Summary: Researchers from the Francis Crick Institute and Charité – Universitätsmedizin Berlin report identifying 27 protein biomarkers in blood that can help predict which COVID-19 patients are likely to develop severe illness.
Source: Francis Crick Institute
Researchers at the Francis Crick Institute and Charité – Universitätsmedizin Berlin have identified 27 protein biomarkers that could be used to predict whether a patient with COVID-19 is likely to become severely ill with the disease.
Individuals infected with SARS-CoV-2 experience a wide range of clinical outcomes: some remain asymptomatic, others require hospitalization, and a subset progress to life-threatening disease. To help clinicians anticipate which patients are most at risk, scientists applied an advanced proteomics workflow to measure protein levels in blood samples and identify patterns linked to disease severity.
Published in Cell Systems, the study reports 27 proteins whose concentrations vary consistently with COVID-19 severity. These proteins span pathways involved in the complement system, blood coagulation, inflammation, and processes upstream and downstream of interleukin-6 (IL-6), a cytokine already associated with severe inflammatory responses in COVID-19. The set of biomarkers could be used to develop prognostic tests and highlight molecular targets for drug development.
The team refined a mass spectrometry–based approach to rapidly quantify many proteins in serum and plasma. The high-throughput platform, developed at the Francis Crick Institute, follows rigorous standardisation and makes use of high-flow liquid chromatography to improve throughput and reproducibility in a clinical laboratory setting. In this study the researchers analysed serum from 31 hospitalized COVID-19 patients treated at Charité, and validated their findings in a separate group of 17 COVID-19 patients and 15 healthy controls from the same institution.
Because the workflow is designed for speed and consistency, it supports large-scale and longitudinal studies: the authors report that a single mass spectrometer can quantify roughly 180 proteomes per day under their protocol, while maintaining high precision and reduced batch effects. This throughput and robustness are intended to enable development of routine, clinically applicable proteomic assays.
The researchers hope these biomarkers can be translated into simple clinical tests that measure one or a few proteins to help guide treatment decisions. A reliable prognostic assay could assist clinicians in identifying patients most likely to deteriorate, prioritising monitoring or therapeutic interventions, and optimising resource allocation in hospitals.
Christoph Messner, a lead author and postdoctoral researcher in the Molecular Biology of Metabolism Laboratory at the Crick, commented that a predictive test for progression to critical illness would be “invaluable” for tailoring care and identifying high-risk patients. The study highlights potential drug targets among proteins associated with IL-6 and other inflammatory pathways, suggesting that modulating these proteins might reduce severe symptoms in some patients.
Markus Ralser, another paper author and group leader at the Crick and Charité, emphasised that the robust, standardised proteomics method offers a powerful tool both for predicting disease progression and for uncovering candidate therapeutic targets. He noted the approach can be readily adapted to study other diseases to better understand their systemic molecular effects.
Vadim Demichev, co-lead author and scientist in the same laboratory, added that although the platform was not developed specifically for COVID-19, it has proven highly useful for gaining new insights into this disease. The team hopes the technology will contribute to the development of prognostic tests across a broad range of conditions.
All protocols and software required to implement the described workflow are freely available from the authors and project resources. The authors state that raw data from the commercial plasma and serum control samples used in the Generation Scotland study were submitted to the ProteomeXchange Consortium via PRIDE under dataset identifier PXD018874. Access to individual-level omics and phenotype data from Generation Scotland is governed by the study’s consent terms and requires review by the GS Access Committee; requests should follow the committee’s application process.
Notes
The study’s software tools include the DIA-NN software suite and the DiaNN R package, which are provided as open-source resources. The research team has released protocols to support adoption of the workflow in other laboratories.
About this coronavirus research article
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Francis Crick Institute
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Original Research: Open access
“Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection” by Christoph Messner et al., published in Cell Systems. DOI: 10.1016/j.cels.2020.05.012
Abstract
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection
The COVID-19 pandemic presents an urgent need for point-of-care diagnostic classifiers. The authors present an ultra-high-throughput serum and plasma proteomics platform built on ISO13485 standardisation and high-flow liquid chromatography to facilitate clinical implementation. Their low-cost workflow enables quantification of about 180 proteomes per day per mass spectrometer, delivers high precision, and reduces batch effects for large-scale studies. Applied to samples from early hospitalized SARS-CoV-2 cases, the approach identified 27 candidate biomarkers that correlate with WHO severity grades of COVID-19. These biomarkers include complement factors, coagulation proteins, inflammation modulators, and pro-inflammatory factors connected to interleukin-6. The authors provide protocols and software openly, supporting the development of routine proteomic assays to aid clinical decisions and to generate hypotheses for potential COVID-19 therapeutic targets.
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