactionsFerdosi, Shadi; Tangeysh, Behzad; Brown, Tristan R.; Everley, Patrick A.; Figa, Michael; McLean, Matthew; Elgierari, Eltaher M.; Zhao, Xiaoyan; Garcia, Veder J.; Wang, Tianyu; Chang, Matthew E. K.; Riedesel, Kateryna; Chu, Jessica; Mahoney, Max; Xia, Hongwei; O'Brien, Evan S.; Stolarczyk, Craig; Harris, Damian; Platt, Theodore L.; Ma, Philip; Goldberg, Martin; Langer, Robert; Flory, Mark R.; Benz, Ryan; Tao, Wei; Cuevas, Juan Cruz; Batzoglou, Serafim; Blume, John E.; Siddiqui, Asim; Hornburg, Daniel; Farokhzad, Omid C.Proceedings of the National Academy of Sciences of the United States of America (2022), 119 (11), e2106053119CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Deep interrogation of plasma proteins on a large scale is a challenge due to the no. and concn. of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochem. properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by sp. surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biol. samples.
- 15Donovan, M. K. R.; Huang, Y.; Blume, J. E.; Wang, J.; Hornburg, D.; Ferdosi, S.; Mohtashemi, I.; Kim, S.; Ko, M.; Benz, R. W.; Platt, T. L.; Batzoglou, S.; Diaz, L. A.; Farokhzad, O. C.; Siddiqui, A. Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls. PLos One 2023, 18, e0282821 DOI: 10.1371/journal.pone.0282821Google Scholar202https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXmvFCjs74%253D&md5=02481ad678d060b26ae6fce85227bac7Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controlsDonovan, Margaret K. R.; Huang, Yingxiang; Blume, John E.; Wang, Jian; Hornburg, Daniel; Ferdosi, Shadi; Mohtashemi, Iman; Kim, Sangtae; Ko, Marwin; Benz, Ryan W.; Platt, Theodore L.; Batzoglou, Serafim; Diaz, Luis A.; Farokhzad, Omid C.; Siddiqui, AsimPLoS One (2023), 18 (3), e0282821CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)Advancements in deep plasma proteomics are enabling high-resoln. measurement of plasma proteoforms, which may reveal a rich source of novel biomarkers previously concealed by aggregated protein methods. Here, we analyze 188 plasma proteomes from non-small cell lung cancer subjects (NSCLC) and controls to identify NSCLC-assocd. protein isoforms by examg. differentially abundant peptides as a proxy for isoform-specific exon usage. We find four proteins comprised of peptides with opposite patterns of abundance between cancer and control subjects. One of these proteins, BMP1, has known isoforms that can explain this differential pattern, for which the abundance of the NSCLC-assocd. isoform increases with stage of NSCLC progression. The presence of cancer and control-assocd. isoforms suggests differential regulation of BMP1 isoforms. The identified BMP1 isoforms have known functional differences, which may reveal insights into mechanisms impacting NSCLC disease progression.
- 16Ferdosi, S.; Stukalov, A.; Hasan, M.; Tangeysh, B.; Brown, T. R.; Wang, T.; Elgierari, E. M.; Zhao, X.; Huang, Y.; Alavi, A.; Lee-McMullen, B.; Chu, J.; Figa, M.; Tao, W.; Wang, J.; Goldberg, M.; O'Brien, E. S.; Xia, H.; Stolarczyk, C.; Weissleder, R.; Farias, V.; Batzoglou, S.; Siddiqui, A.; Farokhzad, O. C.; Hornburg, D. Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes. Adv Mater. 2022, 34, e2206008 DOI: 10.1002/adma.202270307Google Scholar203https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisFSktbjL&md5=74c378319e1066875dd9ecad7544e509Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of ProteomesFerdosi, Shadi; Stukalov, Alexey; Hasan, Moaraj; Tangeysh, Behzad; Brown, Tristan R.; Wang, Tianyu; Elgierari, Eltaher M.; Zhao, Xiaoyan; Huang, Yingxiang; Alavi, Amir; Lee-McMullen, Brittany; Chu, Jessica; Figa, Mike; Tao, Wei; Wang, Jian; Goldberg, Martin; O'Brien, Evan S.; Xia, Hongwei; Stolarczyk, Craig; Weissleder, Ralph; Farias, Vivek; Batzoglou, Serafim; Siddiqui, Asim; Farokhzad, Omid C.; Hornburg, DanielAdvanced Materials (Weinheim, Germany) (2022), 34 (44), 2206008CODEN: ADVMEW; ISSN:0935-9648. (Wiley-VCH Verlag GmbH & Co. KGaA)Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which det. the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona compn., enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7x more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3x more proteins compared to a std. plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in detg. the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.
- 17Koh, B.; Liu, M.; Almonte, R. Multi-omics profiling with untargeted proteomics for blood-based early detection of lung cancer. medRxiv. 2024, DOI: 10.1101/2024.01.03.24300798Google ScholarThere is no corresponding record for this reference.
- 18Meier, F.; Brunner, A. D.; Frank, M. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat. Methods 2020, 17 (12), 1229– 1236, DOI: 10.1038/s41592-020-00998-0Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisVGksbbL&md5=f6987c905570fbd9bdb74fd2452998c5DiaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisitionMeier, Florian; Brunner, Andreas-David; Frank, Max; Ha, Annie; Bludau, Isabell; Voytik, Eugenia; Kaspar-Schoenefeld, Stephanie; Lubeck, Markus; Raether, Oliver; Bache, Nicolai; Aebersold, Ruedi; Collins, Ben C.; Roest, Hannes L.; Mann, MatthiasNature Methods (2020), 17 (12), 1229-1236CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass anal. Here, we make use of the correlation of mol. wt. and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extn. workflow by inclusion of the ion mobility dimension for both signal extn. and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quant. accuracy, even from 10 ng sample amts.
- 19Demichev, V.; Messner, C. B.; Vernardis, S. I.; Lilley, K. S.; Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 2020, 17 (1), 41– 44, DOI: 10.1038/s41592-019-0638-xGoogle Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXksFGjuw%253D%253D&md5=0ee3e1449b4ad6c270b1346f8d5126f6DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughputDemichev, Vadim; Messner, Christoph B.; Vernardis, Spyros I.; Lilley, Kathryn S.; Ralser, MarkusNature Methods (2020), 17 (1), 41-44CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics expts. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatog. methods.
- 20Guergues, J.; Wohlfahrt, J.; Stevens, S. M., Jr. Enhancement of Proteome Coverage by Ion Mobility Fractionation Coupled to PASEF on a TIMS-QTOF Instrument. J. Proteome Res. 2022, 21 (8), 2036– 2044, DOI: 10.1021/acs.jproteome.2c00336Google Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhvVygs7jM&md5=0aab129c156dc783aa5e7267bb137845Enhancement of Proteome Coverage by Ion Mobility Fractionation Coupled to PASEF on a TIMS-QTOF InstrumentGuergues, Jennifer; Wohlfahrt, Jessica; Stevens, Stanley M.Journal of Proteome Research (2022), 21 (8), 2036-2044CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)Trapped ion-mobility spectrometry (TIMS) was used to fractionate ions in the gas phase based on their ion mobility (V s/cm2), followed by parallel accumulation-serial fragmentation (PASEF) using a quadrupole time-of-flight instrument to det. the effect on the depth of proteome coverage. TIMS fractionation (up to four gas-phase fractions) coupled to data-dependent acquisition (DDA)-PASEF resulted in the detection of ~ 7000 proteins and over 70,000 peptides overall from 200 ng of human (HeLa) cell lysate per injection using a com. 25 cm ultra high performance liq. chromatog. (UHPLC) column with a 90 min gradient. This result corresponded to ~ 19 and 30% increases in protein and peptide identifications, resp., when compared to a default, single-range TIMS DDA-PASEF anal. Quantitation precision was not affected by TIMS fractionation as demonstrated by the av. and median coeff. of variation values that were less than 4% upon label-free quantitation of tech. replicates. TIMS fractionation was utilized to generate a DDA-based spectral library for downstream data-independent acquisition (DIA) anal. of lower sample input using a shorter LC gradient. The TIMS-fractionated library, consisting of over 7600 proteins and 82,000 peptides, enabled the identification of ~ 4000 and 6600 proteins from 10 and 200 ng of human (HeLa) cell lysate input, resp., with a 20 min gradient, single-shot DIA anal. Data are available in ProteomeXchange: identifier PXD033129.
- 21Sample Loading Protocol for Evotips, Evosep 2020, 2023. https://www.evosep.com/wp-content/uploads/2020/03/Sample-loading-protocol.pdf.Google ScholarThere is no corresponding record for this reference.
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Abstract
Figure 1
Figure 1. Qualitative performance of timsTOF HT exceeded that of timsTOF Pro 2 across a wide range of plasma peptide loading masses and LC gradients. Compared to timsTOF Pro 2, HT identified up to 76 and 46% more peptide precursors in (A) neat and (B) Proteograph plasma, respectively, with up to 4.5-fold more precursors detected in Proteograph plasma than neat plasma. Red (timsTOF HT) and blue (timsTOF Pro 2) lines represent the number of peptide precursors uniquely detected in all replicate injections (n = 3) of neat and Proteograph plasma samples (NP1–3,5). Proteograph plasma NP4 was not considered for plotting due to insufficient yield for 1200 ng peptide mass load. Abbreviations: LC, liquid chromatography; NP, nanoparticle, SPD, samples per day.
Figure 2
Figure 2. timsTOF HT had a greater quantitative linear range compared to timsTOF Pro 2. (A) Distribution of peptide precursor MS2 peak area (triplicate average) ratios quantified with timsTOF HT vs Pro 2. For visualization purposes, the MS2 peak area ratio cutoff was set to 5. (B) Extracted ion chromatogram of MS2 fragment ion (y9) for a randomly selected precursor, FLVGPDGIPIMR (2+), from within the 3rd quartile of the total precursor intensity range. (C) R-squared distribution for the precursors quantified in all three replicate measurements of each peptide loading mass for Proteograph plasma NP2 at 60 SPD gradient with timsTOF HT (n = 4256) and timsTOF Pro 2 (n = 3331). Abbreviations: NP, nanoparticle; SPD, samples per day.
Figure 3
Figure 3. timsTOF HT had greater reproducibility compared to timsTOF Pro 2. The number of reproducibly quantified peptide precursors (CV < 20% of triplicate measurements) was increased by up to 127% in (A) neat and 81% in (B) Proteograph plasma (NP2) with timsTOF HT relative to Pro 2 across different LC gradients. As the loading mass increased, the quantitative reproducibility of timsTOF HT improved significantly compared to timsTOF Pro 2. Abbreviations: CV, coefficient of variation; LC, liquid chromatography; NP, nanoparticle; SPD, samples per day.
Figure 4
Figure 4. Qualitative and quantitative improvements of timsTOF HT allowed for higher sensitivity and reproducibility of lung cancer biomarkers detected in plasma. (A) Volcano plot comparison of precursors quantified in the control (n = 20) vs. lung cancer (n = 20) samples for representative Proteograph plasma NP2 when data were analyzed by timsTOF HT or Pro 2. (B) Improved sensitivity and reproducibility of timsTOF HT resulted in 48% more identified peptide precursors compared to timsTOF Pro 2, which translated to 52% more statistically significant precursors (q < 0.05) across 5 NPs. Abbreviations: NP, nanoparticle.
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- 5Landegren, U.; Hammond, M. Cancer diagnostics based on plasma protein biomarkers: hard times but great expectations. Mol. Oncol. 2021, 15 (6), 1715– 1726, DOI: 10.1002/1878-0261.128095https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3s%252FntVCrtg%253D%253D&md5=5237bd0e6f1b640be3cd2025d533db0eCancer diagnostics based on plasma protein biomarkers: hard times but great expectationsLandegren Ulf; Hammond MariaMolecular oncology (2021), 15 (6), 1715-1726 ISSN:.Cancer diagnostics based on the detection of protein biomarkers in blood has promising potential for early detection and continuous monitoring of disease. However, the currently available protein biomarkers and assay formats largely fail to live up to expectations, mainly due to insufficient diagnostic specificity. Here, we discuss what kinds of plasma proteins might prove useful as biomarkers of malignant processes in specific organs. We consider the need to search for biomarkers deep down in the lowest reaches of the proteome, below current detection levels. In this regard, we comment on the poor molecular detection sensitivity of current protein assays compared to nucleic acid detection reactions, and we discuss requirements for achieving detection of vanishingly small amounts of proteins, to ensure detection of early stages of malignant growth through liquid biopsy.
- 6Ignjatovic, V.; Geyer, P. E.; Palaniappan, K. K. Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data. J. Proteome Res. 2019, 18 (12), 4085– 4097, DOI: 10.1021/acs.jproteome.9b005036https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvVyls7fJ&md5=e9c3111bdc785929c704f260a1c8ae56Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational DataIgnjatovic, Vera; Geyer, Philipp E.; Palaniappan, Krishnan K.; Chaaban, Jessica E.; Omenn, Gilbert S.; Baker, Mark S.; Deutsch, Eric W.; Schwenk, Jochen M.Journal of Proteome Research (2019), 18 (12), 4085-4097CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)A review. The proteomic analyses of human blood and blood-derived products (e.g. plasma) offers an attractive avenue to translate research progress from the lab. into the clinic. However, due to its unique protein compn., performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technol. advances, but challenges imposed by both complications inherent to studying human biol. (e.g. inter-individual variability), anal. of biospecimen (e.g. sample variability), as well as technol. limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing and bioinformatic anal. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed anal. of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clin. practice.
- 7Geyer, P. E.; Holdt, L. M.; Teupser, D.; Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 2017, 13 (9), 942, DOI: 10.15252/msb.201562977https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1M%252Fis1GqsQ%253D%253D&md5=1d8415701bdc0ce6672552e4828fcad6Revisiting biomarker discovery by plasma proteomicsGeyer Philipp E; Mann Matthias; Geyer Philipp E; Mann Matthias; Holdt Lesca M; Teupser DanielMolecular systems biology (2017), 13 (9), 942 ISSN:.Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.
- 8Nakayasu, E. S.; Gritsenko, M.; Piehowski, P. D. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nat. Protoc. 2021, 16 (8), 3737– 3760, DOI: 10.1038/s41596-021-00566-68https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXhsFejtb%252FJ&md5=c38433549c6e3b03966ac8f365939c58Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validationNakayasu, Ernesto S.; Gritsenko, Marina; Piehowski, Paul D.; Gao, Yuqian; Orton, Daniel J.; Schepmoes, Athena A.; Fillmore, Thomas L.; Frohnert, Brigitte I.; Rewers, Marian; Krischer, Jeffrey P.; Ansong, Charles; Suchy-Dicey, Astrid M.; Evans-Molina, Carmella; Qian, Wei-Jun; Webb-Robertson, Bobbie-Jo M.; Metz, Thomas O.Nature Protocols (2021), 16 (8), 3737-3760CODEN: NPARDW; ISSN:1750-2799. (Nature Portfolio)A review. Mass-spectrometry-based proteomic anal. is a powerful approach for discovering new disease biomarkers. However, certain crit. steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during exptl. design and execution. This tutorial discusses important steps for designing and implementing a liq.-chromatog.-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including exptl. design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical anal. for meaningful biol. interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a ref. for improving rigor and reproducibility of biomarker development studies.
- 9Naylor, C. N.; Reinecke, T.; Ridgeway, M. E.; Park, M. A.; Clowers, B. H. Validation of Calibration Parameters for Trapped Ion Mobility Spectrometry. J. Am. Soc. Mass Spectrom. 2019, 30 (10), 2152– 2162, DOI: 10.1007/s13361-019-02289-19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhsFKjsLzL&md5=4a3ac8d87d87a9b348706f97da03168dValidation of Calibration Parameters for Trapped Ion Mobility SpectrometryNaylor, Cameron N.; Reinecke, Tobias; Ridgeway, Mark E.; Park, Melvin A.; Clowers, Brian H.Journal of the American Society for Mass Spectrometry (2019), 30 (10), 2152-2162CODEN: JAMSEF; ISSN:1044-0305. (Springer)Using contemporary theory for ion mobility spectrometry (IMS), gas-phase ion mobilities within a trapped ion mobility-mass spectrometer (TIMS) are not easily deduced using first principle equations due to non-linear pressure changes and consequently variations in E/N. It is for this reason that prior literature values have traditionally been used for TIMS calibration. Addnl., given that verified mobility stds. currently do not exist and the that the exact conditions used to measure reported literature values may not always represent the environment within the TIMS, a direct approach to validating the behavior of the TIMS system is warranted. A calibration procedure is presented where an ambient pressure, ambient temp., two-gate, printed circuit board drift-tube IMS (PCBIMS) is coupled to the front of a TIMS allowing reduced mobilities to be directly measured on the same instrument as the TIMS. These measured mobilities were used to evaluate the TIMS calibration procedure which correlates reduced mobility and TIMS elution voltages with literature values. When using the measured PCBIMS-reduced mobilities of tetraalkyl ammonium salts and tune mix for TIMS calibration of the alkyltrimethyl ammonium salts, the percent error is less than 1% as compared with using the reported literature K0 values where the percent error approaches 5%. This method provides a way to obtain accurate ref. mobilities for ion mobility techniques that require a calibration step (i.e., TIMS and TWAVE).
- 10Michelmann, K.; Silveira, J. A.; Ridgeway, M. E.; Park, M. A. Fundamentals of trapped ion mobility spectrometry. J. Am. Soc. Mass Spectrom. 2015, 26 (1), 14– 24, DOI: 10.1007/s13361-014-0999-410https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvVSntb%252FM&md5=9b1f6ee16d7081ea56aabceca67d74daFundamentals of Trapped Ion Mobility SpectrometryMichelmann, Karsten; Silveira, Joshua A.; Ridgeway, Mark E.; Park, Melvin A.Journal of the American Society for Mass Spectrometry (2015), 26 (1), 14-24CODEN: JAMSEF; ISSN:1044-0305. (Springer)Trapped ion mobility spectrometry (TIMS) is a relatively new gas-phase sepn. method that was coupled to quadrupole orthogonal acceleration time-of-flight mass spectrometry. The TIMS analyzer is a segmented radiofrequency ion guide wherein ions are mobility-analyzed using an elec. field that holds ions stationary against a moving gas, unlike conventional drift tube ion mobility spectrometry where the gas is stationary. Ions are initially trapped, and subsequently eluted from the TIMS analyzer over time according to their mobility (K). Though TIMS has achieved a high level of performance (R > 250) in a small device (<5 cm) using modest operating potentials (<300 V), a proper theory has yet to be produced. Here, the authors develop a quant. theory for TIMS via math. derivation and simulations. A 1-dimensional anal. model, used to predict the transit time and theor. resolving power, is described. Theor. trends are in agreement with exptl. measurements performed as a function of K, pressure, and the axial elec. field scan rate. The linear dependence of the transit time with 1/K provides a fundamental basis for detn. of reduced mobility or collision cross section values by calibration. The quant. description of TIMS provides an operational understanding of the analyzer, outlines the current performance capabilities, and provides insight into future avenues for improvement. [Figure not available: see fulltext.].
- 11timsTOF-HT─Expanding the Capabilities of High-Throughput, 4D-proteomics, Bruker Scientific LLC, 2023. https://www.bruker.com/en/products-and-solutions/mass-spectrometry/timstof/timstof-ht.html.There is no corresponding record for this reference.
- 12Blume, J. E.; Manning, W. C.; Troiano, G. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat. Commun. 2020, 11 (1), 3662 DOI: 10.1038/s41467-020-17033-712https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVKmur%252FK&md5=5825436c998c601d196e52744ab662aeRapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein coronaBlume, John E.; Manning, William C.; Troiano, Gregory; Hornburg, Daniel; Figa, Michael; Hesterberg, Lyndal; Platt, Theodore L.; Zhao, Xiaoyan; Cuaresma, Rea A.; Everley, Patrick A.; Ko, Marwin; Liou, Hope; Mahoney, Max; Ferdosi, Shadi; Elgierari, Eltaher M.; Stolarczyk, Craig; Tangeysh, Behzad; Xia, Hongwei; Benz, Ryan; Siddiqui, Asim; Carr, Steven A.; Ma, Philip; Langer, Robert; Farias, Vivek; Farokhzad, Omid C.Nature Communications (2020), 11 (1), 3662CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liq. chromatog.-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochem. properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biol. samples, including deep sampling of the plasma proteome. Spike expts. confirm a linear signal response. The median coeff. of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quant. proteomic studies.
- 13Huang, T.; Wang, J.; Stukalov, A. Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics. bioRxiv 2023, DOI: 10.1101/2023.08.555225There is no corresponding record for this reference.
- 14Ferdosi, S.; Tangeysh, B.; Brown, T. R.; Everley, P. A.; Figa, M.; McLean, M.; Elgierari, E. M.; Zhao, X.; Garcia, V. J.; Wang, T.; Chang, M. E. K.; Riedesel, K.; Chu, J.; Mahoney, M.; Xia, H.; O’Brien, E. S.; Stolarczyk, C.; Harris, D.; Platt, T. L.; Ma, P.; Goldberg, M.; Langer, R.; Flory, M. R.; Benz, R.; Tao, W.; Cuevas, J. C.; Batzoglou, S.; Blume, J. E.; Siddiqui, A.; Hornburg, D.; Farokhzad, O. C. Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions. Proc Natl Acad Sci U S A 2022, 119, e2106053119 DOI: 10.1073/pnas.2106053119201https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XotVSmt7g%253D&md5=833a5fde158da3aceabc1529d7e0d8faEngineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactionsFerdosi, Shadi; Tangeysh, Behzad; Brown, Tristan R.; Everley, Patrick A.; Figa, Michael; McLean, Matthew; Elgierari, Eltaher M.; Zhao, Xiaoyan; Garcia, Veder J.; Wang, Tianyu; Chang, Matthew E. K.; Riedesel, Kateryna; Chu, Jessica; Mahoney, Max; Xia, Hongwei; O'Brien, Evan S.; Stolarczyk, Craig; Harris, Damian; Platt, Theodore L.; Ma, Philip; Goldberg, Martin; Langer, Robert; Flory, Mark R.; Benz, Ryan; Tao, Wei; Cuevas, Juan Cruz; Batzoglou, Serafim; Blume, John E.; Siddiqui, Asim; Hornburg, Daniel; Farokhzad, Omid C.Proceedings of the National Academy of Sciences of the United States of America (2022), 119 (11), e2106053119CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Deep interrogation of plasma proteins on a large scale is a challenge due to the no. and concn. of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochem. properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by sp. surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biol. samples.
- 15Donovan, M. K. R.; Huang, Y.; Blume, J. E.; Wang, J.; Hornburg, D.; Ferdosi, S.; Mohtashemi, I.; Kim, S.; Ko, M.; Benz, R. W.; Platt, T. L.; Batzoglou, S.; Diaz, L. A.; Farokhzad, O. C.; Siddiqui, A. Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls. PLos One 2023, 18, e0282821 DOI: 10.1371/journal.pone.0282821202https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXmvFCjs74%253D&md5=02481ad678d060b26ae6fce85227bac7Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controlsDonovan, Margaret K. R.; Huang, Yingxiang; Blume, John E.; Wang, Jian; Hornburg, Daniel; Ferdosi, Shadi; Mohtashemi, Iman; Kim, Sangtae; Ko, Marwin; Benz, Ryan W.; Platt, Theodore L.; Batzoglou, Serafim; Diaz, Luis A.; Farokhzad, Omid C.; Siddiqui, AsimPLoS One (2023), 18 (3), e0282821CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)Advancements in deep plasma proteomics are enabling high-resoln. measurement of plasma proteoforms, which may reveal a rich source of novel biomarkers previously concealed by aggregated protein methods. Here, we analyze 188 plasma proteomes from non-small cell lung cancer subjects (NSCLC) and controls to identify NSCLC-assocd. protein isoforms by examg. differentially abundant peptides as a proxy for isoform-specific exon usage. We find four proteins comprised of peptides with opposite patterns of abundance between cancer and control subjects. One of these proteins, BMP1, has known isoforms that can explain this differential pattern, for which the abundance of the NSCLC-assocd. isoform increases with stage of NSCLC progression. The presence of cancer and control-assocd. isoforms suggests differential regulation of BMP1 isoforms. The identified BMP1 isoforms have known functional differences, which may reveal insights into mechanisms impacting NSCLC disease progression.
- 16Ferdosi, S.; Stukalov, A.; Hasan, M.; Tangeysh, B.; Brown, T. R.; Wang, T.; Elgierari, E. M.; Zhao, X.; Huang, Y.; Alavi, A.; Lee-McMullen, B.; Chu, J.; Figa, M.; Tao, W.; Wang, J.; Goldberg, M.; O'Brien, E. S.; Xia, H.; Stolarczyk, C.; Weissleder, R.; Farias, V.; Batzoglou, S.; Siddiqui, A.; Farokhzad, O. C.; Hornburg, D. Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes. Adv Mater. 2022, 34, e2206008 DOI: 10.1002/adma.202270307203https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisFSktbjL&md5=74c378319e1066875dd9ecad7544e509Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of ProteomesFerdosi, Shadi; Stukalov, Alexey; Hasan, Moaraj; Tangeysh, Behzad; Brown, Tristan R.; Wang, Tianyu; Elgierari, Eltaher M.; Zhao, Xiaoyan; Huang, Yingxiang; Alavi, Amir; Lee-McMullen, Brittany; Chu, Jessica; Figa, Mike; Tao, Wei; Wang, Jian; Goldberg, Martin; O'Brien, Evan S.; Xia, Hongwei; Stolarczyk, Craig; Weissleder, Ralph; Farias, Vivek; Batzoglou, Serafim; Siddiqui, Asim; Farokhzad, Omid C.; Hornburg, DanielAdvanced Materials (Weinheim, Germany) (2022), 34 (44), 2206008CODEN: ADVMEW; ISSN:0935-9648. (Wiley-VCH Verlag GmbH & Co. KGaA)Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which det. the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona compn., enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7x more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3x more proteins compared to a std. plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in detg. the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.
- 17Koh, B.; Liu, M.; Almonte, R. Multi-omics profiling with untargeted proteomics for blood-based early detection of lung cancer. medRxiv. 2024, DOI: 10.1101/2024.01.03.24300798There is no corresponding record for this reference.
- 18Meier, F.; Brunner, A. D.; Frank, M. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat. Methods 2020, 17 (12), 1229– 1236, DOI: 10.1038/s41592-020-00998-013https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXisVGksbbL&md5=f6987c905570fbd9bdb74fd2452998c5DiaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisitionMeier, Florian; Brunner, Andreas-David; Frank, Max; Ha, Annie; Bludau, Isabell; Voytik, Eugenia; Kaspar-Schoenefeld, Stephanie; Lubeck, Markus; Raether, Oliver; Bache, Nicolai; Aebersold, Ruedi; Collins, Ben C.; Roest, Hannes L.; Mann, MatthiasNature Methods (2020), 17 (12), 1229-1236CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass anal. Here, we make use of the correlation of mol. wt. and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extn. workflow by inclusion of the ion mobility dimension for both signal extn. and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quant. accuracy, even from 10 ng sample amts.
- 19Demichev, V.; Messner, C. B.; Vernardis, S. I.; Lilley, K. S.; Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 2020, 17 (1), 41– 44, DOI: 10.1038/s41592-019-0638-x14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXksFGjuw%253D%253D&md5=0ee3e1449b4ad6c270b1346f8d5126f6DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughputDemichev, Vadim; Messner, Christoph B.; Vernardis, Spyros I.; Lilley, Kathryn S.; Ralser, MarkusNature Methods (2020), 17 (1), 41-44CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics expts. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatog. methods.
- 20Guergues, J.; Wohlfahrt, J.; Stevens, S. M., Jr. Enhancement of Proteome Coverage by Ion Mobility Fractionation Coupled to PASEF on a TIMS-QTOF Instrument. J. Proteome Res. 2022, 21 (8), 2036– 2044, DOI: 10.1021/acs.jproteome.2c0033615https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhvVygs7jM&md5=0aab129c156dc783aa5e7267bb137845Enhancement of Proteome Coverage by Ion Mobility Fractionation Coupled to PASEF on a TIMS-QTOF InstrumentGuergues, Jennifer; Wohlfahrt, Jessica; Stevens, Stanley M.Journal of Proteome Research (2022), 21 (8), 2036-2044CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)Trapped ion-mobility spectrometry (TIMS) was used to fractionate ions in the gas phase based on their ion mobility (V s/cm2), followed by parallel accumulation-serial fragmentation (PASEF) using a quadrupole time-of-flight instrument to det. the effect on the depth of proteome coverage. TIMS fractionation (up to four gas-phase fractions) coupled to data-dependent acquisition (DDA)-PASEF resulted in the detection of ~ 7000 proteins and over 70,000 peptides overall from 200 ng of human (HeLa) cell lysate per injection using a com. 25 cm ultra high performance liq. chromatog. (UHPLC) column with a 90 min gradient. This result corresponded to ~ 19 and 30% increases in protein and peptide identifications, resp., when compared to a default, single-range TIMS DDA-PASEF anal. Quantitation precision was not affected by TIMS fractionation as demonstrated by the av. and median coeff. of variation values that were less than 4% upon label-free quantitation of tech. replicates. TIMS fractionation was utilized to generate a DDA-based spectral library for downstream data-independent acquisition (DIA) anal. of lower sample input using a shorter LC gradient. The TIMS-fractionated library, consisting of over 7600 proteins and 82,000 peptides, enabled the identification of ~ 4000 and 6600 proteins from 10 and 200 ng of human (HeLa) cell lysate input, resp., with a 20 min gradient, single-shot DIA anal. Data are available in ProteomeXchange: identifier PXD033129.
- 21Sample Loading Protocol for Evotips, Evosep 2020, 2023. https://www.evosep.com/wp-content/uploads/2020/03/Sample-loading-protocol.pdf.There 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.3c00646.
Plasma sample processing and data acquisition; plasma peptide precursors identified in neat and Proteograph plasma (NP1–5) by timsTOF HT and Pro 2 across a wide range of plasma peptide loading masses and LC gradients (Figure S1); qualitative and quantitative assessment of spike-in PQ500 QC peptides (Figure S2); density distribution of MS1 fold-change quantities for neat and Proteograph plasma (NP1–5) precursors measured in timsTOF HT vs. Pro 2 (Figure S3); density distribution of MS2 fold-change quantities for neat and Proteograph plasma (NP1–5) precursors measured in timsTOF HT vs. Pro 2 (Figure S4); density distribution of MS2 peak area quantities for neat and Proteograph plasma (NP1–5) peptide precursors measured in timsTOF HT vs. Pro 2 (Figure S5); timsTOF HT showed increased sensitivity compared to timsTOF Pro 2 (Figure S6); timsTOF HT demonstrated a superior linear response compared to timsTOF Pro 2 for neat and Proteograph plasma (NP1-NP5) across a wide range of peptide loading masses and LC gradients (Figure S7) timsTOF HT resulted in a higher number of reproducibly quantified precursors (CV < 10% and CV < 20%) in neat and Proteograph plasma (NP1-NP5) compared to timsTOF Pro 2 across a wide range of peptide loading masses and LC gradients (Figure S8); comparison of reproducibly quantified precursors (CV < 20%) in neat and Proteograph plasma (NP1–5) analyzed with timsTOF HT and Pro 2 (Figure S9); demographic plots for lung cancer and control cohorts (Figure S10); timsTOF HT increased total and statistically significant peptide precursors identified in lung cancer and control cohorts (Figure S11); overlap of statistically significant precursors, peptides, and proteins in a cohort of 20 lung cancer and 20 control samples analyzed with timsTOF HT and Pro 2 (Figure S12); peptide dilution schema for neat and Proteograph plasma (Table S1); patient demographic information for the cohorts of 20 lung cancer and 20 control patient samples (Table S2); DIA m/z and IM window ranges within the MS2 fragmentation cycle (Table S3); peptide precursors identified in timsTOF HT and Pro 2 with CV < 10% (Table S4A); peptide precursors identified in timsTOF HT and Pro 2 with CV < 20% (Table S4B); total peptide precursor identified in timsTOF HT vs. Pro 2 in neat and Proteograph plasma; total peptide precursors identified in timsTOF HT vs. Pro 2 in Proteograph plasma NP1–5 (Table S5) (PDF)
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