An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Devon Kohler, Mateusz Staniak, Fengchao Yu, Alexey I. Nesvizhskii, Olga Vitek

Published: 2024-05-19 DOI: 10.1038/s41596-024-01000-3

statistical analysis

proteomics

DIA experiments

MSstats

FragPipe

AI 解读

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

查看全部

Sections

Figures

References

Introduction
Materials
- Equipment
- Equipment setup
Procedure 1: MSstatsShiny analysis pipeline
Procedure 2: MSstats code analysis pipeline
Troubleshooting
Timing
- Procedure 1
- Procedure 2
Anticipated results
Acknowledgements

Shuken, S. R. An introduction to mass spectrometry-based proteomics. J. Proteom. Res. 22, 2151–2171 (2023).
Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).
Ong, S.-E. & Mann, M. Mass spectrometry–based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).
Borràs, E. & Sabidó, E. What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry. Proteomics 17, 1700180 (2017).
Mann, M. & Jensen, O. N. Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21, 255–261 (2003).
Li, Z. et al. Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J. Proteome Res. 11, 1582–1590 (2012).
Poulos, R. C. et al. Strategies to enable large-scale proteomics for reproducible research. Nat. Commun. 11, 3793 (2020).
Cai, X. et al. PulseDIA: data-independent acquisition mass spectrometry using multi-injection pulsed gas-phase fractionation. J. Proteome Res. 20, 279–288 (2021).
Krzywinski, M. & Altman, N. Power and sample size. Nat. Methods 10, 1139–1140 (2013).
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nat. Methods 14, 513–520 (2017).
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).
Bernhardt, O. M. et al. Spectronaut: A Fast and Efficient Algorithm for MRM-Like Processing of Data Independent Acquisition (SWATH-MS) Data. Presented at Proceedings of the 60th ASMS Conference on Mass Spectrometry and Allied Topics, Vancouver, BC, Canada, (unpublished), http://f1000research.com/posters/1096450 (2012).
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).
Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).
Zhang, F., Ge, W., Ruan, G., Cai, X. & Guo, T. Data-independent acquisition mass spectrometry-based proteomics and software tools: a glimpse in 2020. Proteomics 20, e1900276 (2020).
Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022).
Yu, F. et al. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat. Commun. 14, 4154 (2023).
Käll, L. & Vitek, O. Computational mass spectrometry–based proteomics. PLoS Comput. Biol. 7, e1002277 (2011).
Molloy, M. P., Brzezinski, E. E., Hang, J., McDowell, M. T. & VanBogelen, R. A. Overcoming technical variation and biological variation in quantitative proteomics. Proteomics 3, 1912–1919 (2003).
Clough, T., Thaminy, S., Ragg, S., Ruedi, A. & Vitek, O. Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs. BMC Bioinforma. 13, S6 (2012).
Tsai, T.-H. et al. Selection of features with consistent profiles improves relative protein quantification in mass spectrometry experiments. Mol. Cell. Proteom. 19, 944–959 (2020).
Girden, E. R. ANOVA: Repeated Measures (Sage Publications, 1992).
Kohler, D. et al. MSstats version 4.0: statistical analyses of quantitative mass spectrometry-based proteomic experiments with chromatography-based quantificationat scale. J. Proteome Res. 22, 1466–1482 (2023).
Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).
Goeminne, L. J. E., Sticker, A., Martens, L., Gevaert, K. & Clement, L. MSqRob takes the missing hurdle: uniting intensity- and count-based proteomics. Anal. Chem. 92, 6278–6287 (2020).
Sticker, A., Goeminne, L., Martens, L. & Clement, L. Robust summarization and inference in proteomewide label-free quantification. Mol. Cell. Proteom. 19, 1209–1219 (2020).
Goeminne, L. J. E., Gevaert, K. & Clement, L. Peptide-level robust ridge regression improves estimation, sensitivity, and specificity in data-dependent quantitative label-free shotgun proteomics. Mol. Cell. Proteom. 15, 657–668 (2016).
Zhu, et al. DEqMS: a method for accurate variance estimation in differential protein expression analysis. Mol. Cell. Proteom. 19, 1047–1057 (2020).
Wolski, W. E. et al. prolfqua: a comprehensive R-package for proteomics differential expression analysis. J. Proteome Res. 22, 1092–1104 (2023).
Bai, M. et al. LFQ-based peptide and protein intensity differential expression analysis. J. Proteome Res. 22, 2114–2123 (2023).
Gatto, L. & Vanderaa, C. R Package Version 1.13.1, http://github.com/RforMassSpectrometry/QFeatures (2023).
Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011).
Kohler, D. et al. MSstatsShiny: a GUI for versatile, scalable, and reproducible statistical analyses of quantitative proteomic experiments. J. Proteome Res. 22, 551–556 (2023).
Yang, K. L. et al. MSBooster: improving peptide identification rates using deep learning-based features. Nat. Commun. 14, 4539 (2023).
Leprevost, F. D. V. et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat. Methods 17, 869–870 (2020).
Kohler, D. et al. MSstatsPTM: statistical relative quantification of posttranslational modifications in bottom-up mass spectrometry-based proteomics. Mol. Cell. Proteom. 22, 100477 (2023).
Huang, T. et al. MSstatsTMT: statistical detection of differentially abundant proteins in experiments with isobaric labeling and multiple mixtures. Mol. Cell. Proteom. 19, 1706–1723 (2020).
Malinovska, L. et al. Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications. Nat. Protoc. 18, 659–682 (2022).
Richardson, N., et al. Apache/Arrow, http://github.com/apache/arrow/, http://arrow.apache.org/docs/r/ (2023).
Zaharia, M., Xin, R. S., Wendell, P., Das, T. & Armbrust, M. Apache Spark: a unified engine for big data processing. Commun. ACM 59, 56–65 (2016).
Feng, et al. Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 32, 1036–1044 (2014).
Clark, D. J., Dhanasekaran, S. M., Petralia, F., Wang, P. & Zhang, H. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell 179, 964–983 (2019).
Dowle, M. & Srinivasan, A. data.table, http://r-datatable.com, http://Rdatatable.gitlab.io/data.table, http://github.com/Rdatatable/data.table (2023).
Venables, W. & Ripley, B. Modern Applied Statistics with S 359–364 (Springer, 2002).

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Extended

Supplementary information

Supplementary Information

推荐阅读