The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry
Open accessAbstract
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. This protocol describes a workflow for multiplexed deep-scale, quantitative proteome and phosphoproteome analysis of tumor tissue samples. The procedure includes step-by-step instructions for all stages, from sample preparation to data analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Self-citationRoss, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., et al. (2004) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m400129-mcp200 · Cited by 4,401
Proteogenomics connects somatic mutations to signalling in breast cancer
CPTAC, N., Mertins, P., Mani, D. R., Ruggles, K. V., Gillette, M. A., Clauser, K. R., Wang, P., Wang, X., Qiao, J. W., Cao, S., et al. (2016) · Nature · doi:10.1038/nature18003 · Cited by 1,676
Self-citationMass spectrometry–based proteomics turns quantitative
Ong, S.-E., & Mann, M. (2005) · Nature Chemical Biology · doi:10.1038/nchembio736 · Cited by 1,572
Proteogenomic characterization of human colon and rectal cancer
CPTAC, T. N., Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., Chambers, M. C., Zimmerman, L. J., Shaddox, K. F., et al. (2014) · Nature · doi:10.1038/nature13438 · Cited by 1,462
Self-citationMS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics
Ting, L., Rad, R., Gygi, S. P., & Haas, W. (2011) · Nature Methods · doi:10.1038/nmeth.1714 · Cited by 1,009
Geiger, T., Wehner, A., Schaab, C., Cox, J., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.014050 · Cited by 797
Research works printed but not linked: 37
- Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016). · DOI · Paper page
- Ntai, I. et al. Integrated bottom-up and top-down proteomics of patient-derived breast tumor xenografts. Mol. Cell. Proteomics 15, 45–56 (2016). · DOI
- Coscia, F. et al. Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status. Nat. Commun. 7, 1–14 (2016). · DOI
- Geiser, L., Dayon, L., Vaezzadeh, A. R. & Hochstrasser, D. F. Shotgun proteomics: a relative quantitative approach using off-gel electrophoresis and LC-MS/MS. Methods Mol. Biol. 681, 459–472 (2011). · DOI
- Dwivedi, R. C. et al. Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal. Chem. 80, 7036–7042 (2008). · DOI
- Gilar, M., Olivova, P., Daly, A. E. & Gebler, J. C. Orthogonality of separation in two-dimensional liquid chromatography. Anal. Chem. 77, 6426–6434 (2005). · DOI · Paper page
- Delmotte, N., Lasaosa, M., Tholey, A., Heinzle, E. & Huber, C. G. Two-dimensional reversed-phase × ion-pair reversed-phase HPLC: an alternative approach to high-resolution peptide separation for shotgun proteome analysis. J. Proteome Res. 6, 4363–4373 (2007). · DOI
- Song, C. et al. Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides. Anal. Chem. 82, 53–56 (2010). · DOI
- Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011). · DOI · Paper page
- Mertins, P. et al. Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat. Methods 10, 634–637 (2013). · DOI · Paper page
- Batth, T. S., Francavilla, C. & Olsen, J. V. Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J. Proteome Res. 13, 6176–6186 (2014). · DOI · Paper page
- Rauniyar, N. & Yates, J. R. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293-5309 (2014). · DOI · Paper page
- Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J. R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010). · DOI · Paper page
- Molina, H. et al. Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J. Proteome Res. 8, 48–58 (2009). · DOI
- Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007). · DOI · Paper page
- Rauniyar, N. & Yates, J. R. III Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014). · DOI · Paper page
- Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003). · DOI · Paper page
- Pichler, P. et al. Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal. Chem. 82, 6549–6558 (2010). · DOI
- Raso, C. et al. Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-Orbitrap Velos mass spectrometry, and pathway analysis. J. Proteome Res. 11, 3199–3210 (2012). · DOI
- Werner, T. et al. Ion coalescence of neutron encoded TMT 10-plex reporter ions. Anal. Chem. 86, 3594–3601 (2014). · DOI · Paper page
- Mertins, P. et al. Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels. Mol. Cell. Proteomics. 13, 1690–1704 (2014). · DOI · Paper page
- Svinkina, T. et al. Deep, quantitative coverage of the lysine acetylome using novel anti-acetyl-lysine antibodies and an optimized proteomic workflow. Mol. Cell. Proteomics 14, 2429–2440 (2015). · DOI
- Böhm, G. et al. Low-pH solid-phase amino labeling of complex peptide digests with TMTs improves peptide identification rates for multiplexed global phosphopeptide analysis. J. Proteome Res. 14, 2500–2510 (2015). · DOI
- Wiktorowicz, J. E., English, R. D., Wu, Z. & Kurosky, A. Model studies on iTRAQ modification of peptides: sequence-dependent reaction specificity. J. Proteome Res. 11, 1512–1520 (2012). · doi:10.1021/pr2003165
- Thingholm, T. E., Palmisano, G., Kjeldsen, F. & Larsen, M. R. Undesirable charge-enhancement of isobaric tagged phosphopeptides leads to reduced identification efficiency. J. Proteome Res. 9, 4045–4552 (2010). · DOI
- Karp, N. A. et al. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell. Proteomics 9, 1885–1897 (2010). · DOI · Paper page
- Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013). · DOI · Paper page
- May, J. C. & McLean, J. A. Ion mobility-mass spectrometry: time-dispersive instrumentation. Anal. Chem. 87, 1422–1436 (2014). · DOI · Paper page
- Pfammatter, S., Bonneil, W. & Thibault, P. Improvement of quantitative measurements in multiplex proteomics using high-field asymmetric waveform ion mobility spectrometry. J. Proteome Res. 15, 4653 (2016). · DOI
- Ibrahim, Y. M. et al. New frontiers for mass spectrometry based upon structures for lossless ion manipulations. Analyst 142, 1010–1021 (2017). · DOI
- Shadforth, I. P., Dunkley, T. P., Lilley, K. S. & Bessant, C. i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 6, 145–150 (2005). · DOI · Paper page
- Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protocols 2, 1896–1906 (2007). · DOI · Paper page
- Rodriguez, J., Gupta, N., Smith, R. D. & Pevzner, P. A. Does trypsin cut before proline? J. Proteome Res. 7, 300–305 (2008). · DOI · Paper page
- Mertins, P. et al. iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol. Cell. Proteomics 11, M111.014423 (2012). · DOI
- Wenger, C. D. et al. Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nat. Methods 8, 933–935 (2011). · DOI
- Tabb, D. et al. Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts. J. Proteome Res. 15, 691–706 (2016). · DOI
- Hogrebe, A. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9, 1045 (2018). · DOI · Paper page
Cited by
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001
Self-citationProteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma
Gillette, M. A., Satpathy, S., Cao, S., Dhanasekaran, S. M., Vasaikar, S. V., Krug, K., Petralia, F., Li, Y., Liang, W.-W., Reva, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.013 · Cited by 820
Self-citationIntegrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma
Clark, D. J., Dhanasekaran, S. M., Petralia, F., Pan, J., Song, X., Hu, Y., da Veiga Leprevost, F., Reva, B., Lih, T.-S. M., Chang, H.-Y., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.007 · Cited by 730
Self-citationProteogenomic characterization of pancreatic ductal adenocarcinoma
Cao, L., Huang, C., Cui Zhou, D., Hu, Y., Lih, T. M., Savage, S. R., Krug, K., Clark, D. J., Schnaubelt, M., Chen, L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.08.023 · Cited by 578
Self-citationOpenCell: Endogenous tagging for the cartography of human cellular organization
Cho, N. H., Cheveralls, K. C., Brunner, A.-D., Kim, K., Michaelis, A. C., Raghavan, P., Kobayashi, H., Savy, L., Li, J. Y., Canaj, H., et al. (2022) · Science · doi:10.1126/science.abi6983 · Cited by 567
Johnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00999-y · Cited by 561
Proximity labeling in mammalian cells with TurboID and split-TurboID
Cho, K. F., Branon, T. C., Udeshi, N. D., Myers, S. A., Carr, S. A., & Ting, A. Y. (2020) · Nature Protocols · doi:10.1038/s41596-020-0399-0 · Cited by 534
Self-citationSARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528
Mitochondrial Reprogramming Underlies Resistance to BCL-2 Inhibition in Lymphoid Malignancies
Guièze, R., Liu, V. M., Rosebrock, D., Jourdain, A. A., Hernández-Sánchez, M., Martinez Zurita, A., Sun, J., Ten Hacken, E., Baranowski, K., Thompson, P. A., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.08.005 · Cited by 361
Self-citationEpigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity
Griffin, G. K., Wu, J., Iracheta-Vellve, A., Patti, J. C., Hsu, J., Davis, T., Dele-Oni, D., Du, P. P., Halawi, A. G., Ishizuka, J. J., et al. (2021) · Nature · doi:10.1038/s41586-021-03520-4 · Cited by 370
Self-citationHigginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Hurst, C., Glass, J. D., Factor, S. A., Johnson, E. C. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz9360 · Cited by 367
Higginbotham, L., Ping, L., Dammer, E. B., Duong, D. M., Zhou, M., Gearing, M., Johnson, E. C. B., Hajjar, I., Lah, J. J., Levey, A. I., et al. (2019) · doi:10.1101/806752 · Cited by 367
Wingo, A. P., Liu, Y., Gerasimov, E. S., Gockley, J., Logsdon, B. A., Duong, D. M., Dammer, E. B., Robins, C., Beach, T. G., Reiman, E. M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00773-z · Cited by 366
The SARS-CoV-2 RNA–protein interactome in infected human cells
Schmidt, N., Lareau, C. A., Keshishian, H., Ganskih, S., Schneider, C., Hennig, T., Melanson, R., Werner, S., Wei, Y., Zimmer, M., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-00846-z · Cited by 319
Self-citationCancer proteogenomics: current impact and future prospects
Mani, D. R., Krug, K., Zhang, B., Satpathy, S., Clauser, K. R., Ding, L., Ellis, M., Gillette, M. A., & Carr, S. A. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00446-5 · Cited by 229
Self-citationShared mechanisms across the major psychiatric and neurodegenerative diseases
Wingo, T. S., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Wynne, M. E., Liu, J., Lori, A., Faundez, V., Bennett, D. A., Seyfried, N. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31873-5 · Cited by 190
Thompson, A., Wölmer, N., Koncarevic, S., Selzer, S., Böhm, G., Legner, H., Schmid, P., Kienle, S., Penning, P., Höhle, C., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b04474 · Cited by 186
Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain
Wingo, A. P., Fan, W., Duong, D. M., Gerasimov, E. S., Dammer, E. B., Liu, Y., Harerimana, N. V., White, B., Thambisetty, M., Troncoso, J. C., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0635-5 · Cited by 161
Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease
Ping, L., Kundinger, S. R., Duong, D. M., Yin, L., Gearing, M., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2020) · Scientific Data · doi:10.1038/s41597-020-00650-8 · Cited by 127
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104
Determining the ERK-regulated phosphoproteome driving KRAS-mutant cancer
Klomp, J. E., Diehl, J. N., Klomp, J. A., Edwards, A. C., Yang, R., Morales, A. J., Taylor, K. E., Drizyte-Miller, K., Bryant, K. L., Schaefer, A., et al. (2024) · Science · doi:10.1126/science.adk0850 · Cited by 83
Mari, T., Mösbauer, K., Wyler, E., Landthaler, M., Drosten, C., & Selbach, M. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00198 · Cited by 70
Shi, W., Wang, Y., Xu, C., Li, Y., Ge, S., Bai, B., Zhang, K., Wang, Y., Zheng, N., Wang, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35797-6 · Cited by 50
Self-citationA Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics
Bramer, L. M., Irvahn, J., Piehowski, P. D., Rodland, K. D., & Webb-Robertson, B.-J. M. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00123 · Cited by 47
Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing
Yang, M., Matan-Lithwick, S., Wang, Y., De Jager, P. L., Bennett, D. A., & Felsky, D. (2023) · Brain Communications · doi:10.1093/braincomms/fcad110 · Cited by 35
Reddy, J. S., Heath, L., Linden, A. V., Allen, M., Lopes, K. D. P., Seifar, F., Wang, E., Ma, Y., Poehlman, W. L., Quicksall, Z. S., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14208 · Cited by 29
Tate, M., Wijeratne, H. R. S., Kim, B., Philtjens, S., You, Y., Lee, D., Gutierrez, D. A., Sharify, D., Wells, M., Perez‐Cardelo, M., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14243 · Cited by 21
Kim, B., Tate, M. D., Karahan, H., Wijeratne, H. R. S., Sharify, A. D., Wang, S. S., Kim, J. R., Al-Amin, M. M., Hartigan, K., Chung, S., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.03.011 · Cited by 6
Deletion of neuronal Idol ameliorates Alzheimer's disease–related pathologies via APOE receptors
Karahan, H., Hartigan, K., Al‐Amin, M. M., John, S. K., McCord, B., Wijeratne, H. R. S., Acri, D. J., Smith, D. C., Dabin, L. C., Cordero, H. M. R., et al. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70949 · Cited by 2
Waghmare, I., Gangwani, K., Rai, A., Singh, A., & Kango-Singh, M. (2023) · doi:10.1101/2023.10.18.561369 · Cited by 1
Regulation of vacuole fusion in stomata by dephosphorylation of the HOPS subunit VPS39
Pullen, A., Billings, G., Hodgens, C., White, G., Akpa, B. S., & Rojas‐Pierce, M. (2025) · The Plant Journal · doi:10.1111/tpj.70640 · Cited by 1
Metadata
- Authors
- Mertins, P., Tang, L. C., Krug, K., Clark, D. J., Gritsenko, M. A., Chen, L., Clauser, K. R., Clauss, T. R., Shah, P., Gillette, M. A., Petyuk, V. A., Thomas, S. N., Mani, D. R., Mundt, F., Moore, R. J., Hu, Y., Zhao, R., Schnaubelt, M., Keshishian, H., Monroe, M. E., Zhang, Z., Udeshi, N. D., Mani, D., Davies, S. R., Townsend, R. R., Chan, D. W., Smith, R. D., Zhang, H., Liu, T., & Carr, S. A.
- Venue
- Nature Protocols
- Published
- 2018-07
- Volume
- 13
- Issue
- 7
- Pages
- 1632-1661
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2857722519
- Cited by
- 479
- References
- 7
- FWCI
- 15.93
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.