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

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.

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Fig. 1: Optimized workflow and experimental design of global proteome and phosphoproteome analysis in tissues using TMT.
Fig. 2: Deep and reproducible coverage of tumor tissue proteomes and phosphoproteomes across three laboratories.
Fig. 3: Assessment of the variability of TMT quantitation.
Fig. 4: Breast cancer subtype–specific protein and phosphorylation site expression identified by three laboratories.

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Acknowledgements

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.

Author information

Conceptualized and designed overall study: K.R.C., L.C.T., M.A. Gillette, P.M., and S.A.C. Led experimental and data analysis efforts at the performance sites: P.M., M.A. Gritsenko., S.A.C., K.K., D.R.M., K.R.C., D.J.C., M.A. Gillette, V.A.P., S.N.T., Z.Z., R.D.S., D.W.C., H.Z., and T.L. Developed PDX benchmarking reference material and distributed to all centers: S.R.D. and R.R.T. Tested and optimized aspects of the experimental protocol: D.J.C., F.M., H.K., L.C.T., M.A. Gritsenko, N.D.U., P.M., P.S., R.J.M., R.Z., S.N.T., and T.R.C. Analyzed the results: D.R.M., F.M., K.R.C., K.K., M.A.G., M.S., M.E.M., P.M., T.L., V.A.P., and Y.H. Wrote the manuscript: H.Z., L.C.T., M.A. Gillette, P.M., K.R.C., and S.A.C. Edited and revised aspects of the text: D.J.C., D.W.C., H.Z., K.K., L.C.T., R.D.S., S.N.T., T.L., V.A.P., Z.Z., R.R.T., and S.R.D.

Correspondence to Steven A. Carr.

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Related links

1. Mertins, P. et al. Nature 534, 55–62 (2016) http://dx.doi.org/10.1038/nature18003

2. Zhang, H. et al. Cell 166, 755–765 (2016) http://dx.doi.org/10.1016/j.cell.2016.05.069

3. Mundt, F. et al. Cancer Res. 78, 2732–2746 (2018) http://dx.doi.org/10.1158/0008-5472.CAN-17-1990

Integrated supplementary information

Supplementary Figure 1 Uniqueness per basic RP fraction for identified peptides.

The pie charts depicts the distribution of the number of fractions a peptide has been detected per TMT plex, which is highly reproducible across laboratories. More the 75% of peptides have been detected in a single fraction. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 2 Efficiency of tryptic protein digestion.

Missed cleavage rate for proteome (A) and phosphoproteome (B) data. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 3 QC of basic reversed-phase chromatography.

A) Chromatogram of non-human peptide standard mix on the offline HPLC column. B) Example of 4 mg TMT-labeled sample fractionated on an Agilent Xtend Column

Supplementary Figure 4 Enrichment specificity for phosphopeptides.

The barchart depicts the percentage of phosphorylated peptides in the metal-affinity enriched fractions. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 5 Number of quantified proteins and phosphorylation sites across three laboratories.

The total number of quantified A) proteins and B) phosphorylation sites that could be assigned to human are illustrated as ‘UpSet’ plots (Lex, 2014). Horizontal bars indicate total number of proteins or phosphorylation sites detected by each laboratory; vertical bars depict the number of jointly detected features, as indicated by the layout matrix below. C) Comparison of detected and quantified human phosphosites in two TMT10-plexes acquired in PCC1. D) and E) Analogous plots for PCC2 and PCC3, respectively. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 6 Intra-plex correlations calculated on human+mouse and human-specific proteins and phosphorylation sites.

The box-whisker plots depict Pearson correlations of TMT ratios between intraplex replicate measurements, separated into human+mouse and human-specific. A) Intraplex correlations of human+mouse and B) human-specific proteins. C) Intraplex correlations of human+mouse and B) human-specific phosphorylation sites. Focusing on proteins and phosphorylation sites with human origin resulted in lower intraplex variability. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 7 Mouse-specific proteins contribute to high intra-plex variability.

The example shown illustrates the impact of mouse proteins on reproducibility. A) Scatter plot of protein TMT ratios from human+mouse proteins measured in two luminal intraplex replicate measurements. A small population of mouse proteins could not be reproducibly quantified in the two replicates, resulting in a moderate Pearson correlation of 0.78. B) After removal of mouse-specific proteins, the correlation significantly improves to 0.88. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 8 Impact of ratio count prefiltering on coverage and reproducibility.

Interplex correlations were calculated for different numbers of ratio counts (x-axis) required to calculate A) protein and B) phosphorylation site ratios, respectively. Median Pearson correlations as a function of minimal ratio counts are depicted as lines (left axis). Error bars correspond to +/- 1 M.A.D. The number of quantified features at different minimal ratio counts are illustrated as bar plots (right axis). Red dashed boxes indicate the number of minimal ratio counts providing the best trade-off between reproducibility and coverage, and were subsequently used in this study. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 9 Breast cancer-relevant proteins were consistently measured within and across laboratories.

Proteins for ESR1, FOXA1, GATA3, and TP53 show higher expression in luminal PDX samples, whereas proteins for EGFR and KRT5 are more abundant in basal PDX samples. Normalized TMT ratios of basal or luminal samples over a multimean denominator are shown. Red lines indicate the median of five measurements per TMT10-plex experiment with interquartile range bars. PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

Supplementary Figure 10 Biology is very well recapitulated between all three centers.

A) Normalized enrichment scores (NES) show high correlation with Pearson r’s of 0.96 to 0.97. The four most enriched genesets are color coded and include SMID_BREAST_CANCER_BASAL_UP and SMID_BREAST_CANCER_RELAPSE_IN_BONE_DN, which are enriched in proteins and phosphoproteins that are high in basal tumors, compared with the luminal tumors. Conversely, SMID_BREAST_CANCER_BASAL_DN and VANTEER_BREAST_CANCER_ESR1_UP are enriched in proteins and phosphoproteins that are high in luminal tumors (and therefore have low NES-scores). B) All the genesets containing the terms: “BASAL”, “ESR1”, or “LUMINAL” are plotted as three heat--maps; one for each search term. Red indicates a high NES score and blue a low NES score. Protein Characterization Center (PCC). PDX models used in this study were approved by the institutional animal care and use committee at Washington University in St. Louis

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Mertins, P., Tang, L.C., Krug, K. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry. Nat Protoc 13, 1632–1661 (2018). https://doi.org/10.1038/s41596-018-0006-9

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