We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.

We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.

Mass spectrometry is the premier tool for identifying and quantifying protein phosphorylation on a global scale. Analysis of phosphopeptides requires enrichment, and even after the samples remain highly complex and exhibit broad dynamic range of abundance. Achieving maximal depth of coverage for phosphoproteomics therefore typically necessitates offline liquid chromatography prefractionation, a time-consuming and laborious approach. Here, we incorporate a recently commercialized aerodynamic high-field asymmetric waveform ion mobility spectrometry (FAIMS) device into the phosphoproteomic workflow. We characterize the effects of phosphorylation on the FAIMS separation, describe optimized compensation voltage settings for unlabeled phosphopeptides, and demonstrate the advantages of FAIMS-enabled gas-phase fractionation. Standard FAIMS single-shot analyses identified around 15−20% additional phosphorylation sites than control experiments without FAIMS. In comparison to liquid chromatography prefractionation, FAIMS experiments yielded similar or superior results when analyzing up to four discrete gas-phase fractions. Although using FAIMS led to a modest reduction in the precision of quantitative measurements when using label-free approaches, the data collected with FAIMS yielded a 26% increase in total reproducible measurements. Overall, we conclude that the new FAIMS technology is a valuable addition to any phosphoproteomic workflow, with greater benefits emerging from longer analyses and higher amounts of material.