Journal
NATURE BIOTECHNOLOGY
Volume 40, Issue 1, Pages 30-41Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-021-01131-y
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Funding
- NIH Human Immunology Project Consortium [U19AI128914]
- Vaccine and Immunology Statistical Center (Bill and Melinda Gates Foundation) [OPP1032317]
- Fred Hutchinson Cancer Research Center New Development funds
- Andy Hill Endowment Distinguished Researcher CARE fund
- ORIP [S10OD028685]
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This review summarizes existing studies using single-cell technologies to investigate the immune responses and molecular mechanisms in COVID-19. Understanding the immune cell subsets and molecular factors associated with protective or pathological immunity may contribute to the development of vaccines and therapeutics for COVID-19. Single-cell technologies show promise in uncovering the heterogeneity of immune responses and pathogenesis mechanisms of COVID-19.
This Review provides an overview of existing studies using single-cell technologies to provide insights over the immune responses and molecular mechanisms at work in COVID-19. Gaining a better understanding of the immune cell subsets and molecular factors associated with protective or pathological immunity against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 could aid the development of vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Single-cell technologies, such as flow cytometry, mass cytometry, single-cell transcriptomics and single-cell multi-omic profiling, offer considerable promise in dissecting the heterogeneity of immune responses among individual cells and uncovering the molecular mechanisms of COVID-19 pathogenesis. Single-cell immune-profiling studies reported to date have identified innate and adaptive immune cell subsets that correlate with COVID-19 disease severity, as well as immunological factors and pathways of potential relevance to the development of vaccines and treatments for COVID-19. For facilitation of integrative studies and meta-analyses into the immunology of SARS-CoV-2 infection, we provide standardized, download-ready versions of 21 published single-cell sequencing datasets (over 3.2 million cells in total) as well as an interactive visualization portal for data exploration.
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