COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

doi:10.1038/s41586-021-03570-8

. 2021 Jul;595(7865):107-113.

doi: 10.1038/s41586-021-03570-8. Epub 2021 Apr 29.

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

Toni M Delorey^#¹, Carly G K Ziegler^#^{2

3

4

5

6

7}, Graham Heimberg^#¹, Rachelly Normand^#^{2

8

9

10

11}, Yiming Yang^#^{1

8}, Åsa Segerstolpe^#¹, Domenic Abbondanza^#¹, Stephen J Fleming^#^{12

13}, Ayshwarya Subramanian^#¹, Daniel T Montoro^#², Karthik A Jagadeesh^#¹, Kushal K Dey^#¹⁴, Pritha Sen^#^{2

8

15

16}, Michal Slyper^#¹, Yered H Pita-Juárez^#^{2

10

17

18

19}, Devan Phillips^#¹, Jana Biermann^#^{20

21}, Zohar Bloom-Ackermann²², Nikolaos Barkas¹², Andrea Ganna^{23

24}, James Gomez²², Johannes C Melms^{20

21}, Igor Katsyv²⁵, Erica Normandin^{2

10}, Pourya Naderi^{10

17

18}, Yury V Popov^{10

26

27}, Siddharth S Raju^{2

28

29}, Sebastian Niezen^{10

26

27}, Linus T-Y Tsai^{2

10

26

30

31}, Katherine J Siddle^{2

32}, Malika Sud¹, Victoria M Tran²², Shamsudheen K Vellarikkal^{2

33}, Yiping Wang^{20

21}, Liat Amir-Zilberstein¹, Deepak S Atri^{2

33}, Joseph Beechem³⁴, Olga R Brook³⁵, Jonathan Chen^{2

36}, Prajan Divakar³⁴, Phylicia Dorceus¹, Jesse M Engreitz^{2

37}, Adam Essene^{26

30

31}, Donna M Fitzgerald³⁸, Robin Fropf³⁴, Steven Gazal³⁹, Joshua Gould^{1

12}, John Grzyb⁴⁰, Tyler Harvey¹, Jonathan Hecht^{10

17}, Tyler Hether³⁴, Judit Jané-Valbuena¹, Michael Leney-Greene², Hui Ma^{1

8}, Cristin McCabe¹, Daniel E McLoughlin³⁸, Eric M Miller³⁴, Christoph Muus^{2

41}, Mari Niemi²³, Robert Padera^{40

42

43}, Liuliu Pan³⁴, Deepti Pant^{26

30

31}, Carmel Pe'er¹, Jenna Pfiffner-Borges¹, Christopher J Pinto^{16

38}, Jacob Plaisted⁴⁰, Jason Reeves³⁴, Marty Ross³⁴, Melissa Rudy², Erroll H Rueckert³⁴, Michelle Siciliano⁴⁰, Alexander Sturm²², Ellen Todres¹, Avinash Waghray^{44

45}, Sarah Warren³⁴, Shuting Zhang²², Daniel R Zollinger³⁴, Lisa Cosimi⁴⁶, Rajat M Gupta^{2

33}, Nir Hacohen^{2

9

47}, Hanina Hibshoosh²⁵, Winston Hide^{10

17

18

19}, Alkes L Price¹⁴, Jayaraj Rajagopal³⁸, Purushothama Rao Tata⁴⁸, Stefan Riedel^{10

17}, Gyongyi Szabo^{2

10

26}, Timothy L Tickle^{1

12}, Patrick T Ellinor⁴⁹, Deborah Hung^{22

50

51}, Pardis C Sabeti^{2

32

52

53

54}, Richard Novak⁵⁵, Robert Rogers^{26

56}, Donald E Ingber^{41

55

57}, Z Gordon Jiang^{10

26

27}, Dejan Juric^{16

38}, Mehrtash Babadi^{12

13}, Samouil L Farhi¹, Benjamin Izar^{20

21

58

59}, James R Stone³⁶, Ioannis S Vlachos^{60

61

62

63

64}, Isaac H Solomon⁴⁰, Orr Ashenberg¹, Caroline B M Porter¹, Bo Li^{1

8

16}, Alex K Shalek^{65

66

67

68

69

70

71

72

73

74

75}, Alexandra-Chloé Villani^{76

77

78

79}, Orit Rozenblatt-Rosen^{80

81}, Aviv Regev^{82

83

84

85}

Affiliations

¹ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Program in Health Sciences & Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA.
⁴ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁶ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA.
⁷ Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA.
⁸ Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁹ Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
¹⁰ Harvard Medical School, Boston, MA, USA.
¹¹ Massachusetts Institute of Technology, Cambridge, MA, USA.
¹² Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹³ Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁴ Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.
¹⁵ Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹⁷ Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
¹⁸ Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA.
¹⁹ Cancer Research Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁰ Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA.
²¹ Columbia Center for Translational Immunology, New York, NY, USA.
²² Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
²³ Institute for Molecular Medicine Finland, Helsinki, Finland.
²⁴ Analytical & Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
²⁵ Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
²⁶ Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁷ Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁸ Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
²⁹ FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
³⁰ Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA.
³¹ Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core Boston, Boston, MA, USA.
³² Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
³³ Divisions of Cardiovascular Medicine and Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁴ NanoString Technologies Inc, Seattle, WA, USA.
³⁵ Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
³⁶ Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
³⁷ Department of Genetics and BASE Initiative, Stanford University School of Medicine, Stanford, CA, USA.
³⁸ Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
³⁹ Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴⁰ Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
⁴¹ John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
⁴² Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
⁴³ Department of Pathology, Harvard Medical School, Boston, MA, USA.
⁴⁴ Harvard Stem Cell Institute, Cambridge, MA, USA.
⁴⁵ Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴⁶ Infectious Diseases Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
⁴⁷ Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁴⁸ Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA.
⁴⁹ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵⁰ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁵¹ Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
⁵² Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.
⁵³ Howard Hughes Medical Institute, Chevy Chase, MD, USA.
⁵⁴ Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA.
⁵⁵ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
⁵⁶ Massachusetts General Hospital, Boston, MA, USA.
⁵⁷ Vascular Biology Program and Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
⁵⁸ Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
⁵⁹ Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY, USA.
⁶⁰ Broad Institute of MIT and Harvard, Cambridge, MA, USA. ivlachos@bidmc.harvard.edu.
⁶¹ Harvard Medical School, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶² Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶³ Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶⁴ Cancer Research Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶⁵ Broad Institute of MIT and Harvard, Cambridge, MA, USA. shalek@mit.edu.
⁶⁶ Program in Health Sciences & Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA. shalek@mit.edu.
⁶⁷ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁶⁸ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁶⁹ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA. shalek@mit.edu.
⁷⁰ Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA. shalek@mit.edu.
⁷¹ Harvard Medical School, Boston, MA, USA. shalek@mit.edu.
⁷² Harvard Stem Cell Institute, Cambridge, MA, USA. shalek@mit.edu.
⁷³ Program in Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁷⁴ Program in Immunology, Harvard Medical School, Boston, MA, USA. shalek@mit.edu.
⁷⁵ Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁷⁶ Broad Institute of MIT and Harvard, Cambridge, MA, USA. avillani@mgh.harvard.edu.
⁷⁷ Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. avillani@mgh.harvard.edu.
⁷⁸ Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. avillani@mgh.harvard.edu.
⁷⁹ Department of Medicine, Harvard Medical School, Boston, MA, USA. avillani@mgh.harvard.edu.
⁸⁰ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. orit.r.rosen@gmail.com.
⁸¹ Genentech, South San Francisco, CA, USA. orit.r.rosen@gmail.com.
⁸² Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. aviv.regev.sc@gmail.com.
⁸³ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. aviv.regev.sc@gmail.com.
⁸⁴ Howard Hughes Medical Institute, Chevy Chase, MD, USA. aviv.regev.sc@gmail.com.
⁸⁵ Genentech, South San Francisco, CA, USA. aviv.regev.sc@gmail.com.

^# Contributed equally.

PMID: 33915569
PMCID: PMC8919505
DOI: 10.1038/s41586-021-03570-8

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

Toni M Delorey et al. Nature. 2021 Jul.

. 2021 Jul;595(7865):107-113.

doi: 10.1038/s41586-021-03570-8. Epub 2021 Apr 29.

Authors

Affiliations

¹ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Program in Health Sciences & Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA.
⁴ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁶ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA.
⁷ Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA.
⁸ Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁹ Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
¹⁰ Harvard Medical School, Boston, MA, USA.
¹¹ Massachusetts Institute of Technology, Cambridge, MA, USA.
¹² Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹³ Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹⁴ Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.
¹⁵ Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁶ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹⁷ Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
¹⁸ Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA.
¹⁹ Cancer Research Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁰ Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA.
²¹ Columbia Center for Translational Immunology, New York, NY, USA.
²² Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
²³ Institute for Molecular Medicine Finland, Helsinki, Finland.
²⁴ Analytical & Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
²⁵ Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
²⁶ Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁷ Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
²⁸ Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
²⁹ FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
³⁰ Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA.
³¹ Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core Boston, Boston, MA, USA.
³² Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
³³ Divisions of Cardiovascular Medicine and Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁴ NanoString Technologies Inc, Seattle, WA, USA.
³⁵ Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
³⁶ Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
³⁷ Department of Genetics and BASE Initiative, Stanford University School of Medicine, Stanford, CA, USA.
³⁸ Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
³⁹ Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴⁰ Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
⁴¹ John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
⁴² Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
⁴³ Department of Pathology, Harvard Medical School, Boston, MA, USA.
⁴⁴ Harvard Stem Cell Institute, Cambridge, MA, USA.
⁴⁵ Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴⁶ Infectious Diseases Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
⁴⁷ Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁴⁸ Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA.
⁴⁹ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵⁰ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁵¹ Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
⁵² Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.
⁵³ Howard Hughes Medical Institute, Chevy Chase, MD, USA.
⁵⁴ Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA.
⁵⁵ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
⁵⁶ Massachusetts General Hospital, Boston, MA, USA.
⁵⁷ Vascular Biology Program and Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
⁵⁸ Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
⁵⁹ Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY, USA.
⁶⁰ Broad Institute of MIT and Harvard, Cambridge, MA, USA. ivlachos@bidmc.harvard.edu.
⁶¹ Harvard Medical School, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶² Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶³ Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶⁴ Cancer Research Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA. ivlachos@bidmc.harvard.edu.
⁶⁵ Broad Institute of MIT and Harvard, Cambridge, MA, USA. shalek@mit.edu.
⁶⁶ Program in Health Sciences & Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA. shalek@mit.edu.
⁶⁷ Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁶⁸ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁶⁹ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA. shalek@mit.edu.
⁷⁰ Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA. shalek@mit.edu.
⁷¹ Harvard Medical School, Boston, MA, USA. shalek@mit.edu.
⁷² Harvard Stem Cell Institute, Cambridge, MA, USA. shalek@mit.edu.
⁷³ Program in Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁷⁴ Program in Immunology, Harvard Medical School, Boston, MA, USA. shalek@mit.edu.
⁷⁵ Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.
⁷⁶ Broad Institute of MIT and Harvard, Cambridge, MA, USA. avillani@mgh.harvard.edu.
⁷⁷ Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. avillani@mgh.harvard.edu.
⁷⁸ Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. avillani@mgh.harvard.edu.
⁷⁹ Department of Medicine, Harvard Medical School, Boston, MA, USA. avillani@mgh.harvard.edu.
⁸⁰ Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. orit.r.rosen@gmail.com.
⁸¹ Genentech, South San Francisco, CA, USA. orit.r.rosen@gmail.com.
⁸² Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. aviv.regev.sc@gmail.com.
⁸³ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. aviv.regev.sc@gmail.com.
⁸⁴ Howard Hughes Medical Institute, Chevy Chase, MD, USA. aviv.regev.sc@gmail.com.
⁸⁵ Genentech, South San Francisco, CA, USA. aviv.regev.sc@gmail.com.

^# Contributed equally.

PMID: 33915569
PMCID: PMC8919505
DOI: 10.1038/s41586-021-03570-8

Abstract

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure^1-4, 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.

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Figures

**Extended Data Figure 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, S/s: time from symptom onset to death in days; PMI: post-mortem interval. **b-d.** Comparison of cell composition by scRNA-seq and snRNA-seq in matched samples. Proportion of cells (x axis) of each type (color 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, colored 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 pre-CellBender (left) and post-CellBender (right) processing, colored by expression of the surfactant protein *SFTPA1* (f) or signature score (Scanpy’s⁶³ score_genes function, color bar) for genes sets specific to lung AT2 (g) cells. Color bar saturation chosen to emphasize low expression. h. Improved specificity of surfactant gene expression with CellBender (same sample). Expression level (log(average UMI count per cell), color) and percent of cells with nonzero 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).

**Extended Data Figure 2.. Quality control and annotation in the COVID-19 lung cell atlas**
**a-d.** QC metrics for 24 lung samples (n=16 donors). Number of cells/nuclei (a, y axis) and distributions (median and first and third quartiles) of number of UMI per cell/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 labeled 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 post-Harmony⁶⁵ correction (Methods) colored 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. Percent of cells (color bar) annotated in a class by Schiller *et al.⁷³* that we predict for each class (columns). i. Identification of main lineage annotations by manual annotation. UMAP of 106,792 sc/snRNA-Seq profiles post-Harmony⁶⁵ correction (as in Fig. 2a) colored by manual annotation done in sub-clustering of each lineage. Dashed lines: chosen compartments for sub-clustering. **j-n.** Refined annotation of cell subsets within lineages. UMAP embeddings of each selected cell lineages with cells colored by manually annotated sub-clusters. Color 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).

**Extended Data Figure 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 = SD estimates from bulk RNA-Seq deconvolution (hatched bars; from MuSiC⁸⁶) 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 (color dots) in each of 16 bulk RNA-Seq lung samples (panels) when using three independent samples of 1,000 to 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 (color 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/nuclei) (d) and T and NK (k=9,950 cells), (e) cell profiles colored by manually annotated subclusters (left) or automated predictions (right).

**Extended Data Figure 4.. Manual annotation in the COVID-19 lung cell atlas**
**a,b.** Identification of main immune lineage annotations. a. UMAP of 106,792 sc/snRNA-Seq profiles post- Harmony correction (as in Fig. 2a) colored by expression of genes (color bar, genes listed below) used to separate immune cell sub-lineages (Methods). b. Differentially expressed genes between sub-clusters within each lineage. Expression (color bar) of genes (rows) that are differentially expressed (Methods) across the sub-clusters (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 sub-cluster (x axis) after batch correction with Harmony⁶⁵ within each lineage.

**Extended Data Figure 5.. Cell intrinsic programs and epithelial regenerative cell states in the COVID-19 lung cell atlas**
**a,b.** Differences in cell composition across donors. Percent of cells (y axis) from each myeloid subset (legend) in each donor (x axis). b. Percent 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-value <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 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) colored by IPBLPs or basal cells (orange, leftmost panels) or characteristic markers (purple, remaining panels).

**Extended Data Figure 6.. SARS-CoV-2-RNA+ cells distinguished by sc/snRNA-Seq**
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 percent (bottom) of SARS-CoV-2 RNA+ cells after ambient correction (m=24 specimens). **b,c.** Impact 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)¹¹⁰ (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, Methods). d. Variation in SARS-CoV-RNA+ cells across donors. Percent of cells (y axis) assigned as SARS-CoV-2 RNA⁻(white), SARS-CoV-2 RNA⁺(red), or SARS-CoV-2 ambient (grey, 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 (prior to ambient viral correction) for each cell (y axis) *vs.* 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 % 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) following 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 ⍴ 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-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 *VCAN*^highFCN1^high (q), macrophages *CD163*^highMERTK^high (r), macrophages *LDB2*^highOSMR^highYAP1^high (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 following Bonferroni correction (p). v. Impact of viral load on bulk RNA profiles. Significance (−log₁₀(P-value), yaxis) and magnitude (log₂(fold-change), xaxis) of differential expression of each gene (dots) between three donors with highest viral load and six donors with lowest/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 (color) across major categories (w, x axis), myeloid subsets (x, inflammatory monocytes: 40 cells, 5 donors; *LDB2*^highOSMR^highYAP1^high macrophages: 27 cells, 5 donors; x axis), or endothelial subsets (y, capillary endothelial cells: 16 cells, 4 donors; lymphatic endothelial cells: 9 cells, 3 donors; 16 cells, 4 donors, x axis).

**Extended Data Figure 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 sub-types. **a,c.** UMAP embeddings of either myeloid cells (a), or endothelial cells (c) from seven donors containing any SARS-CoV-2 RNA+ cell, and colored by viral enrichment score (color 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 color: FDR ( dark blue: higher significance, Methods; * FDR < 0.05. ). **b. 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), colored by major cell categories (e), expression of SARS-CoV-2 entry factors (f), or SARS-CoV-2 RNA enrichment per cluster (g, red/blue colorbar; 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 color: 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 GSEA^,, 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 Inflammatory monocytes *CD14*^highCD16^highcells.

**Extended Data Figure 8.. NanoString GeoMx experiment design and analysis**
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 (color bar) of cell type signatures (rows) in PanCK⁺ (left) and PanCK⁻ (right) alveolar AOIs (columns) in CTA data from different donors (top color bar). **i-k.** Differential gene expression in COVID-19 *vs.* healthy lung. Left: Significance (−log₁₀(p-value), y axis) and magnitude (log₂(fold-change), x axis) of differential expression of each gene (dots) in WTA for PanCK⁻ (i, 112 COVID-19 *vs.* 20 healthy), and in CTA for PanCK⁺ (j, 69 COVID-19 *vs.* 18 healthy) and PanCK⁻ (k, 92 COVID-19 *vs.* 22 healthy) alveoli. Horizontal dashed line: FDR = 0.05, vertical dashed lines: |log₂(fold-change)| = 2. Right: Significance (−log₁₀(q-value)) of enrichment (permutation test) of different pathways (rows). **l,m.** Changes in gene expression in SARS-CoV-2 high *vs.* 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-value), 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, 6 medium, 95 low). Horizontal dashed line: FDR = 0.05.

**Extended Data Figure 9.. GeoMx WTA DSP analysis of lung biopsies reveals region- and inflammation-specific expression programs**
a. Region selection. Serial sections of lung biopsies (five donors, D13-17; image depicts serial sections of D14) processed with GeoMx WTA-DSP with 4-color staining (DNA, *CD45*, *CD68,* PanCK), RNAscope with probes against (SARS-CoV-2 S-gene (utilized to derive semi-quantitative viral load scores), *ACE2*, *TMPRSS2*), H&E staining, and immunohistochemistry with anti-SARS-CoV-2 S-protein. Scale bar: 100 μm. **b-d**. Regions 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 interferon-γ 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 (color bar, log2(counts per million)) of IFNγ pathway genes (rows) from normal-appearing (green, n=6) and inflamed alveoli (magenta, n=18) AOIs (columns) from D14 lung biopsy.

**Extended Data Figure 10.. A single nucleus atlas of heart, kidney, and liver COVID-19 tissues**
**a-c.** COVID-19 heart cell atlas. UMAP embedding of 40,880 heart nuclei (dots) (n=18 donors, m=19 specimens) colored by Leiden resolution 1.5 clustering with manual *post hoc* annotations (a) or donors (c). b. Proportions of cells (y axis) in each sample. **d-f.** COVID-19 kidney cell atlas. UMAP embedding of 33,872 kidney nuclei (dots) (n=16, m=16) colored 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), colored by clustering with manual *post hoc* annotations (g) or donors (i). h. Proportion of cells (y-axis) in each sample. **j-l.** Automatic annotations. UMAP embeddings, colored by predicted cell type labels by automatic annotation for heart (j), kidney (k) and liver (l).

**Extended Data Figure 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 color) and fraction of expressing cells (color, 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 color) 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, 2 studies) samples (boxplots: middle line=mean, box bounds=first and third quartiles,whiskers=1.5x the interquartile range, minima=smallest observed proportion, maxima=highest observed proportion). f. UMAP embedding of integrated COVID-19 and healthy snRNA-seq profiles (dots) colored 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 *vs.* healthy nuclei. Differential expression (log₂(fold change), x axis), and associated significance (−log₁₀(P-value), y axis, Methods) for each gene (dot) between COVID-19 *vs.* 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) colored by expression of *PLCG2* (j) or *AFDN* (k). l. Low levels of viral UMIs in heart, liver and kidney, compared to lung. Cumulative viral read counts as a function of droplet UMI count. In lung (red) most viral-positive droplets are empty droplets (total UMI count ~ 100) with some viral-positive droplets which contain nuclei (UMI count > ~1,000), but in heart (green), liver (blue), and kidney (orange), most of the “viral-positive” droplets have fewer than 10 total UMI counts, suggesting these reads are not trustworthy.

**Extended Data Figure 12.. Expression of GWAS curated genes across lung, heart, liver and kidney atlases**
**a-d.** Mean expression (dot color, 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 to the other three tissues. **e,f.** Mean expression (e, z-score relative to all other cell types, color bar) or differential expression (f, z-score of DE analysis of expression in COVID-19 *vs.* healthy cells of the same type) of 25 out of 26 GWAS implicated genes (rows) from 6 genomic loci associated with COVID-19 (based on summary statistics data from COVID-19 HGI meta analysis 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 (color, −log₁₀(P-value)) of the enrichment of cell type programs and cell-types 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-value), y-axis) of GWAS association signal at locus (x-axis). Blue boxes: Significantly associated loci at a genome-wide significance level (purple horizontal bar). j. Schematic summarizing the key findings and contributions of this study.

**Figure 1.. Experimental and computational pipeline for a COVID-19 autopsy atlas.**
a. Sample processing pipeline. Up to 11 tissue types from 32 donors were collected. PMI: post-mortem interval. b. sc/snRNA-Seq analysis pipeline.

**Figure 2.. A single cell and single nucleus atlas of COVID-19 lung**
a. Automatic prediction identifies 28 cell subsets across compartments. UMAP embedding of 106,792 harmonized sc/snRNA-Seq profiles (dots) from 24 tissue samples of 16 COVID-19 lung donors, colored by automatic annotations (legend). b. Epithelial cell subsets. UMAP embedding of 21,661 epithelial cells/nuclei profiles, colored by manual annotations, with highly expressed marker genes (boxes). **c,d.** Cell composition and expression differences between COVID-19 and healthy lung. c. Cell proportions (x axis, mean: bar, and 95% confidence intervals: line) in each automatically annotated subset (y axis) in COVID-19 snRNA-Seq (red, n=16), healthy snRNA-Seq (grey, n=3), and healthy scRNA-seq (n=8, blue). Cell types shown have a COVID-19 *vs.* healthy snRNA-Seq FDR < 0.05 (Dirichlet-multinomial regression). d. Significance (−Log₁₀(P-value), y axis) vs. magnitude (log₂(fold-change), x axis) of differential expression of each gene (dots; horizontal dashed line: FDR < 0.05) between COVID-19 and healthy lung from a total of 2,000 AT2 cells and 14 studies (2 sided test, Methods). **e,f.** An increased PATS^– program in pneumocytes in COVID-19 *vs.* healthy lung. e. Distribution of PATS signature scores (y axis) for 17,655 cells from COVID-19 and 24,000 cells from healthy lung pneumocytes (x axis) p-value< 2.2*10⁻¹⁶ (one-sided Mann–Whitney U test). f. UMAP embedding of 21,661 epithelial cell profiles (dots) colored by signature level (color legend, lower right) for the PATS (top) or IPBLP (bottom) programs. g. Model of epithelial cell regeneration in healthy and COVID-19 lung. In healthy alveoli (top), AT2 cells self-renew (1) and differentiate into AT1 cells (2). In COVID-19 alveoli (bottom), AT2 cell self-renewal (1) and AT1 differentiation (2) are inhibited, resulting in PATS accumulation (3) and recruitment of airway-derived IPBLP progenitors to alveoli (4).

**Figure 3.. SARS-CoV-2 RNA+ single cells are enriched for phagocytic and endothelial cells**
**a,b.** Many SARS-CoV-2 RNA+ single cells do not express known SARS-CoV-2 entry factors. UMAP embedding of all 106,792 lung cells/nuclei (as in Fig. 2a), showing either a. only the 40,581 cells from seven donors containing any SARS-CoV-2 RNA+ cell, colored by viral enrichment score (Methods, red: stronger enrichment) and by SARS-CoV-2 RNA+ cells (black points), and marked by annotation and FDR of enrichment (legend) or b. all 106,792 cells/nuclei, colored by expression of SARS-CoV-2 entry factors (co-expression combinations with at least 10 cells are shown). Dashed lines: major cell types. c. Reduction in SARS-CoV-2 RNA with prolonged S/s to death interval (Spearman ρ = −0.68, p <0.005, two-sided test). S/s to death (x axis, days) and lung SARS-CoV-2 copies/ng input RNA (y axis) for each donor (n=16). d. Expression changes in SARS-CoV-2 RNA+ myeloid cells. Significantly differentially expressed host genes (log-normalized and scaled digital gene expression, rows; cutoff: FDR < 0.05 and log₂fold change > 0.5) across SARS-CoV-2 RNA+ (n=158) and SARS-CoV-2 RNA− myeloid cells (n=790) (columns).

**Figure 4.. Composition and expression differences between COVID-19 and healthy lungs and between infected and uninfected regions within COVID-19 lungs.**
a. Example of analyzed regions. Top: RNAscope (left) and immunofluorescent staining (right) of donor D20 with collection ROIs and matched areas in white rectangles. Bottom: One ROI (yellow rectangle) from each scan (left and middle), and the segmented collection AOIs (right). b. Cell composition differences between PanCK⁺ and PanCK⁻ alveolar AOIs and between AOIs from COVID-19 (n=9, 190 AOIs) and healthy (D22-24, 38 AOIs) lungs. Expression scores (color bar) of cell type signatures (rows) in PanCK⁺ (left) and PanCK⁻ (right) alveolar AOIs (columns) in WTA data from different donors (top color bar).c. Differential gene expression in COVID-19 *vs.* healthy lung. Left: Significance (−log₁₀(p-value), y axis) and magnitude (log₂(fold-change), x axis) of differential expression of each gene (dots) in WTA data between PanCK⁺ alveoli AOIs from COVID-19 (n=78) vs. healthy (n=18) lung . Right: Significance (−log₁₀(q-value)) of enrichment (permutation test) of different pathways (rows). **d,e.** Changes in gene expression in SARS-CoV-2 high *vs.* low AOIs within COVID-19 lungs in WTA data. d. SARS-CoV-2 high and low alveolar AOIs. PanCK+ alveolar AOIs (dots) rank ordered by their SARS-CoV-2 signature scores (y axis) in WTA data, and partitioned to high (red), medium (grey) and low (blue) SARS-CoV-2 AOIs. e. Significance (−log10(p-value), 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 (AOIs: 17 high, 3 medium, 58 low). Horizontal dashed line: FDR = 0.05. Vertical dashed lines: |log₂(fold-change)| = 2. Top 10 DE genes by fold change marked.

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References

1. Guan W-J et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med 382, 1708–1720 (2020). - PMC - PubMed
1. Puelles VG et al. Multiorgan and Renal Tropism of SARS-CoV-2. N. Engl. J. Med 383, 590–592 (2020). - PMC - PubMed
1. Huang C et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020). - PMC - PubMed
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1. Varga Z et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet 395, 1417–1418 (2020). - PMC - PubMed

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[1] Guan W-J et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med 382, 1708–1720 (2020). - PMC - PubMed

[2] Guan W-J et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med 382, 1708–1720 (2020). - PMC - PubMed

[3] Puelles VG et al. Multiorgan and Renal Tropism of SARS-CoV-2. N. Engl. J. Med 383, 590–592 (2020). - PMC - PubMed

[4] Puelles VG et al. Multiorgan and Renal Tropism of SARS-CoV-2. N. Engl. J. Med 383, 590–592 (2020). - PMC - PubMed

[5] Huang C et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020). - PMC - PubMed

[6] Huang C et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020). - PMC - PubMed

[7] Xu Z et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 8, 420–422 (2020). - PMC - PubMed

[8] Xu Z et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 8, 420–422 (2020). - PMC - PubMed

[9] Varga Z et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet 395, 1417–1418 (2020). - PMC - PubMed

[10] Varga Z et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet 395, 1417–1418 (2020). - PMC - PubMed

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COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

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