The Francis Crick Institute
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Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection

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journal contribution
posted on 2020-07-30, 13:14 authored by Christoph B Messner, Vadim Demichev, Daniel Wendisch, Laura Michalick, Matthew White, Anja Freiwald, Kathrin Textoris-Taube, Spyros I Vernardis, Anna-Sophia Egger, Marco Kreidl, Daniela Ludwig, Christiane Kilian, Federica Agostini, Aleksej Zelezniak, Charlotte Thibeault, Moritz Pfeiffer, Stefan Hippenstiel, Andreas Hocke, Christof von Kalle, Archie Campbell, Caroline Hayward, David J Porteous, Riccardo E Marioni, Claudia Langenberg, Kathryn S Lilley, Wolfgang M Kuebler, Michael Mülleder, Christian Drosten, Norbert Suttorp, Martin Witzenrath, Florian Kurth, Leif Erik Sander, Markus Ralser
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.


Crick (Grant ID: 10134, Grant title: Ralser FC001134) Wellcome Trust (Grant ID: 200829/Z/16/Z, Grant title: WT 200829/Z/16/Z) Biotechnology and Biological Sciences Research Council (Grant ID: BB/N015215/1, Grant title: BBSRC BB/N015215/1)