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Current landscape of plasma proteomics from technical innovations to biological insights and biomarker discovery
Open accessAbstract
Abstract Plasma is a rich source of biomolecules, including proteins, that reflect both health and disease. Due to their key roles in biological processes, proteins hold significant potential as biomarkers, fueling the rise of plasma proteome profiling in recent years. Despite widespread adoption, few studies have directly compared different plasma proteomics platforms, particularly those using mass spectrometry. Our study provides a comprehensive comparison of seven platforms across three leading technologies - SomaLogic, Olink, and Mass Spectrometry (MS) - including affinity-based approaches and various MS techniques, covering over 13,000 proteins. By applying these methods to the same cohort, we assess their performance, revealing key differences and complementary strengths. Our findings offer valuable insights for researchers, highlighting trade-offs in coverage and their implications for biomarker discovery and clinical applications. This study serves as an essential resource, offering both technical evaluation and biological insights to support the development of novel diagnostics and therapeutics through plasma proteomics.
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Platform comparisons incorporating MS protein measures are already proving informative to further depict genetically encoded isoform differences and impacts of splicing on detected protein structures.
“Platform comparisons incorporating MS protein measures are already proving informative to further depict genetically encoded isoform differences and impacts of splicing on detected protein structures24,40,41, and will be especially important to expand across ancestrally diverse sets of participants.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [40]
In Results and discussion, under “Correlations between shared proteins among platforms” (Fig. 4; page not provided), the paper uses MS peptides to compare genetically encoded ApoE isoforms across platforms and assess isoform specificity. It does not report effects of splicing on detected protein structures; a search for alternative splicing found no passage addressing that part of the claim. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe SomaScan assay uses DNA-based slow off-rate modified aptamers (SOMAmer reagents) to quantify the relative abundance of approximately 7000 proteins with high specificity and limits of detection largely comparable to antibody-based assays.
“The assay uses DNA-based slow off-rate modified aptamers (SOMAmerTM reagents) to quantify the relative abundance of approximately 7000 proteins with high specificity and limits of detection largely comparable to antibody-based assays.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [40]
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Plasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,880
Assarsson, E., Lundberg, M., Holmquist, G., Björkesten, J., Bucht Thorsen, S., Ekman, D., Eriksson, A., Rennel Dickens, E., Ohlsson, S., Edfeldt, G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095192 · Cited by 1,874
Plasma Proteome Profiling to Assess Human Health and Disease
Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.015 · Cited by 734
Measuring adiposity in patients: the utility of body mass index (BMI), percent body fat, and leptin
Shah, N. R., & Braverman, E. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033308 · Cited by 554
The role of adipokines in health and disease
Clemente-Suárez, V. J., Redondo-Flórez, L., Beltrán-Velasco, A. I., Martín-Rodríguez, A., Martínez-Guardado, I., Navarro-Jiménez, E., Laborde-Cárdenas, C. C., & Tornero-Aguilera, J. F. (2023) · Biomedicines · doi:10.3390/biomedicines11051290 · Cited by 463
Katz, D. H., Robbins, J. M., Deng, S., Tahir, U. A., Bick, A. G., Pampana, A., Yu, Z., Ngo, D., Benson, M. D., Chen, Z.-Z., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm5164 · Cited by 206
Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Raffield, L. M., Dang, H., Pratte, K. A., Jacobson, S., Gillenwater, L. A., Ampleford, E., Barjaktarevic, I., Basta, P., Clish, C. B., Comellas, A. P., et al. (2020) · PROTEOMICS · doi:10.1002/pmic.201900278 · Cited by 184
Wang, Y.-Y., Ge, Y.-J., Tan, C.-C., Cao, X.-P., Tan, L., & Xu, W. (2021) · Journal of Alzheimer's Disease · doi:10.3233/jad-201606 · Cited by 49
Flagged first, then by citations
Research works printed but not linked: 49
- Yurkovich, J. T. & Hood, L. Blood is a window into health and disease. Clin. Chem. 65, 1204–1206 (2019). · DOI
- Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002). · DOI · Paper page
- Geyer, P. E., Holdt, L. M., Teupser, D. & Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol Syst. Biol. 13, 942 (2017). · DOI · Paper page
- Birhanu, A. G. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clin. Proteomics 20, https://doi.org/10.1186/s12014-023-09424-x (2023). · DOI
- Deutsch, E. W. et al. Advances and utility of the human plasma proteome. J. Proteome Res. 20, 5241–5263 (2021). · DOI
- Ignjatovic, V. et al. Mass spectrometry-based plasma proteomics: considerations from sample collection to achieving translational data. J. Proteome Res. 18, 4085–4097 (2019). · DOI
- Geyer, P. E. et al. The Circulating Proteome─technological developments, current challenges, and future trends. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.4c00586.(2024). · DOI
- Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019). · DOI · Paper page
- Carrasco-Zanini, J. et al. Mapping biological influences on the human plasma proteome beyond the genome. Nat. Metab. https://doi.org/10.1038/s42255-024-01133-5 (2024). · DOI
- Zaghlool, S. B. et al. Revealing the role of the human blood plasma proteome in obesity using genetic drivers. Nat. Commun. 12, 1279 (2021). · DOI
- Zander, J. et al. Effect of biobanking conditions on short-term stability of biomarkers in human serum and plasma. Clin. Chem. Lab Med. 52, 629–639 (2014). · DOI
- Daniels, J. R. et al. Stability of the human plasma proteome to pre-analytical variability as assessed by an aptamer-based approach. J. Proteome Res. 18, 3661–3670 (2019). · DOI
- Cao, Z. et al. An integrated analysis of metabolites, peptides, and inflammation biomarkers for assessment of preanalytical variability of human plasma. J. Proteome Res. 18, 2411–2421 (2019). · DOI
- Lan, J. et al. Systematic evaluation of the use of human plasma and serum for mass-spectrometry-based shotgun proteomics. J. Proteome Res. 17, 1426–1435 (2018). · DOI
- Tognetti, M. et al. Biomarker candidates for tumors identified from deep-profiled plasma stem predominantly from the low abundant area. J. Proteome Res. 21, 1718–1735 (2022). · DOI
- Keshishian, H. et al. Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat. Protoc. 12, 1683–1701 (2017). · DOI
- Viode, A. et al. A Simple, Time-and Cost-Effective, High-Throughput Depletion Strategy for Deep Plasma Proteomics. Sci. Adv. 9, https://doi.org/10.1126/sciadv.adf9717 (2023). · DOI
- Soni, R. K. Frontiers in plasma proteome profiling platforms: innovations and applications. Clin. Proteomics 21, 43 (2024). · DOI
- Blume, J. E. et al. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat. Commun. 11, 3662 (2020). · DOI
- Sun, B. B. et al. Genomic Atlas of the human plasma proteome. Nature 558, 73–79 (2018). · DOI · Paper page
- Lundberg, M. et al. Multiplexed homogeneous proximity ligation assays for high-throughput protein biomarker research in serological material. Mol. Cell Proteomics 10, https://doi.org/10.1074/mcp.M110.004978 (2011). · DOI
- Feng, W. et al. NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing. Nat. Commun. 14, 7238 (2023). · DOI
- Van Eyk, J. E. & Sobhani, K. Precision medicine: establishing proteomic assessment criteria from discovery to clinical diagnostics. Circulation 138, 2172–2174 (2018). · DOI
- Suhre, K. et al. Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping. Nat. Commun. 15, 989 (2024). · DOI · Paper page
- Ferdosi, S. et al. Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions. https://doi.org/10.1073/pnas (2022). · DOI
- Candia, J. et al. Assessment of variability in the SOMAscan Assay. Sci. Rep. 7, 14248 (2017). · DOI
- Rooney, M. R. et al. Plasma proteomic comparisons change as coverage expands for SomaLogic and Olink. https://doi.org/10.1101/2024.07.11.24310161 (2024). · DOI
- Geyer, P. E. et al. Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol. Med. 11, e10427 (2019). · DOI · Paper page
- Hendricks, N. G. et al. An inflection point in high-throughput proteomics with Orbitrap Astral: analysis of biofluids, cells, and tissues. https://doi.org/10.1101/2024.04.26.591396 (2024). · DOI
- Bhowmick, P., Roome, S., Borchers, C. H., Goodlett, D. R. & Mohammed, Y. An update on MRMAssayDB: a comprehensive resource for targeted proteomics assays in the community. J. Proteome Res. 20, 2105–2115 (2021). · DOI
- Eldjarn, G. H. et al. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 622, 348–358 (2023). · DOI · Paper page
- Hellström, L., Wahrenberg, H., Hruska, K., Reynisdottir, S. & Arner, P. Mechanisms behind gender differences in circulating leptin levels. J. Intern. Med. 247, 457–462 (2000). · DOI
- Reed, E. R. et al. Cross-platform proteomics signatures of extreme old age. https://doi.org/10.1101/2024.04.10.588876 (2024). · DOI
- Rauzier, C., Lamarche, B., Tremblay, A. J., Couture, P. & Picard, F. Associations between insulin-like growth factor binding protein-2 and lipoprotein kinetics in men. J. Lipid Res. 63, 100269 (2022). · DOI
- Llobet, M. O., Johansson, Å., Gyllensten, U., Allen, M. & Enroth, S. Forensic prediction of sex, age, height, body mass index, hip-to-waist ratio, smoking status and lipid lowering drugs using epigenetic markers and plasma proteins. Forensic Sci. Int. Genet. 65, 102871 (2023). · DOI
- Wang, H. et al. Quantitative iTRAQ-based proteomic analysis of differentially expressed proteins in aging in human and monkey. BMC Genomics 20, 725 (2019). · DOI
- Sathyan, S. et al. Plasma proteomic profile of age, health span, and all-cause mortality in older adults. Aging Cell 19, e13250 (2020). · DOI
- Naval, J., Alonso, V. & Herranz, M. A. Genetic polymorphisms and skin aging: the identification of population genotypic groups holds potential for personalized treatments. Clin. Cosmet. Investig. Dermatol. 7, 207–214 (2014). · DOI
- Siino, V. et al. Plasma proteome profiling of healthy individuals across the life span in a Sicilian cohort with long-lived individuals. Aging Cell 21, e13684 (2022). · DOI
- Gao, H. et al. Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics. https://doi.org/10.1101/2025.04.26.650757 (2025). · DOI
- Korff, K. et al. Pre-analytical drivers of bias in bead-enriched plasma proteomics. https://doi.org/10.1101/2025.05.07.652495 (2025). · DOI
- Beimers, W. F. et al. Technical evaluation of plasma proteomics technologies. J. Proteome Res. 24, 3074–3087 (2025). · DOI
- Sun, Z. et al. Deep blood proteomics identifies over 12,000 proteins providing valuable information about the state of the human body. https://doi.org/10.1101/2025.05.15.654311 (2025). · DOI
- Rooney, M. R. et al. Comparison of proteomic measurements across platforms in the Atherosclerosis Risk in Communities (ARIC) study. Clin. Chem. 69, 68–79 (2023). · DOI · Paper page
- Candia, J. et al. Variability of 7K and 11K SomaScan plasma proteomics assays. J. Proteome Res. 23, 5531–5539 (2024). · DOI
- Argentieri, M. A. et al. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat. Med. https://doi.org/10.1038/s41591-024-03164-7 (2024). · DOI
- Coenen, L., Lehallier, B., de Vries, H. E. & Middeldorp, J. Markers of aging: unsupervised integrated analyses of the human plasma proteome. Front. Aging 4, 1112109 (2023). · DOI
- Pham, T. V., Henneman, A. A. & Jimenez, C. R. Iq: An R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 36, 2611–2613 (2020). · DOI
- Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). · DOI · Paper page
Cited by
Sorted by citations
Cross-ancestry comparison of aptamer and antibody protein measures
Nicholas, J. C., Katz, D. H., Tahir, U. A., Debban, C. L., Aguet, F., Blackwell, T., Bowler, R. P., Broadaway, K. A., Chen, J., Clish, C. B., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67814-1 · Cited by 13
Metadata
- Authors
- Kirsher, D. Y., Chand, S., Phong, A., Nguyen, B., Szoke, B. G., & Ahadi, S.
- Affiliations
- Alkahest, Inc. (United States) (US)
- Corresponding author
- Ahadi, Sara
- Venue
- Communications Chemistry
- Published
- 2025-09-25
- Volume
- 8
- Issue
- 1
- Pages
- 279-279
- ISSN
- 2399-3669 (ISSN-L)
- PMC
- PMC12462477
- OpenAlex
- W4414514925
- Cited by
- 111
- References
- 8
- Citations by year
- 2026: 96 · 2025: 14
- FWCI
- 26.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Communications Chemistry (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Grifols
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- plasma proteomics, biomarker discovery
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