Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
Autor: | Moritz Pfeiffer, Aleksej Zelezniak, Charlotte Thibeault, Florian Kurth, Federica Agostini, Vadim Demichev, Leif E. Sander, Daniel Wendisch, Christian Drosten, Wolfgang M. Kuebler, Christoph B. Messner, Riccardo E. Marioni, Markus Ralser, Claudia Langenberg, David J. Porteous, Christof von Kalle, Laura Michalick, Matthew White, Anna Sophia Egger, Kathrin Textoris-Taube, Christiane Kilian, Spyros I. Vernardis, Michael Mülleder, Stefan Hippenstiel, Kathryn S. Lilley, Martin Witzenrath, Anja Freiwald, Norbert Suttorp, Caroline Hayward, Andreas C. Hocke, Archie Campbell, Marco Kreidl, Daniela Ludwig |
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Přispěvatelé: | Langenberg, Claudia [0000-0002-5017-7344], Lilley, Kathryn [0000-0003-0594-6543], Apollo - University of Cambridge Repository |
Rok vydání: | 2020 |
Předmět: |
Adult
Male Proteomics Histology Standardization Coronavirus disease 2019 (COVID-19) Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Pneumonia Viral Computational biology Article Pathology and Forensic Medicine 03 medical and health sciences Betacoronavirus Young Adult 0302 clinical medicine Humans Throughput (business) Pandemics mass spectrometry 030304 developmental biology Aged Aged 80 and over 0303 health sciences SARS-CoV-2 COVID-19 Cell Biology Blood Proteins Middle Aged clinical classifiers COVID-19 infection 3. Good health Workflow high-throughput proteomics Potential biomarkers SWATH-MS Female Plasma proteomics antiviral immune response Coronavirus Infections 030217 neurology & neurosurgery Biomarkers |
Zdroj: | Messner, C B, Demichev, V, Wendisch, D, Michalick, L, White, M, Freiwald, A, Textoris-taube, K, Vernardis, S I, Egger, A, Kreidl, M, Ludwig, D, Kilian, C, Agostini, F, Zelezniak, A, Thibeault, C, Pfeiffer, M, Hippenstiel, S, Hocke, A, Von Kalle, C, Campbell, A, Hayward, C, Porteous, D J, Marioni, R E, Langenberg, C, Lilley, K S, Kuebler, W M, Mülleder, M, Drosten, C, Witzenrath, M, Kurth, F, Sander, L E & Ralser, M 2020, ' Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection ', Cell Systems . https://doi.org/10.1016/j.cels.2020.05.012 Cell Systems |
ISSN: | 2405-4720 |
Popis: | Summary 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 standardisation and high-flow liquid chromatography to facilitate implementation in clinical laboratories. Our low-cost workflow quantifies 180 proteomes per day per mass spectrometer, 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 upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, our platform supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets. Graphical Abstract Highlights - A standardized, ultra-highthroughput clinical platform for serum and plasma proteomics - Platform enables high precision quantification of 180 patient samples/day at low cost - 27 biomarkers are differentially expressed between WHO severity grades for COVID-19 - Biomarkers include proteins not previously associated with COVID-19 infection Messner and Demichev et al. present a standardized, low cost, ultra-highthroughput platform for serum and plasma proteomics designed for clinical use and apply it to a cohort of hospitalized COVID-19 patients. They identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. |
Databáze: | OpenAIRE |
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