Proteomic analysis of circulating immune cells identifies novel cellular phenotypes associated with COVID-19 severity (preprint)

Certain serum proteins, including CRP and D-dimer, have prognostic value in patients with SARS-CoV-2 infection. Nonetheless, these factors are non-specific, and provide limited mechanistic insight into the peripheral blood mononuclear cell (PBMC) populations which drive the pathogenesis of severe COVID-19. To identify novel cellular phenotypes associated with disease progression, we here describe a comprehensive, unbiased analysis of the total and plasma membrane proteomes of PBMCs from a cohort of 40 unvaccinated individuals with SARS-CoV-2 infection, spanning the whole spectrum of disease severity. Combined with RNA-seq and flow cytometry data from the same donors, we define a comprehensive multi-omic profile for each severity level, revealing cumulative immune cell dysregulation in progressive disease. In particular, the cell surface proteins CEACAMs1, 6 and 8, CD177, CD63 and CD89 are strongly associated with severe COVID-19, corresponding to the emergence of atypical CD3+CD4+CD177+ and CD16+CEACAM1/6/8+ mononuclear cells. Utilisation of these markers may facilitate real-time patient assessment by flow cytometry, and identify immune cell populations that could be targeted to ameliorate immunopathology.

A patient-centric characterization of systemic recovery from SARS-CoV-2 infection (preprint)

The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data, covering a year post disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct "systemic recovery" profiles with specific progression and resolution of the inflammatory, immune, metabolic and clinical responses, over weeks to several months after infection. In particular, we found a strong intra-patient temporal covariation of innate immune cell numbers, kynurenine- and host lipid-metabolites, which suggested candidate immunometabolic pathways putatively influencing restoration of homeostasis, the risk of death and of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery on the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-systemic-recovery-prediction-app, designed to test our findings prospectively.