Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies
- Ipputa TadaIpputa TadaDepartment of Genetics, The Graduate University for Advanced Studies, SOKENDAI, 1111 Yata, Mishima, Shizuoka 411-8540, JapanMore by Ipputa Tada
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- Romanas ChaleckisRomanas ChaleckisGunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, JapanDivision of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, SwedenMore by Romanas Chaleckis
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- Hiroshi TsugawaHiroshi TsugawaRIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, JapanRIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, JapanMore by Hiroshi Tsugawa
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- Isabel MeisterIsabel MeisterGunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, JapanDivision of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, SwedenMore by Isabel Meister
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- Pei ZhangPei ZhangGunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, JapanDivision of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, SwedenMore by Pei Zhang
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- Nikolaos LazarinisNikolaos LazarinisDivision of Respiratory Medicine and Allergy, Department of Medicine, Karolinska University Hospital Huddinge, Stockholm, 141-86, SwedenMore by Nikolaos Lazarinis
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- Barbro DahlénBarbro DahlénDivision of Respiratory Medicine and Allergy, Department of Medicine, Karolinska University Hospital Huddinge, Stockholm, 141-86, SwedenMore by Barbro Dahlén
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- Craig E. Wheelock*Craig E. Wheelock*E-mail: [email protected]Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, JapanDivision of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, SwedenMore by Craig E. Wheelock
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- Masanori Arita*Masanori Arita*E-mail: [email protected]. Phone: +81-55-981-9449.RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, JapanNational Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, JapanMore by Masanori Arita
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Abstract
Data-independent acquisition mass spectrometry (DIA-MS) is essential for information-rich spectral annotations in untargeted metabolomics. However, the acquired MS2 spectra are highly complex, posing significant annotation challenges. We have developed a correlation-based deconvolution (CorrDec) method that uses ion abundance correlations in multisample studies using DIA-MS as an update of our MS-DIAL software. CorrDec is based on the assumption that peak intensities of precursor and fragment ions correlate across samples and exploits this quantitative information to deconvolute complex DIA spectra. CorrDec clearly improved deconvolution of the original MS-DIAL deconvolution method (MS2Dec) in a dilution series of chemical standards and a 224-sample urinary metabolomics study. The primary advantage of CorrDec over MS2Dec is the ability to discriminate coeluting low-abundance compounds. CorrDec requires the measurement of multiple samples to successfully deconvolute DIA spectra; however, our randomized assessment demonstrated that CorrDec can contribute to studies with as few as 10 unique samples. The presented methodology improves compound annotation and identification in multisample studies and will be useful for applications in large cohort studies.
For compound identification, high-resolution tandem mass spectra (MS2) with public spectral library and associated computational tools are indispensable. A number of resources are available including MassBank, (1) GNPS, (2) CSI:FingerID, (3) and MS-FINDER. (4) In the classical data-dependent acquisition mass spectrometry (DDA-MS), ions are isolated in a narrow m/z window to obtain clean spectra (typically 1 Da, sometimes up to 9 Da). (5,6) In contrast, for data-independent acquisition mass spectrometry (DIA-MS), wider m/z windows of 10–1000 Da are used to obtain complex spectra from coeluting precursors, thereby requiring computational approaches to interpret. (7)
To overcome the trade-off between cleanness and comprehensiveness of DIA spectra, various deconvolution tools have been proposed, such as OpenSWATH, (8) Specter, (9) MetDIA, (10) MS-DIAL, (11) and decoMetDIA. (12) The first three tools were designed for targeted analyses that utilize predefined spectral libraries to deconvolute spectra. The latter two can deconvolute MS2 spectra de novo by fitting MS2 chromatograms to their precursor chromatogram in a single sample (i.e., using retention time). These powerful methods are suitable for the SWATH (Sequential Window Acquisition of all THeoretical fragment ion spectra) type of DIA data. (7) However, MS2 spectra become highly complex when precursor ions of all m/z are fragmented together: e.g., all ion fragmentation (AIF), MSALL, or MSE. (7) In particular, busy chromatographic regions with multiple coeluting compounds pose a significant challenge. In the case of the original MS-DIAL, at least two data-point differences between the chromatographic peak tops is required for deconvolution, which is a challenging condition for AIF data. Previous tools are therefore not suitable for untangling complex MS2 spectra from the AIF acquisition and its equivalent.
We present a new MS2 deconvolution method based on the correlation of ion abundances between precursor and product ions among biological samples, named CorrDec (Correlation-based Deconvolution). This method, implemented in MS-DIAL version 3.22 and later, is designed to deconvolute MS2 spectra from untargeted, multisample AIF metabolomics without requiring a predefined spectral library. The method is based on three assumptions: (1) metabolite concentrations differ across study samples in multisample studies; (2) the MS2 fragmentation pattern is identical under identical experimental conditions; (3) intensities of fragment ions correlate with those of their precursors.
Correlation has been widely used in mass spectrometry-based metabolomics. (13,14) For example, the Pearson correlation is used in CAMERA to estimate the similarity of different mass chromatograms to extract compound spectra and to annotate adduct ions and isotopic peaks. (15) For DIA, data correlation-based approaches such as RAMClust assigns precursor-product relationships based on detected features in MS1 and MS2. (16,17) In contrast to the previous approaches, CorrDec is not designed to retrieve as many characteristic product ions as possible from the DIA-MS2 spectra. Rather, it excludes noise peaks effectively by integrating multisample profiles. We demonstrate the concept and utility of CorrDec in a dilution series of chemical standards in urine and a case study from a urinary metabolomics cohort.
Experimental Section
ARTICLE SECTIONS
Correlation-Based Deconvolution
CorrDec starts with the aligned peak list from multiple samples. The peak list consists of “aligned features”, which include the averages of retention time, m/z, peak height, and width obtained from the detected peaks in the samples, their ion abundances, and corresponding MS2 spectra. The peak height is used for the quantification of MS1 and MS2 peaks. The MS2 deconvolution is performed as follows (Figure 1).
Figure 1
Figure 1. Flowchart of the CorrDec method for a target feature Ft1. A. For each feature, the Pearson correlations are calculated for all pairs of precursor (MS1 vector) and product ions (MS2 matrix). B. All correlation values of all features are merged into a single matrix. C. Product ions satisfying the three criteria (see the main text for details) are selected to produce the deconvoluted MS2 spectrum of Ft1.
Step 1: For each aligned feature Ft1, Pearson correlations are calculated between all product ions and their precursors. The MS2 spectra of Ft1 for all samples are retrieved to create a “MS2Mat” data matrix, consisting of the ion abundances of each product ion (P) binned by an m/z threshold in all samples (0.01 in this study). The precursor ion abundances of all samples are retrieved to create a “MS1Vec” data vector, and Pearson correlations are calculated for all pairs of the features in MS1Vec and product ions in MS2Mat (Figure 1A). For each product ion, its existence ratio within the samples (the number of samples having the product ion above the threshold (1000 in this study) divided by the number of all samples) is also recorded.
Step 2: All correlation values in all features are integrated into a matrix based on the m/z of the product ion using the same m/z threshold (0.01 in this study) as MS2Mat (Figure 1B).
Step 3: Each product ion is assessed using the correlation value CorrMS1vsMS2 for its inclusion to the deconvoluted spectrum of Ft1. Three criteria are applied (Figure 1C):
(Criterion 1) CorrMS1vsMS2 > minimum threshold,
(Criterion 2) CorrMS1vsMS2> MaxCorrFt-margin1, and
(Criterion 3) CorrMS1vsMS2> MaxCorrP-margin2.
Criterion 1 is an overall cutoff to suppress noise signals. Correlations between the ion abundances of a MS1 precursor ion and the ion abundances of a MS2 product ion must be higher than a predefined minimum correlation threshold for all peaks. The recommended threshold is between 0.3 and 0.7, and we used 0.7 in this study. A lower threshold indicates a higher possibility to introduce noise peaks into spectra.
Criterion 2 is a threshold to filter product ions for fragments from each MS1 feature Ft1. MaxCorrFt is the maximum of all correlations for Ft1, and relatively low-correlating peaks from ionization enhancement and/or biochemical proximation are removed. The recommended margin1 is between 0.1 and 0.3, and we used 0.2 in this study. A larger margin indicates a higher possibility to introduce noise peaks into spectra. For example, in Figure 1B, the MS2 peak P1 (0.73) is removed because MaxCorrFt for the feature is 0.98.
Criterion 3 is used to avoid false-positive assignments by Criterion 2 when the same product ion shows high correlation values for multiple precursor ions. For each product ion Px, a maximum correlation MaxCorrP with its neighboring features (eluting within ±0.5 × peak width of Ft1) is determined. When the correlation value between the Ft1 and Px is less than MaxCorrP-margin2, Px is excluded from the deconvoluted spectrum of Ft1. The recommended range is between 0.1 and 0.3, and we used 0.1 in this study. A larger margin indicates a higher possibility to introduce noise peaks into spectra. For example, the product ion P2 is excluded from the Ft1 deconvoluted spectrum because the value of 0.81 is less than MaxCorrP (0.93) – 0.1 (Figure 1B).
These threshold values require tuning when applied to different data sets. The m/z value and the intensity in a deconvoluted spectrum are represented by their respective median value of m/z and intensities in biological samples, where the intensities are normalized by the abundance of the precursor ion in each sample.
Sample Information and Data Acquisition
Information on samples and experiments are detailed in Supporting Information. Liquid chromatography (LC)-MS measurements in AIF mode were performed as described previously. (18,19) Metabolites were separated on a 15 min gradient using HILIC chromatography with acidified water and acetonitrile. Data were acquired in positive ionization mode on an Agilent 6550 Q-TOF-MS system with a mass range of 40–1200 m/z in AIF mode with three alternating collision energies (0, 10, and 30 eV). The data acquisition rate was 2 scans/s for each segment.
A dilution series of eight chemical standards (proline betaine, trigonelline, dimethylglycine, trimethylamine N-oxide, tyrosine, glycine betaine, proline, 3-hydroxy-kynurenine; Table S1) was prepared using 10-fold diluted urine as a matrix. The starting spiked-in concentration of 4 μM in urine was diluted 1.5-fold with an equal amount of urine 10 times, resulting in an 11-point series to the final concentration of 69 nM (Figure S1). In addition, we also acquired data with a smaller dilution step (1.07-fold, 3.27–4.00 μM) for tyrosine. Two dilution series of trimethoprim (1.07-fold starting at 0.06 μM and 1.5-fold starting at 0.3 μM and) were acquired in urine samples with differing matrix composition.
Urine samples (n = 224) were used as the proof of concept for assessing the CorrDec performance. A detailed description of the full study is given in the original publication. (20) Samples were measured in four analytical batches, with pooled quality control (QC) sample injections every five samples and a water blank at the end of the batch sequence. The data sets have been deposited in the EMBL-EBI MetaboLights repository (21) with the identifiers MTBLS787 (chemical standards) and MTBLS816 (urine metabolomics).
Chemical Standard Library
An in-house MS2 spectral library combined from various open and closed sources containing 128,039 experimental MS2 spectra (high-resolution, mostly DDA) for 13 597 compounds was used for identification. The retention times (RT) for 280 compounds were obtained from purchased chemical standards. (18,22)
Data Processing and Analysis
The CorrDec method was implemented into MS-DIAL. (11) Data were processed in MS-DIAL version 4.12 (peak detection, alignment, and deconvolution). Important parameters were as follows: minimum peak height MS1:3000, noise level of MS2:1000, total identified score cutoff: 80%, detected in at least 20% of all samples, not in blank (maximum sample intensity/average blank intensity >5). As our library contained records of both DDA and DIA spectra, we used the deconvoluted spectra with and without the ions heavier than the precursor during the identification process; the higher matching score was kept. Detailed data processing settings of MS-DIAL can be found in the Tables S2 and S3. The MS2 spectra were deconvoluted independently using the MS2Dec (11) and the CorrDec methods (after the alignment of features).
For the urine data, we manually confirmed and curated the alignment results to correct missed or doubtful peak picking, feature alignment, and compound identification. We also annotated all features using three criteria: (i) accurate mass (AM) match (tolerance: 0.01 Da), (ii) RT match (tolerance: 1 min), and (iii) MS2 spectrum match (similarity >80%). The MS2 similarity was scored by the simple dot product without any weighting, (23) for clearer understanding of our method:
where Am and Ar are the arrays of m/z intensities in a measured and reference mass spectrum, respectively. To avoid erroneous high similarity matches resulting from only a few peaks, we adopted the following additional criteria for MS2 spectrum match: (1) if AMRT, a match of at least two MS2 peaks with the reference spectra, and (2) if AM only, a match of at least three MS2 peaks with the reference spectra. The MS2 similarities with reference spectra were compared between the CorrDec and the MS2Dec using three different collision energies (0, 10, and 30 eV).
Random Sampling Analysis
We evaluated the performance of CorrDec for different sample sizes by randomized resampling analysis of the urine metabolomics data set. After chromatographic alignment was performed using all samples, we reselected the study and QC samples for deconvolution by the CorrDec. The number of samples varied from four to the number of detected samples (depending upon the chosen compound) with 100 iterations. For each iteration, we calculated the MS2 similarity between the deconvoluted spectrum from the resampling and the reference spectrum. The MS2 similarity of resampling was the average of 100 iterations.
Results and Discussion
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CorrDec Demonstration Using Compound Dilution Series in Urine
Using a dilution series of chemical standards, we verified high correlation of the intensities of MS2 fragments with those of their precursors. We measured the 11-point dilution series (0.069–4 μM) of eight chemical standards in AIF mode with diluted urine as the matrix. In such a setup, only the concentrations of the spiked compound vary (partially masked by the endogenous compounds present in the matrix) while concentrations of other compounds in the urine matrix remain stable. In the case of the tyrosine dilution series (Figure 2), the MS2 spectra of tyrosine contained 193 and 280 peaks for 10 and 30 eV, respectively. The similarity scores (simple dot product) of all raw MS2 spectra with the reference spectra were less than 30%. When processed by CorrDec, 12 peaks in 10 eV and 13 peaks in 30 eV showed >0.9 correlations with their precursors, clearly deviating from the normal distribution formed by the correlation values of the other peaks (Figure 2B bottom). These highly correlated peaks exhibited intensities proportional to the dilution (Figure 2B top in the log scale), and the MS2 similarity scores with the reference spectra were 90.5% and 86.5% for 10 and 30 eV, respectively. Similar results were obtained for the other 7 compounds: MS2 spectra were successfully generated with high MS2 matches (1 compound >80%, 6 compounds >90% for at least one collision energy) by CorrDec (Table S4 and Figure S2).
Figure 2
Figure 2. Demonstration of the CorrDec method using tyrosine dilution series spiked into diluted urine as background matrix. A. Raw MS2 spectra of tyrosine [M + H]+ (m/z: 182.082) at the lowest (69 nM) and the highest (4 μM) spiked concentrations in dilution series. Raw MS2 spectra contain over one hundred peaks masking the ions derived from tyrosine, especially at low spiked-in concentrations. B. Linked scatter plots visualizing the intensity correlations between the MS1 m/z 182.082 and MS2 peaks in 11 dilution series samples. Only 12 out of 193 (10 eV) and 13 out of 280 peaks (30 eV) correlated >0.9 (highlighted lines). C. Deconvoluted MS2 spectra (above, in black) matched well with the library reference spectra (below, in red). The MS2 similarities of deconvoluted spectra were 90.5% (10 eV) and 86.5% (30 eV), while the MS2 similarities of raw spectra at 0, 10, and 30 eV were less than 30% in the all samples.
In addition to the MS2 spectra at 10 and 30 eV, deconvoluted spectra were also obtained for 0 eV, justifying the use of in-source fragmentation for metabolite identification. (16,17,24,25) The degree of in-source fragmentation depends on the ionization source setting. In this study, in-source fragmentation is facilitated by a high fragmentor voltage (380 V), and the MS2 spectra at 0 eV for all eight chemical standards provided >80% matches in the library (Table S4), corroborating the usability of the in-source fragmentation.
To further confirm the usability of CorrDec for the cases where the concentration of the compounds varies only little across samples, we measured a 1.07-fold dilution series of tyrosine and trimethoprim. For tyrosine, the same QC background was used because the variation of endogenous tyrosine overwhelmed the 1.07-fold variation; we could obtain clean CorrDec spectra in such cases. With the constant QC background, CorrDec could generate MS2 spectra, showing >80% MS2 match at 10 eV using four samples (spiked-in concentration range 3.27–4.00 μM; Figure S3). To ensure the performance of CorrDec with the minimum concentration variations on different urine backgrounds, we measured an exogenous compound, trimethoprim, under different background matrixes. CorrDec could again deconvolute spectra using four samples (Figure S3). We could confirm that small concentration changes between the samples (<25%) suffice for the correlation-based method, (16) when heavy coelution is avoided.
Urine Metabolomics Data Set
To verify the practical performance of CorrDec, we analyzed a LC-MS (HILIC chromatography) metabolomics data set consisting of 224 unique urine samples, 58 pooled QCs, and 4 blanks acquired in positive ionization AIF mode. Data were processed by MS-DIAL version 4.12. In the CorrDec deconvolution process, we discarded product ions that appeared in <50% of all samples for computational efficiency. This threshold of 50% is arbitrary and should be set for each study considering the sample number and the desired level of reliability. The remaining 4159 features were aligned, among which the alignment of 64 features was manually corrected to separate fortuitously merged, coeluting compounds. By matching AM, RT, and MS2 spectra to the reference library, 105 compounds were confidently identified at the MSI level 1. (26)
For all of the 105 compounds, both MS2Dec and CorrDec could generate MS2 spectra. The number of spectra achieving >80% match, however, were 34 and 85 for MS2Dec and CorrDec, respectively (Figure 3A). Furthermore, the distribution of MS2 similarity scores reveals that MS2Dec spectra showed <60% match for 50 compounds. Median similarity values were 59.1% and 91.3% for MS2Dec and CorrDec, respectively (Figure 3B). The reason for the disparity is that CorrDec is especially effective in obtaining cleaner spectra for compounds of low abundance or smaller peak intensity (Figure 3C). (27)
Figure 3
Figure 3. CorrDec MS2 spectra provide increased confidence in compound identification than those obtained by MS2Dec in the urinary metabolomics DIA data set. A. Number of compounds in each identification category identified using MS2Dec and CorrDec. B. Distribution of the MS2 similarity scores for the MSI level-1 compounds spectra deconvoluted by the CorrDec and MS2Dec. C. MS2 similarity scores from CorrDec were higher than MS2Dec, especially for low-intensity peaks.
In addition to the 105 compounds identified at the AMRT and MS2 match level, we could also identify six metabolites as high match (>80%) to the applied MS2 library using CorrDec spectra but not with MS2Dec spectra. These compounds have been previously reported in human urine: imidazole acetic acid, (28) homocitrulline, (29) aminohippuric acid, (30) isobutyryl (C4) carnitine, (30) liquiritigenin, (31) and AICA-riboside. (32) Among the 111 identified compounds, over half (61) were amino acids and their metabolites: standard amino acids (13), methylated (9), acetylated (6), other amino acid metabolites (22), and conjugates (11). The other major compound groups include products of nucleic acid metabolism (13), and food/drug metabolites (8) (Table S5 and Figure S4).
In addition to the MS2 library matching, CorrDec can provide a more reliable MS2 spectra for structure-prediction tools such as MS-FINDER. (4) For example, we could annotate two features based on their CorrDec spectra as acetaminophen sulfate and valerylcarnitine (Figure S5), two compounds not present in our MS2 spectral library but likely present in urine. (30,33) The method is particularly suitable for compounds with variable levels in the samples, such as drugs and dietary components. Indeed, among the confirmed 85 AMRT+MS2 compounds (Figure 3A), 25 were first annotated by MS2 match only and were later purchased for confirmation.
MS2 Spectra Deconvolution of Coeluting Compounds
Nontargeted LC methods often contain regions with multiple coeluting compounds. In our analytical method, the distribution of the 4159 features ranged from a few to over 250 peaks per 20 s (approximate average peak width at base) across the 0.8–15 min of gradient elution (Figure S6A,B). Such coeluting peaks pose a challenge to deconvolution methods relying on mass chromatograms. With CorrDec, even completely coeluting compounds could be deconvoluted, such as abundant glutamine and sparse N-acetylcarnosine (Figure 4A,B). The relative peak intensities of the two compounds fit well with the reported average concentrations in the literature: 18–72 and 1–2 μM/mmol creatinine for glutamine (30) and N-acetylcarnosine (see Supporting Information), respectively. Using MS2Dec, the deconvoluted spectrum of N-acetylcarnosine contained all fragment peaks of glutamine, reducing the MS2 match to only 29.3%. The MS2Dec deconvoluted spectrum of glutamine showed the MS2 match of 86.5% (Figure 4C). With the same data set, CorrDec could deconvolute the MS2 spectrum of N-acetylcarnosine with 95.1% match and provided an equivalent high match for glutamine (84.1%) as well (Figure 4D). Low abundance metabolites such as N-acetylcarnosine arguably constitute the larger part of most metabolomics data sets. (27) The high-quality MS2 spectra deconvoluted by the CorrDec enabled us to untangle the complex AIF data set, by improving the identifications and annotations of smaller peaks in chromatographically dense sections.
Figure 4
Figure 4. CorrDec can successfully deconvolute the MS2 spectra of completely coeluting compounds, glutamine and N-acetylcarnosine. A. The raw MS2 spectrum and extracted ion chromatograms in MS1 (0 eV) of completely coeluting glutamine and N-acetylcarnosine as well as B. their fragments in MS2 (10 eV) from the urine data (QC1 sample in batch 1). C. MS2 spectra of glutamine and N-acetylcarnosine deconvoluted by the MS2Dec. D. MS2 spectra of glutamine and N-acetylcarnosine deconvoluted by the CorrDec.
The benefits of CorrDec are summarized as (1) cleaner MS2 spectra, and (2) statistical annotations (frequency and correlation) for MS2 peaks. CorrDec can generate clean spectra without noise signals from the matrix, mobile phase, or mass spectrometer artifacts, enabling better match to spectral databases or libraries. In the deconvolution process, each MS2 peak is assigned with a correlation value and frequency among samples. Using these statistical annotations, advanced users can manually interpret deconvoluted MS2 spectra of unknown or marginally matching metabolites.
On the other hand, CorrDec has two disadvantages: (1) the requirement of multiple samples with varying compound concentrations, and (2) the possibility of removing shared fragments among coeluting compounds. First, in principle, CorrDec cannot be performed on a single sample and at least three samples are required to calculate the correlation. While we observed that four spiked samples were sufficient to obtain >80% similarity match (Figure S3), we investigated further to estimate the required sample size using random resampling of the urine metabolomics data set in the next section. Second, if coeluting compounds produce the same m/z product ions, their intensity correlations become small enough to be removed from deconvoluted spectra depending on the CorrDec parameters. MS2Dec spectra are useful to complement such missing peaks, and advanced users can recover them through careful interpretation of statistical annotations and MS2 chromatograms. One such example for a group of betaines is provided in Figure S7.
Verification by Random Resampling
Estimating the number of samples required for CorrDec is difficult; it depends on multiple factors (study design, sample matrix, metabolite, etc.). Here for a rough estimation, we used 85 compounds confidently annotated (AMRT and MS2 match) in the urine study to perform random resampling analysis. For each of the 85 compounds, we created a scatter plot between the number of samples and MS2 similarity with the preselected library reference spectrum (Figure S8). On the basis of the median MS2 similarity from 100 iterations for each resampling, we plotted the number of compounds (total 85) of high MS2 similarity scores for each sampling size (Figure 5). Already with 10 samples, 47% (40 of 85) of the compounds showed >80% MS2 similarity; when using 30 samples, the number rose to 85% (72 of 85). Therefore, small studies with tens of samples can benefit from the CorrDec method. Note that urine is more variable compared to homeostatic fluids such as blood. A larger number of samples might be required for successful application of CorrDec in studies with less metabolite variations. On the other hand, the quality of MS2 spectra are largely dependent on compound classes and study designs. Defining the best parameters or the minimum sample number required for all studies is therefore difficult.
Figure 5
In MS-DIAL, CorrDec is not intended to replace MS2Dec. Both deconvolution methods are based on different concepts and have different usage scenarios. The CorrDec method provides a reasonably clean deconvoluted MS2 spectrum per feature and sample set, and it is suitable for annotating and identifying a feature at the level of the whole sample set. MS2Dec can deconvolute MS2 spectra for each feature in a single sample; therefore, while noisier, the MS2Dec can be utilized to evaluate the feature identification for each sample in the data set. In DIA metabolomics, MS2 spectra are obtained from only a small number of MS scans. For such complex and noisy data, traditional deconvolution methods such as multivariate curve resolution (MCR) are difficult to apply because the multivariate method requires proper constraints to deconvolute spectra. When the number of coeluting compounds and peak shapes are much interfered by noise, error-minimization is not a good algorithmic choice. For such a data set, MS2Dec and CorrDec methods can function in complement to clean MS2 spectra. Lastly, regardless of how clean the MS2 spectra or how good the MS2 library similarity matches are, it is still necessary to further confirm compound annotations with chemical standards.
Conclusions
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We have developed CorrDec, a new MS2 spectra deconvolution method for DIA data based on the correlations of the peak intensities across samples. CorrDec has been implemented in MS-DIAL and is available in version 3.22 or later (version 4 also covers ion-mobility data processing (34)). The improved quality of the MS2 spectra and the ability to deconvolute completely coeluting compounds are the main advantages over retention-time based deconvolution methods. Therefore, CorrDec enables more reliable compound annotations and identifications in multisample studies.
Supporting Information
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.0c01980.
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Author Information
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- Craig E. Wheelock - Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan; Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden;
http://orcid.org/0000-0002-8113-0653;
- Masanori Arita - RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, Japan; National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan;http://orcid.org/0000-0001-6706-0487;
- Ipputa Tada - Department of Genetics, The Graduate University for Advanced Studies, SOKENDAI, 1111 Yata, Mishima, Shizuoka 411-8540, Japan;http://orcid.org/0000-0003-4149-7191
- Romanas Chaleckis - Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan; Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden;http://orcid.org/0000-0001-8042-1005
- Hiroshi Tsugawa - RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, Japan; RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro, Tsurumi, Yokohama, Kanagawa 240-0045, Japan;http://orcid.org/0000-0002-2015-3958
- Isabel Meister - Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan; Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden;http://orcid.org/0000-0001-9063-0492
- Pei Zhang - Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22, Showa-machi, Maebashi, Gunma 371-8511, Japan; Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 171-77, Sweden;http://orcid.org/0000-0003-2054-928X
Acknowledgments
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We thank Hideru Obinata, Stacey Reinke, and David Broadhurst for discussions and suggestions. This work was supported by JSPS KAKENHI grant numbers JP18J23133, 17H03621, 15H05898, 15K21738, 18H02432, 18K19155, and 19K17662. We acknowledge support from the Gunma University Initiative for Advanced Research (GIAR), the STINT Foundation, the Swedish Heart Lung Foundation (HLF 20170734, HLF 20180290), the Swedish Research Council (2016-02798), the Stockholm County Council Research Funds (ALF), the Asthma and Allergy Research Foundation, the Centre for Allergy Research and Karolinska Institutet, NBDC, and AMED (JP17gm1010006, JP18gm0910005). This work was supported in part by The Environment Research and Technology Development Fund (ERTDF) (grant no. 5-1752). I.M. was supported by Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship (P17774).
References
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This article references 34 other publications.
- 1Horai, H. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 2010, 45, 703– 714, DOI: 10.1002/jms.1777Google Scholar1https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXovVCgsLg%253D&md5=229b850934e957b3ed841d191ad5bebfMassBank: a public repository for sharing mass spectral data for life sciencesHorai, Hisayuki; Arita, Masanori; Kanaya, Shigehiko; Nihei, Yoshito; Ikeda, Tasuku; Suwa, Kazuhiro; Ojima, Yuya; Tanaka, Kenichi; Tanaka, Satoshi; Aoshima, Ken; Oda, Yoshiya; Kakazu, Yuji; Kusano, Miyako; Tohge, Takayuki; Matsuda, Fumio; Sawada, Yuji; Hirai, Masami Yokota; Nakanishi, Hiroki; Ikeda, Kazutaka; Akimoto, Naoshige; Maoka, Takashi; Takahashi, Hiroki; Ara, Takeshi; Sakurai, Nozomu; Suzuki, Hideyuki; Shibata, Daisuke; Neumann, Steffen; Iida, Takashi; Tanaka, Ken; Funatsu, Kimito; Matsuura, Fumito; Soga, Tomoyoshi; Taguchi, Ryo; Saito, Kazuki; Nishioka, TakaakiJournal of Mass Spectrometry (2010), 45 (7), 703-714CODEN: JMSPFJ; ISSN:1076-5174. (John Wiley & Sons Ltd.)MassBank is the first public repository of mass spectra of small chem. compds. for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry(EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MSn data of 2337 authentic compds. of metabolites, 11 545 EI-MS and 834 other-MS data of 10 286 volatile natural and synthetic compds., and 3045 ESI-MS2 data of 679 synthetic drugs contributed by 16 research groups (Jan. 2010). ESI-MS2 data were analyzed under nonstandardized, independent exptl. conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more exptl. conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calcd. by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS2 data. MassBank also provides a merged spectrum for each compd. prepd. by merging the analyzed ESI-MS2 data on an identical compd. under different collision-induced dissocn. conditions. Data merging has significantly improved the precision of the identification of a chem. compd. by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chem. compds. and the publication of exptl. data.
- 2Wang, M. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 2016, 34, 828– 837, DOI: 10.1038/nbt.3597Google Scholar2https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtlaitLnE&md5=e6ca23ede2d85dd1460a5d73da542444Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular NetworkingWang, Mingxun; Carver, Jeremy J.; Phelan, Vanessa V.; Sanchez, Laura M.; Garg, Neha; Peng, Yao; Nguyen, Don Duy; Watrous, Jeramie; Kapono, Clifford A.; Luzzatto-Knaan, Tal; Porto, Carla; Bouslimani, Amina; Melnik, Alexey V.; Meehan, Michael J.; Liu, Wei-Ting; Crusemann, Max; Boudreau, Paul D.; Esquenazi, Eduardo; Sandoval-Calderon, Mario; Kersten, Roland D.; Pace, Laura A.; Quinn, Robert A.; Duncan, Katherine R.; Hsu, Cheng-Chih; Floros, Dimitrios J.; Gavilan, Ronnie G.; Kleigrewe, Karin; Northen, Trent; Dutton, Rachel J.; Parrot, Delphine; Carlson, Erin E.; Aigle, Bertrand; Michelsen, Charlotte F.; Jelsbak, Lars; Sohlenkamp, Christian; Pevzner, Pavel; Edlund, Anna; McLean, Jeffrey; Piel, Jorn; Murphy, Brian T.; Gerwick, Lena; Liaw, Chih-Chuang; Yang, Yu-Liang; Humpf, Hans-Ulrich; Maansson, Maria; Keyzers, Robert A.; Sims, Amy C.; Johnson, Andrew R.; Sidebottom, Ashley M.; Sedio, Brian E.; Klitgaard, Andreas; Larson, Charles B.; Boya P, Cristopher A.; Torres-Mendoza, Daniel; Gonzalez, David J.; Silva, Denise B.; Marques, Lucas M.; Demarque, Daniel P.; Pociute, Egle; O'Neill, Ellis C.; Briand, Enora; Helfrich, Eric J. N.; Granatosky, Eve A.; Glukhov, Evgenia; Ryffel, Florian; Houson, Hailey; Mohimani, Hosein; Kharbush, Jenan J.; Zeng, Yi; Vorholt, Julia A.; Kurita, Kenji L.; Charusanti, Pep; McPhail, Kerry L.; Nielsen, Kristian Fog; Vuong, Lisa; Elfeki, Maryam; Traxler, Matthew F.; Engene, Niclas; Koyama, Nobuhiro; Vining, Oliver B.; Baric, Ralph; Silva, Ricardo R.; Mascuch, Samantha J.; Tomasi, Sophie; Jenkins, Stefan; Macherla, Venkat; Hoffman, Thomas; Agarwal, Vinayak; Williams, Philip G.; Dai, Jingqui; Neupane, Ram; Gurr, Joshua; Rodriguez, Andres M. C.; Lamsa, Anne; Zhang, Chen; Dorrestein, Kathleen; Duggan, Brendan M.; Almaliti, Jehad; Allard, Pierre-Marie; Phapale, Prasad; Nothias, Louis-Felix; Alexandrov, Theodore; Litaudon, Marc; Wolfender, Jean-Luc; Kyle, Jennifer E.; Metz, Thomas O.; Peryea, Tyler; Nguyen, Dac-Trung; Van Leer, Danielle; Shinn, Paul; Jadhav, Ajit; Muller, Rolf; Waters, Katrina M.; Shi, Wenyuan; Liu, Xueting; Zhang, Lixin; Knight, Rob; Jensen, Paul R.; Palsson, Bernhard O.; Pogliano, Kit; Linington, Roger G.; Gutierrez, Marcelino; Lopes, Norberto P.; Gerwick, William H.; Moore, Bradley S.; Dorrestein, Pieter C.; Bandeira, NunoNature Biotechnology (2016), 34 (8), 828-837CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)The potential of the diverse chemistries present in natural products (NP) for biotechnol. and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Mol. Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide ref. MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanal. of deposited data.
- 3Dührkop, K.; Shen, H.; Meusel, M.; Rousu, J.; Böcker, S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 12580– 12585, DOI: 10.1073/pnas.1509788112Google Scholar3https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhsFamsLzF&md5=8ccf25648836ca6271ebe8516b4c257cSearching molecular structure databases with tandem mass spectra using CSI:FingerIDDuehrkop, Kai; Shen, Huibin; Meusel, Marvin; Rousu, Juho; Boecker, SebastianProceedings of the National Academy of Sciences of the United States of America (2015), 112 (41), 12580-12585CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics expts. usually rely on tandem MS to identify the thousands of compds. in a biol. sample. Today, the vast majority of metabolites remain unknown. The authors present a method for searching mol. structure databases using tandem MS data of small mols. The authors' method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown mol. The authors use the fragmentation tree to predict the mol. structure fingerprint of the unknown compd. using machine learning. This fingerprint is then used to search a mol. structure database such as PubChem. The authors' method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.
- 4Tsugawa, H.; Nakabayashi, R.; Mori, T.; Yamada, Y.; Takahashi, M.; Rai, A.; Sugiyama, R.; Yamamoto, H.; Nakaya, T.; Yamazaki, M.; Kooke, R.; Bac-Molenaar, J. A.; Oztolan-Erol, N.; Keurentjes, J. J. B.; Arita, M.; Saito, K. A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms. Nat. Methods 2019, 16, 295– 298, DOI: 10.1038/s41592-019-0358-2Google Scholar4https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosFKjtrc%253D&md5=e919e14137cbbac67a5acd2d9a632d51A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organismsTsugawa, Hiroshi; Nakabayashi, Ryo; Mori, Tetsuya; Yamada, Yutaka; Takahashi, Mikiko; Rai, Amit; Sugiyama, Ryosuke; Yamamoto, Hiroyuki; Nakaya, Taiki; Yamazaki, Mami; Kooke, Rik; Bac-Molenaar, Johanna A.; Oztolan-Erol, Nihal; Keurentjes, Joost J. B.; Arita, Masanori; Saito, KazukiNature Methods (2019), 16 (4), 295-298CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)We report a computational approach (implemented in MS-DIAL 3.0; http://prime.psc.riken.jp/) for metabolite structure characterization using fully 13C-labeled and non-labeled plants and LC-MS/MS. Our approach facilitates carbon no. detn. and metabolite classification for unknown mols. Applying our method to 31 tissues from 12 plant species, we assigned 1,092 structures and 344 formulas to 3,604 carbon-detd. metabolite ions, 69 of which were found to represent structures currently not listed in metabolome databases.
- 5Nikolskiy, I.; Mahieu, N. G.; Chen, Y. J.; Tautenhahn, R.; Patti, G. J. An untargeted metabolomic workflow to improve structural characterization of metabolites. Anal. Chem. 2013, 85, 7713– 7719, DOI: 10.1021/ac400751jGoogle Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtVGqtr7E&md5=e724f08767fe8d4d370f33c41c72c86eAn Untargeted Metabolomic Workflow to Improve Structural Characterization of MetabolitesNikolskiy, Igor; Mahieu, Nathaniel G.; Chen, Ying-Jr; Tautenhahn, Ralf; Patti, Gary J.Analytical Chemistry (Washington, DC, United States) (2013), 85 (16), 7713-7719CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Mass spectrometry-based metabolomics relies on MS2 data for structural characterization of metabolites. To obtain the high-quality MS2 data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS2 data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS2 fragments, even narrow windows are not always selective enough, and they can complicate data anal. by removing isotopic patterns from MS2 spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS2 scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS2 fragment intensities as a function of retention time and precursor mass targeted for MS2 anal., we obtain deconvolved MS2 spectra that are consistent with pure stds. and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic exts. from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite stds. in combination with simulations based on raw MS2 data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our lab. website (http://pattilab.wustl.edu/decoms2.php).
- 6Lawson, T. N.; Weber, R. J. M.; Jones, M. R.; Chetwynd, A. J.; Rodríguez-Blanco, G.; Di Guida, R.; Viant, M. R.; Dunn, W. B. msPurity: Automated evaluation of precursor ion purity for mass spectrometry-based fragmentation in metabolomics. Anal. Chem. 2017, 89, 2432– 2439, DOI: 10.1021/acs.analchem.6b04358Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFGhtLo%253D&md5=63e4240cec8f7df306126be4d6ea6e54msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in MetabolomicsLawson, Thomas N.; Weber, Ralf J. M.; Jones, Martin R.; Chetwynd, Andrew J.; Rodriguez-Blanco, Giovanny; Di Guida, Riccardo; Viant, Mark R.; Dunn, Warwick B.Analytical Chemistry (Washington, DC, United States) (2017), 89 (4), 2432-2439CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Tandem mass spectrometry (MS/MS or MS2) is a widely used approach for structural annotation and identification of metabolites in complex biol. samples. The importance of assessing the contribution of the precursor ion within an isolation window for MS2 expts. has been previously detailed in proteomics, where precursor ion purity influences the quality and accuracy of matching to mass spectral libraries, but to date there has been little attention to this data-processing technique in metabolomics. Here the authors present msPurity, a vendor-independent R package for liq. chromatog. (LC) and direct infusion (DI) MS2 that calcs. a simple metric to describe the contribution of the selected precursor. The precursor purity metric is calcd. as "intensity of a selected precursor divided by the summed intensity of the isolation window". The metric is interpolated at the recorded point of MS2 acquisition using bordering full-scan spectra, isotopic peaks of the selected precursor can be removed and low abundance peaks that are believed to have limited contribution to the resulting MS2 spectra are removed. Addnl., the isolation efficiency of the mass spectrometer can be taken into account. The package was applied to Data Dependent Acquisition (DDA)-based MS2 metabolomics datasets derived from three metabolomics data repositories. For the ten LC-MS2 DDA datasets with > ± 1 Da isolation windows the median precursor purity score ranged from 0.60 to 0.96 (scale = 0 to +1). The R package was also used to assess precursor purity of theor. isolation windows from LC-MS datasets of differing sample types. The theor. isolation windows being the same width used for an anticipated DDA expt. (± 0.5 Da). The most complex sample had a median precursor purity score of 0.46 for the 64,498 XCMS detd. features, in comparison to the less spectrally complex sample that had a purity score of 0.66 for 5071 XCMS features. It has been previously reported in proteomics that a purity score of < 0.5 can produce unreliable spectra matching results, with this assumption for complex samples there will be a large no. of metabolites where traditional DDA approaches will struggle to provide reliable annotations or accurate matches to mass spectral libraries.
- 7Zhu, X.; Chen, Y.; Subramanian, R. Comparison of information-dependent acquisition, SWATH, and MS(All) techniques in metabolite identification study employing ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Anal. Chem. 2014, 86, 1202– 1209, DOI: 10.1021/ac403385yGoogle Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvFKhtr7P&md5=f9919a286119365d83dc011515277097Comparison of Information-Dependent Acquisition, SWATH, and MSAll Techniques in Metabolite Identification Study Employing Ultrahigh-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass SpectrometryZhu, Xiaochun; Chen, Yuping; Subramanian, RajuAnalytical Chemistry (Washington, DC, United States) (2014), 86 (2), 1202-1209CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Sensitive and selective liq. chromatog.-mass spectrometry (LC-MS) anal. is a powerful and essential tool for metabolite identification in drug discovery and development. An MS2 (or tandem, MS/MS) mass spectrum is acquired from the fragmentation of a precursor ion by multiple methods including information-dependent acquisition (IDA), SWATH (sequential window acquisition of all theor. fragment-ion spectra), and MSAll (also called MSE) techniques. The authors compared these three techniques in their capabilities to produce comprehensive MS2 data by assessing both metabolite MS2 acquisition hit rate and the quality of MS2 spectra. Rat liver microsomal incubations from eight test compds. were analyzed with four methods (IDA, MMDF (multiple mass defect filters)-IDA, SWATH, or MSAll) using an ultraHPLC-qudrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) platform. A combined total of 227 drug-related materials (DRM) were detected from all eight test article incubations, and among those, 5% and 4% of DRM were not triggered for MS2 acquisition with IDA and MMDF-IDA methods, resp. When the same samples were spiked to an equal vol. of blank rat urine (urine sample), the DRM without MS2 acquisition increased to 29% and 18%, correspondingly. In contrast, 100% of DRM in both matrixes were subjected to MS2 acquisition with either the SWATH or MSAll method. However, the quality of the acquired MS2 spectra decreased in the order of IDA, SWATH, and MSAll methods. An av. of 10, 9, and 6 out of 10 most abundant ions in MS2 spectra were the real product ions of DRM detected in microsomal samples from IDA, SWATH, and MSAll methods, resp. The corresponding nos. declined to 9, 6, and 3 in the urine samples. Overall, IDA-based methods acquired qual. better MS2 spectra but with a lower MS2 acquisition hit rate than the other two methods. SWATH outperformed the MSAll method given its better quality of MS2 spectra with an identical MS2 acquisition hit rate.
- 8Röst, H. L.; Rosenberger, G.; Navarro, P.; Gillet, L.; Miladinović, S. M.; Schubert, O. T.; Wolski, W.; Collins, B. C.; Malmström, J.; Malmström, L.; Aebersold, R. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 2014, 32, 219– 223, DOI: 10.1038/nbt.2841Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2cnjtlSjug%253D%253D&md5=26e7673812152653a61a9ea2d953367dOpenSWATH enables automated, targeted analysis of data-independent acquisition MS dataRost Hannes L; Rosenberger George; Navarro Pedro; Gillet Ludovic; Collins Ben C; Malmstrom Lars; Miladinovic Sasa M; Schubert Olga T; Wolski Witold; Malmstrom Johan; Aebersold RuediNature biotechnology (2014), 32 (3), 219-23 ISSN:.There is no expanded citation for this reference.
- 9Peckner, R.; Myers, S. A.; Jacome, A. S. V.; Egertson, J. D.; Abelin, J. G.; MacCoss, M. J.; Carr, S. A.; Jaffe, J. D. Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nat. Methods 2018, 15, 371– 378, DOI: 10.1038/nmeth.4643Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXmslKrur0%253D&md5=7768f1798c3424e91a7da4e717ea977bSpecter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomicsPeckner, Ryan; Myers, Samuel A.; Jacome, Alvaro Sebastian Vaca; Egertson, Jarrett D.; Abelin, Jennifer G.; MacCoss, Michael J.; Carr, Steven A.; Jaffe, Jacob D.Nature Methods (2018), 15 (5), 371-378CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biol. sample. However, the anal. challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixt. spectra directly through comparison to a spectral library, thus circumventing the problems assocd. with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA anal. methods.
- 10Li, H.; Cai, Y.; Guo, Y.; Chen, F.; Zhu, Z. J. MetDIA: Targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition. Anal. Chem. 2016, 88, 8757– 8764, DOI: 10.1021/acs.analchem.6b02122Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1CjsrrP&md5=f4e056f97a6189866883f54e58bbf382MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent AcquisitionLi, Hao; Cai, Yuping; Guo, Yuan; Chen, Fangfang; Zhu, Zheng-JiangAnalytical Chemistry (Washington, DC, United States) (2016), 88 (17), 8757-8764CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)With recent advances in mass spectrometry, there is an increased interest in data-independent acquisition (DIA) techniques for metabolomics. With DIA technique, all metabolite ions are sequentially selected and isolated using a wide window to generate multiplexed MS/MS spectra. Therefore, DIA strategy enables a continuous and unbiased acquisition of all metabolites and increases the data dimensionality, but presents a challenge to data anal. due to the loss of the direct link between precursor ion and fragment ions. However, very few DIA data processing methods are developed for metabolomics application. Here, the authors developed a new DIA data anal. approach, namely MetDIA, for targeted extn. of metabolites from multiplexed MS/MS spectra generated using DIA technique. MetDIA approach considers each metabolite in the spectral library as an anal. target. Ion chromatographs for each metabolite (both precursor ion and fragment ions) and MS2 spectra are readily detected, extd., and scored for metabolite identification, referred as metabolite-centric identification. A min. metabolite-centric identification score responsible for 1% false pos. rate of identification is detd. as 0.8 using fully 13C labeled biol. exts. Finally, the comparisons of the MetDIA method with data-dependent acquisition (DDA) method demonstrated that MetDIA could significantly detect more metabolites in biol. samples, and is more accurate and sensitive for metabolite identifications. The MetDIA program and the metabolite spectral library is freely available on the internet.
- 11Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 2015, 12, 523– 526, DOI: 10.1038/nmeth.3393Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXnslOns78%253D&md5=1466eda7f23af352e7342fba1b3009e2MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysisTsugawa, Hiroshi; Cajka, Tomas; Kind, Tobias; Ma, Yan; Higgins, Brendan; Ikeda, Kazutaka; Kanazawa, Mitsuhiro; Vander Gheynst, Jean; Fiehn, Oliver; Arita, MasanoriNature Methods (2015), 12 (6), 523-526CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)Data-independent acquisition (DIA) in liq. chromatog. (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of mol. data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small mols. by mass spectral deconvolution. For a reversed-phase LC-MS/MS anal. of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compds., highlighting the chemotaxonomic relationships between the algal strains.
- 12Yin, Y.; Wang, R.; Cai, Y.; Wang, Z.; Zhu, Z.-J. DecoMetDIA: Deconvolution of multiplexed MS/MS spectra for metabolite identification in SWATH-MS-based untargeted metabolomics. Anal. Chem. 2019, 91, 11897– 11904, DOI: 10.1021/acs.analchem.9b02655Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhs1egurvI&md5=db3bac501b985c48e63c4a9717400e43DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted MetabolomicsYin, Yandong; Wang, Ruohong; Cai, Yuping; Wang, Zhuozhong; Zhu, Zheng-JiangAnalytical Chemistry (Washington, DC, United States) (2019), 91 (18), 11897-11904CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technol. has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quant. accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatog. peaks of fragment ions in multiplexed spectra and decomp. them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biol. samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the exptl. MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biol. samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA.
- 13Brown, M.; Wedge, D. C.; Goodacre, R.; Kell, D. B.; Baker, P. N.; Kenny, L. C.; Mamas, M. A.; Neyses, L.; Dunn, W. B. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics 2011, 27, 1108– 1112, DOI: 10.1093/bioinformatics/btr079Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXksFKlu74%253D&md5=b34aea0350101a753358bd294f885fe1Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasetsBrown, Marie; Wedge, David C.; Goodacre, Royston; Kell, Douglas B.; Baker, Philip N.; Kenny, Louise C.; Mamas, Mamas A.; Neyses, Ludwig; Dunn, Warwick B.Bioinformatics (2011), 27 (8), 1108-1112CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)Motivation: The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chem. identifying metabolites from mass spectrometric signals present in complex datasets. Results: Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral mols. and assocd. mol. formula and matching of the mol. formulas to a ref. file of metabolites. The software is independent of the instrument and data pre-processing applied. The no. of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of exptl. data. Availability: The workflows, std. operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html. Contact: [email protected].
- 14Alonso, A.; Marsal, S.; Julià, A. Analytical methods in untargeted metabolomics: state of the art in 2015. Front. Bioeng. Biotechnol. 2015, 3, 23, DOI: 10.3389/fbioe.2015.00023Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2MnmvFGktw%253D%253D&md5=129c8c7cb2c56ded51cd56dd6ea8c114Analytical methods in untargeted metabolomics: state of the art in 2015Alonso Arnald; Marsal Sara; Julia AntonioFrontiers in bioengineering and biotechnology (2015), 3 (), 23 ISSN:2296-4185.Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
- 15Kuhl, C.; Tautenhahn, R.; Böttcher, C.; Larson, T. R.; Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 2012, 84, 283– 289, DOI: 10.1021/ac202450gGoogle Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsFansLvL&md5=46bc1d612000928d7a4a57fac84290c1CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data SetsKuhl, Carsten; Tautenhahn, Ralf; Boettcher, Christoph; Larson, Tony R.; Neumann, SteffenAnalytical Chemistry (Washington, DC, United States) (2012), 84 (1), 283-289CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Liq. chromatog. coupled to mass spectrometry is routinely used for metabolomics expts. In contrast to the fairly routine and automated data acquisition steps, subsequent compd. annotation and identification require extensive manual anal. and thus form a major bottleneck in data interpretation. Here the authors present CAMERA, a Bioconductor package integrating algorithms to ext. compd. spectra, annotate isotope and adduct peaks, and propose the accurate compd. mass even in highly complex data. To evaluate the algorithms, the authors compared the annotation of CAMERA against a manually defined annotation for a mixt. of known compds. spiked into a complex matrix at different concns. CAMERA successfully extd. accurate masses for 89.7% and 90.3% of the annotatable compds. in pos. and neg. ion modes, resp. Furthermore, the authors present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. The authors demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics expts., where the application of CAMERA drastically reduced the amt. of manual anal.
- 16Broeckling, C. D.; Heuberger, A. L.; Prince, J. A.; Ingelsson, E.; Prenni, J. E. Assigning precursor–product ion relationships in indis. criminant MS/MS data from non-targeted metabolite profiling studies. Metabolomics 2013, 9, 33– 43, DOI: 10.1007/s11306-012-0426-4Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtVymtbg%253D&md5=ba7b1051702c81493931bf981c5c1e3bAssigning precursor-product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studiesBroeckling, Corey D.; Heuberger, Adam L.; Prince, Jonathan A.; Ingelsson, E.; Prenni, Jessica E.Metabolomics (2013), 9 (1), 33-43CODEN: METAHQ; ISSN:1573-3882. (Springer)Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small mols. In non-targeted metabolite profiling studies, instrument duty cycle limitations and exptl. costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor-product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor-product relationships in idMS/MS data by appending a dataset-wide correlational anal. to existing tools. The utility of the approach was demonstrated using a dataset of std. compds. spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to std. MS/MS spectra of authentic stds., even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable mol. feature, which will streamline the identification process for non-targeted metabolite profiling studies.
- 17Broeckling, C. D.; Afsar, F. A.; Neumann, S.; Ben-Hur, A.; Prenni, J. E. RAMClust: A novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal. Chem. 2014, 86, 6812– 6817, DOI: 10.1021/ac501530dGoogle Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXps1Kmurk%253D&md5=81d8746ff6040dfa20ec017b7c744e18RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics DataBroeckling, C. D.; Afsar, F. A.; Neumann, S.; Ben-Hur, A.; Prenni, J. E.Analytical Chemistry (Washington, DC, United States) (2014), 86 (14), 6812-6817CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Metabolomic data are frequently acquired using chromatog. coupled mass spectrometry (MS) platforms. For such datasets, the first step in data anal. relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compd., a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We addnl. address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature-feature relationships are detd. simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single expt., reduces quant. anal. variation compared to single-feature measures, and decreases false pos. annotations of unpredictable phenomenon as novel compds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatog.-spectrometric platform or feature-finding software.
- 18Naz, S.; Gallart-Ayala, H.; Reinke, S. N.; Mathon, C.; Blankley, R.; Chaleckis, R.; Wheelock, C. E. Development of a liquid chromatography-high resolution mass spectrometry metabolomics method with high specificity for metabolite identification using all ion fragmentation acquisition. Anal. Chem. 2017, 89, 7933– 7942, DOI: 10.1021/acs.analchem.7b00925Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVClsrbE&md5=13c57859bbc8f8564794e39c871cd4d4Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation AcquisitionNaz, Shama; Gallart-Ayala, Hector; Reinke, Stacey N.; Mathon, Caroline; Blankley, Richard; Chaleckis, Romanas; Wheelock, Craig E.Analytical Chemistry (Washington, DC, United States) (2017), 89 (15), 7933-7942CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)High-resoln. mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biol. context. In the current study, a liq. chromatog. (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (1) accurate mass (AM), (2) retention time (RT), (3) MS/MS spectrum, and (4) product/precursor ion intensity ratios. The authors constructed an inhouse mass spectral library of 408 metabolites contg. AMRT and MS/MS spectra information at four collision energies. The percent relative std. deviations between ion ratios of a metabolite in an anal. std. vs. sample matrix were used as an addnl. metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, resp. The authors also included a scheme to distinguish coeluting isobaric compds. by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. The authors' data acquisition strategy enables a simultaneous mixt. of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biol. information acquired in a metabolomics expt.
- 19Chaleckis, R.; Naz, S.; Meister, I.; Wheelock, C. E. LC-MS-based metabolomics of biofluids using All-Ion Fragmentation (AIF) acquisition. Methods Mol. Biol. 2018, 1730, 45– 58, DOI: 10.1007/978-1-4939-7592-1_3Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitVWhsbvF&md5=62e2e0751bdc20baeac249eaac41454cLC-MS-based metabolomics of biofluids using all-ion fragmentation (AIF) acquisitionChaleckis, Romanas; Naz, Shama; Meister, Isabel; Wheelock, Craig E.Methods in Molecular Biology (New York, NY, United States) (2018), 1730 (Clinical Metabolomics), 45-58CODEN: MMBIED; ISSN:1940-6029. (Springer)The field of liq. chromatog.-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compds. in biol. samples. However, compd. identification remains a major challenge, which is crucial in interpreting the biol. function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an inhouse spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative std. deviation between ion ratios of a metabolite in a biofluid vs. its anal. std. is used as an addnl. metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compds. Our method enables database-dependent targeted as well as nontargeted metabolomics anal. from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biol. information.
- 20Lazarinis, N.; Bood, J.; Gomez, C.; Kolmert, J.; Lantz, A. S.; Gyllfors, P.; Davis, A.; Wheelock, C. E.; Dahlén, S. E.; Dahlén, B. Leukotriene E4 induces airflow obstruction and mast cell activation through the cysteinyl leukotriene type 1 receptor. J. Allergy Clin. Immunol. 2018, 142, 1080– 1089, DOI: 10.1016/j.jaci.2018.02.024Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtVCksrfK&md5=686cb1d2702fee17b1f07e3230147a63Leukotriene E4 induces airflow obstruction and mast cell activation through the cysteinyl leukotriene type 1 receptorLazarinis, Nikolaos; Bood, Johan; Gomez, Cristina; Kolmert, Johan; Lantz, Ann-Sofie; Gyllfors, Paer; Davis, Andy; Wheelock, Craig E.; Dahlen, Sven-Erik; Dahlen, BarbroJournal of Allergy and Clinical Immunology (2018), 142 (4), 1080-1089CODEN: JACIBY; ISSN:0091-6749. (Elsevier)Leukotriene (LT) E4 is the final active metabolite among the cysteinyl leukotrienes (CysLTs). Animal studies have identified a distinct LTE4 receptor, suggesting that current cysteinyl leukotriene type 1 (CysLT1) receptor antagonists can provide incomplete inhibition of CysLT responses. We tested this hypothesis by assessing the influence of the CysLT1 antagonist montelukast on responses induced by means of inhalation of LTE4 in asthmatic patients. Fourteen patients with mild intermittent asthma and 2 patients with aspirin-exacerbated respiratory disease received 20 mg of montelukast twice daily and placebo for 5 to 7 days in a randomized, double-blind, crossover study (NCT01841164). The PD20 value was detd. at the end of each treatment period based on an increasing dose challenge. Measurements included lipid mediators in urine and sputum cells 4 h after LTE4 challenge. Montelukast completely blocked LTE4-induced bronchoconstriction. Despite tolerating an at least 10 times higher dose of LTE4 after montelukast, there was no difference in the percentage of eosinophils in sputum. Urinary excretion of all major lipid mediators increased after LTE4 inhalation. Montelukast blocked release of the mast cell product prostaglandin (PG) D2, as well as release of PGF2α and thromboxane (Tx) A2, but not increased excretion of PGE2 and its metabolites or isoprostanes. LTE4 induces airflow obstruction and mast cell activation through the CysLT1 receptor.
- 21Haug, K.; Salek, R. M.; Conesa, P.; Hastings, J.; de Matos, P.; Rijnbeek, M.; Mahendraker, T.; Williams, M.; Neumann, S.; Rocca-Serra, P.; Maguire, E.; González-Beltrán, A.; Sansone, S. A.; Griffin, J. L.; Steinbeck, C. MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 2013, 41, D781– 786, DOI: 10.1093/nar/gks1004Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvV2ktrnO&md5=b4fd7aa651ff814b038cb87db58bb10fMetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-dataHaug, Kenneth; Salek, Reza M.; Conesa, Pablo; Hastings, Janna; de Matos, Paula; Rijnbeek, Mark; Mahendraker, Tejasvi; Williams, Mark; Neumann, Steffen; Rocca-Serra, Philippe; Maguire, Eamonn; Gonzalez-Beltran, Alejandra; Sansone, Susanna-Assunta; Griffin, Julian L.; Steinbeck, ChristophNucleic Acids Research (2013), 41 (D1), D781-D786CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw exptl. data and assocd. metadata, maintained by one of the major open-access data providers in mol. biol. Metabolomic profiling is an important tool for research into biol. functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of exptl. methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their ref. spectra as well as their biol. roles, locations, concns. and raw data from metabolic expts. Studies automatically receive a stable unique accession no. that can be used as a publication ref. (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
- 22Tada, I.; Tsugawa, H.; Meister, I.; Zhang, P.; Shu, R.; Katsumi, R.; Wheelock, C. E.; Arita, M.; Chaleckis, R. Creating a reliable mass spectral–retention time library for all ion fragmentation-based metabolomics. Metabolites 2019, 9, 251, DOI: 10.3390/metabo9110251Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVSiu7fF&md5=98500e02cad3b843658033583284ab76Creating a reliable mass spectral-retention time library for all ion fragmentation-based metabolomicsTada, Ipputa; Tsugawa, Hiroshi; Meister, Isabel; Zhang, Pei; Shu, Rie; Katsumi, Riho; Wheelock, Craig E.; Arita, Masanori; Chaleckis, RomanasMetabolites (2019), 9 (11), 251CODEN: METALU; ISSN:2218-1989. (MDPI AG)Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chem. spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liq. chromatog. coupled to mass spectrometry (LC-MS) methods across labs. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across labs. This library construction strategy improves the confidence in annotation for AIF data in LC-MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.
- 23Moorthy, A. S.; Wallace, W. E.; Kearsley, A. J.; Tchekhovskoi, D. V.; Stein, S. E. Combining fragment-ion and neutral-loss matching during mass spectral library searching: A new general purpose algorithm applicable to illicit drug identification. Anal. Chem. 2017, 89, 13261– 13268, DOI: 10.1021/acs.analchem.7b03320Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvVars7bN&md5=180c05ff3b01a5a255dcac8e136774ffCombining Fragment-Ion and Neutral-Loss Matching during Mass Spectral Library Searching: A New General Purpose Algorithm Applicable to Illicit Drug IdentificationMoorthy, Arun S.; Wallace, William E.; Kearsley, Anthony J.; Tchekhovskoi, Dmitrii V.; Stein, Stephen E.Analytical Chemistry (Washington, DC, United States) (2017), 89 (24), 13261-13268CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A mass spectral library search algorithm that identifies compds. that differ from library compds. by a single 'inert' structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compds., to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum. When the spectra being compared differ by a single structural feature, these matching neutral loss peaks should contain that structural feature. This method extends the scope of the library to include spectra of 'nearest-neighbor' compds. that differ from library compds. by a single chem. moiety. Addnl., detn. of the structural origin of the shifted peaks can aid in the detn. of the chem. structure and fragmentation mechanism of the query compd. A variety of examples are presented, including the identification of designer drugs and chem. derivs. not present in the library.
- 24Domingo-Almenara, X.; Montenegro-Burke, J. R.; Benton, H. P.; Siuzdak, G. Annotation: A computational solution for streamlining metabolomics analysis. Anal. Chem. 2018, 90, 480– 489, DOI: 10.1021/acs.analchem.7b03929Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1KnsL3L&md5=6491b60f6ad7834b6e773de66adc0e05Annotation: A Computational Solution for Streamlining Metabolomics AnalysisDomingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Benton, H. Paul; Siuzdak, GaryAnalytical Chemistry (Washington, DC, United States) (2018), 90 (1), 480-489CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. The aim of any untargeted metabolomics expt. is to identify and quantify dysregulated compds. relevant to a particular disease or stressor. However, metabolite identification is still considered an imposing bottleneck in untargeted metabolomics. Untargeted data anal. workflows for liq. chromatog./electrospray ionization/mass spectrometry (LC/ESI/MS) usually consist in applying peak-picking algorithms, align those peaks across multiple samples to obtain the so-called peak features (defined as a peak, or a set of aligned peaks across samples with a unique m/z and a specific retention time), and subsequently discover statistically significant variations between exptl. groups or conditions. Once features of interest are prioritized, those can be annotated by searching their mass values against metabolite libraries. Next, those features can be putatively identified via fragmentation expts. (tandem MS or MS/MS), usually with Quadrupole-Time-of-Flight (Q-ToF) instrumentation, by comparing exptl. fragmentation patterns with spectral libraries. Ultimately, unambiguous identification, according to the Metabolomics Stds. Initiative (MSI) guidelines, can only be achieved by comparing the exptl. tandem MS spectra with std. materials analyzed under identical conditions.
- 25Domingo-Almenara, X.; Montenegro-Burke, J. R.; Guijas, C.; Majumder, E. L.; Benton, H. P.; Siuzdak, G. Autonomous METLIN-guided in-source fragment annotation for untargeted metabolomics. Anal. Chem. 2019, 91, 3246– 3253, DOI: 10.1021/acs.analchem.8b03126Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitVCns7k%253D&md5=944118feb8d3cd4b292643cf413ee435Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted MetabolomicsDomingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Guijas, Carlos; Majumder, Erica L.-W.; Benton, H. Paul; Siuzdak, GaryAnalytical Chemistry (Washington, DC, United States) (2019), 91 (5), 3246-3253CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Computational metabolite annotation in untargeted profiling aims at uncovering neutral mol. masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to det. metabolite neutral masses. However, a significant fraction of features usually detected in untargeted expts. remains unannotated, which limits the ability to det. neutral mol. masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liq. chromatog.-electrospray ionization-mass spectrometry. In this study, the authors introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. The algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation anal. of a total of 140 metabolites across three different sets of biol. samples analyzed with liq. chromatog.-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral mol. masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the no. of in-source fragments matched and the relative intensity similarity between the exptl. data and the ref. low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu.
- 26Sumner, L. W. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 2007, 3, 211– 221, DOI: 10.1007/s11306-007-0082-2Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtFOntbvL&md5=e636dad276f9f1b2e756db169368eb53Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.Metabolomics (2007), 3 (3), 211-221CODEN: METAHQ; ISSN:1573-3882. (Springer)There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of std. metadata provides a biol. and empirical context for the data, facilitates exptl. replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Stds. Initiative is building a general consensus concerning the min. reporting stds. for metabolomics expts. of which the Chem. Anal. Working Group (CAWG) is a member of this community effort. This article proposes the min. reporting stds. related to the chem. anal. aspects of metabolomics expts. including: sample prepn., exptl. anal., quality control, metabolite identification, and data pre-processing. These min. stds. currently focus mostly upon mass spectrometry and NMR spectroscopy due to the popularity of these techniques in metabolomics. However, addnl. input concerning other techniques is welcomed and can be provided via the CAWG online discussion forum at http://msi-workgroups.sourceforge.net/ or http://[email protected]. Further, community input related to this document can also be provided via this electronic forum.
- 27Chaleckis, R.; Meister, I.; Zhang, P.; Wheelock, C. E. Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics. Curr. Opin. Biotechnol. 2019, 55, 44– 50, DOI: 10.1016/j.copbio.2018.07.010Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhsVGrtr%252FI&md5=bbb3b5341edd5f92d133537659a1b6c8Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomicsChaleckis, Romanas; Meister, Isabel; Zhang, Pei; Wheelock, Craig E.Current Opinion in Biotechnology (2019), 55 (), 44-50CODEN: CUOBE3; ISSN:0958-1669. (Elsevier B.V.)A review. Accurate annotation is vital for data interpretation; however, metabolite identification is a major bottleneck in untargeted metabolomics. Although community guidelines for metabolite identification were published over a decade ago, adaptation of the recommended stds. has been limited. The complexity of LC-MS data due to combinations of various chromatog. and mass spectrometric acquisition methods has resulted in the advent of diverse workflows, which often involve non-standardized manual curation. Herein, we review the parameters involved in metabolite reporting and provide a workflow to est. the level of confidence in reported metabolite annotation. The future of metabolite identification will be heavily based upon the use of metabolome data repositories and assocd. data anal. tools, which will enable data to be shared, re-analyzed and re-annotated in an automated fashion.
- 28Tsuruta, Y.; Tomida, H.; Kohashi, K.; Ohkura, Y. Simultaneous determination of imidazoleacetic acid and N tau- and N pi-methylimidazoleacetic acids in human urine by high-performance liquid chromatography with fluorescence detection. J. Chromatogr., Biomed. Appl. 1987, 416, 63– 69, DOI: 10.1016/0378-4347(87)80485-1Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2sXktVSqsLY%253D&md5=904a7b3289d0b25d42e2c3a2fb9987a4Simultaneous determination of imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acids in human urine by high-performance liquid chromatography with fluorescence detectionTsuruta, Yasuto; Tomida, Hisao; Kohashi, Kazuya; Ohkura, YosukeJournal of Chromatography, Biomedical Applications (1987), 416 (1), 63-9CODEN: JCBADL; ISSN:0378-4347.A sensitive method for the simultaneous detn. of urinary imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acid which employs HPLC with fluorescence detection is described. The compds. are converted into the corresponding fluorescent esters by reaction with 4-bromomethyl-7-methoxycoumarin. These derivs. are sepd. by liq. chromatog. on a Radial-Pak silica column. The detection limits for imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acid in urine are 15, 10, and 20 pmol/mL, resp. The 24-h urinary excretions of imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acids by healthy persons are 5.7-39.9, 4.3-24.6, and 1.5-19.3 nmol/mg creatinine, resp. The simultaneous detns. of these compds. should be useful in investigations of the turnover of histamine.
- 29Pohjanpelto, P.; Niemi, K.; Sarmela, T. Anterior chamber haemorrhage in the newborn after spontaneous delivery. A case report. Acta Ophthalmol. 1979, 57, 443– 446, DOI: 10.1111/j.1755-3768.1979.tb01827.xGoogle Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaE1M3lt1Squg%253D%253D&md5=6e626908c52d4fe09d3edc8670c31036Anterior chamber haemorrhage in the newborn after spontaneous delivery. A case reportPohjanpelto P; Niemi K; Sarmela TActa ophthalmologica (1979), 57 (3), 443-6 ISSN:0001-639X.A case report is presented of anterior chamber haemorrhage occurring in one eye in a newborn after spontaneous delivery. At the age of two weeks the anterior chamber was clear but the vitreous cloudy. At the age of five weeks the vitreous had also cleared. The infant's later development was normal and there were no disorders in the function of the eye.
- 30Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A. C.; Wilson, M. R.; Knox, C.; Bjorndahl, T. C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z. T.; Poelzer, J.; Huynh, J.; Yallou, F. S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D. S. The human urine metabolome. PLoS One 2013, 8, e73076 DOI: 10.1371/journal.pone.0073076Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVCrsrnJ&md5=0467920d8bf44214bb2b557648612c36The human urine metabolomeBouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick; Dong, Edison; Bogumil, Ralf; Roehring, Cornelia; Wishart, David S.PLoS One (2013), 8 (9), e73076CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)A review. Urine has long been a "favored" biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large vols., largely free from interfering proteins or lipids and chem. complex. However, this chem. complexity has also made urine a particularly difficult substrate to fully understand. As a biol. waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial byproducts. Many of these compds. are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quant., metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quant. exptl. assessment/validation. The exptl. portion employed NMR spectroscopy, gas chromatog. mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liq. chromatog. (HPLC) expts. performed on multiple human urine samples. This multi-platform metabolomic anal. allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different anal. platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compds. and was used to help guide the subsequent exptl. studies. An online database contg. the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concns., related literature refs. and links to their known disease assocns. are freely available at online.
- 31Li, C.; Homma, M.; Oka, K. Characteristics of delayed excretion of flavonoids in human urine after administration of Shosaiko-to, a herbal medicine. Biol. Pharm. Bull. 1998, 21, 1251– 1257, DOI: 10.1248/bpb.21.1251Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXivFGh&md5=07ddff93fbf7cccf77ce2ac965815d04Characteristics of delayed excretion of flavonoids in human urine after administration of Shosaiko-to, a herbal medicineLi, Chuan; Homma, Masato; Oka, KitaroBiological & Pharmaceutical Bulletin (1998), 21 (12), 1251-1257CODEN: BPBLEO; ISSN:0918-6158. (Pharmaceutical Society of Japan)There has been little explanation of herbal medicines by modern medical sciences, including pharmacokinetics, whereas physicians follow empirical indications written in classical literature. Recent reports of herb-induced adverse reactions compelled the authors to proceed the investigation of a herbal medicine Shosaiko-to (TJ-9) from a pharmacokinetic point of view. To five healthy volunteers, a single 5 g dose of TJ-9, consisting of 7 herbs, was administered. The authors conducted HPLC anal. of the timed-urine specimens to disclose the type and amt. of compds. excreted. Excretion rate-time curves were analyzed individually. Four flavonoids, liquiritigenin, baicalein, wogonin and oroxylin A, were found both in the urine and TJ-9. The glycosides in TJ-9 were absorbed after microflora hydrolysis. Davidigenin, which was not found in TJ-9, was an intestinal metabolite of liquiritigenin. Also, two flavanones, S-dihydrowogonin and S-dihydrooroxylin A, were identified as the metabolites of wogonin and oroxylin A, resp. Excretion rate-time curves of the flavonoids were divided into three types of structure-dependent absorption, i.e. (1) the fast absorption of herbal-origin aglycons, (2) the moderately-delayed absorption of aglycons derived from herbal glycosides, and (3) markedly-delayed absorption after the mol. transformation of herbal compds. Individual excretion profiles seemed to depend on microflora activities. Two types of flavanones, S-dihydrowogonin and S-dihydrooroxylin A, were found in a half of the volunteers, suggesting there might be two kinds of volunteers, namely, rapid and poor metabolizers of flavonoids.
- 32Hornik, P.; Vyskocilová, P.; Friedecký, D.; Adam, T. Diagnosing AICA-ribosiduria by capillary electrophoresis. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2006, 843, 15– 19, DOI: 10.1016/j.jchromb.2006.05.020Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtVyktrzP&md5=c67ce29daa0b3c15f90e12e47d17b33fDiagnosing AICA-ribosiduria by capillary electrophoresisHornik, Petr; Vyskocilova, Petra; Friedecky, David; Adam, TomasJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences (2006), 843 (1), 15-19CODEN: JCBAAI; ISSN:1570-0232. (Elsevier B.V.)AICA-ribosiduria is a recently discovered inherited metabolic disease caused by a defect in final steps of purine de novo biosynthesis-5-amino-4-imidazolecarboxamide ribodide (AICAR)-transformylase/inosinemonophosphate (IMP)-cyclohydrolase (ATIC). A rapid and selective capillary electrophoretic method for screening of patients with AICA-ribosiduria is described. The method is based on direct UV detection of 5-amino-4-imidazolecarboxamide (AICA) and 5-amino-4-imidazolecarboxamide riboside (AICAr) in untreated urine. Background electrolyte consists of 100 mM malonic acid adjusted with γ-aminobutyric acid (pH 2.7). Under the given sepn. conditions both compds. of interest are well sepd. from other substances with sepn. efficiency of 1 020 000 and 130 000 theor. plates/m for AICA and AICAr, resp. Total anal. time is 3 min with the limits of detection of 3.6 μM and 4.5 μM for AICA and AICAr, resp. The usefulness of the presented method for screening of patients with ATIC deficiency is demonstrated on samples of Chinese hamster ovary cell line defective in ATIC activity, spiked urine samples and urine samples from patients treated with high-dose MTX which do not excrete increased amts. of AICA and AICAr compared to untreated controls (p < 0.05). The described method is fast and effective enough for diagnostic applications.
- 33Bales, J. R.; Sadler, P. J.; Nicholson, J. K.; Timbrell, J. A. Urinary excretion of acetaminophen and its metabolites as studied by proton NMR spectroscopy. Clin. Chem. 1984, 30, 1631– 1636, DOI: 10.1093/clinchem/30.10.1631Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2cXmt1ajtrw%253D&md5=66288b251a1c8ac9261aafd3eb229898Urinary excretion of acetaminophen and its metabolites as studied by proton NMR spectroscopyBales, John R.; Sadler, Peter J.; Nicholson, Jeremy K.; Timbrell, John A.Clinical Chemistry (Washington, DC, United States) (1984), 30 (10), 1631-6CODEN: CLCHAU; ISSN:0009-9147.Acetaminophen [103-90-2] and its glucuronide [16110-10-4], sulfate [10066-90-7], N-acetyl-L-cysteinyl [52372-86-8], and L-cysteinyl [53446-10-9] metabolites can be rapidly detected by 1H NMR spectroscopy of intact, untreated human urine. Study of the time course of excretion of these metabolites in 5 clin. normal men after ingestion of the usual 1-g therapeutic dose of the drug showed that the mean 24-h excretion of the drug and these metabolites as detd. by NMR was 77.3% of the dose. Resp. relative proportions of the above metabolites were 49.9%, 37.6%, 3.0%, and 9.5% (L-cysteinyl plus free drug). Excretion of some other metabolites in urine, including creatinine [60-27-5], citrate [77-92-9], hippurate [495-69-2], and sarcosine [107-97-1] was measured concurrently. Excretion of creatinine and sarcosine was closely correlated.
- 34Tsugawa, H., Ikeda, K., Takahashi, M., Satoh, A., Mori, Y., Uchino, H., Okahashi, N., Yamada, Y., Tada, I., Bonini, P., Higashi, Y., Okazaki, Y., Zhou, Z., Zhu, Z. J., Koelmel, J., Cajka, T., Fiehn, O., Saito, K., Arita, M., Arita, M. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 2020, DOI: 10.1038/s41587-020-0531-2 .Google ScholarThere is no corresponding record for this reference.
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Abstract
Figure 1
Figure 1. Flowchart of the CorrDec method for a target feature Ft1. A. For each feature, the Pearson correlations are calculated for all pairs of precursor (MS1 vector) and product ions (MS2 matrix). B. All correlation values of all features are merged into a single matrix. C. Product ions satisfying the three criteria (see the main text for details) are selected to produce the deconvoluted MS2 spectrum of Ft1.
Figure 2
Figure 2. Demonstration of the CorrDec method using tyrosine dilution series spiked into diluted urine as background matrix. A. Raw MS2 spectra of tyrosine [M + H]+ (m/z: 182.082) at the lowest (69 nM) and the highest (4 μM) spiked concentrations in dilution series. Raw MS2 spectra contain over one hundred peaks masking the ions derived from tyrosine, especially at low spiked-in concentrations. B. Linked scatter plots visualizing the intensity correlations between the MS1 m/z 182.082 and MS2 peaks in 11 dilution series samples. Only 12 out of 193 (10 eV) and 13 out of 280 peaks (30 eV) correlated >0.9 (highlighted lines). C. Deconvoluted MS2 spectra (above, in black) matched well with the library reference spectra (below, in red). The MS2 similarities of deconvoluted spectra were 90.5% (10 eV) and 86.5% (30 eV), while the MS2 similarities of raw spectra at 0, 10, and 30 eV were less than 30% in the all samples.
Figure 3
Figure 3. CorrDec MS2 spectra provide increased confidence in compound identification than those obtained by MS2Dec in the urinary metabolomics DIA data set. A. Number of compounds in each identification category identified using MS2Dec and CorrDec. B. Distribution of the MS2 similarity scores for the MSI level-1 compounds spectra deconvoluted by the CorrDec and MS2Dec. C. MS2 similarity scores from CorrDec were higher than MS2Dec, especially for low-intensity peaks.
Figure 4
Figure 4. CorrDec can successfully deconvolute the MS2 spectra of completely coeluting compounds, glutamine and N-acetylcarnosine. A. The raw MS2 spectrum and extracted ion chromatograms in MS1 (0 eV) of completely coeluting glutamine and N-acetylcarnosine as well as B. their fragments in MS2 (10 eV) from the urine data (QC1 sample in batch 1). C. MS2 spectra of glutamine and N-acetylcarnosine deconvoluted by the MS2Dec. D. MS2 spectra of glutamine and N-acetylcarnosine deconvoluted by the CorrDec.
Figure 5
References
ARTICLE SECTIONSThis article references 34 other publications.
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- 4Tsugawa, H.; Nakabayashi, R.; Mori, T.; Yamada, Y.; Takahashi, M.; Rai, A.; Sugiyama, R.; Yamamoto, H.; Nakaya, T.; Yamazaki, M.; Kooke, R.; Bac-Molenaar, J. A.; Oztolan-Erol, N.; Keurentjes, J. J. B.; Arita, M.; Saito, K. A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organisms. Nat. Methods 2019, 16, 295– 298, DOI: 10.1038/s41592-019-0358-2Google Scholar4https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXosFKjtrc%253D&md5=e919e14137cbbac67a5acd2d9a632d51A cheminformatics approach to characterize metabolomes in stable-isotope-labeled organismsTsugawa, Hiroshi; Nakabayashi, Ryo; Mori, Tetsuya; Yamada, Yutaka; Takahashi, Mikiko; Rai, Amit; Sugiyama, Ryosuke; Yamamoto, Hiroyuki; Nakaya, Taiki; Yamazaki, Mami; Kooke, Rik; Bac-Molenaar, Johanna A.; Oztolan-Erol, Nihal; Keurentjes, Joost J. B.; Arita, Masanori; Saito, KazukiNature Methods (2019), 16 (4), 295-298CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)We report a computational approach (implemented in MS-DIAL 3.0; http://prime.psc.riken.jp/) for metabolite structure characterization using fully 13C-labeled and non-labeled plants and LC-MS/MS. Our approach facilitates carbon no. detn. and metabolite classification for unknown mols. Applying our method to 31 tissues from 12 plant species, we assigned 1,092 structures and 344 formulas to 3,604 carbon-detd. metabolite ions, 69 of which were found to represent structures currently not listed in metabolome databases.
- 5Nikolskiy, I.; Mahieu, N. G.; Chen, Y. J.; Tautenhahn, R.; Patti, G. J. An untargeted metabolomic workflow to improve structural characterization of metabolites. Anal. Chem. 2013, 85, 7713– 7719, DOI: 10.1021/ac400751jGoogle Scholar5https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtVGqtr7E&md5=e724f08767fe8d4d370f33c41c72c86eAn Untargeted Metabolomic Workflow to Improve Structural Characterization of MetabolitesNikolskiy, Igor; Mahieu, Nathaniel G.; Chen, Ying-Jr; Tautenhahn, Ralf; Patti, Gary J.Analytical Chemistry (Washington, DC, United States) (2013), 85 (16), 7713-7719CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Mass spectrometry-based metabolomics relies on MS2 data for structural characterization of metabolites. To obtain the high-quality MS2 data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS2 data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS2 fragments, even narrow windows are not always selective enough, and they can complicate data anal. by removing isotopic patterns from MS2 spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS2 scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS2 fragment intensities as a function of retention time and precursor mass targeted for MS2 anal., we obtain deconvolved MS2 spectra that are consistent with pure stds. and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic exts. from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite stds. in combination with simulations based on raw MS2 data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our lab. website (http://pattilab.wustl.edu/decoms2.php).
- 6Lawson, T. N.; Weber, R. J. M.; Jones, M. R.; Chetwynd, A. J.; Rodríguez-Blanco, G.; Di Guida, R.; Viant, M. R.; Dunn, W. B. msPurity: Automated evaluation of precursor ion purity for mass spectrometry-based fragmentation in metabolomics. Anal. Chem. 2017, 89, 2432– 2439, DOI: 10.1021/acs.analchem.6b04358Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFGhtLo%253D&md5=63e4240cec8f7df306126be4d6ea6e54msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in MetabolomicsLawson, Thomas N.; Weber, Ralf J. M.; Jones, Martin R.; Chetwynd, Andrew J.; Rodriguez-Blanco, Giovanny; Di Guida, Riccardo; Viant, Mark R.; Dunn, Warwick B.Analytical Chemistry (Washington, DC, United States) (2017), 89 (4), 2432-2439CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Tandem mass spectrometry (MS/MS or MS2) is a widely used approach for structural annotation and identification of metabolites in complex biol. samples. The importance of assessing the contribution of the precursor ion within an isolation window for MS2 expts. has been previously detailed in proteomics, where precursor ion purity influences the quality and accuracy of matching to mass spectral libraries, but to date there has been little attention to this data-processing technique in metabolomics. Here the authors present msPurity, a vendor-independent R package for liq. chromatog. (LC) and direct infusion (DI) MS2 that calcs. a simple metric to describe the contribution of the selected precursor. The precursor purity metric is calcd. as "intensity of a selected precursor divided by the summed intensity of the isolation window". The metric is interpolated at the recorded point of MS2 acquisition using bordering full-scan spectra, isotopic peaks of the selected precursor can be removed and low abundance peaks that are believed to have limited contribution to the resulting MS2 spectra are removed. Addnl., the isolation efficiency of the mass spectrometer can be taken into account. The package was applied to Data Dependent Acquisition (DDA)-based MS2 metabolomics datasets derived from three metabolomics data repositories. For the ten LC-MS2 DDA datasets with > ± 1 Da isolation windows the median precursor purity score ranged from 0.60 to 0.96 (scale = 0 to +1). The R package was also used to assess precursor purity of theor. isolation windows from LC-MS datasets of differing sample types. The theor. isolation windows being the same width used for an anticipated DDA expt. (± 0.5 Da). The most complex sample had a median precursor purity score of 0.46 for the 64,498 XCMS detd. features, in comparison to the less spectrally complex sample that had a purity score of 0.66 for 5071 XCMS features. It has been previously reported in proteomics that a purity score of < 0.5 can produce unreliable spectra matching results, with this assumption for complex samples there will be a large no. of metabolites where traditional DDA approaches will struggle to provide reliable annotations or accurate matches to mass spectral libraries.
- 7Zhu, X.; Chen, Y.; Subramanian, R. Comparison of information-dependent acquisition, SWATH, and MS(All) techniques in metabolite identification study employing ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Anal. Chem. 2014, 86, 1202– 1209, DOI: 10.1021/ac403385yGoogle Scholar7https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhvFKhtr7P&md5=f9919a286119365d83dc011515277097Comparison of Information-Dependent Acquisition, SWATH, and MSAll Techniques in Metabolite Identification Study Employing Ultrahigh-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass SpectrometryZhu, Xiaochun; Chen, Yuping; Subramanian, RajuAnalytical Chemistry (Washington, DC, United States) (2014), 86 (2), 1202-1209CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Sensitive and selective liq. chromatog.-mass spectrometry (LC-MS) anal. is a powerful and essential tool for metabolite identification in drug discovery and development. An MS2 (or tandem, MS/MS) mass spectrum is acquired from the fragmentation of a precursor ion by multiple methods including information-dependent acquisition (IDA), SWATH (sequential window acquisition of all theor. fragment-ion spectra), and MSAll (also called MSE) techniques. The authors compared these three techniques in their capabilities to produce comprehensive MS2 data by assessing both metabolite MS2 acquisition hit rate and the quality of MS2 spectra. Rat liver microsomal incubations from eight test compds. were analyzed with four methods (IDA, MMDF (multiple mass defect filters)-IDA, SWATH, or MSAll) using an ultraHPLC-qudrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) platform. A combined total of 227 drug-related materials (DRM) were detected from all eight test article incubations, and among those, 5% and 4% of DRM were not triggered for MS2 acquisition with IDA and MMDF-IDA methods, resp. When the same samples were spiked to an equal vol. of blank rat urine (urine sample), the DRM without MS2 acquisition increased to 29% and 18%, correspondingly. In contrast, 100% of DRM in both matrixes were subjected to MS2 acquisition with either the SWATH or MSAll method. However, the quality of the acquired MS2 spectra decreased in the order of IDA, SWATH, and MSAll methods. An av. of 10, 9, and 6 out of 10 most abundant ions in MS2 spectra were the real product ions of DRM detected in microsomal samples from IDA, SWATH, and MSAll methods, resp. The corresponding nos. declined to 9, 6, and 3 in the urine samples. Overall, IDA-based methods acquired qual. better MS2 spectra but with a lower MS2 acquisition hit rate than the other two methods. SWATH outperformed the MSAll method given its better quality of MS2 spectra with an identical MS2 acquisition hit rate.
- 8Röst, H. L.; Rosenberger, G.; Navarro, P.; Gillet, L.; Miladinović, S. M.; Schubert, O. T.; Wolski, W.; Collins, B. C.; Malmström, J.; Malmström, L.; Aebersold, R. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 2014, 32, 219– 223, DOI: 10.1038/nbt.2841Google Scholar8https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2cnjtlSjug%253D%253D&md5=26e7673812152653a61a9ea2d953367dOpenSWATH enables automated, targeted analysis of data-independent acquisition MS dataRost Hannes L; Rosenberger George; Navarro Pedro; Gillet Ludovic; Collins Ben C; Malmstrom Lars; Miladinovic Sasa M; Schubert Olga T; Wolski Witold; Malmstrom Johan; Aebersold RuediNature biotechnology (2014), 32 (3), 219-23 ISSN:.There is no expanded citation for this reference.
- 9Peckner, R.; Myers, S. A.; Jacome, A. S. V.; Egertson, J. D.; Abelin, J. G.; MacCoss, M. J.; Carr, S. A.; Jaffe, J. D. Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nat. Methods 2018, 15, 371– 378, DOI: 10.1038/nmeth.4643Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXmslKrur0%253D&md5=7768f1798c3424e91a7da4e717ea977bSpecter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomicsPeckner, Ryan; Myers, Samuel A.; Jacome, Alvaro Sebastian Vaca; Egertson, Jarrett D.; Abelin, Jennifer G.; MacCoss, Michael J.; Carr, Steven A.; Jaffe, Jacob D.Nature Methods (2018), 15 (5), 371-378CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biol. sample. However, the anal. challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixt. spectra directly through comparison to a spectral library, thus circumventing the problems assocd. with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA anal. methods.
- 10Li, H.; Cai, Y.; Guo, Y.; Chen, F.; Zhu, Z. J. MetDIA: Targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition. Anal. Chem. 2016, 88, 8757– 8764, DOI: 10.1021/acs.analchem.6b02122Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1CjsrrP&md5=f4e056f97a6189866883f54e58bbf382MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent AcquisitionLi, Hao; Cai, Yuping; Guo, Yuan; Chen, Fangfang; Zhu, Zheng-JiangAnalytical Chemistry (Washington, DC, United States) (2016), 88 (17), 8757-8764CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)With recent advances in mass spectrometry, there is an increased interest in data-independent acquisition (DIA) techniques for metabolomics. With DIA technique, all metabolite ions are sequentially selected and isolated using a wide window to generate multiplexed MS/MS spectra. Therefore, DIA strategy enables a continuous and unbiased acquisition of all metabolites and increases the data dimensionality, but presents a challenge to data anal. due to the loss of the direct link between precursor ion and fragment ions. However, very few DIA data processing methods are developed for metabolomics application. Here, the authors developed a new DIA data anal. approach, namely MetDIA, for targeted extn. of metabolites from multiplexed MS/MS spectra generated using DIA technique. MetDIA approach considers each metabolite in the spectral library as an anal. target. Ion chromatographs for each metabolite (both precursor ion and fragment ions) and MS2 spectra are readily detected, extd., and scored for metabolite identification, referred as metabolite-centric identification. A min. metabolite-centric identification score responsible for 1% false pos. rate of identification is detd. as 0.8 using fully 13C labeled biol. exts. Finally, the comparisons of the MetDIA method with data-dependent acquisition (DDA) method demonstrated that MetDIA could significantly detect more metabolites in biol. samples, and is more accurate and sensitive for metabolite identifications. The MetDIA program and the metabolite spectral library is freely available on the internet.
- 11Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 2015, 12, 523– 526, DOI: 10.1038/nmeth.3393Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXnslOns78%253D&md5=1466eda7f23af352e7342fba1b3009e2MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysisTsugawa, Hiroshi; Cajka, Tomas; Kind, Tobias; Ma, Yan; Higgins, Brendan; Ikeda, Kazutaka; Kanazawa, Mitsuhiro; Vander Gheynst, Jean; Fiehn, Oliver; Arita, MasanoriNature Methods (2015), 12 (6), 523-526CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)Data-independent acquisition (DIA) in liq. chromatog. (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of mol. data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small mols. by mass spectral deconvolution. For a reversed-phase LC-MS/MS anal. of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compds., highlighting the chemotaxonomic relationships between the algal strains.
- 12Yin, Y.; Wang, R.; Cai, Y.; Wang, Z.; Zhu, Z.-J. DecoMetDIA: Deconvolution of multiplexed MS/MS spectra for metabolite identification in SWATH-MS-based untargeted metabolomics. Anal. Chem. 2019, 91, 11897– 11904, DOI: 10.1021/acs.analchem.9b02655Google Scholar12https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhs1egurvI&md5=db3bac501b985c48e63c4a9717400e43DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted MetabolomicsYin, Yandong; Wang, Ruohong; Cai, Yuping; Wang, Zhuozhong; Zhu, Zheng-JiangAnalytical Chemistry (Washington, DC, United States) (2019), 91 (18), 11897-11904CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technol. has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quant. accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatog. peaks of fragment ions in multiplexed spectra and decomp. them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biol. samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the exptl. MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biol. samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA.
- 13Brown, M.; Wedge, D. C.; Goodacre, R.; Kell, D. B.; Baker, P. N.; Kenny, L. C.; Mamas, M. A.; Neyses, L.; Dunn, W. B. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics 2011, 27, 1108– 1112, DOI: 10.1093/bioinformatics/btr079Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXksFKlu74%253D&md5=b34aea0350101a753358bd294f885fe1Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasetsBrown, Marie; Wedge, David C.; Goodacre, Royston; Kell, Douglas B.; Baker, Philip N.; Kenny, Louise C.; Mamas, Mamas A.; Neyses, Ludwig; Dunn, Warwick B.Bioinformatics (2011), 27 (8), 1108-1112CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)Motivation: The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chem. identifying metabolites from mass spectrometric signals present in complex datasets. Results: Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral mols. and assocd. mol. formula and matching of the mol. formulas to a ref. file of metabolites. The software is independent of the instrument and data pre-processing applied. The no. of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of exptl. data. Availability: The workflows, std. operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html. Contact: [email protected].
- 14Alonso, A.; Marsal, S.; Julià, A. Analytical methods in untargeted metabolomics: state of the art in 2015. Front. Bioeng. Biotechnol. 2015, 3, 23, DOI: 10.3389/fbioe.2015.00023Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2MnmvFGktw%253D%253D&md5=129c8c7cb2c56ded51cd56dd6ea8c114Analytical methods in untargeted metabolomics: state of the art in 2015Alonso Arnald; Marsal Sara; Julia AntonioFrontiers in bioengineering and biotechnology (2015), 3 (), 23 ISSN:2296-4185.Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
- 15Kuhl, C.; Tautenhahn, R.; Böttcher, C.; Larson, T. R.; Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 2012, 84, 283– 289, DOI: 10.1021/ac202450gGoogle Scholar15https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsFansLvL&md5=46bc1d612000928d7a4a57fac84290c1CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data SetsKuhl, Carsten; Tautenhahn, Ralf; Boettcher, Christoph; Larson, Tony R.; Neumann, SteffenAnalytical Chemistry (Washington, DC, United States) (2012), 84 (1), 283-289CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Liq. chromatog. coupled to mass spectrometry is routinely used for metabolomics expts. In contrast to the fairly routine and automated data acquisition steps, subsequent compd. annotation and identification require extensive manual anal. and thus form a major bottleneck in data interpretation. Here the authors present CAMERA, a Bioconductor package integrating algorithms to ext. compd. spectra, annotate isotope and adduct peaks, and propose the accurate compd. mass even in highly complex data. To evaluate the algorithms, the authors compared the annotation of CAMERA against a manually defined annotation for a mixt. of known compds. spiked into a complex matrix at different concns. CAMERA successfully extd. accurate masses for 89.7% and 90.3% of the annotatable compds. in pos. and neg. ion modes, resp. Furthermore, the authors present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. The authors demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics expts., where the application of CAMERA drastically reduced the amt. of manual anal.
- 16Broeckling, C. D.; Heuberger, A. L.; Prince, J. A.; Ingelsson, E.; Prenni, J. E. Assigning precursor–product ion relationships in indis. criminant MS/MS data from non-targeted metabolite profiling studies. Metabolomics 2013, 9, 33– 43, DOI: 10.1007/s11306-012-0426-4Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtVymtbg%253D&md5=ba7b1051702c81493931bf981c5c1e3bAssigning precursor-product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studiesBroeckling, Corey D.; Heuberger, Adam L.; Prince, Jonathan A.; Ingelsson, E.; Prenni, Jessica E.Metabolomics (2013), 9 (1), 33-43CODEN: METAHQ; ISSN:1573-3882. (Springer)Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small mols. In non-targeted metabolite profiling studies, instrument duty cycle limitations and exptl. costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor-product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor-product relationships in idMS/MS data by appending a dataset-wide correlational anal. to existing tools. The utility of the approach was demonstrated using a dataset of std. compds. spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to std. MS/MS spectra of authentic stds., even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable mol. feature, which will streamline the identification process for non-targeted metabolite profiling studies.
- 17Broeckling, C. D.; Afsar, F. A.; Neumann, S.; Ben-Hur, A.; Prenni, J. E. RAMClust: A novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal. Chem. 2014, 86, 6812– 6817, DOI: 10.1021/ac501530dGoogle Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXps1Kmurk%253D&md5=81d8746ff6040dfa20ec017b7c744e18RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics DataBroeckling, C. D.; Afsar, F. A.; Neumann, S.; Ben-Hur, A.; Prenni, J. E.Analytical Chemistry (Washington, DC, United States) (2014), 86 (14), 6812-6817CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Metabolomic data are frequently acquired using chromatog. coupled mass spectrometry (MS) platforms. For such datasets, the first step in data anal. relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compd., a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We addnl. address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature-feature relationships are detd. simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single expt., reduces quant. anal. variation compared to single-feature measures, and decreases false pos. annotations of unpredictable phenomenon as novel compds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatog.-spectrometric platform or feature-finding software.
- 18Naz, S.; Gallart-Ayala, H.; Reinke, S. N.; Mathon, C.; Blankley, R.; Chaleckis, R.; Wheelock, C. E. Development of a liquid chromatography-high resolution mass spectrometry metabolomics method with high specificity for metabolite identification using all ion fragmentation acquisition. Anal. Chem. 2017, 89, 7933– 7942, DOI: 10.1021/acs.analchem.7b00925Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVClsrbE&md5=13c57859bbc8f8564794e39c871cd4d4Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation AcquisitionNaz, Shama; Gallart-Ayala, Hector; Reinke, Stacey N.; Mathon, Caroline; Blankley, Richard; Chaleckis, Romanas; Wheelock, Craig E.Analytical Chemistry (Washington, DC, United States) (2017), 89 (15), 7933-7942CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)High-resoln. mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biol. context. In the current study, a liq. chromatog. (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (1) accurate mass (AM), (2) retention time (RT), (3) MS/MS spectrum, and (4) product/precursor ion intensity ratios. The authors constructed an inhouse mass spectral library of 408 metabolites contg. AMRT and MS/MS spectra information at four collision energies. The percent relative std. deviations between ion ratios of a metabolite in an anal. std. vs. sample matrix were used as an addnl. metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, resp. The authors also included a scheme to distinguish coeluting isobaric compds. by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. The authors' data acquisition strategy enables a simultaneous mixt. of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biol. information acquired in a metabolomics expt.
- 19Chaleckis, R.; Naz, S.; Meister, I.; Wheelock, C. E. LC-MS-based metabolomics of biofluids using All-Ion Fragmentation (AIF) acquisition. Methods Mol. Biol. 2018, 1730, 45– 58, DOI: 10.1007/978-1-4939-7592-1_3Google Scholar19https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitVWhsbvF&md5=62e2e0751bdc20baeac249eaac41454cLC-MS-based metabolomics of biofluids using all-ion fragmentation (AIF) acquisitionChaleckis, Romanas; Naz, Shama; Meister, Isabel; Wheelock, Craig E.Methods in Molecular Biology (New York, NY, United States) (2018), 1730 (Clinical Metabolomics), 45-58CODEN: MMBIED; ISSN:1940-6029. (Springer)The field of liq. chromatog.-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compds. in biol. samples. However, compd. identification remains a major challenge, which is crucial in interpreting the biol. function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an inhouse spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative std. deviation between ion ratios of a metabolite in a biofluid vs. its anal. std. is used as an addnl. metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compds. Our method enables database-dependent targeted as well as nontargeted metabolomics anal. from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biol. information.
- 20Lazarinis, N.; Bood, J.; Gomez, C.; Kolmert, J.; Lantz, A. S.; Gyllfors, P.; Davis, A.; Wheelock, C. E.; Dahlén, S. E.; Dahlén, B. Leukotriene E4 induces airflow obstruction and mast cell activation through the cysteinyl leukotriene type 1 receptor. J. Allergy Clin. Immunol. 2018, 142, 1080– 1089, DOI: 10.1016/j.jaci.2018.02.024Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhtVCksrfK&md5=686cb1d2702fee17b1f07e3230147a63Leukotriene E4 induces airflow obstruction and mast cell activation through the cysteinyl leukotriene type 1 receptorLazarinis, Nikolaos; Bood, Johan; Gomez, Cristina; Kolmert, Johan; Lantz, Ann-Sofie; Gyllfors, Paer; Davis, Andy; Wheelock, Craig E.; Dahlen, Sven-Erik; Dahlen, BarbroJournal of Allergy and Clinical Immunology (2018), 142 (4), 1080-1089CODEN: JACIBY; ISSN:0091-6749. (Elsevier)Leukotriene (LT) E4 is the final active metabolite among the cysteinyl leukotrienes (CysLTs). Animal studies have identified a distinct LTE4 receptor, suggesting that current cysteinyl leukotriene type 1 (CysLT1) receptor antagonists can provide incomplete inhibition of CysLT responses. We tested this hypothesis by assessing the influence of the CysLT1 antagonist montelukast on responses induced by means of inhalation of LTE4 in asthmatic patients. Fourteen patients with mild intermittent asthma and 2 patients with aspirin-exacerbated respiratory disease received 20 mg of montelukast twice daily and placebo for 5 to 7 days in a randomized, double-blind, crossover study (NCT01841164). The PD20 value was detd. at the end of each treatment period based on an increasing dose challenge. Measurements included lipid mediators in urine and sputum cells 4 h after LTE4 challenge. Montelukast completely blocked LTE4-induced bronchoconstriction. Despite tolerating an at least 10 times higher dose of LTE4 after montelukast, there was no difference in the percentage of eosinophils in sputum. Urinary excretion of all major lipid mediators increased after LTE4 inhalation. Montelukast blocked release of the mast cell product prostaglandin (PG) D2, as well as release of PGF2α and thromboxane (Tx) A2, but not increased excretion of PGE2 and its metabolites or isoprostanes. LTE4 induces airflow obstruction and mast cell activation through the CysLT1 receptor.
- 21Haug, K.; Salek, R. M.; Conesa, P.; Hastings, J.; de Matos, P.; Rijnbeek, M.; Mahendraker, T.; Williams, M.; Neumann, S.; Rocca-Serra, P.; Maguire, E.; González-Beltrán, A.; Sansone, S. A.; Griffin, J. L.; Steinbeck, C. MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 2013, 41, D781– 786, DOI: 10.1093/nar/gks1004Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvV2ktrnO&md5=b4fd7aa651ff814b038cb87db58bb10fMetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-dataHaug, Kenneth; Salek, Reza M.; Conesa, Pablo; Hastings, Janna; de Matos, Paula; Rijnbeek, Mark; Mahendraker, Tejasvi; Williams, Mark; Neumann, Steffen; Rocca-Serra, Philippe; Maguire, Eamonn; Gonzalez-Beltran, Alejandra; Sansone, Susanna-Assunta; Griffin, Julian L.; Steinbeck, ChristophNucleic Acids Research (2013), 41 (D1), D781-D786CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw exptl. data and assocd. metadata, maintained by one of the major open-access data providers in mol. biol. Metabolomic profiling is an important tool for research into biol. functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of exptl. methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their ref. spectra as well as their biol. roles, locations, concns. and raw data from metabolic expts. Studies automatically receive a stable unique accession no. that can be used as a publication ref. (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
- 22Tada, I.; Tsugawa, H.; Meister, I.; Zhang, P.; Shu, R.; Katsumi, R.; Wheelock, C. E.; Arita, M.; Chaleckis, R. Creating a reliable mass spectral–retention time library for all ion fragmentation-based metabolomics. Metabolites 2019, 9, 251, DOI: 10.3390/metabo9110251Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXisVSiu7fF&md5=98500e02cad3b843658033583284ab76Creating a reliable mass spectral-retention time library for all ion fragmentation-based metabolomicsTada, Ipputa; Tsugawa, Hiroshi; Meister, Isabel; Zhang, Pei; Shu, Rie; Katsumi, Riho; Wheelock, Craig E.; Arita, Masanori; Chaleckis, RomanasMetabolites (2019), 9 (11), 251CODEN: METALU; ISSN:2218-1989. (MDPI AG)Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chem. spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liq. chromatog. coupled to mass spectrometry (LC-MS) methods across labs. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across labs. This library construction strategy improves the confidence in annotation for AIF data in LC-MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.
- 23Moorthy, A. S.; Wallace, W. E.; Kearsley, A. J.; Tchekhovskoi, D. V.; Stein, S. E. Combining fragment-ion and neutral-loss matching during mass spectral library searching: A new general purpose algorithm applicable to illicit drug identification. Anal. Chem. 2017, 89, 13261– 13268, DOI: 10.1021/acs.analchem.7b03320Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvVars7bN&md5=180c05ff3b01a5a255dcac8e136774ffCombining Fragment-Ion and Neutral-Loss Matching during Mass Spectral Library Searching: A New General Purpose Algorithm Applicable to Illicit Drug IdentificationMoorthy, Arun S.; Wallace, William E.; Kearsley, Anthony J.; Tchekhovskoi, Dmitrii V.; Stein, Stephen E.Analytical Chemistry (Washington, DC, United States) (2017), 89 (24), 13261-13268CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A mass spectral library search algorithm that identifies compds. that differ from library compds. by a single 'inert' structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compds., to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum. When the spectra being compared differ by a single structural feature, these matching neutral loss peaks should contain that structural feature. This method extends the scope of the library to include spectra of 'nearest-neighbor' compds. that differ from library compds. by a single chem. moiety. Addnl., detn. of the structural origin of the shifted peaks can aid in the detn. of the chem. structure and fragmentation mechanism of the query compd. A variety of examples are presented, including the identification of designer drugs and chem. derivs. not present in the library.
- 24Domingo-Almenara, X.; Montenegro-Burke, J. R.; Benton, H. P.; Siuzdak, G. Annotation: A computational solution for streamlining metabolomics analysis. Anal. Chem. 2018, 90, 480– 489, DOI: 10.1021/acs.analchem.7b03929Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1KnsL3L&md5=6491b60f6ad7834b6e773de66adc0e05Annotation: A Computational Solution for Streamlining Metabolomics AnalysisDomingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Benton, H. Paul; Siuzdak, GaryAnalytical Chemistry (Washington, DC, United States) (2018), 90 (1), 480-489CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)A review. The aim of any untargeted metabolomics expt. is to identify and quantify dysregulated compds. relevant to a particular disease or stressor. However, metabolite identification is still considered an imposing bottleneck in untargeted metabolomics. Untargeted data anal. workflows for liq. chromatog./electrospray ionization/mass spectrometry (LC/ESI/MS) usually consist in applying peak-picking algorithms, align those peaks across multiple samples to obtain the so-called peak features (defined as a peak, or a set of aligned peaks across samples with a unique m/z and a specific retention time), and subsequently discover statistically significant variations between exptl. groups or conditions. Once features of interest are prioritized, those can be annotated by searching their mass values against metabolite libraries. Next, those features can be putatively identified via fragmentation expts. (tandem MS or MS/MS), usually with Quadrupole-Time-of-Flight (Q-ToF) instrumentation, by comparing exptl. fragmentation patterns with spectral libraries. Ultimately, unambiguous identification, according to the Metabolomics Stds. Initiative (MSI) guidelines, can only be achieved by comparing the exptl. tandem MS spectra with std. materials analyzed under identical conditions.
- 25Domingo-Almenara, X.; Montenegro-Burke, J. R.; Guijas, C.; Majumder, E. L.; Benton, H. P.; Siuzdak, G. Autonomous METLIN-guided in-source fragment annotation for untargeted metabolomics. Anal. Chem. 2019, 91, 3246– 3253, DOI: 10.1021/acs.analchem.8b03126Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitVCns7k%253D&md5=944118feb8d3cd4b292643cf413ee435Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted MetabolomicsDomingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Guijas, Carlos; Majumder, Erica L.-W.; Benton, H. Paul; Siuzdak, GaryAnalytical Chemistry (Washington, DC, United States) (2019), 91 (5), 3246-3253CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)Computational metabolite annotation in untargeted profiling aims at uncovering neutral mol. masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to det. metabolite neutral masses. However, a significant fraction of features usually detected in untargeted expts. remains unannotated, which limits the ability to det. neutral mol. masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liq. chromatog.-electrospray ionization-mass spectrometry. In this study, the authors introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. The algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation anal. of a total of 140 metabolites across three different sets of biol. samples analyzed with liq. chromatog.-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral mol. masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the no. of in-source fragments matched and the relative intensity similarity between the exptl. data and the ref. low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu.
- 26Sumner, L. W. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 2007, 3, 211– 221, DOI: 10.1007/s11306-007-0082-2Google Scholar26https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtFOntbvL&md5=e636dad276f9f1b2e756db169368eb53Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave; Beale, Michael H.; Beger, Richard; Daykin, Clare A.; Fan, Teresa W.-M.; Fiehn, Oliver; Goodacre, Royston; Griffin, Julian L.; Hankemeier, Thomas; Hardy, Nigel; Harnly, James; Higashi, Richard; Kopka, Joachim; Lane, Andrew N.; Lindon, John C.; Marriott, Philip; Nicholls, Andrew W.; Reily, Michael D.; Thaden, John J.; Viant, Mark R.Metabolomics (2007), 3 (3), 211-221CODEN: METAHQ; ISSN:1573-3882. (Springer)There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of std. metadata provides a biol. and empirical context for the data, facilitates exptl. replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Stds. Initiative is building a general consensus concerning the min. reporting stds. for metabolomics expts. of which the Chem. Anal. Working Group (CAWG) is a member of this community effort. This article proposes the min. reporting stds. related to the chem. anal. aspects of metabolomics expts. including: sample prepn., exptl. anal., quality control, metabolite identification, and data pre-processing. These min. stds. currently focus mostly upon mass spectrometry and NMR spectroscopy due to the popularity of these techniques in metabolomics. However, addnl. input concerning other techniques is welcomed and can be provided via the CAWG online discussion forum at http://msi-workgroups.sourceforge.net/ or http://[email protected]. Further, community input related to this document can also be provided via this electronic forum.
- 27Chaleckis, R.; Meister, I.; Zhang, P.; Wheelock, C. E. Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics. Curr. Opin. Biotechnol. 2019, 55, 44– 50, DOI: 10.1016/j.copbio.2018.07.010Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhsVGrtr%252FI&md5=bbb3b5341edd5f92d133537659a1b6c8Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomicsChaleckis, Romanas; Meister, Isabel; Zhang, Pei; Wheelock, Craig E.Current Opinion in Biotechnology (2019), 55 (), 44-50CODEN: CUOBE3; ISSN:0958-1669. (Elsevier B.V.)A review. Accurate annotation is vital for data interpretation; however, metabolite identification is a major bottleneck in untargeted metabolomics. Although community guidelines for metabolite identification were published over a decade ago, adaptation of the recommended stds. has been limited. The complexity of LC-MS data due to combinations of various chromatog. and mass spectrometric acquisition methods has resulted in the advent of diverse workflows, which often involve non-standardized manual curation. Herein, we review the parameters involved in metabolite reporting and provide a workflow to est. the level of confidence in reported metabolite annotation. The future of metabolite identification will be heavily based upon the use of metabolome data repositories and assocd. data anal. tools, which will enable data to be shared, re-analyzed and re-annotated in an automated fashion.
- 28Tsuruta, Y.; Tomida, H.; Kohashi, K.; Ohkura, Y. Simultaneous determination of imidazoleacetic acid and N tau- and N pi-methylimidazoleacetic acids in human urine by high-performance liquid chromatography with fluorescence detection. J. Chromatogr., Biomed. Appl. 1987, 416, 63– 69, DOI: 10.1016/0378-4347(87)80485-1Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2sXktVSqsLY%253D&md5=904a7b3289d0b25d42e2c3a2fb9987a4Simultaneous determination of imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acids in human urine by high-performance liquid chromatography with fluorescence detectionTsuruta, Yasuto; Tomida, Hisao; Kohashi, Kazuya; Ohkura, YosukeJournal of Chromatography, Biomedical Applications (1987), 416 (1), 63-9CODEN: JCBADL; ISSN:0378-4347.A sensitive method for the simultaneous detn. of urinary imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acid which employs HPLC with fluorescence detection is described. The compds. are converted into the corresponding fluorescent esters by reaction with 4-bromomethyl-7-methoxycoumarin. These derivs. are sepd. by liq. chromatog. on a Radial-Pak silica column. The detection limits for imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acid in urine are 15, 10, and 20 pmol/mL, resp. The 24-h urinary excretions of imidazoleacetic acid and Nτ- and Nπ-methylimidazoleacetic acids by healthy persons are 5.7-39.9, 4.3-24.6, and 1.5-19.3 nmol/mg creatinine, resp. The simultaneous detns. of these compds. should be useful in investigations of the turnover of histamine.
- 29Pohjanpelto, P.; Niemi, K.; Sarmela, T. Anterior chamber haemorrhage in the newborn after spontaneous delivery. A case report. Acta Ophthalmol. 1979, 57, 443– 446, DOI: 10.1111/j.1755-3768.1979.tb01827.xGoogle Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaE1M3lt1Squg%253D%253D&md5=6e626908c52d4fe09d3edc8670c31036Anterior chamber haemorrhage in the newborn after spontaneous delivery. A case reportPohjanpelto P; Niemi K; Sarmela TActa ophthalmologica (1979), 57 (3), 443-6 ISSN:0001-639X.A case report is presented of anterior chamber haemorrhage occurring in one eye in a newborn after spontaneous delivery. At the age of two weeks the anterior chamber was clear but the vitreous cloudy. At the age of five weeks the vitreous had also cleared. The infant's later development was normal and there were no disorders in the function of the eye.
- 30Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A. C.; Wilson, M. R.; Knox, C.; Bjorndahl, T. C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z. T.; Poelzer, J.; Huynh, J.; Yallou, F. S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D. S. The human urine metabolome. PLoS One 2013, 8, e73076 DOI: 10.1371/journal.pone.0073076Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVCrsrnJ&md5=0467920d8bf44214bb2b557648612c36The human urine metabolomeBouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick; Dong, Edison; Bogumil, Ralf; Roehring, Cornelia; Wishart, David S.PLoS One (2013), 8 (9), e73076CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)A review. Urine has long been a "favored" biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large vols., largely free from interfering proteins or lipids and chem. complex. However, this chem. complexity has also made urine a particularly difficult substrate to fully understand. As a biol. waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial byproducts. Many of these compds. are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quant., metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quant. exptl. assessment/validation. The exptl. portion employed NMR spectroscopy, gas chromatog. mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liq. chromatog. (HPLC) expts. performed on multiple human urine samples. This multi-platform metabolomic anal. allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different anal. platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compds. and was used to help guide the subsequent exptl. studies. An online database contg. the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concns., related literature refs. and links to their known disease assocns. are freely available at online.
- 31Li, C.; Homma, M.; Oka, K. Characteristics of delayed excretion of flavonoids in human urine after administration of Shosaiko-to, a herbal medicine. Biol. Pharm. Bull. 1998, 21, 1251– 1257, DOI: 10.1248/bpb.21.1251Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXivFGh&md5=07ddff93fbf7cccf77ce2ac965815d04Characteristics of delayed excretion of flavonoids in human urine after administration of Shosaiko-to, a herbal medicineLi, Chuan; Homma, Masato; Oka, KitaroBiological & Pharmaceutical Bulletin (1998), 21 (12), 1251-1257CODEN: BPBLEO; ISSN:0918-6158. (Pharmaceutical Society of Japan)There has been little explanation of herbal medicines by modern medical sciences, including pharmacokinetics, whereas physicians follow empirical indications written in classical literature. Recent reports of herb-induced adverse reactions compelled the authors to proceed the investigation of a herbal medicine Shosaiko-to (TJ-9) from a pharmacokinetic point of view. To five healthy volunteers, a single 5 g dose of TJ-9, consisting of 7 herbs, was administered. The authors conducted HPLC anal. of the timed-urine specimens to disclose the type and amt. of compds. excreted. Excretion rate-time curves were analyzed individually. Four flavonoids, liquiritigenin, baicalein, wogonin and oroxylin A, were found both in the urine and TJ-9. The glycosides in TJ-9 were absorbed after microflora hydrolysis. Davidigenin, which was not found in TJ-9, was an intestinal metabolite of liquiritigenin. Also, two flavanones, S-dihydrowogonin and S-dihydrooroxylin A, were identified as the metabolites of wogonin and oroxylin A, resp. Excretion rate-time curves of the flavonoids were divided into three types of structure-dependent absorption, i.e. (1) the fast absorption of herbal-origin aglycons, (2) the moderately-delayed absorption of aglycons derived from herbal glycosides, and (3) markedly-delayed absorption after the mol. transformation of herbal compds. Individual excretion profiles seemed to depend on microflora activities. Two types of flavanones, S-dihydrowogonin and S-dihydrooroxylin A, were found in a half of the volunteers, suggesting there might be two kinds of volunteers, namely, rapid and poor metabolizers of flavonoids.
- 32Hornik, P.; Vyskocilová, P.; Friedecký, D.; Adam, T. Diagnosing AICA-ribosiduria by capillary electrophoresis. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2006, 843, 15– 19, DOI: 10.1016/j.jchromb.2006.05.020Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtVyktrzP&md5=c67ce29daa0b3c15f90e12e47d17b33fDiagnosing AICA-ribosiduria by capillary electrophoresisHornik, Petr; Vyskocilova, Petra; Friedecky, David; Adam, TomasJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences (2006), 843 (1), 15-19CODEN: JCBAAI; ISSN:1570-0232. (Elsevier B.V.)AICA-ribosiduria is a recently discovered inherited metabolic disease caused by a defect in final steps of purine de novo biosynthesis-5-amino-4-imidazolecarboxamide ribodide (AICAR)-transformylase/inosinemonophosphate (IMP)-cyclohydrolase (ATIC). A rapid and selective capillary electrophoretic method for screening of patients with AICA-ribosiduria is described. The method is based on direct UV detection of 5-amino-4-imidazolecarboxamide (AICA) and 5-amino-4-imidazolecarboxamide riboside (AICAr) in untreated urine. Background electrolyte consists of 100 mM malonic acid adjusted with γ-aminobutyric acid (pH 2.7). Under the given sepn. conditions both compds. of interest are well sepd. from other substances with sepn. efficiency of 1 020 000 and 130 000 theor. plates/m for AICA and AICAr, resp. Total anal. time is 3 min with the limits of detection of 3.6 μM and 4.5 μM for AICA and AICAr, resp. The usefulness of the presented method for screening of patients with ATIC deficiency is demonstrated on samples of Chinese hamster ovary cell line defective in ATIC activity, spiked urine samples and urine samples from patients treated with high-dose MTX which do not excrete increased amts. of AICA and AICAr compared to untreated controls (p < 0.05). The described method is fast and effective enough for diagnostic applications.
- 33Bales, J. R.; Sadler, P. J.; Nicholson, J. K.; Timbrell, J. A. Urinary excretion of acetaminophen and its metabolites as studied by proton NMR spectroscopy. Clin. Chem. 1984, 30, 1631– 1636, DOI: 10.1093/clinchem/30.10.1631Google Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2cXmt1ajtrw%253D&md5=66288b251a1c8ac9261aafd3eb229898Urinary excretion of acetaminophen and its metabolites as studied by proton NMR spectroscopyBales, John R.; Sadler, Peter J.; Nicholson, Jeremy K.; Timbrell, John A.Clinical Chemistry (Washington, DC, United States) (1984), 30 (10), 1631-6CODEN: CLCHAU; ISSN:0009-9147.Acetaminophen [103-90-2] and its glucuronide [16110-10-4], sulfate [10066-90-7], N-acetyl-L-cysteinyl [52372-86-8], and L-cysteinyl [53446-10-9] metabolites can be rapidly detected by 1H NMR spectroscopy of intact, untreated human urine. Study of the time course of excretion of these metabolites in 5 clin. normal men after ingestion of the usual 1-g therapeutic dose of the drug showed that the mean 24-h excretion of the drug and these metabolites as detd. by NMR was 77.3% of the dose. Resp. relative proportions of the above metabolites were 49.9%, 37.6%, 3.0%, and 9.5% (L-cysteinyl plus free drug). Excretion of some other metabolites in urine, including creatinine [60-27-5], citrate [77-92-9], hippurate [495-69-2], and sarcosine [107-97-1] was measured concurrently. Excretion of creatinine and sarcosine was closely correlated.
- 34Tsugawa, H., Ikeda, K., Takahashi, M., Satoh, A., Mori, Y., Uchino, H., Okahashi, N., Yamada, Y., Tada, I., Bonini, P., Higashi, Y., Okazaki, Y., Zhou, Z., Zhu, Z. J., Koelmel, J., Cajka, T., Fiehn, O., Saito, K., Arita, M., Arita, M. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 2020, DOI: 10.1038/s41587-020-0531-2 .Google ScholarThere is no corresponding record for this reference.
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