There remains a significant burden of long-term illness from COVID-19, but the possible causes are poorly understood, which limits mechanistic understanding and therapeutic intervention.

Analysing the plasma proteome in acute COVID-19 has been able to identify distinct signatures and potential therapeutic targets. Utilising this method in long COVID could provide insights and identify individuals who are more likely to suffer from persistent symptoms.

We used mass spectrometry (MS)-based proteomics to analyse 220 individual plasma samples. 5ul of plasma per sample was processed and analysed with the use of standard flow LC-MS in a data independent acquisition (DIA) mode. Raw data were processed by the DIANN software and the statistical analysis was performed in R (Messner, C.B. et al. Cell Systems 2020; 11(1):11-24).

Of 220 plasma samples, 138 (63%) were from patients with confirmed SARS-CoV-2 infection. Median time from symptom onset to sampling date for all patients was 16 weeks (IQR 12-19). Median age was 48 years (IQR 39-61) and 55% were female. In those with confirmed infection, 80 (58%) were hospitalised and in the unconfirmed group 83 (99%) were community managed. We identified 190 unique proteins and in the hospitalised group, these cluster according to severity of illness with either increasing or decreasing levels; P<0.05.  

Unbiased hierarchical clustering was able to identify subsets of patients stratified according to severity of acute illness. Further analysis will explore defined clinical phenotypes correlating symptoms, physiology and imaging with protein abundance.