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Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.
- Source :
-
Cell systems [Cell Syst] 2020 Jul 22; Vol. 11 (1), pp. 11-24.e4. Date of Electronic Publication: 2020 Jun 02. - Publication Year :
- 2020
-
Abstract
- The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.<br />Competing Interests: Declaration of Interests The authors declare no competing interests.<br /> (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)
- Subjects :
- Adult
Aged
Aged, 80 and over
Betacoronavirus isolation & purification
Biomarkers blood
Blood Proteins analysis
COVID-19
Coronavirus Infections classification
Coronavirus Infections pathology
Coronavirus Infections virology
Female
Humans
Male
Middle Aged
Pandemics classification
Pneumonia, Viral classification
Pneumonia, Viral pathology
Pneumonia, Viral virology
SARS-CoV-2
Young Adult
Blood Proteins metabolism
Coronavirus Infections blood
Pneumonia, Viral blood
Proteomics methods
Subjects
Details
- Language :
- English
- ISSN :
- 2405-4720
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Cell systems
- Publication Type :
- Academic Journal
- Accession number :
- 32619549
- Full Text :
- https://doi.org/10.1016/j.cels.2020.05.012