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Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques
Open accessAbstract
INTRODUCTION: While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.
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“Therefore, DIA results in the identification of nearly all detectable peptides within a selected mass range, allowing for comprehensive and accurate quantification of identified proteins in the sample with minimal missing values [81, 82].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [81]
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MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M., Finney, G. L., Frewen, B., Kern, R., Tabb, D. L., Liebler, D. C., & MacCoss, M. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq054 · Cited by 4,697
Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877
Absolute Quantification of Proteins by LCMSE
Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C., & Geromanos, S. J. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500230-mcp200 · Cited by 1,529
Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions
Picotti, P., & Aebersold, R. (2012) · Nature Methods · doi:10.1038/nmeth.2015 · Cited by 1,302
Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinović, S. M., Cheng, L.-Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044305 · Cited by 1,238
Choi, M., Chang, C.-Y., Clough, T., Broudy, D., Killeen, T., MacLean, B., & Olga Vitek, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu305 · Cited by 1,041
OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data
Röst, H. L., Rosenberger, G., Navarro, P., Gillet, L. C. J., Miladinović, S. M., Schubert, O. T., Wolski, W., Collins, B. C., Malmström, J. A., Malmström, L., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2841 · Cited by 762
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Tsou, C., Avtonomov, D. M., Larsen, B. G., Tucholska, M., Choi, H., Gingras, A., & Nesvizhskii, A. I. (2015) · Nature Methods · doi:10.1038/nmeth.3255 · Cited by 642
Collins, B. C., Hunter, C. L., Liu, Y., Schilling, B., Rosenberger, G., Bader, S. L., Chan, D. W., Gibson, B. W., Gingras, A.-C., Held, J. M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00249-5 · Cited by 576
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Aguilan, J. T., Lim, J., Racine-Brzostek, S., Fischer, J. L., Silvescu, C., Cornett, S., Nieves, E., Mendu, D. R., Aliste, C.-M., Semple, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26742890
Aguilan, J. T., Lim, J., Racine-Brzostek, S., Fischer, J. L., Silvescu, C., Cornett, S., Nieves, E., Mendu, D. R., Aliste, C.-M., Semple, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26742899
Aguilan, J. T., Lim, J., Racine‐Brzostek, S., Fischer, J. L., Silvescu, C., Cornett, S., Nieves, E., Mendu, D. R., Aliste, C.-M., Semple, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26742893
Aguilan, J. T., Lim, J., Racine‐Brzostek, S., Fischer, J. L., Silvescu, C., Cornett, S., Nieves, E., Mendu, D. R., Aliste, C.-M., Semple, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26742896
Parrotta, E. I., Benedetto, G. L., Cuda, G., Covello, R., Longo, U. G., Carnevale, A., Galasso, O., Gasparini, G., & Mercurio, M. (2026) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2026.101069
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Liu, F., Zhang, H., Qu, Y., Zheng, H., Liu, S., & Zhang, X.-F. (2026) · Journal of Chromatography A · doi:10.1016/j.chroma.2026.467531
Yakubu, R. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-3-032-29437-1_28
Metadata
- Authors
- Meyer, J. G., & Schilling, B.
- Affiliations
- Buck Institute for Research on Aging (US)
- Corresponding author
- Schilling, Birgit
- Venue
- Expert Review of Proteomics
- Publisher
- Informa UK Limited
- Published
- 2017-05-04
- Volume
- 14
- Issue
- 5
- Pages
- 419-429
- ISSN
- 1478-9450 (ISSN-L)
- 1744-8387
- PMC
- PMC5671767
- OpenAlex
- W2610156696
- Cited by
- 151
- References
- 80
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 20 · 2023: 15 · 2022: 24 · 2021: 17 · 2020: 28 · 2019: 18 · 2018: 7 · 2017: 3
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Expert Review of Proteomics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (R24DK085610)
- NIH (S10 OD016281)
- NIH T32 (T32G000266)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- quantitative proteomics, data-independent acquisition, multiple reaction monitoring, parallel reaction monitoring, SWATH, biomarker discovery, clinical proteomics, high-resolution mass spectrometry, bioinformatics pipeline, disease pathways
- MeSH terms
- Humans, Biomarkers, Molecular Diagnostic Techniques, Proteomics
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