Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry

Philipp Mertins, Lauren C. Tang, Karsten Krug, David J. Clark, Marina A. Gritsenko, Lijun Chen, Karl R. Clauser, Therese R. Clauss, Punit Shah, Michael A. Gillette, Vladislav A. Petyuk, Stefani N. Thomas, D. R. Mani, Filip Mundt, Ronald J. Moore, Yingwei Hu, Rui Zhao, Michael Schnaubelt, Hasmik Keshishian, Matthew E. MonroeZhen Zhang, Namrata D. Udeshi, Sherri R. Davies, R. Reid Townsend, Daniel W. Chan, Richard D. Smith, Hui Zhang, Tao Liu, Steven A. Carr

Research output: Contribution to journalArticlepeer-review

66 Scopus citations

Abstract

Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra-and inter-laboratory reproducibility. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Each plex consisted of ten samples, each being 300 μg of peptide derived from <50 mg of wet-weight tissue. Of the 10,000 proteins quantified per sample, we could distinguish 7,700 human proteins derived from tumor cells and 3100 mouse proteins derived from the surrounding stroma and blood. The maximum deviation across replicates and laboratories was <7%, and the inter-laboratory correlation for TMT ratio-based comparison of the two breast cancer subtypes was r > 0.88. The maximum deviation for the phosphoproteome coverage was <24% across laboratories, with an average of >37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in ∼4 months using a single LC-MS/MS instrument. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration.

Original languageEnglish (US)
Pages (from-to)1632-1661
Number of pages30
JournalNature Protocols
Volume13
Issue number7
DOIs
StatePublished - Jul 1 2018
Externally publishedYes

Bibliographical note

Funding Information:
We thank M.J. Ellis of the Lester and Sue Smith Breast Center, the Dan L. Duncan Comprehensive Cancer Center and the Departments of Medicine and Molecular and Cellular Biology, Baylor College of Medicine, and S. Li of the Human and Mouse Linked Evaluation of Tumor Core, Division of Oncology, Washington University School of Medicine, for development of the breast xenograft samples used; and J. Snider and P. Erdmann-Gilmore for the large-scale preparation of the cryopulverized tumor tissue. We also thank S. Stein and S. Markey of the National Institutes of Science and Technology for insightful comments. This work was supported by grants from the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium ((CPTAC) to S.A.C. and M.A.G. at the Broad Institute of MIT and Harvard (1U24CA210986); to T.L. and R.D.S. at the Pacific Northwest National Laboratories (U24CA210955); to D.W.C., H.Z. and Z.Z. at Johns Hopkins University (U24CA210985); and to R.R.T. (U24CA160035). Proteomics work at PNNL described herein was carried out in the Environmental Molecular Sciences Laboratory, a U.S. Department of Energy (DOE) national scientific user facility located at PNNL in Richland, Washington. PNNL is a multiprogram national laboratory operated by the Battelle Memorial Institute for the DOE under contract DE-AC05-76RL01830. Research reported in this publication was supported by a Washington University Institute of Clinical and Translational Sciences grant (UL1 TR000448) from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.

Publisher Copyright:
© 2018 The Author(s).

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