Article
Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

https://doi.org/10.1016/j.xcrm.2023.101079Get rights and content
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open access

Highlights

  • Distinct baseline and temporal patterns are associated with the clinical course

  • Persistent viral levels, despite high antibody titers, are associated with severity

  • Severity is linked to reduced cytotoxic NK cells, increased inflammation, and thrombosis

  • Myocardial damage markers distinguish critical patients who recover from those who die

Summary

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1–3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Keywords

COVID-19
SARS-CoV-2
multi-omics
systems immunology
immunophenotyping
longitudinal modeling

Data and code availability

Data files are available at ImmPort under accession number SDY1760 and dbGAP accession number phs002686.v1.p1. Accession numbers are listed in the key resources table. Additional supplementary items are available from Mendeley Data at https://doi.org/10.17632/vcskpv8tjk.1.

All analysis codes have been deposited at Bitbucket: https://bitbucket.org/kleinstein/impacc-public-code120 and are publicly available as of the date of publication. DOIs are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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16

These authors contributed equally

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Lead contact