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Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Nature Protocols volume 16, pages 3737–3760 (2021)Cite this article

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Abstract

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography–mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

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Fig. 1: Phases of biomarker development studies.
Fig. 2: Considerations for each step of the discovery-phase workflow.
Fig. 3: Monitoring instrument performance with standard samples.
Fig. 4: Identification of unexpected peptide modifications with data QC analysis.
Fig. 5: Considerations for each step of the validation-phase workflow.

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Data availability

All the data discussed in this review are associated with the supporting primary research papers.

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Acknowledgements

The authors thank N. Johnson for his help in designing figures used in this publication. This work was supported by National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases grants UC4 DK104166 (to C.E.M. and T.O.M.), U01 DK127786 (to C.E.M., B.J.M.W.R and T.O.M.), U01 DK124020 (to W.J.Q), R01 DK032493 (to M.R.) and P30DK097512 (to C.E.M), R01 DK093954 (to C.E.M) and R21 DK119800-01A1 (to C.E.M). M.R., E.S.N., B.J.M.W.R. and T.O.M. were also supported by the Helmsley Trust grant G-1901-03687. C.E.M was also supported by VA Merit Award I01BX001733, JDRF 2-SRA-2018-493-A-B and gifts from the Sigma Beta Sorority, the Ball Brothers Foundation, and the George and Frances Ball Foundation. The TEDDY Study is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Centers for Disease Control and Prevention (CDC), and JDRF. TEDDY is supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR002535). Work was performed in the Environmental Molecular Sciences Laboratory, a US Department of Energy (DOE) national scientific user facility at Pacific Northwest National Laboratory (PNNL) in Richland, WA. Battelle operates PNNL for the DOE under contract DE-AC05-76RLO01830.

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Authors and Affiliations

  1. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Ernesto S. Nakayasu, Marina Gritsenko, Paul D. Piehowski, Yuqian Gao, Daniel J. Orton, Athena A. Schepmoes, Thomas L. Fillmore, Charles Ansong, Wei-Jun Qian, Bobbie-Jo M. Webb-Robertson & Thomas O. Metz

  2. Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, USA

    Brigitte I. Frohnert & Marian Rewers

  3. Morsani College of Medicine, University of South Florida, Tampa, FL, USA

    Jeffrey P. Krischer

  4. Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, USA

    Astrid M. Suchy-Dicey

  5. Center for Diabetes and Metabolic Diseases and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA

    Carmella Evans-Molina

  6. Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Bobbie-Jo M. Webb-Robertson

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  1. Ernesto S. Nakayasu
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Contributions

E.S.N. wrote the abstract, introduction and concluding remarks, contributed to the data analysis section and edited the manuscript; A.M.D.S., J.P.K., M.R. and B.I.F. wrote the sections on subject selection, power calculation and considerations for sample handling. C.E.M. wrote the section on specimen collection, storage and tracking; M.G., P.D.P. and A.S. wrote the sample preparation sections of both discovery and validation phases; D.O. wrote the section on data collection for the discovery phase; C.A. wrote the section on data quality control; B.J.W.R. contributed to the power analysis section and wrote the data analysis section; Y.G., P.D.P., T.F. and W.J.Q wrote about the different sections of the validation phase; T.O.M. wrote the phases of biomarker development. All the authors read, provided inputs and approved the final version of the manuscript.

Corresponding authors

Correspondence to Ernesto S. Nakayasu or Thomas O. Metz.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Bing Zhang and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Key references using this review

Zhang, Q. et al. J. Exp. Med. 210, 191–203 (2013): https://doi.org/10.1084/jem.20111843

Carnielli, C. M. et al. Nat. Commun. 9, 3598 (2018): https://doi.org/10.1038/s41467-018-05696-2

Tofte, N. et al. Lancet Diabetes Endocrinol. 8, 301–312 (2020): https://doi.org/10.1016/S2213-8587(20)30026-7

Zhang, Z. et al. Cancer Res. 64, 5882–5890 (2004): https://doi.org/10.1158/0008-5472.CAN-04-0746

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Nakayasu, E.S., Gritsenko, M., Piehowski, P.D. et al. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nat Protoc 16, 3737–3760 (2021). https://doi.org/10.1038/s41596-021-00566-6

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