Tissues contain more tumor-type specific information than biofluids, such as blood, rendering them valuable resources for biomarker studies. However, considering the characteristics of tissue homogenization, it is difficult to obtain reproducible samples and analyze many samples simultaneously. To address these issues, we developed a robust and reproducible method for preparing tissues for targeted proteomics—multiple reaction monitoring-mass spectrometry (MRM-MS)—using a Bioruptor Pico sonicator. This approach uses sodium deoxycholate (SDC) as a detergent and can extract proteins from up to 20 mg of tissue using a lysis buffer volume of 300 µL and a sonication time of 30 s, with 30 on/off cycles. The tryptic digestion was optimized as follows: digestion base buffer, ammonium bicarbonate (ABC); reduction and alkylation reagent, dithiothreitol (DTT) and iodoacetamide (IAA), respectively; and trypsin amount and incubation time, 1:50 (enzyme: substrate) and 10 h, respectively. With regard to reproducibility, the intra-assay and inter-assay CVs for the target peptides were less than 20% (intra-CV, 0.87% to 19.13%; inter-CV, 2.3% to 13.62%). Our method was robust and reproducible in the quantitative analysis of tissue by MRM-MS, rendering it applicable to the large-scale study of tissue-based biomarkers.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price includes VAT (USA)
Tax calculation will be finalised during checkout.
Keshishian, H., T. Addona, M. Burgess, E. Kuhn, and S. A. Carr (2007) Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell. Proteomics. 6: 2212–2229.
Picotti, P., B. Bodenmiller, L. N. Mueller, B. Domon, and R. Aebersold (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell. 138: 795–806.
Whiteaker, J. R., L. Zhao, L. Anderson, and A. G. Paulovich (2010) An automated and multiplexed method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers. Mol. Cell. Proteomics. 9: 184–196.
Shi, T., D. Su, T. Liu, K. Tang, D. G. Camp 2nd, W. J. Qian, and R. D. Smith (2012) Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics. Proteomics. 12: 1074–1092.
Abbatiello, S. E., B. Schilling, D. R. Mani, L. J. Zimmerman, S. C. Hall, B. MacLean, M. Albertolle, S. Allen, M. Burgess, M. P. Cusack, M. Gosh, V. Hedrick, J. M. Held, H. D. Inerowicz, A. Jackson, H. Keshishian, C. R. Kinsinger, J. Lyssand, L. Makowski, M. Mesri, H. Rodriguez, P. Rudnick, P. Sadowski, N. Sedransk, K. Shaddox, S. J. Skates, E. Kuhn, D. Smith, J. R. Whiteaker, C. Whitwell, S. Zhang, C. H. Borchers, S. J. Fisher, B. W. Gibson, D. C. Liebler, M. J. MacCoss, T. A. Neubert, A. G. Paulovich, F. E. Regnier, P. Tempst, and S. A. Carr (2015) Large-scale interlaboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma. Mol. Cell. Proteomics. 14: 2357–2374.
Kuhn, E., J. R. Whiteaker, D. R. Mani, A. M. Jackson, L. Zhao, M. E. Pope, D. Smith, K. D. Rivera, N. L. Anderson, S. J. Skates, T. W. Pearson, A. G. Paulovich, and S. A. Carr (2012) Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma. Mol. Cell. Proteomics. 11: M111.013854.
Kim, H., J. Park, Y. Kim, A. Sohn, I. Yeo, S. J. Yu, J. H. Yoon, T. Park, and Y. Kim (2017) Serum fibronectin distinguishes the early stages of hepatocellular carcinoma. Sci. Rep. 7: 9449.
Kim, H., A. Sohn, I. Yeo, S. J. Yu, J. H. Yoon, and Y. Kim (2018) Clinical assay for AFP-L3 by using multiple reaction monitoringmass spectrometry for diagnosing hepatocellular carcinoma. Clin. Chem. 64: 1230–1238.
Kim, H., S. J. Yu, I. Yeo, Y. Y. Cho, D. H. Lee, Y. Cho, E. J. Cho, J. H. Lee, Y. J. Kim, S. Lee, J. Jun, T. Park, J. H. Yoon, and Y. Kim (2017) Prediction of response to sorafenib in hepatocellular carcinoma: A putative marker panel by multiple reaction monitoring-mass spectrometry (MRM-MS). Mol. Cell. Proteomics. 16: 1312–1323.
Sohn, A., H. Kim, I. Yeo, Y. Kim, M. Son, S. J. Yu, J. H. Yoon, and Y. Kim (2018) Fully validated SRM-MS-based method for absolute quantification of PIVKA-II in human serum: Clinical applications for patients with HCC. J. Pharm. Biomed. Anal. 156: 142–146.
Domon, B. and S. Gallien (2015) Recent advances in targeted proteomics for clinical applications. Proteomics Clin. Appl. 9: 423–431.
He, J., A. A. Schepmoes, T. Shi, C. Wu, T. L. Fillmore, Y. Gao, R. D. Smith, W. J. Qian, K. D. Rodland, T. Liu, D. G. Camp 2nd, A. Rastogi, S. H. Tan, W. Yan, A. A. Mohamed, W. Huang, S. Banerjee, J. Kagan, S. Srivastava, D. G. McLeod, S. Srivastava G. Petrovics, A. Dobi, and A. Srinivasan (2015) Analytical platform evaluation for quantification of ERG in prostate cancer using protein and mRNA detection methods. J. Transl. Med. 13: 54.
Son, M., H. Kim, I. Yeo, Y. Kim, A. Sohn, and Y. Kim (2019) Method validation by CPTAC guidelines for multi-protein marker assays using multiple reaction monitoring-mass spectrometry. Biotechnol. Bioprocess Eng. 24: 343–358.
Minikel, E. V., E. Kuhn, A. R. Cocco, S. M. Vallabh, C. R. Hartigan, A. G. Reidenbach, J. G. Safar, G. J. Raymond, M. D. McCarthy, R. O’Keefe, F. Llorens, I. Zerr, S. Capellari, P. Parchi, S. L. Schreiber, and S. A. Carr (2019) Domain-specific quantification of prion protein in cerebrospinal fluid by targeted mass spectrometry. Mol. Cell. Proteomics. 18: 2388–2400.
Wildsmith, K. R., S. P. Schauer, A. M. Smith, D. Arnott, Y. Zhu, J. Haznedar, S. Kaur, W. R. Mathews, and L. A. Honigberg (2014) Identification of longitudinally dynamic biomarkers in Alzheimer’s disease cerebrospinal fluid by targeted proteomics. Mol. Neurodegener. 9: 22.
Liu, X., W. Zheng, W. Wang, H. Shen, L. Liu, W. Lou, X. Wang, and P. Yang (2017) A new panel of pancreatic cancer biomarkers discovered using a mass spectrometry-based pipeline. Br. J. Cancer. 117: 1846–1854.
Huttenhain, R., M. Choi, L. M. de la Fuente, K. Oehl, C. Y. V. Chang, A. K. Zimmermann, S. Malander, H. Olsson, S. Surinova, T. Clough, V. Heinzelmann-Schwarz, P. J. Wild, D. M. Dinulescu, E. Niméus, O. Vitek, and R. Aebersold (2019) A targeted mass spectrometry strategy for developing proteomic biomarkers: a case study of epithelial ovarian cancer. Mol. Cell. Proteomics. 18: 1836–1850.
Yu, J., K. Kim, M. Kang, H. Kim, S. W. Kim, J. Y. Jang, and Y. Kim (2013) Development of candidate biomarkers for pancreatic ductal adenocarcinoma using multiple reaction monitoring. Biotechnol. Bioprocess Eng. 18: 1038–1047.
Chen, Y. T., H. W. Chen, C. F. Wu, L. J. Chu, W. F. Chiang, C. C. Wu, J. S. Yu, C. H. Tsai, K. H. Liang, Y. S. Chang, M. Wu, and W. T. Ou Yang (2017) Development of a multiplexed liquid chromatography multiple-reaction-monitoring mass spectrometry (LC-MRM/MS) method for evaluation of salivary proteins as oral cancer biomarkers. Mol. Cell. Proteomics. 16: 799–811.
Duriez, E., C. D. Masselon, C. Mesmin, M. Court, K. Demeure, Y. Allory, N. Malats, M. Matondo, F. Radvanyi, J. Garin, and B. Domon (2017) Large-scale SRM screen of urothelial bladder cancer candidate biomarkers in urine. J. Proteome Res. 16: 1617–1631.
Chen, Y., D. Britton, E. R. Wood, S. Brantley, A. Magliocco, I. Pike, and J. M. Koomen (2017) Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers. Proteomics. 17: 1600335.
Uzozie, A. C., N. Selevsek, A. Wahlander, P. Nanni, J. Grossmann, A. Weber, F. Buffoli, and G. Marra (2017) Targeted proteomics for multiplexed verification of markers of colorectal tumorigenesis. Mol. Cell. Proteomics. 16: 407–427.
Naboulsi, W., D. A. Megger, T. Bracht, M. Kohl, M. Turewicz, M. Eisenacher, D. M. Voss, J. F. Schlaak, A. C. Hoffmann, F. Weber, H. A. Baba, H. E. Meyer, and B. Sitek (2016) Quantitative tissue proteomics analysis reveals versican as potential biomarker for early-stage hepatocellular carcinoma. J. Proteome Res. 15: 38–47.
Frantzi, M., K. E. Van Kessel, E. C. Zwarthoff, M. Marquez, M. Rava, N. Malats, A. S. Merseburger, I. Katafigiotis, K. Stravodimos, W. Mullen, J. Zoidakis, M. Makridakis, M. Pejchinovski, E. Critselis, R. Lichtinghagen, K. Brand, M. Dakna, M. G. Roubelakis, D. Theodorescu, A. Vlahou, H. Mischak, and N. P. Anagnou (2016) Development and validation of urine-based peptide biomarker panels for detecting bladder cancer in a multicenter study. Clin. Cancer Res. 22: 4077–4086.
Ohnishi, M., T. Matsumoto, R. Nagashio, T. Kageyama, S. Utsuki, H. Oka, I. Okayasu, and Y. Sato (2009) Proteomics of tumor-specific proteins in cerebrospinal fluid of patients with astrocytoma: usefulness of gelsolin protein. Pathol. Int. 59: 797–803.
Kumar, D. M., B. Thota, S. V. Shinde, K. V. Prasanna, A. S. Hegde, A. Arivazhagan, B. A. Chandramouli, V. Santosh, and K. Somasundaram (2010) Proteomic identification of haptoglobin α2 as a glioblastoma serum biomarker: implications in cancer cell migration and tumor growth. J. Proteome Res. 9: 5557–5567.
Nirmalan, N. J., P. Harnden, P. J. Selby, and R. E. Banks (2008) Mining the archival formalin-fixed paraffin-embedded tissue proteome: opportunities and challenges. Mol. Biosyst. 4: 712–720.
Rifai, N., M. A. Gillette, and S. A. Carr (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24: 971–983.
Cottingham, K. (2007) Tissues tell the real tale of breast cancer. J. Proteome Res. 6: 2052.
Zhang, B., J. Wang, X. Wang, J. Zhu, Q. Liu, Z. Shi, M. C. Chambers, L. J. Zimmerman, K. F. Shaddox, S. Kim, S. R. Davies, S. Wang, P. Wang, C. R. Kinsinger, R. C. Rivers, H. Rodriguez, R. R. Townsend, M. J. C. Ellis, S. A. Carr, D. L. Tabb, R. J. Coffey, R. J. C. Slebos, and D. C. Liebler (2014) Proteogenomic characterization of human colon and rectal cancer. Nature. 513: 382–387.
Dounce, A. L., R. F. Witter, K. J. Monty, S. Pate, and M. A. Cottone (1955) A method for isolating intact mitochondria and nuclei from the same homogenate, and the influence of mitochondrial destruction on the properties of cell nuclei. J Biophys Biochem. Cytol. 1: 139–153.
Buczak, K., A. Ori, J. M. Kirkpatrick, K. Holzer, D. Dauch, S. Roessler, V. Endris, F. Lasitschka, L. Parca, A. Schmidt, L. Zender, P. Schirmacher, J. Krijgsveld, S. Singer, and M. Beck (2018) Spatial tissue proteomics quantifies inter- and intratumor heterogeneity in hepatocellular carcinoma (HCC). Mol. Cell. Proteomics. 17: 810–825.
Emmett, M. R. and R. M. Caprioli (1994) Micro-electrospray mass spectrometry: Ultra-high-sensitivity analysis of peptides and proteins. J. Am. Soc. Mass Spectrom. 5: 605–613.
Vivo-Truyols, G. and P. J. Schoenmakers (2006) Automatic selection of optimal Savitzky-Golay smoothing. Anal. Chem. 78: 4598–4608.
Leon, I. R., V. Schwammle, O. N. Jensen, and R. R. Sprenger (2013) Quantitative assessment of in-solution digestion efficiency identifies optimal protocols for unbiased protein analysis. Mol. Cell. Proteomics. 12: 2992–3005.
Lin, Y., J. Zhou, D. Bi, P. Chen, X. Wang, and S. Liang (2008) Sodium-deoxycholate-assisted tryptic digestion and identification of proteolytically resistant proteins. Anal. Biochem. 377: 259–266.
Proc, J. L., M. A. Kuzyk, D. B. Hardie, J. Yang, D. S. Smith, A. M. Jackson, C. E. Parker, and C. H. Borchers (2010) A quantitative study of the effects of chaotropic agents, surfactants, and solvents on the digestion efficiency of human plasma proteins by trypsin. J. Proteome Res. 9: 5422–5437.
Hao, P., Y. Ren, A. Datta, J. P. Tam, and S. K. Sze (2015) Evaluation of the effect of trypsin digestion buffers on artificial deamidation. J. Proteome Res. 14: 1308–1314.
Rosenfeld, J., J. Capdevielle, J. C. Guillemot, and P. Ferrara (1992) In-gel digestion of proteins for internal sequence analysis after one- or two-dimensional gel electrophoresis. Anal. Biochem. 203: 173–179.
Ren, D., G. D. Pipes, D. Liu, L. Y. Shih, A. C. Nichols, M. J. Treuheit, D. N. Brems, and P. V. Bondarenko (2009) An improved trypsin digestion method minimizes digestion-induced modifications on proteins. Anal. Biochem. 392: 12–21.
Ruhl, M., V. Golghalyani, G. Barka, U. Bahr, and M. Karas (2017) Enhanced on-plate digestion of proteins using a MALDI-digestion chamber. Int. J. Mass Spectrom. 416: 37–45.
Dittrich, J., S. Becker, M. Hecht, and U. Ceglarek (2015) Sample preparation strategies for targeted proteomics via proteotypic peptides in human blood using liquid chromatography tandem mass spectrometry. Proteomics Clin. Appl. 9: 5–16.
Ceglarek, U., J. Dittrich, S. Becker, F. Baumann, L. Kortz, and J. Thiery (2013) Quantification of seven apolipoproteins in human plasma by proteotypic peptides using fast LC-MS/MS. Proteomics Clin. Appl. 7: 794–801.
Uchida, Y., M. Tachikawa, W. Obuchi, Y. Hoshi, Y. Tomioka, S. Ohtsuki, and T. Terasaki (2013) A study protocol for quantitative targeted absolute proteomics (QTAP) by LC-MS/MS: application for inter-strain differences in protein expression levels of transporters, receptors, claudin-5, and marker proteins at the blood-brain barrier in ddY, FVB, and C57BL/6J mice. Fluids Barriers CNS. 10: 21.
Addona, T. A., S. E. Abbatiello, B. Schilling, S. J. Skates, D. R. Mani, D. M. Bunk, C. H. Spiegelman, L. J. Zimmerman, A. J. L. Ham, H. Keshishian, S. C. Hall, S. Allen, R. K. Blackman, C. H. Borchers, C. Buck, H. L. Cardasis, M. P. Cusack, N. G. Dodder, B. W. Gibson, J. M. Held, T. Hiltke, A. Jackson, E. B. Johansen, C. R. Kinsinger, J. Li, M. Mesri, T. A. Neubert, R. K. Niles, T. C. Pulsipher, D. Ransohoff, H. Rodriguez, P. A. Rudnick, D. Smith, D. L. Tabb, T. J. Tegeler, A. M. Variyath, L. J. Vega-Montoto, A. Wahlander, S. Waldemarson, M. Wang, J. R. Whiteaker, L. Zhao, N. L. Anderson, S. J. Fisher, D. C. Liebler, A. G. Paulovich, F. E. Regnier, P. Tempst, and S. A. Carr (2009) Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27: 633–641.
Lundell, N. and T. Schreitmuller (1999) Sample preparation for peptide mapping - A pharmaceutical quality-control perspective. Anal. Biochem. 266: 31–47.
Smith, J. G. and R. E. Gerszten (2017) Emerging affinity-based proteomic technologies for large-scale plasma profiling in cardiovascular disease. Circulation. 135: 1651–1664.
Anderson, L. and C. L. Hunter (2006) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics. 5: 573–588.
This work was supported by the Industrial Strategic Technology Development Program (#10079271 and #20000134), funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea); the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (# HL19C0020); and the Collaborative Genome Program for Fostering New Post-Genome Industry (NRF-2017M3C9A5031597). This study was also supported by a grant from Seoul National University Hospital (2020).
The authors declare no conflict of interest.
Neither ethical approval nor informed consent was required for this study.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary materials
About this article
Cite this article
Kim, Y., Yeo, I., Kim, H. et al. Preparation of Tissue Samples for Large-scale Quantitative Mass Spectrometric Analysis. Biotechnol Bioproc E 25, 551–561 (2020). https://doi.org/10.1007/s12257-019-0495-6
- sample preparation method
- targeted proteomics