COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1,2,3,4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.
The host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection ranges from asymptomatic infection to severe coronavirus disease 2019 (COVID-19) and death. The leading cause of mortality is acute lung injury and acute respiratory distress syndrome, or direct complications with multiple organ failure1,2,3,4. Clinical deterioration in acute illness leads to ineffective viral clearance and collateral tissue damage1,2,3,4,5. Severe COVID-19 is also accompanied by an inappropriate pro-inflammatory host immune response and a diminished antiviral interferon response6,7,8.
Many molecular and cellular questions related to COVID-19 pathophysiology remain unanswered, including how cell composition and gene programs shift, which cells are infected, and how associated genetic loci drive disease. Autopsies are crucial to understanding severe COVID-19 pathophysiology9,10,11,12, but comprehensive genomic studies are challenged by long post-mortem intervals (PMIs).
Here, we developed a large cross-body COVID-19 autopsy biobank of 420 autopsy specimens, spanning 11 organs, and used it to generate a single-cell atlas of lung, kidney, liver and heart associated with COVID-19 and a lung spatial atlas, in a subset of 14–18 donors per organ. Our atlases provide crucial insights into the pathogenesis of severe COVID-19.
A COVID-19 autopsy cohort and biobank
We assembled an autopsy cohort of 20 male and 12 female donors, of various ages (>30–>89 years), racial/ethnic backgrounds, intermittent mandatory ventilation (IMV) periods (0–30 days) and days from symptom start to death (Fig. 1a, Supplementary Table 1). A biobank was created with a subset of 17 donors. From most donors, we collected at least lung, heart and liver tissue (Fig. 1a, Extended Data Fig. 1a, Supplementary Methods), preserving specimens for single-cell and spatial analysis. We optimized single-cell and single-nucleus RNA sequencing (sc/snRNA-Seq) protocols for Biosafety Level 3 and NanoString GeoMx workflows to spatially profile RNA from different tissue compartments by cell composition or viral RNA (Supplementary Methods).
COVID-19 cell atlases
We generated sc/snRNA-Seq atlases of lung (n = 16 donors, k = 106,792 cells/nuclei, m = 24 specimens; donors D1–8, 10–17), heart (n = 18, k = 40,880, m = 19; D1–8, 10–11, 14–17, 27–28, 31–32), liver (n = 15, k = 47,001, m = 16; D1–7, 10–17) and kidney (n = 16, k = 33,872, m = 16; D4–8, 10–12, 14–15, 17, 25–26, 28–30). Although initial tests showed some differences in proportions of cell types between snRNA-Seq and scRNA-Seq, snRNA-Seq performed better overall13 (Extended Data Fig. 1b–d and data not shown) and was used for the remaining samples.
We developed a computational pipeline (Fig. 1b) to tackle unique technical challenges. We used CellBender remove-background14 to remove ambient RNA, which enhanced cell distinction and marker specificity (Extended Data Fig. 1e–h; Supplementary Methods), we rapidly quality-controlled, pre-processed and batch-corrected the data with cloud-based Cumulus15 (Extended Data Fig. 2a–g, Supplementary Methods) and we automatically annotated cells and nuclei by transferring labels from previous atlases (Fig. 2a, Extended Data Fig. 2h, Supplementary Methods). We refined these labels with manual annotation of subclusters in each main lineage (Fig. 2b, Extended Data Fig. 2i–n, Supplementary Methods). The automated annotation approach allowed us to compare against other data resources (without clustering or batch correction), while the manual approach enabled us to refine cell identity assignments with detailed domain knowledge.
A cell census of the COVID-19 lung
Automatic annotation defined 28 subsets of parenchymal, endothelial and immune cells (Fig. 2a, Supplementary Table 2, Supplementary Methods), with further manual annotation within subgroupings (Fig. 2b, Extended Data Figs. 2, 4, Supplementary Methods). Deconvolution of bulk RNA-Seq from the same samples largely agreed (Extended Data Fig. 3a, b, Supplementary Methods), and our two annotation strategies had 94% agreement (Extended Data Fig. 3c–e).
Among immune cells we distinguished six cell myeloid subsets: CD14highCD16high inflammatory monocytes with antimicrobial properties and five macrophage subsets (Extended Data Figs. 2j, 4b) that were enriched for scavenger receptors, toll-like receptor ligands, inflammatory transcriptional regulators or metabolism genes; four B cell and plasma cell subsets: BLIMP1high plasma cells16,17, BLIMP1intermediate plasma cells, B cells and JCHAIN-expressing plasmablasts (Extended Data Figs. 2k, 4b); five T and natural killer (NK) cell subsets: two CD4+ subsets: regulatory T cells (Treg cells) and a metabolically active subset; one CD8+ subset; and two T or NK cell subsets (Extended Data Figs. 2l, 4b), including one with cytotoxic effector genes. The dearth of neutrophils (Fig. 2a, 419 cells) is likely due to freezing or limitations of droplet-based sc/snRNA-Seq13.
We identified seven endothelial cell (EC) subsets18,19 (Extended Data Figs. 2m, 4b): arterial, venous and lymphatic, capillary aerocytes, capillary EC-1 and capillary EC-2 and a mixed subset (Supplementary Methods), and three stromal subsets: fibroblasts, proliferative fibroblasts and myofibroblasts19 (Extended Data Fig. 2n, Supplementary Table 3).
There were eight epithelial subsets, including club/secretory cells, AT1 cells, AT2 cells, and proliferative AT2 cells (Fig. 2b). One subset corresponded to a previously described AT2 to AT1 transitional cell state (KRT8+ pre-alveolar type 1 transitional cell state (PATS); PATS/ADI/DATP)20,21,22 (Fig. 2b).
Changes in lung cell composition
In comparison with normal lung from a matching region (Fig. 2c, Supplementary Methods), numbers of AT2 cells were significantly decreased (false discovery rate (FDR) = 2.8 × 10−15, Dirichlet multinomial regression; Supplementary Methods), possibly reflecting virally induced cell death23,24,25. Numbers of dendritic cells (FDR = 0.004), macrophages (FDR = 3.6 × 10−10), NK cells (FDR = 0.018), fibroblasts (FDR = 0.013), lymphatic endothelial cells (FDR = 0.00058) and vascular endothelial cells (FDR = 0.00011) all increased.
Cell proportions varied between donors (Extended Data Fig. 5a, b). Whereas variation was not significantly correlated with PMI, age or sex, IMV was positively correlated with epithelial cell fraction (FDR = 0.007; Spearman ρ = 0.765) and negatively correlated with T and NK cell fraction (FDR = 0.041; ρ = −0.62). Fewer days on a ventilator may indicate a rapidly deteriorating condition. This is corroborated by the nominally significant positive correlation between epithelial cell fraction and days from symptom start to death (ρ = 0.671, P = 0.004, but FDR = 0.053).
Induced programs in epithelial cells
There were widespread, cell-type-specific transcriptional changes in lung cell types associated with COVID-19 (Extended Data Fig. 5c, Supplementary Methods), most notably in CD16+ monocytes (1,580 upregulated genes), lymphatic endothelial (578), vascular endothelial (317), AT2 (309) and AT1 (307) cells. Within AT2 cells, there was higher expression (P < 0.0004) of genes associated with host viral response (Fig. 2d), including those for programmed cell death (STAT1), inflammation and adaptive immune response (Supplementary Table 4). Lung surfactant genes were downregulated, consistent with reports from in vitro studies21.
Failed paths for AT1 cell regeneration
The PATS program signature was increased in COVID-19 pneumocytes (P < 2.2 × 10−16, one-sided Mann–Whitney U test) (Fig. 2e, Extended Data Fig. 5d). This progenitor program is induced during lung injury20,21,22 (for example, idiopathic pulmonary fibrosis), consistent with fibrosis in severe COVID-1926,27. These studies also highlight fibroblast expansion, which we also observed (Fig. 2c).
A subset of PATS program cells, distinct from KRT5+TP63+ airway basal cells, expressed canonical (KRT8/CLDN4/CDKN1A) and non-canonical (KRT5/TP63/KRT17) PATS markers (Fig. 2f, Extended Data Fig. 5d, Supplementary Table 3). These may be TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, which were identified in H1N1 influenza mouse models28 and act as an emergency cellular reserve for severely damaged alveoli29. The putative IPBLP cells express interferon virus defence and progenitor cell differentiation genes (Supplementary Table 3). Thus, multiple emergency pathways for alveolar cell regeneration are activated in lung (Fig. 2g, Discussion).
Changed cell composition with viral load
To determine viral load and associated host responses, we analysed donor- and cell-type-specific distribution of SARS-CoV-2 reads (Fig. 3a, b, Extended Data Fig. 6a–d, Supplementary Methods). Reads spanned the entire SARS-CoV-2 genome, with bias towards positive-sense alignments. A few cells had reads aligning to all viral segments, including the negative strand (Extended Data Fig. 6e), potentially indicating productive infection. Virus detection was not technically driven (Extended Data Fig. 6f–i), and inter-donor variation was consistent with SARS-CoV-2 qRT–PCR on bulk RNA (Extended Data Fig. 6j–l, Supplementary Methods). Viral load was negatively correlated with days from symptom start to death (Fig. 3c), as previously reported30,31. Bulk RNA-Seq yielded nine unique complete viral genomes from nine donors with high viral loads (Extended Data Fig. 6m, Supplementary Methods); all genomes carried the D614G allele. We identified no other common respiratory viral co-infections (Extended Data Fig. 6n). Total viral burden per sample (including ambient RNA; Supplementary Methods) positively correlated with proportions of mast cells, specific macrophage subsets, venular endothelial cells and capillary aerocyte endothelial cells (Extended Data Fig. 6o–u).
Genes upregulated in biopsy samples with high versus low or no viral load (Supplementary Methods) included viral response and innate immune processes (log2(fold change) > 1.4, Wald test, FDR-corrected P < 0.05; Extended Data Fig. 6v, Supplementary Table 4) and significantly overlapped with those in bulk RNA-Seq of post-mortem COVID-19 lungs in another study32 (FDR = 3.12 × 10−6, Kolmogorov–Smirnov test). Downregulated genes (log2(fold change) < 1.4, Wald test, FDR-corrected P < 0.05) were involved in surfactant metabolism dysfunction and lamellar bodies (secretory vesicles in AT2 cells33).
Lung cells enriched for SARS-CoV-2 RNA
Myeloid cells were the cell category most enriched for SARS-CoV-2 RNA (158 cells after correction for ambient RNA, FDR < 0.013; Fig. 3a, Extended Data Fig. 6w–y, Supplementary Methods), with particular enrichment in CD14highCD16high inflammatory monocytes (FDR < 0.005) and LDB2highOSMRhighYAP1high macrophages (FDR < 0.02; Extended Data Figs. 6x, 7a, b), although enrichment scores in individual donors varied. There was elevated, but non-significantly enriched, viral RNA in endothelial cells, with the capillary EC-2 (cluster 3, FDR < 0.017) and lymphatic endothelial cells (cluster 7, FDR < 0.006) enriched compared with other endothelial subsets (Fig. 3a, Extended Data Figs. 6w, y, 7c, d). There were also SARS-CoV-2 RNA+ cells among mast cells, and B and plasma cells, and viral RNA reads in multiple other cell types (Fig. 3a, Extended Data Fig. 6w). Notably, SARS-CoV-2 RNA+ cells did not co-express the entry factors ACE2 and TMPRSS2, or other hypothesized entry cofactors (Fig. 3b, Extended Data Fig. 7e–h).
Immune programs in SARS-CoV-2 RNA+ cells
SARS-CoV-2 RNA+ cells had distinct transcriptional programs compared with SARS-CoV-2 RNA− counterparts, with differentially expressed genes (FDR < 0.05; Supplementary Methods) in epithelial and myeloid cells, including PPARGhighCD15Lhigh macrophages and CD14highCD16high inflammatory monocytes (Supplementary Table 5). Genes upregulated in epithelial SARS-CoV-2 RNA+ cells were enriched for TNF, AP1 and chemokine and cytokine signalling, SARS-CoV-2-driven cell responses in vitro32, and keratinization pathways, which may reflect an injury response (Extended Data Fig. 7i). Genes upregulated in myeloid SARS-CoV-2 RNA+ cells were those associated with chemokine and cytokine signalling, and responses to interferon, TNF, intracellular pathogens and viruses (Fig. 3d, Extended Data Fig. 7j–m, Supplementary Table 5), as previously described34,35. Cytokines and viral host response genes were upregulated in both CD14highCD16high inflammatory monocytes and PPARGhighCD15Lhigh macrophages (Extended Data Fig. 7m, Supplementary Table 5), including CXCL10 and CXCL11, which were upregulated in nasopharyngeal swabs35 and bronchoalveolar lavages34.
A spatial atlas of COVID-19 lung
To provide tissue context, we used Nanostring GeoMx Digital Spatial Profiling (DSP) for transcriptomic profiling from regions of interest (Supplementary Methods) in 14 donors, including three deceased healthy donors (‘healthy’) (Extended Data Fig. 1a). Regions of interest spanned a range of anatomical structures and viral abundance on the basis of SARS-CoV-2 RNA hybridization signals; when possible, we segmented them to PanCK+ and PanCK−, and inflamed and normal-appearing alveoli areas of illumination (AOIs) to capture RNA (Fig. 4a, Extended Data Figs. 8a, 9a, Supplementary Methods). We acquired high-quality profiles (Extended Data Fig. 8b) from matched AOIs on the basis of distance to morphological landmarks (Supplementary Methods). SARS-CoV-2 RNA expression varied by donor, with elevated levels in four donors (Extended Data Fig. 8c, d, Supplementary Methods), consistent with viral qRT–PCR and sc/snRNA-Seq. Given the good agreement between a targeted 1,811-gene panel and a whole transcriptome (WTA) panel (18,335 genes) (Extended Data Fig. 8e–g, Supplementary Table 6), we focused our analyses on WTA data. For D8–12, 18–24, we contrasted donors with COVID-19 and healthy donors and COVID-19 epithelial and non-epithelial AOIs; for D13–17, we focused on distinct anatomical regions and inflamed versus normal-appearing regions within donors.
Inflammatory activation in alveoli
Deconvolution of major cell type composition (Fig. 4b, Extended Data Fig. 8h, Supplementary Table 7, 8, Supplementary Methods) showed inferred AT1 and AT2 cells dominating the PanCK+ compartments and greater cellular diversity in the PanCK− compartment. COVID-19 PanCK− AOIs had increased fibroblast and myofibroblast scores compared with controls, in line with parallel spatial studies36,37.
Comparing COVID-19 alveolar AOIs with control lungs from deceased healthy donors, there was upregulation of IFNα and IFNγ response genes and oxidative phosphorylation pathways (Fig. 4c, Extended Data Fig. 8i–k, Supplementary Table 6), similar to bulk RNA-Seq of highly infected tissue (IFIT1, IFIT3, IDO1, GZMB, LAG3, NKG7 and PRF1) and to SARS-CoV-2+ myeloid cells (TNFAIP6, CXCL11, CCL8, ISG1 and GBP5) and consistent with PANoptosis in a COVID-19 model38. Conversely, TNF, IL2–STAT5 and TGFβ signalling as well as apical junction and hypoxia were downregulated. Decreased TNF signalling expression in PanCK+ alveoli contrasts with its increase in SARS-CoV-2+ epithelial cells in snRNA-Seq and with reported38 synergy between TNF and IFNγ in mouse models of COVID-19.
Comparison of inflamed and normal-appearing AOIs within the same alveolar biopsy samples of COVID-19 lungs (Extended Data Fig. 9, Supplementary Table 9, D13–D17), showed that upregulated genes were enriched for innate immune and inflammatory pathways39,40, including neutrophil degranulation (FDR = 5.2 × 10−17) and IFNγ (FDR = 3.4 × 10−15) and interleukin (FDR = 1.4 × 10−13) signalling. TNF pathway expression was elevated in inflamed tissue, albeit not significantly (FDR = 0.097). Claudins and tight junction pathways were downregulated, corroborating a disrupted alveolar barrier, as in influenza41,42. Cilium assembly genes were enriched when comparing bronchial epithelial AOIs and matched normal-appearing alveoli (Extended Data Fig. 9d, Supplementary Table 9).
Comparison of SARS-CoV-2 high and low AOIs (Fig. 4d, e, Extended Data Fig. 8l, m, Supplementary Methods) revealed induction of the viral ORF1ab and S genes and upregulation of chemokine genes (CXCL2 and CXCL3) and immediate early genes in the PanCK+ compartment, consistent with snRNA-Seq (Supplementary Table 9, Extended Data Fig. 7i). NT5C, which encodes a nucleotidase with a preference for 5′-dNTPs, is consistently upregulated in SARS-CoV-2-high AOIs (Fig. 4e, Extended Data Fig. 8m, Supplementary Table 9). This gene is not known to have a role in lung injury and should be further studied.
COVID-19 effect on heart, kidney and liver
We next profiled liver, heart and kidney by snRNA-Seq with automated and manual annotation of parenchymal, endothelial and immune cells (Supplementary Methods, Extended Data Figs. 10, 11). Although other studies have reported viral reads in COVID-19 non-lung tissues43, we detected very few viral RNA reads in all three tissues, most of which could not be assigned to nuclei (Extended Data Fig. 11l); this absence was confirmed by NanoString DSP and RNAscope (data not shown).
Focusing on heart, both cell composition and gene programs changed between COVID-19 and healthy heart. There was a significant reduction in the proportion of cardiomyocytes and pericytes, and an increase in vascular endothelial cells (Extended Data Fig. 11e). Genes upregulated (FDR < 0.01) in cardiomyocytes, pericytes or fibroblasts (Extended Data Fig. 11g–i, Supplementary Table 10) included PLCG2, the cardiac role of which is unknown but which was induced in all major heart cell subtypes (Extended Data Fig. 11j), and AFDN, which is upregulated in endothelial cells (Extended Data Fig. 11k), and which encodes a junction adherens complex component44 that is necessary for endothelial barrier function. Upregulated pathways include oxidative-stress-induced apoptosis in pericytes, cell adhesion and immune pathways in cardiomyocytes, and cell differentiation processes in fibroblasts (Supplementary Table 10).
COVID-19 cell types related through GWAS
Finally, we aimed to identify genes and cell types associated with COVID-19 risk by integrating our atlas data with genome-wide association studies (GWAS)45 for common46 variants associated with COVID-19 (Supplementary Methods). Among 26 genes proximal to six COVID-19 GWAS regions (Supplementary Table 11, Supplementary Methods), 14 genes had higher average expression in the lung (P < 0.05, t-test; Extended Data Fig. 12a–d), 21 had significant (FDR < 0.05) expression specificity in at least one lung cell type, including FOXP4 (chromosome (chr.) 6, AT1 and AT2 cells), and CCR1 and CCRL2 (chr. 3, macrophages) (Extended Data Fig. 12e, Supplementary Table 11), and 18 were differentially expressed (FDR < 0.05) in COVID-19 compared with healthy lung (for example, SLC6A20 in goblet cells, CCR5 in CD8+ T cells and Treg cells, and CCR1 in macrophage and CD16+ monocytes (Extended Data Fig. 12f, Supplementary Table 11).
We related heritability from GWAS of COVID-19 severity traits to either cell type programs (genes enriched in a cell type in each tissue) or disease progression programs (genes differentially expressed between COVID-19 and controls in a cell type) in each tissue using sc-linker47 (Supplementary Methods). AT2 (4.8× heritability enrichment, P = 0.04), CD8+ T cells (4.4×, P = 0.009) and ciliated cell programs in the lung, proximal convoluted tubule and connecting tubule programs in kidney, and cholangiocyte programs in liver attained nominal (but not Bonferroni-corrected) significance (Extended Data Fig. 12g, h, Supplementary Table 11). Of all disease progression programs, only the club cell program (single-cell level model) had nominally significant heritability enrichment (10.5×, P = 0.04 for severe COVID-19) (Extended Fig. 12g, Supplementary Table 11).
The highest number of driving genes was observed for lung AT2 cells and spanned several loci, hinting at a polygenic architecture linking AT2 cells with severe COVID-19 (Supplementary Methods, Supplementary Table 11). Implicated GWAS proximity genes include OAS3 in lung AT2 and club cells, and SLC4A7 in lung CD8+ T cells (Supplementary Table 11), as well as genes at unresolved significantly associated GWAS loci (Extended Data Fig. 12i), such as FYCO1 (AT2, ciliated, club; chr. 3p), NFKBIZ (AT2; chr. 3q) and DPP9 (AT2; chr. 19) (Supplementary Table 11).
Among the changes in lung cell composition in COVID-19, is a reduction in AT2 cells and the presence of PATS and IPBLP-like cells, indicating that multiple regenerative strategies are invoked to re-establish alveolar epithelial cells lost to infection. A serial failure of epithelial progenitors to regenerate at a sufficient rate, first by secretory progenitor cells in the nasal passages and large and small airways, followed by alveolar AT2 cells, PATS and IPBLP cells, may eventually lead to lung failure.
Viral RNA in the lung varied, was negatively correlated with time from symptom start to death, and was primarily detected in myeloid and endothelial cells (as in nonhuman primates49); spatial analysis supports high virus levels at the earlier stages of infection36,37,50. Epithelial cells were not enriched in high viral RNA samples or in SARS-CoV-2+ cells, consistent with their excessive death. Cell-associated SARS-CoV-2 unique molecular identifiers may represent a mixture of replicating virus, immune cell engulfment and virions or virally infected cells attached to the cell surface. We did not detect viral RNA in the heart, liver or kidney, but observed other changes, including broad upregulation of PLCG2, a target of Bruton’s tyrosine kinase (BTK), in the heart51.
Combining our profiles with GWAS of COVID-19, we related specific cell types to heritable risk, especially AT2, ciliated and CD8+ T cells and macrophages, as well as genes in multi-gene regions underlying the association. This analysis can improve as GWAS grows and atlases expand.
Our study was limited by a modest number of donors without pre-selection of features, the terminal time point, limited distinction between viral RNA and true infection, and technical confounders such as PMIs. Nevertheless, our methods would enable studies in diverse diseased or damaged tissues. Future meta-analyses will further enhance its power and provide crucial resources for the community studying host–SARS-CoV-2 biology.
Further information on research design is available in the Nature Research Reporting Summary linked to this paper.
All samples were initially processed using Cumulus (https://github.com/klarman-cell-observatory/cumulus), which we ran on the Terra Cloud platform (https://app.terra.bio/). Code for all other analyses is available on GitHub (https://github.com/klarman-cell-observatory/covid19-autopsy).
Processed sequencing data (sc/snRNA-Seq and bulk) are available in the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE171668 and raw human sequencing data are available in the controlled access repository DUOS (https://duos.broadinstitute.org/), under dataset IDs DUOS-000126, DUOS-000127, DUOS-000128 and DUOS-000129. Viral genome assemblies and short-read sequencing data are publicly available in NCBI’s GenBank and SRA databases, respectively, under BioProject PRJNA720544. GenBank accessions for SARS-CoV-2 genomes are MW885875–MW885883. Data for other tissues in the biobank will be released as they are acquired. Further queries can be addressed to: firstname.lastname@example.org .
The processed data are available on the Single Cell Portal: Lung, https://singlecell.broadinstitute.org/single_cell/study/SCP1052/.
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We are deeply grateful to all donors and their families. This paper is part of the Human Cell Atlas (https://www.humancellatlas.org/publications/). We acknowledge the contribution of C. Kania, E. Kounaves, N. Lemelin, J. Susterich, J. Teixeira, C. Bernal, M. Berstein, A. Morris, J. N. Ray, A. Awley, A. Araujo and E. Figueroa who all assisted in performing the autopsies at the Massachusetts General Hospital. We thank A. Hupalowska and L. Gaffney for help with figure preparation. We thank M. Veregge, Z. Kramer and C. Jacobs for their contributions in the execution of experimental procedures, and D. Pouli who supported the creation of tissue annotation resources; 10x Genomics, Illumina, BD Biosciences, and NanoString for help and support with instruments and/or lab reagents and technical advice. Portions of this research were conducted on the Ithaca High Performance Computing system, Department of Pathology, BIDMC, and the O2 High Performance Compute Cluster at Harvard Medical School. This project has been funded in part with funds from the Manton Foundation, Klarman Family Foundation, HHMI, the Chan Zuckerberg Initiative and the Human Tumor Atlas Network trans-network projects SARDANA (Shared Repositories, Data, Analysis and Access). A.R. was an Investigator of the Howard Hughes Medical Institute. This project was also funded by DARPA grant HR0011-20-2-0040 (to D.E.I.) and the US Food and Drug Administration grant HHSF223201810172C (to P.C.S. and A.K.S.). A.-C.V. acknowledges funding support from the National Institute of Health Director’s New Innovator Award (DP2CA247831), the Massachusetts General Hospital (MGH) Transformative Scholar in Medicine Award, a COVID-19 Clinical Trials Pilot grant from the Executive Committee on Research at MGH, and the Damon Runyon-Rachleff Innovation Award. G.S. acknowledges support from the NIH R01AA0207440 and U01AA026933 research grants. A.P. acknowledges funding from the following sources U01 HG009379, R01 MH101244, R37 MH107649. We thank all members of the Department of Pathology and Cell Biology at Columbia University Irving Medical Center who led the procurement of autopsy tissues used in this work. This work was supported by National Institute of Health (NIH) grants K08CA222663 (B.I.), R37CA258829 (B.I.), U54CA225088 (B.I.), FastGrants (B.I.) and the Burroughs Wellcome Fund Career Award for Medical Scientists (B.I.). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30CA013696 at Columbia University and used the Molecular Pathology Shared Resource and its Tissue Bank.
A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until 31 July 2020. Since 1 August 2020, A.R. has been an employee of Genentech. O.R.-R. is an employee of Genentech as of 19 October 2020. P.Di., R.F., E.M.M., M.Ro., E.H.R., L.P., T.He., J.R., J.B. and S.W. are employees and stockholders at Nanostring Technologies Inc. D.R.Z. is a former employee and stockholder at NanoString Technologies. N.H. holds equity in BioNTech and Related Sciences. T.H. is an employee and stockholder of Prime Medicine as of 13 October 2020. G.H. is an employee of Genentech as of 16 November 2020. R.N. is a founder, shareholder, and member of the board at Rhinostics Inc. P.C.S. is a co-founder and shareholder of Sherlock Biosciences, and a Board member and shareholder of Danaher Corporation. A.K.S. reports compensation for consulting and/or SAB membership from Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Ochre Bio, Third Rock Ventures and Dahlia Biosciences. Z.G.J. reports grant support from Gilead Science, Pfizer and compensation for consulting from Olix Pharmaceuticals. Y.V.P. reports grant support from Enanta Pharmaceuticals, CymaBay Therapeutics, Morphic Therapeutic; consulting and/or SAB in Ambys Medicines, Morphic Therapeutics, Enveda Therapeutics, BridgeBio Pharma, as well as being an Editor of American Journal of Physiology-Gastrointestinal and Liver Physiology. G.S. reports consultant service in Alnylam Pharmaceuticals, Merck, Generon, Glympse Bio, Inc., Mayday Foundation, Novartis Pharmaceuticals, Quest Diagnostics, Surrozen, Terra Firma, Zomagen Bioscience, Pandion Therapeutics, Inc., Durect Corporation; royalty from UpToDate Inc. and Editor service for Hepatology Communications. P.R.T. receives consulting fees from Cellarity Inc. and Surrozen Inc. for work not related to this manuscript. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. P.T.E. has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. B.I. is a consultant for Merck and Volastra Therapeutics. All other authors declare no competing interests.
Peer review information Nature thanks Christopher Mason, Michael Matthay and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Fig. 1 A COVID-19 autopsy cohort, data quality and ambient RNA removal for a single-cell/nucleus lung atlas.
a, COVID-19 cohort overview. IMV, intermittent mandatory ventilation days; PMI, post-mortem interval; S/s, time from symptom start to death in days. b–d, Comparison of cell composition by scRNA-Seq and snRNA-Seq in matched samples. Proportion of cells (x axis) of each type (colour code) in sc/snRNA-Seq samples from the same three donors (D3, D8, D12). e–h, Cellbender remove-background on a single sample (D1). e, CellBender improves cell clustering and expression specificity by removing ambient RNA and empty (non-cell) droplets. UMAP plot of snRNA-Seq profiles (dots) either before (left) or after (right) CellBender processing, coloured by clusters, with CellBender-determined empty droplets in black (k = 2,508 droplets removed, k = 10,687 cells remaining). f, g, CellBender improves specificity of individual genes and cell type signatures. UMAP embedding of single nucleus profiles before CellBender (left) and after CellBender (right) processing, coloured by expression of the surfactant protein gene SFTPA1 (f) or signature score (SCANPY52 score_genes function, colour bar) for gene sets specific to lung AT2 (g) cells. Colour bar saturation chosen to emphasize low expression. h, Improved specificity of surfactant gene expression with CellBender (same sample). Expression level (log (average unique molecular identifier (UMI) count per cell), colour) and percentage of cells with non-zero expression (dot size) of surfactant genes (columns) across cell clusters (rows) before (left) and after (right) CellBender processing. Also shown, for comparison, are the results of an alternative method, DecontX (middle). Source data
a–d, Quality-control metrics for 24 lung samples (n = 16 donors). Number of cells or nuclei (a, y axis) and distributions (median and first and third quartiles) of number of UMIs per cell or nucleus (b, y axis), number of genes per cell/nucleus (c, y axis) and fraction of mitochondrial genes per cell/nucleus (d, y axis) across the samples (x axis) in the lung atlas. ScRNA-Seq samples are labelled by a grey circle. e–g, Cross-sample integration corrects batch effects. e, UMAP (as in Fig. 2a) of 106,792 sc/snRNA-Seq profiles after Harmony53 correction (Supplementary Methods) coloured by sample ID. f, g, Donors and processing protocols across clusters. Number of cells (y axis) from different donors (f) or processing protocols (g) in each Leiden cluster (x axis). h, Cross validation of automatic annotation. Percentage of cells (colour bar) annotated in a class by Schiller et al.54 that we predict for each class (columns). i, Identification of main lineage annotations by manual annotation. UMAP of 106,792 sc/snRNA-Seq profiles after Harmony53 correction (as in Fig. 2a) coloured by manual annotation done in subclustering of each lineage. Dashed lines: chosen compartments for subclustering. j–n, Refined annotation of cell subsets within lineages. UMAP embeddings of each selected cell lineage with cells coloured by manually annotated subclusters. Colour legends highlight highly expressed marker genes for select subsets. j, myeloid cells (k = 24,417 cells/nuclei); k, B and plasma cells (k = 1,693); l, T and NK cells (k = 9,950); m, endothelial cells (k = 20,366); and n, fibroblast (k = 20,925). Source data
Extended Data Fig. 3 Bulk RNA-Seq deconvolution and comparison of automatic and manual annotations in the COVID-19 lung cell atlas.
a, b, Deconvolution of bulk RNA-Seq libraries from adjacent lung tissue. a, Mean proportion (y axis, error bars = s.d. estimates from bulk RNA-Seq deconvolution (hatched bars; from MuSiC55) and from sc/snRNA-Seq (filled bars) for each of 11 cell subsets (x axis) in each of 16 bulk RNA-Seq lung samples (panels) from 10 random samples of 10,000 cells each. b, Robustness of cell proportion estimates to the number of single cells sampled for the reference data. Mean proportion (y axis, from MuSiC) estimates for each of 11 cell subsets (colour dots) in each of 16 bulk RNA-Seq lung samples (panels) when using three independent samples of 1,000–10,000 cells from the single-cell reference (x axis). c–e, Agreement between automated and manual annotations. c. High consistency between automatic and manual annotations. The proportion (colour intensity) and number (dot size) of cells with a given predicted annotation (rows) in each manual annotation category (columns). d, e, UMAP embedding of myeloid (k = 24,417 cells or nuclei) (d) and T and NK (k = 9,950 cells); (e) cell profiles coloured by manually annotated subclusters (left) or automated predictions (right). Source data
a, b, Identification of main immune lineage annotations. a, UMAP of 106,792 sc/snRNA-Seq profiles after Harmony correction (as in Fig. 2a) coloured by expression of genes (colour bar, genes listed below) used to separate immune cell sub-lineages (Supplementary Methods). b, Differentially expressed (DE) genes between subclusters within each lineage. Expression (colour bar) of genes (rows) that are differentially expressed (Supplementary Methods) across the subclusters (columns) within each compartment. DE genes shown are a union of the following: (i) top 10 DE genes between clusters, (ii) DE genes above an AUC of 0.8 and 0.75 for B/Plasma cells, (iii) pseudo-bulk DE genes above a log(fold change) threshold (thresholds: endothelial = 4.2, T/NK = 3, myeloid = 4, B/plasma = 2) (label on top). c, Batch correction within lineage. Fraction of cells/nuclei (y axis) from different processing protocols (left) or different donors (right, n = 17) in each subcluster (x axis) after batch correction with Harmony53 within each lineage. Source data
Extended Data Fig. 5 Cell-intrinsic programs and epithelial regenerative cell states in the COVID-19 lung cell atlas.
a, b, Differences in cell composition across donors. Percentage of cells (y axis) from each myeloid subset (legend) in each donor (x axis). b, Percentage of cells (y axis) from each main lineage (legend) in each donor (x axis), rank ordered by proportion of epithelial cells (blue). c, Myeloid, endothelial and pneumocyte cells show substantial changes in cell intrinsic expression profiles in COVID-19 lung. log2(fold change) (y axis) between COVID-19 and healthy lung for each elevated gene (dot) in each cell subset (x axis, by automatic annotation). Black bars, number of genes with significantly increased expression (adjusted P < 7.5 × 10−6). Computed using a single cell-based differential expression model applied to a meta-differential expression analysis between COVID-19 and healthy samples across 14 studies (see Supplementary Methods). d, PATS and IBPLP cells in COVID-19 lung. UMAP embeddings of 1,550 KRT8+ PATS-expressing cells (top) or of 1,394 airway epithelial cells (bottom) coloured by IPBLP cells or basal cells (orange, leftmost panels) or characteristic markers (purple, remaining panels). Source data
a, Detection of SARS-CoV-2 UMIs from sc/snRNA-Seq data. SARS-CoV-2 UMIs from all cell barcodes (top) and after ambient correction (second from top). Number (second from bottom) and percentage (bottom) of SARS-CoV-2 RNA+ cells after ambient correction (m = 24 specimens). b, c, Effect of ambient RNA on SARS-CoV-2 RNA+ detection. Number of SARS-CoV-2 aligning UMI per cell barcode (CB) (y axis) in healthy lung (b, black), in vitro SARS-CoV-2 infected human bronchial epithelial cells (HBEC)56 (b, blue) or lung samples from COVID-19 donors at autopsy either with CB with high-quality capture of human mRNA (b, red) or after removal of cells whose viral alignments were attributed to ambient contamination (c, Supplementary Methods). d, Variation in SARS-CoV-RNA+ cells across donors. Percentage of cells (y axis) assigned as SARS-CoV-2 RNA− (white), SARS-CoV-2 RNA+ (red) or SARS-CoV-2 ambient (grey, Supplementary Methods) across the donors (x axis), sorted by proportion of SARS-CoV-RNA+ cells. e–i, Viral RNA detection does not correlate with cell quality metrics. e–h, Number of SARS-CoV-2 UMIs (before ambient viral correction) for each cell (y axis) versus either number of SARS-CoV-2 genes for that cell (e, x axis), number of human (GRCh38) genes per cell (f, x axis), number of human (GRCh38) UMI per cell (g, x axis) or percentage of human (GRCh38) mitochondrial UMIs per cell (h, x axis). i, Number of retained high-quality cells (x axis) and number of SARS-CoV-2 RNA+ cells (y axis) in each sample (dots) after correction for ambient viral reads. Pearson’s r = 0.07, two-sided P = 0.73. j–l, Agreement in viral RNA detection between qPCR and sn/scRNA-Seq. Number of SARS-CoV-2 copies measured by CDC N1 qPCR on bulk RNA extracted from matched tissue samples (x axis) and the number of SARS-CoV-2 aligning UMI (y axis) for each sample (dot) from all reads (j, P < 0.0001, two-sided), all reads from high-quality cell barcodes (k, P < 0.0001), and after viral ambient RNA correction (l, P = 0.0042). Spearman’s ρ reported, two-sided test. m, Genetic diversity of SARS-CoV-2. Maximum likelihood phylogenetic tree of 772 SARS-CoV-2 genomes from cases in Massachusetts between January and May 2020. Orange points, donors in this cohort. n, Specificity of SARS-CoV-2 infection. log10(1+ reads) in each donor (columns) assigned to different viruses (rows) by metagenomic classification using Kraken2 from bulk RNA-Seq. Asterisks denote targeted capture. o–u, Relation between SARS-CoV-2 RNA and different cell types. Number of SARS-CoV-2 aligning UMIs in each (including all CB) and the proportion of epithelial (o), mast (p), macrophage VCANhighFCN1high (q), macrophages CD163highMERTKhigh (r), macrophages LDB2highOSMRhighYAP1high (s), venular endothelial (t) or capillary aerocytes (u) cells in these samples (x axes). Pearson’s r denoted in the upper left corner with significance after Bonferroni correction (P). v, Effect of viral load on bulk RNA profiles. Significance (−log10(P), y axis) and magnitude (log2(fold change), x axis) of differential expression of each gene (dots) between three donors with highest viral load and six donors with lowest or undetectable viral load profiled by bulk RNA-Seq. Red points, FDR < 0.05. w–y, Distribution of SARS-CoV-2 RNA+ cells across cell types and subsets. Number of SARS-CoV-2 RNA+ cells (y axis) from each donor (colour) across major categories (w, x axis), myeloid subsets (x, inflammatory monocytes: 40 cells, five donors; LDB2highOSMRhighYAP1high macrophages: 27 cells, five donors; x axis), or endothelial subsets (y, capillary endothelial cells: 16 cells, four donors; lymphatic endothelial cells: nine cells, three donors; x axis). Source data
Extended Data Fig. 7 Donor-specific enrichment of SARS-CoV-2 RNA+ cells and host responses to viral RNA.
a–d, SARS-CoV-2 RNA+ cells are enriched in specific lineages and subtypes. a, c, UMAP embeddings of either myeloid cells (a), or endothelial cells (c) from seven donors containing any SARS-CoV-2 RNA+ cell, and coloured by viral enrichment score (colour bar; red, stronger enrichment) and by SARS-CoV-2 RNA+ cells (black points). b, d, Number of SARS-CoV-2 RNA+ cells (y axis) per cell type/subset (x axis) in myeloid (b) or endothelial (d) subsets. Bar colour, FDR (dark blue, higher significance, Supplementary Methods; *FDR < 0.05). e–h, Variation across donors. e–g, UMAP embeddings of sc/snRNA-Seq profiles from each of seven donors containing any SARS-CoV-2 RNA+ cell (columns), coloured by major cell categories (e), expression of SARS-CoV-2 entry factors (f) or SARS-CoV-2 RNA enrichment per cluster (g, red/blue colour bar; red, high enrichment; black points, SARS-CoV-2 RNA+ cells). h, Number of SARS-CoV-2 RNA+ cells (y axis) across major cell types (x axis) from each of seven donors containing any SARS-CoV-2 RNA+ cell (columns). Bar colour, FDR (dark blue, higher significance). *FDR < 0.05. i, j, Normalized enrichment score (bars, right y axis) and significance (points, FDR, left y axis) (by GSEA39,40, Supplementary Methods) of different functional gene sets (x axis) in genes upregulated in SARS-CoV-2 RNA+ epithelial (i) or myeloid (j) cells. k, Expression of SARS-CoV-2 genomic features (log-normalized UMI counts; rows) across SARS-CoV-2 RNA+ (k = 158 cells) and SARS-CoV-2 RNA− (k = 790) myeloid cells (columns). l, m, Distribution of normalized expression levels (y axis) for select significantly differentially expressed genes between SARS-CoV-2 RNA− and SARS-CoV-2 RNA+ cells from all myeloid cells or CD14highCD16high inflammatory monocytes. DGE, differential gene expression. Source data
a, Overview of spatial profiling experiments. b, Distribution of sequencing saturation (y axis, %) for WTA and CTA AOIs (x axis). c, d, SARS-CoV-2 signature score (y axis) for each WTA (c) and CTA (d) AOI (dots) from each donor (x axis). e, Overlap of WTA and CTA genes. f, g, Agreement between WTA and CTA. f, Distribution (box, interquartile range; white point, median; violin range, min–max) of Pearson correlation coefficients (y axis) between WTA and CTA profiles (for common genes across 296 AOIs). g, Pearson correlation coefficient (y axis) of WTA and CTA common genes for each AOI pair (dot) from each donor (x axis), sorted by distance between WTA and CTA sections (blue, 10 mm; orange, 20 mm; green, 40 mm). h, Cell composition differences between PanCK+ and PanCK− alveolar AOIs and between AOIs from COVID-19 (n = 9, 161 AOIs) and healthy (D22–24, 40 AOIs) lungs. Expression scores (colour bar) of cell type signatures (rows) in PanCK+ (left) and PanCK− (right) alveolar AOIs (columns) in CTA data from different donors (top colour bar). i–k, Differential gene expression in COVID-19 versus healthy lung. Left: significance (−log10(P), y axis) and magnitude (log2(fold change), x axis) of differential expression of each gene (dots) in WTA for PanCK− (i, 112 COVID-19 versus 20 healthy), and in CTA for PanCK+ (j, 69 COVID-19 versus 18 healthy) and PanCK− (k, 92 COVID-19 versus 22 healthy) alveoli. Horizontal dashed line, FDR = 0.05; vertical dashed lines, |log2(fold change)| = 2. Right: significance (−log10(q)) of enrichment (permutation test) of different pathways (rows). l, m, Changes in gene expression in SARS-CoV-2 high versus low AOIs within COVID-19 lungs in WTA data. l, PanCK− alveolar AOIs (dots) rank ordered by their SARS-CoV-2 signature score (y axis) in WTA data, and partitioned to high (red), medium (grey) and low (blue) SARS-CoV-2 AOIs. m. Significance (−log10(P), y axis) and magnitude (log2(fold change), x axis) of differential expression of each gene (dots) in WTA data between SARS-CoV-2 high and low AOIs for PanCK− alveoli (ROIs: 11 high, six medium, 95 low). Horizontal dashed line, FDR = 0.05. Source data
Extended Data Fig. 9 GeoMx WTA DSP analysis of lung biopsy samples reveals region- and inflammation-specific expression programs.
a, Region selection. Serial sections of lung biopsy samples (five donors, D13–17; image depicts serial sections of D14) processed with GeoMx WTA DSP with four-colour staining (DNA, CD45, CD68, PanCK), RNAscope with probes against (SARS-CoV-2 S gene (used to derive semiquantitative viral load scores), ACE2, TMPRSS2), H&E staining and immunohistochemistry (IHC) with anti-SARS-CoV-2 S-protein. Scale bar, 100 μm. b–d, Region- and inflammation-specific expression programs. b, The first two principal components (PCs, x and y axes) from lung ROI gene expression profiles from donors D13–17, spanning normal-appearing alveoli (green; D14 = 6 AOIs, D15 = 2 AOIs, D16 = 5 AOIs, D17 = 4 AOIs); inflamed alveoli (magenta; D13 = 14 AOIs, D14 = 18 AOIs, D15 = 7 AOIs, D16 = 3 AOIs, D17 = 8 AOIs); bronchial epithelium (blue; D14 = 2 AOIs, D15 = 1 AOI, D16 = 2 AOIs, D17 = 3 AOIs) and arterial blood vessels (black; D13 = 2 AOIs, D15 = 3 AOIs). c, GSEA score (circle size, legend) of the enrichment of the IFNγ pathway in each normal-appearing (green; 6 AOIs) and inflamed (magenta; 18 AOIs) alveolar AOIs (dot) from the section of donor D14 (in a), placed in their respective physical coordinates on the tissue section (as in a). d, Expression (colour bar, log2(counts per million)) of IFNγ pathway genes (rows) from normal-appearing (green, n = 6) and inflamed (magenta, n = 18) alveoli AOIs (columns) from D14 lung biopsy. Source data
a–c, COVID-19 heart cell atlas. UMAP embedding of 40,880 heart nuclei (dots) (n = 18 donors, m = 19 specimens) coloured by Leiden resolution 1.5 clustering with manual post hoc annotations (a) or donors (c). b. Proportions of cell types (y axis) in each sample. d–f, COVID-19 kidney cell atlas. UMAP embedding of 33,872 kidney nuclei (dots) (n = 16, m = 16) coloured by clustering with manual post hoc annotations (d) or donors (f). e, Proportion of cells (y axis) in each sample. g–i, COVID-19 liver cell atlas. g, i, UMAP embedding of 47,001 liver nuclei (dots) (n = 15, m = 16), coloured by clustering with manual post hoc annotations (g) or donors (i). h, Proportions of cell types (y axis) in each sample. j–l, Automatic annotations. UMAP embeddings, coloured by predicted cell type labels by automatic annotation for heart (j), kidney (k) and liver (l). Source data
Extended Data Fig. 11 Entry factors in heart, kidney and liver COVID-19 tissues and differential gene expression in heart cell atlas.
a–c, SARS-CoV-2 entry factors are expressed in kidney, liver and heart cells. Average expression (dot colour) and fraction of expressing cells (colour, size) of SARS-CoV-2 entry factors (rows) across cell subsets (columns) in the kidney (a), liver (b) and heart (c). d–k, Genes and pathways differentially expressed between COVID-19 and healthy heart cells. d, log mean expression per cell (dot colour) and fraction of expressing cells (dot size) across cell types from healthy or COVID-19 heart (rows) for select genes (columns) that are differentially expressed between COVID-19 and healthy cells. e, Proportions of each cell type for COVID-19 (n = 15) and healthy (n = 28, two studies) samples (boxplots: middle line, mean; box bounds, first and third quartiles; whiskers, 1.5× the interquartile range; minima, smallest observed proportion; maxima, highest observed proportion). f, UMAP embedding of integrated COVID-19 and healthy snRNA-Seq profiles (dots) coloured by major cell types. Plot limited to a subset of 151,373 high-quality cells for visualization purposes. g–i, Cell-type-specific differentially expressed genes in COVID-19 versus healthy nuclei. Differential expression (log2(fold change), x axis), and associated significance (−log10(P), y axis; Supplementary Methods) for each gene (dot) between COVID-19 and healthy nuclei of cardiomyocytes (g), pericytes (h) and fibroblasts (i). Dashed line, FDR = 0.01. j, k, UMAP embedding of the meta-analysis atlas (as in f) but showing only COVID-19 (top) or healthy (bottom) nuclei profiles (dots) coloured by expression of PLCG2 (j) or AFDN (k). l, Low levels of viral UMIs in heart, liver and kidney, compared with lung. Cumulative viral read counts as a function of droplet UMI count. In lung (red) most virus-positive droplets are empty droplets (total UMI count approximately 100) with some virus-positive droplets that contain nuclei (UMI count > approximately 1,000), but in heart (green), liver (blue) and kidney (orange), most of the ‘virus-positive’ droplets have fewer than ten total UMI counts, indicating that these reads are not trustworthy. Source data
Extended Data Fig. 12 Expression of GWAS curated genes across lung, heart, liver and kidney atlases.
a–d, Mean expression (dot colour, log(TP10K + 1)) and proportion of expressing cells (dot size) for each of 26 curated GWAS implicated genes (columns) in each cell subset (rows) for lung (a), heart (b), liver (c) and kidney (d) COVID-19 autopsy atlases. Results only reported for genes with expression in at least one cell subset in the underlying tissue. Some GWAS genes have higher expression in the lung compared with the other three tissues. e, f, Mean expression (e, z-score relative to all other cell types, colour bar) or differential expression (f, z-score of DE analysis of expression in COVID-19 versus healthy cells of the same type) of 25 out of 26 GWAS implicated genes (rows) from six genomic loci associated with COVID-19 (based on summary statistics data from COVID-19 HGI meta analysis45 across lung cell types (columns). ABO was not considered as it was not reliably recovered in scRNA-Seq data. g, h, Cell type and disease progression gene programs in the lung (g), liver and kidney (h) that contribute to heritability of COVID-19 severity. Magnitude (circle size, E score) and significance (colour, −log10(P)) of the enrichment of cell type programs and cell-type-specific disease programs (columns) that were significantly enriched for COVID-19 or severe COVID-19 phenotypes (rows). All results are conditional on 86 baseline-LDv2.1 model annotations. i, Nomination of single best candidate genes at unresolved GWAS significant loci by aggregating gene level information across program classes and cell types. Significance (−log10(P), y axis) of GWAS association signal at locus (x axis). Blue boxes, significantly associated loci45 at a genome-wide significance level (purple horizontal bar). j, Schematic summarizing the key findings and contributions of this study. Source data
This file contains the Supplementary Methods and full descriptions for Supplementary Tables 1-14.
Clinical meta-data for all donors and extended sample information – see Supplementary Information document for full legend.
Studies included for building tissue meta-atlases and lung classifier coefficients – see Supplementary Information document for full legend.
Differentially expressed genes within KRT8+/PATS cell subsets and between the IPBLP and basal cell subsets – see Supplementary Information document for full legend.
Differentially expressed genes between healthy vs. COVID-19 lung (sc/snRNA-Seq) and between lung biopsies high vs. low/no viral load (bulkRNA-Seq) – see Supplementary Information document for full legend.
Analyses of SARS-CoV-2 RNA+ cells by cell type and viral sequencing – see Supplementary Information document for full legend.
Spatial assay probe list and DE genes between COVID-19 and healthy donors – see Supplementary Information document for full legend.
Differential expression results of snRNA-Seq based on the reconciled annotation – see Supplementary Information document for full legend.
Cell type markers used for cell type deconvolution of WTA and CTA data in lung spatial data – see Supplementary Information document for full legend.
Spatial differentially expressed genes in SARS-CoV-2 high vs. low and inflamed vs. non-inflamed – see Supplementary Information document for full legend.
COVID-19 heart differential expression analysis and GSEA results – see Supplementary Information document for full legend.
COVID-19 GWAS gene list, differential expression analysis and Sc-linker heritability results – see Supplementary Information document for full legend.
Gene signatures used for high-level manual annotation of COVID-19 lung cells.
List of differential gene expression between sub-clusters of myeloid cells, T and NK cells, B and plasma cells, and endothelial cells in COVID-19 lung, as computed by Pegasus – see Supplementary Information document for full legend.
List of top genes for LIGER factors of epithelial and fibroblast cells in COVID-19 lung – see Supplementary Information document for full legend.
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Delorey, T.M., Ziegler, C.G.K., Heimberg, G. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature (2021). https://doi.org/10.1038/s41586-021-03570-8
European Journal of Translational Myology (2021)
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