Background
Despite a huge research effort in recent years, the biological determinants underlying COVID-19 severity are not fully understood. A study from Stanford University used plasma proteomics measured by Olink together with single-cell mass cytometry: measuring over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands in 97 patients with mild, moderate, and severe COVID-19, as well as 40 uninfected patients.
Outcome
Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, they identified and independently validated a multi-variate model that classified COVID-19 severity with high accuracy (AUC=0.799 in the training dataset and AUC=0.773 in the validation dataset). Examination of connections between components of the model showed that 29% of those occurred between plasma proteome and single-cell proteomic features, with many of the proteins enriched for associations with cytokine signaling. Pathway analysis further revealed biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks. These early determinants of COVID-19 severity may point to novel therapeutic targets for prevention and/or treatment of COVID-19 progression.
