Single-cell immunology of SARS-CoV-2 infection

Abstract

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.

Figure 1.

Visual representation of characteristics of the 64 published articles or publicly posted preprints on COVID-19 that have used one or more single-cell technologies (March 2020 – March 2021). (a,b) Scatter plots showing the number of participants versus (a) the number of flow cytometry markers or (b) the number of cells for which data are available in each study. Each symbol represents a dataset using one of the single-cell technologies from a single article/preprint. Opacity indicates dataset availability to the public (light, no; dark, yes), shape indicates whether the dataset has longitudinal data (circle, no; triangle, yes), and color indicates assay type [(a) red, flow cytometry; cyan, mass cytometry; (b) red, repertoires; gold, RNA; green, RNA plus protein; blue, RNA plus protein plus +repertoires; and magenta, RNA plus repertoires].

Figure 2.

Visual representation of single-cell transcriptomics data. (a) Plots showing the distribution of age (left panel) and sex (right panel) among individuals included in the collected 21 datasets. (b) Bar plot showing the number of samples per tissue type among the collected 21 datasets. (c) Uniform manifold approximation and projection (UMAP) plots showing the projection of over 2.5 million single cells from 16 PBMC and whole blood datasets (the latter minus neutrophils and basophils) mapped to the Seurat CITE-seq reference, colored and labeled by reference-defined cell type annotations at level 1 (left panel), level 2 (middle panel), and level 3 (right panel) granularity. (D) Box plots showing the frequencies of CD4⁺ T cells, CD8⁺ T cells, mucosal-associated invariant T (MAIT) cells, CD56^bright NK cells, CD16⁺ monocytes, and CD14⁺ monocytes among samples grouped by disease severity and time points for COVID-19 samples. Samples from Arunachalam et al. (enriched for DCs), Meckiff et al. (enriched for antigen-specific CD4⁺ T cells), Kusnadi et al. (enriched for antigen-specific CD8⁺ T cells), and Bacher et al. (enriched for antigen-specific CD4⁺ T cells) were excluded. Early, <= 8 days post symptom onset; intermediate, > 8 and <= 15 days post symptom onset; late, > 15 days post symptom onset. Statistical significance between mild and severe was determined by Wilcoxon test. *: p <= 0.05, **: p <= 0.01, ***: p <= 0.001, ****: p <= 0.0001.

Figure 3.

An example screenshot of the visualization portal. The website provides visualization of 21 individual datasets and the merged dataset consisting of 16 datasets. The UMAP plot showing the merged dataset consisting of over 2.5 million cells mapped to the human PBMC CITE-seq reference. The processed datasets can also be downloaded from the website at https://atlas.fredhutch.org/fredhutch/covid.