Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19

Immunity. 2020 Dec 15;53(6):1296-1314.e9. doi: 10.1016/j.immuni.2020.11.017. Epub 2020 Nov 26.


Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

Keywords: COVID-19; RNA-seq; acute respiratory distress; blood; disease trajectory; immune response; infectious disease; methylation; scRNA-seq; virus.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers
  • Blood Circulation
  • COVID-19 / immunology
  • COVID-19 / metabolism*
  • Cells, Cultured
  • Cohort Studies
  • Disease Progression
  • Erythroid Cells / pathology*
  • Female
  • Gene Expression Profiling
  • Humans
  • Male
  • Megakaryocytes / physiology*
  • Middle Aged
  • Plasma Cells / physiology*
  • Proteomics
  • SARS-CoV-2 / physiology*
  • Sequence Analysis, RNA
  • Severity of Illness Index
  • Single-Cell Analysis


  • Biomarkers