| Literature DB >> 34672606 |
Eric W Deutsch1, Gilbert S Omenn1,2, Zhi Sun1, Michal Maes1, Maria Pernemalm3, Krishnan K Palaniappan4, Natasha Letunica5, Yves Vandenbrouck6, Virginie Brun6, Sheng-Ce Tao7, Xiaobo Yu8, Philipp E Geyer9, Vera Ignjatovic5,10, Robert L Moritz1, Jochen M Schwenk11.
Abstract
The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.Entities:
Keywords: DNA aptamers (Somascan); Human Plasma Proteome Project; Human Proteome Project; PeptideAtlas; blood; mass spectrometry; plasma; proteomics; proximity extension assays (PEA by Olink)
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Year: 2021 PMID: 34672606 PMCID: PMC9469506 DOI: 10.1021/acs.jproteome.1c00657
Source DB: PubMed Journal: J Proteome Res ISSN: 1535-3893 Impact factor: 5.370