Abstract
Targeted proteomics based on selected reaction monitoring (SRM) mass spectrometry is commonly used for accurate and reproducible quantification of protein analytes in complex biological mixtures. Strictly hypothesis-driven, SRM assays quantify each targeted protein by collecting measurements on its peptide fragment ions, called transitions. To achieve sensitive and accurate quantitative results, experimental design and data analysis must consistently account for the variability of the quantified transitions. This consistency is especially important in large experiments, which increasingly require profiling up to hundreds of proteins over hundreds of samples. Here we describe a robust and automated workflow for the analysis of large quantitative SRM data sets that integrates data processing, statistical protein identification and quantification, and dissemination of the results. The integrated workflow combines three software tools: mProphet for peptide identification via probabilistic scoring; SRMstats for protein significance analysis with linear mixed-effect models; and PASSEL, a public repository for storage, retrieval and query of SRM data. The input requirements for the protocol are files with SRM traces in mzXML format, and a file with a list of transitions in a text tab-separated format. The protocol is especially suited for data with heavy isotope–labeled peptide internal standards. We demonstrate the protocol on a clinical data set in which the abundances of 35 biomarker candidates were profiled in 83 blood plasma samples of subjects with ovarian cancer or benign ovarian tumors. The time frame to realize the protocol is 1–2 weeks, depending on the number of replicates used in the experiment.
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Acknowledgements
This work was supported by the Swiss National Science Foundation (grant no. 3100A0-107679) and the European Research Council (ERC) advanced grant 'Proteomics v3.0' (grant no. 233226) to R.A., and in part by the US National Science Foundation CAREER grant DBI-1054826 to O.V. We thank M. Choi for help with the development of SRMstats, and O. Schubert for carefully examining the protocol.
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S.S. and R.H. designed the project, performed data analysis and wrote the manuscript. C.-Y.C. assisted in the implementation of SRMstats into the protocol. L.E. wrote the script for formatting and filtering the mProphet output file for SRMstats analysis. O.V. supervised the SRMstats section and wrote the manuscript. R.A. designed and supervised the project and wrote the manuscript.
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Supplementary information
Supplementary Figure 1
Transition examples affected by interferences. a, Two transitions of the reference peptide (y6_2 in green and y7_1 in blue) are affected by interference probably by a coeluting peptide at a similar retention time. b, One transition (b13_2 in black) shows different relative transition intensities for the endogenous and the reference peptides, which may be caused by another transition of the same peptide with very similar Q1 and Q3, since the peak shape and the coelution of the transition is equal to the other transitions in the peak group. c, One transition of the endogenous peptide (y5_2 in red) is affected by interference probably by a coeluting peptide at a similar retention time. (PDF 1062 kb)
Supplementary Figure 2
Example of synthetic decoy transition groups generated by mProphet post-acquisition. The figure shows two examples of post-acquisition generated synthetic decoy transition groups. The chromatogram of the endogenous peptide was shifted by the synthetic_decoy_shift_factor of 0.5, whereas the reference chromatogram was kept constant. The synthetic decoy and reference chromatograms are paired and detected peak groups scored. a, Synthetic decoy transitions group for peptide IGESIELTC[160]PK.2. b, Synthetic decoy transitions group for peptide QLGAGSIEEC[160]AAK.2. (PDF 1253 kb)
Supplementary Figure 3
Incorrectly ranked peak group examples by mProphet. The figure shows per-protein profile plots of proteins which were affected by the assignment of the wrong peak group as best scoring peak group in some of the samples. The effect of the miss assignment in the profile plot can be seen as spikes (indicated with a grey arrow). Profile plots are shown for the protein AACT before (a) and after (b) manual peak group selection, for RET4 (c,d) and THRB (e,f). (PDF 3958 kb)
Supplementary Table 1
mProphet workflows and associated parameter files. The table describes different experimental designs for SRM experiments and suggests the workflow and parameter file required for the mProphet analysis of SRM data derived from the different experimental designs. (PDF 491 kb)
Supplementary Data 1
mProphet input and output files. The folder contains all input files that are required to run the mProphet analysis as well as the output files obtained from the analysis of the example dataset. The mzXML files of the example dataset for the mProphet analysis can be downloaded from the PASSEL website (Experiment Title: Human CAP ovarian cancer plasma, https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/PASS_View?identifier=PASS00041) (ZIP 7917 kb)
Supplementary Data 2
mProphet2SRMstats input and output files. The folder contains all input files that are required to run the mProphet2SRMstats.R script (including the mProphet2SRMstats.R script) and the expected output file for the example dataset. In addition to the files listed in Box 2 the folder contains an excel table that provides information about peptides and transitions that were removed from the dataset before the SRMstats analysis based on the manual inspection of data (SRM_data_manual_inspection.xlsx). The SRM data can either be performed in the mProphet_all_peakgroups.txt after running mProphet or in the SRMstats_input_file.csv after running the mProphet2SRMstats script. (ZIP 7732 kb)
Supplementary Data 3
SRMstats input and output files. The folder contains the input file for the SRMstats analysis based on the example dataset. All commands for running the individual steps of SRMstats on the example dataset are provided in the NatureProtocol_SRMstats_Example.R file. For each step the output file is provided. (ZIP 2058 kb)
Supplementary Data 4
PASSEL files. The folder contains the files that are required to upload a new dataset to PASSEL and can then be downloaded for dissemination. Raw data and mzXML files can be downloaded from the PASSEL website (Experiment Title: Human CAP ovarian cancer plasma, https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/PASS_View?identifier=PASS00041). (ZIP 7546 kb)
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Surinova, S., Hüttenhain, R., Chang, CY. et al. Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies. Nat Protoc 8, 1602–1619 (2013). https://doi.org/10.1038/nprot.2013.091
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DOI: https://doi.org/10.1038/nprot.2013.091
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