Back

A conserved immune trajectory of recovery in hospitalized COVID-19 patients

Okholm, T. L. H.; Burnett, C. E.; Tenvooren, I.; Marquez, D. M.; Tamaki, S.; Sandoval, P. M.; The UCSF COMET Consortium, ; Calfee, C. S.; Hendrickson, C. M.; Kangelaris, K. N.; Langelier, C. R.; Krummel, M. F.; Woodruff, P. G.; Erle, D. J.; Ansel, K. M.; Spitzer, M. H.

2022-03-16 immunology
10.1101/2022.03.15.484467 bioRxiv
Show abstract

Many studies have provided insights into the immune response to COVID-19; however, little is known about the immunological changes and immune signaling occurring during COVID-19 resolution. Individual heterogeneity and variable disease resolution timelines obscure unifying immune characteristics. Here, we collected and profiled >200 longitudinal peripheral blood samples from patients hospitalized with COVID-19, with other respiratory infections, and healthy individuals, using mass cytometry to measure immune cells and signaling states at single cell resolution. COVID-19 patients showed a unique immune composition and an early, coordinated and elevated immune cell signaling profile, which correlated with early hospital discharge. Intra-patient time course analysis tied to clinically relevant events of recovery revealed a conserved set of immunological processes that accompany, and are unique to, disease resolution and discharge. This immunological process, together with additional changes in CD4 regulatory T cells and basophils, accompanies recovery from respiratory failure and is associated with better clinical outcomes at the time of admission. Our work elucidates the biological timeline of immune recovery from COVID-19 and provides insights into the fundamental processes of COVID-19 resolution in hospitalized patients.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.