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Complementary proteomic platforms: When adding the SomaScan® Assay to mass spectrometry research makes sense

Identifying disease biomarkers, both in terms of pathogenic pathways and therapeutic targets, is increasingly
important for realizing the potential of personalized medicine.1,2 This involves profiling thousands of proteins within complex biologic samples.3,4 So far, mass spectrometry has been the workhorse of profiling proteins in such samples.3 However, mass spectrometry faces many challenges in studying novel proteins that may be in low abundance and that can vary widely from sample to sample.3,5

Pairing the SomaScan Assay with mass spectrometry can help overcome these barriers by measuring thousands of proteins in small volumes of biological samples with low limits of detection, a broad dynamic range, and high reproducibility.3,6-9

How the SomaScan Assay works

The SomaScan Assay is based on SOMAmer® (Slow Off-Rate Modified Aptamer) Reagents, which are aptamers that are custom engineered to have enhanced shape complementarity to their targets, as well as slow off-rates for longer target binding. A universal polyanionic competitor further enhances specificity to the target protein.1,6,10

The current menu offers 7,000 unique proteins that can be measured simultaneously in a small volume of sample, with effective detection of low and highly abundant proteins over a 10-log dynamic range (fmol – μmol).6,8,9 High-throughput profiling of the full menu or a customized subset can be performed with excellent reproducibility and industry-leading coefficients of variation around 5%.8,9,11,12

Many researchers are already using the SomaScan Assay to elevate their mass spectrometry research in a
variety of ways.

Pairing the SomaScan Assay and mass spectrometry

Stem cells13
Challenge Mass spectrometry has dynamic range limitations (difficulty detecting low expressed proteins), which poses a challenge in human embryonic and mesenchymal stem cells, where many of the targeted proteins are found in low abundance.
Solution Researchers compared the SomaScan Assay with mass spectrometry (MS) and RNA sequencing (RNAseq) in analyzing proteins from human embryonic and mesenchymal stem cells.
Key findings
  • Of the 1,095 proteins targeted by the SomaScan Assay, only 599 (55%) were
    also measured by nano LC-MS/MS or RNAseq.

    • The proteins quantified by SomaScan that were missed by nano LC-MS/
      MS or RNAseq were signaling molecules in low abundance, such as cytokines, chemokines, and interleukins.
  • Results showed that although mass spectrometry techniques were able to measure more of the proteome, the SomaScan Assay was uniquely able to detect low abundant proteins that may be key biomarkers.
Clinical trial for COVID-1914
Challenge Since COVID-19 is a complex and heterogeneous condition involving multiple metabolic processes, investigating variations in the pathophysiological state of the infection can be difficult.
Solution Researchers used the SomaScan Assay and mass spectrometry to help identify the role of molecular mechanisms in different pathophysiological processes in COVID-19 among 73 hospitalized patients with COVID-19 and 32 negative controls.
Key findings
  • COVID-19 can be staged into early and late events (seroconversion stage 1 and 2, respectively).
  • A multiomics approach was needed to measure the thousands of different proteins and other biologic markers present in plasma in varying abundance.
    • Mass spectrometry measurements contained low-intensity results that had to be dynamically excluded.
    • There were no reported data that had to be excluded from the SomaScan results.
  • These combined proteomic measurements, in addition to other immunoassays and mass cytometry, showed that COVID-19 can be divided into two stages that correlate with seroconversion status.
  • Therapeutic interventions may be improved by stratifying patients with COVID-19 based on their seroconversion status.
Cancer risk15
Challenge The mechanism by which aspirin reduces colorectal cancer risk has not yet been fully elucidated.
Solution
  • Researchers generated proteomic data from aspirin-treated adenoma-derived cells using stable isotope labeling with amino acids in cell culture (SILAC)–based mass spectrometry.
  • To assess genetic predictors for protein and gene expression, measurements were taken for 3,622 plasma proteins in 3,301 participants using the SomaScan Assay.
Key findings
  • Aspirin decreased the expression of 3 proteins (MCM6, RRM2, and ARFIP2).
  • ARFIP2 was found to be involved in regulating the actin cytoskeleton, which may be indicative of its involvement in aspirin’s ability to reduce metastasis.
  • By combining aptamer-based proteomics with mass spectrometry analysis, aspirin-targeted proteins that may affect colorectal cancer risk can be identified.
Abbreviations: ARFIP2, ADP ribosylation factor interacting protein 2; MCM6, minichromosome maintenance complex component 6; nano LC-MS/MS, nanoscale liquid chromatography coupled to tandem mass spectrometry; RRM2, ribonucleoside-diphosphate reductase subunit M2.

These studies show just a few of the exciting discoveries that can be made when the SomaScan Assay is added
to other proteomic technologies.

References

  1. Kim B, Araujo R, Howard M, Magni R, Liotta LA, Luchini A. Affinity enrichment for mass spectrometry: improving the yield of low abundance biomarkers. Exp Rev Proteomics. 2018;15(4):353-366. doi:10.1080/14789450.2018.1450631.
  2. Fiblin MR, Mehta A, Schneider AM, et al. Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Rep Med. 2021;2(5):100287. doi:10.1016/j.xcrm.2021.100287.
  3. Petrera A, von Toerne C, Behler J, et al. Multiplatform approach for plasma proteomics: complementarity of Olink proximity extension assay technology to mass spectrometry-based protein profiling. J Proteome Res. 2021;20(1):751-762. doi:10.1021/acs.jproteome.0c00641.
  4. Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics. 2015;8:33. doi:10.1186/s12920-015-0108-y.
  5. Nakayasu ES, Gritsenko M, Piehowski PD, et al. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nat Protoc. 2021;16(8):3737-3760. doi:10.1038/s41596-021-00566-6.
  6. Gold L, Ayers D, Bertino J, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010;5(12):e15004. doi:10.1371/journal.pone.0015004.
  7. Ngo D, Sinha S, Shen D, et al. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease. Circulation. 2016;134(4):270-285. doi:10.1161/CIRCULATIONAHA.116.021803.
  8. Palstrøm NB, Matthiesen R, Rasmussen LM, Beck HC. Recent developments in clinical plasma proteomics—applied to cardiovascular research. Biomedicines. 2022;10(1):162. doi:10.3390/ biomedicines10010162.
  9. Jiang W, Jones JC, Shankavaram U, Sproull M, Camphausen K, Krauze AV. Analytical considerations of large-scale aptamer-based datasets for translational applications. Cancers (Basel). 2022;14(9):2227. doi:10.3390/cancers14092227.
  10. Lollo B, Steele F, Gold L. Beyond antibodies: new affinity reagents to unlock the proteome. Proteomics. 2014;14(6):638-644. doi:10.1002/pmic.201300187.
  11. Masvekar R, Wu T, Kosa P, Barbour C, Fossati V, Bielekova B. Cerebrospinal fluid biomarkers link toxic astrogliosis and microglial activation to multiple sclerosis severity. Mult Scler Relat Disord. 2019;28:34-43. doi:10.1016/j.msard.2018.11.032.
  12. Raffield LM, Dang H, Pratte KA, et al; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium. Comparison of proteomic assessment methods in multiple cohort studies. Proteomics. 2020;20(12):e1900278. doi:10.1002/pmic.201900278.
  13. Billing AM, Ben Hamidane H, Bhagwat AM, et al. Complementarity of SOMAscan to LC-MS/MS and RNA-seq for quantitative profiling of human embryonic and mesenchymal stem cells. J Proteomics. 2017;150:86-97. doi:10.1016/j.jprot.2016.08.023.
  14. Galbraith MD, Kinning KT, Sullivan KD, et al. Seroconversion stages COVID19 into distinct pathophysiological states. eLife. 2021;10:e65508. doi:10.7554/eLife.65508.
  15. Nounu A, Greenhough A, Heesom KJ, et al. A combined proteomics and Mendelian randomization approach to investigate the effects of aspirin-targeted proteins on colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2021;30(3):564-575. doi:10.1158/1055-9965.EPI-20-1176.

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