CoChat
Sign Up
Cited by 263SARS-CoV-2 and COVID-19 ResearchCOVID-19 Clinical Research Studiesinterferon and immune responses

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Temporal and spatial heterogeneity of host response to SARS-CoV-2 pulmonary infection

Open access
Desai, Niyati, Neyaz, Azfar, Szabolcs, Annamaria, Shih, Angela R., Chen, Jonathan H., Thapar, Vishal, Nieman, Linda T., Solovyov, Alexander, Mehta, Arnav, Lieb, David J., Kulkarni, Anupriya S., Jaicks, Christopher, Xu, Katherine H., Raabe, Michael J., Pinto, Christopher J., Juric, Dejan, Chebib, Ivan, Colvin, Robert B., Kim, Arthur Y., Monroe, Robert, Warren, Sarah E., Danaher, Patrick, Reeves, Jason W., Gong, Jingjing, Rueckert, Erroll H., Greenbaum, Benjamin D., Hacohen, Nir, Lagana, Stephen M., Rivera, Miguel N., Sholl, Lynette M., Stone, James R., Ting, David T., Deshpande, Vikram·2020·Nature Communications
DOI 10.1038/s41467-020-20139-7PMC PMC7725958

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

The relationship of SARS-CoV-2 pulmonary infection and severity of disease is not fully understood. Here we show analysis of autopsy specimens from 24 patients who succumbed to SARS-CoV-2 infection using a combination of different RNA and protein analytical platforms to characterize inter-patient and intra-patient heterogeneity of pulmonary virus infection. There is a spectrum of high and low virus cases associated with duration of disease. High viral cases have high activation of interferon pathway genes and a predominant M1-like macrophage infiltrate. Low viral cases are more heterogeneous likely reflecting inherent patient differences in the evolution of host response, but there is consistent indication of pulmonary epithelial cell recovery based on napsin A immunohistochemistry and RNA expression of surfactant and mucin genes. Using a digital spatial profiling platform, we find the virus corresponds to distinct spatial expression of interferon response genes demonstrating the intra-pulmonary heterogeneity of SARS-CoV-2 infection.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-020-20139-7

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Detection of SARS-CoV-2 in human autopsy samples. a Paraffin embedded sections from the lung of Case 1 show abundant SARS-CoV-2 extracellular RNA-ISH signal (red) predominantly localization to the hyaline membranes (arrow). Top image—10×, scale 200 μm. Bottom image—40×, scale bar = 50 μm. The inset shows the corresponding hematoxylin and eosin stained section shows histologic features of exudative diffuse alveolar damage with prominent hyaline membranes. Image 10×, scale bar = 200 μm. b Percentage of viral load in the lung as determined by a quantitative analysis of SARS-CoV-2 RNA-ISH. c Expression heatmap of RNA-seq aligned counts of genes in the SARS-CoV-2 genome Log2(RPM) from autopsy cases. Consistent with the quantitative analysis on the RNA-ISH platform, Cases 9, 1, C, 11, D and case 8 showed the highest viral load. The non-pulmonary organs were virtually negative for virus, except two bowel tissues. d Swimmers plot highlighting the difference in duration of illness between viral high and viral low cases. p-value two-tailed t-test. e Quantitative protein expression of keratin and Napsin A by immunohistochemistry performed on lung sections (one section per case. n = 20) between viral high (red) and low (blue) cases. Viral low cases showed higher number of keratin and Napsin A positive cells, both markers of pulmonary pneumocytes. Box-and-whisker plot, center line, median; box limits, upper and lower quartiles; whiskers, range. P-value two-tailed t-test. Source data are provided as a Source data file.
Fig. 1 Detection of SARS-CoV-2 in human autopsy samples. a Paraffin embedded sections from the lung of Case 1 show abundant SARS-CoV-2 extracellular RNA-ISH signal (red) predominantly localization to the hyaline membranes (arrow). Top image—10×, scale 200 μm. Bottom image—40×, scale bar = 50 μm. The inset shows the corresponding hematoxylin and eosin stained section shows histologic features of exudative diffuse alveolar damage with prominent hyaline membranes. Image 10×, scale bar = 200 μm. b Percentage of viral load in the lung as determined by a quantitative analysis of SARS-CoV-2 RNA-ISH. c Expression heatmap of RNA-seq aligned counts of genes in the SARS-CoV-2 genome Log2(RPM) from autopsy cases. Consistent with the quantitative analysis on the RNA-ISH platform, Cases 9, 1, C, 11, D and case 8 showed the highest viral load. The non-pulmonary organs were virtually negative for virus, except two bowel tissues. d Swimmers plot highlighting the difference in duration of illness between viral high and viral low cases. p-value two-tailed t-test. e Quantitative protein expression of keratin and Napsin A by immunohistochemistry performed on lung sections (one section per case. n = 20) between viral high (red) and low (blue) cases. Viral low cases showed higher number of keratin and Napsin A positive cells, both markers of pulmonary pneumocytes. Box-and-whisker plot, center line, median; box limits, upper and lower quartiles; whiskers, range. P-value two-tailed t-test. Source data are provided as a Source data file.CC BY · PubMed Central
Fig. 2 Lung samples cluster based on SARS-CoV-2 viral RNA levels. Unsupervised hierarchical clustering of 500 most variant genes across lung specimens from SARS-CoV-2 infected patients separating into high, mixed, and low viral RNA cases. Virus high, low, and mixed samples with gene expression sets enriched or recurring gene classes shown in colored boxes. Purple star notes Case 7 LUL high virus levels that is distinct from the other Case 7 lobes.
Fig. 2 Lung samples cluster based on SARS-CoV-2 viral RNA levels. Unsupervised hierarchical clustering of 500 most variant genes across lung specimens from SARS-CoV-2 infected patients separating into high, mixed, and low viral RNA cases. Virus high, low, and mixed samples with gene expression sets enriched or recurring gene classes shown in colored boxes. Purple star notes Case 7 LUL high virus levels that is distinct from the other Case 7 lobes.CC BY · PubMed Central
Fig. 3 Differential expression of SARS-CoV-2 viral high versus low cases. a The high viral cases (red) were enriched with higher interferon response genes with gene expression heatmap of all significant interferon gamma response genes differentially expressed between high and low viral RNA cases (FDR < 0.01). b The low viral cases (blue) had multiple mucin and surfactant genes enriched compared to high viral cases. Gene expression heatmap of selected mucin and surfactant genes. All mucin genes and SFTPC were statistically significant (FDR < 0.01). Purple star notes Case 7 LUL high virus levels that is distinct from the other Case 7 lobes. Source data are provided as a Source data file.
Fig. 3 Differential expression of SARS-CoV-2 viral high versus low cases. a The high viral cases (red) were enriched with higher interferon response genes with gene expression heatmap of all significant interferon gamma response genes differentially expressed between high and low viral RNA cases (FDR < 0.01). b The low viral cases (blue) had multiple mucin and surfactant genes enriched compared to high viral cases. Gene expression heatmap of selected mucin and surfactant genes. All mucin genes and SFTPC were statistically significant (FDR < 0.01). Purple star notes Case 7 LUL high virus levels that is distinct from the other Case 7 lobes. Source data are provided as a Source data file.CC BY · PubMed Central
Fig. 4 Monocytes and macrophages dominate SARS-CoV-2 lung immune response. a Expression heatmap of genes involved with SARS-CoV-2 viral entry, macrophage/monocytes, and MHC Class I. b Immunohistochemical quantitation of immune cell subsets in lung samples for CD163, CD3, CD4, CD8, CD56, CD20, and CD138. Box-and-whisker plot, center line, median; box limits, upper and lower quartiles; whiskers, range. P-value two-tailed t-test. (one section per case. n = 20). c Representative images of CD163 IHC staining in virus high and virus low case 10×, scale bar = 200 μm. d Cell fraction (%) of immune cell estimated in lung tissue obtained by deconvolution of bulk RNA-seq data using CIBERSORTx. Bar = mean. P-value two-tailed t-test. high (red) and low (blue) expression of immune cells (n = 15). Source data are provided as a Source data file.
Fig. 4 Monocytes and macrophages dominate SARS-CoV-2 lung immune response. a Expression heatmap of genes involved with SARS-CoV-2 viral entry, macrophage/monocytes, and MHC Class I. b Immunohistochemical quantitation of immune cell subsets in lung samples for CD163, CD3, CD4, CD8, CD56, CD20, and CD138. Box-and-whisker plot, center line, median; box limits, upper and lower quartiles; whiskers, range. P-value two-tailed t-test. (one section per case. n = 20). c Representative images of CD163 IHC staining in virus high and virus low case 10×, scale bar = 200 μm. d Cell fraction (%) of immune cell estimated in lung tissue obtained by deconvolution of bulk RNA-seq data using CIBERSORTx. Bar = mean. P-value two-tailed t-test. high (red) and low (blue) expression of immune cells (n = 15). Source data are provided as a Source data file.CC BY · PubMed Central
Fig. 5 Cytokine pathway differences between low and high viral cases. a Expression heatmap of IL-6, IL-22/CXCR6, and JAK/STAT pathway genes in SARS-CoV-2 virus high and low cases and controls. b JAK/STAT pathway genes significantly higher in virus high cases compared to low cases. P-value two-tailed t-test. (n = 15). c IL-22 related genes and CXCR6 higher in virus low cases compared to high cases. IL22RA1 trended higher in virus low cases, but was not significant. P-value two-tailed t-test (n = 15). Source data are provided as a Source data file.
Fig. 5 Cytokine pathway differences between low and high viral cases. a Expression heatmap of IL-6, IL-22/CXCR6, and JAK/STAT pathway genes in SARS-CoV-2 virus high and low cases and controls. b JAK/STAT pathway genes significantly higher in virus high cases compared to low cases. P-value two-tailed t-test. (n = 15). c IL-22 related genes and CXCR6 higher in virus low cases compared to high cases. IL22RA1 trended higher in virus low cases, but was not significant. P-value two-tailed t-test (n = 15). Source data are provided as a Source data file.CC BY · PubMed Central
Fig. 6 Intrapulmonary heterogeneity of SARS-CoV-2 host response. a Selection of ROIs. Left) SARS-CoV-2 RNA-ISH staining was used to guide ROI selection of viral positive and viral negative regions. (Scale bar = 2 mm). Right) multi-color immunofluorescence staining for CD45/red, CD68/yellow, PanCK/green, and DNA/blue were used in parallel to select ROIs. (Scale bar = 2 mm). Example ROIs are shown in insets. (Scale bar = 100 μm). b Distribution of immune subsets and relationship with viral location. Rows show estimates from distinct cell types; columns show distinct tissues. Point position shows the physical location of regions within each tissue. Point size shows a cell type’s estimated proportion of cells in a region. Point color denotes whether a region was classified SARS-CoV-2 positive or negative by RNA-ISH. c tSNE clustering of geometric ROIs highlights two primary clusters exist within the data irrespective of SARS-CoV-2 RNA-ISH status of ROI or patient viral load. d Differential expression analysis of clusters identified by tSNE analysis. Target genes colored by significance and association with tSNE clusters. Targets with FDR < 0.05 are shown in gray. Genes shown in red are associated with higher expression cluster labeled ‘active’ in panel (c); genes shown in blue are associated with higher expression in the cluster labeled ‘inactive’. e Unsupervised clustering analysis of interferon stimulated genes cluster across ROIs. Annotation by patient sample identifier and SARS-CoV-2 RNA-ISH positivity in the ROI as performed by GeoMx Digital Spatial Profiler. Source data are provided as a Source data file.
Fig. 6 Intrapulmonary heterogeneity of SARS-CoV-2 host response. a Selection of ROIs. Left) SARS-CoV-2 RNA-ISH staining was used to guide ROI selection of viral positive and viral negative regions. (Scale bar = 2 mm). Right) multi-color immunofluorescence staining for CD45/red, CD68/yellow, PanCK/green, and DNA/blue were used in parallel to select ROIs. (Scale bar = 2 mm). Example ROIs are shown in insets. (Scale bar = 100 μm). b Distribution of immune subsets and relationship with viral location. Rows show estimates from distinct cell types; columns show distinct tissues. Point position shows the physical location of regions within each tissue. Point size shows a cell type’s estimated proportion of cells in a region. Point color denotes whether a region was classified SARS-CoV-2 positive or negative by RNA-ISH. c tSNE clustering of geometric ROIs highlights two primary clusters exist within the data irrespective of SARS-CoV-2 RNA-ISH status of ROI or patient viral load. d Differential expression analysis of clusters identified by tSNE analysis. Target genes colored by significance and association with tSNE clusters. Targets with FDR < 0.05 are shown in gray. Genes shown in red are associated with higher expression cluster labeled ‘active’ in panel (c); genes shown in blue are associated with higher expression in the cluster labeled ‘inactive’. e Unsupervised clustering analysis of interferon stimulated genes cluster across ROIs. Annotation by patient sample identifier and SARS-CoV-2 RNA-ISH positivity in the ROI as performed by GeoMx Digital Spatial Profiler. Source data are provided as a Source data file.CC BY · PubMed Central
Fig. 7 Spatial distribution of innate immune response linked with presence of SARS-CoV-2 virus. a Differential expression of all genes between SARS-CoV-2 positive/negative regions within each tissue. Horizontal position shows genes’ log2 fold-change, with points farther right having higher expression in SARS-CoV-2 positive regions. Vertical position shows −log10(p-value), which increases with statistical significance. Red points show genes that were consistently up-regulated, blue points show genes that were consistently downregulated in SARS-CoV-2 positive regions across the 6 patient tissues. Each gene has the same point color in all 6 panels. b Genes with consistent differential expression between SARS-CoV-2 positive/negative regions across all tissues. Only consistently up/downregulated genes (red/blue in a) are shown. Grid color shows log2 fold-change, with red indicating higher expression in virus-positive regions. Results with p < 0.01 (heteroscedastic 2-sided t-test) are given color. Columns are ordered by hierarchical clustering. c Protein differential expression between SARS-CoV-2 positive/negative regions. Only proteins with FDR < 0.05 in at least one tissue are shown. Grid color shows log2 fold-change, with red indicating higher expression in virus-positive regions. Results with p < 0.01 (unpaired, heteroscedastic t-test) are given color. Columns are ordered by hierarchical clustering. d Spatially-resolved expression of viral and interferon signaling genes. Rows show distinct gene sets; columns show distinct tissues. Pie position shows the physical location of regions within each tissue. Wedge volume shows a gene’s background-subtracted expression; within each row, all genes are scaled to have the same maximum. Wedge color denotes whether a region was classified SARS-CoV-2 positive or negative by RNA-ISH. Source data are provided as a Source data file.
Fig. 7 Spatial distribution of innate immune response linked with presence of SARS-CoV-2 virus. a Differential expression of all genes between SARS-CoV-2 positive/negative regions within each tissue. Horizontal position shows genes’ log2 fold-change, with points farther right having higher expression in SARS-CoV-2 positive regions. Vertical position shows −log10(p-value), which increases with statistical significance. Red points show genes that were consistently up-regulated, blue points show genes that were consistently downregulated in SARS-CoV-2 positive regions across the 6 patient tissues. Each gene has the same point color in all 6 panels. b Genes with consistent differential expression between SARS-CoV-2 positive/negative regions across all tissues. Only consistently up/downregulated genes (red/blue in a) are shown. Grid color shows log2 fold-change, with red indicating higher expression in virus-positive regions. Results with p < 0.01 (heteroscedastic 2-sided t-test) are given color. Columns are ordered by hierarchical clustering. c Protein differential expression between SARS-CoV-2 positive/negative regions. Only proteins with FDR < 0.05 in at least one tissue are shown. Grid color shows log2 fold-change, with red indicating higher expression in virus-positive regions. Results with p < 0.01 (unpaired, heteroscedastic t-test) are given color. Columns are ordered by hierarchical clustering. d Spatially-resolved expression of viral and interferon signaling genes. Rows show distinct gene sets; columns show distinct tissues. Pie position shows the physical location of regions within each tissue. Wedge volume shows a gene’s background-subtracted expression; within each row, all genes are scaled to have the same maximum. Wedge color denotes whether a region was classified SARS-CoV-2 positive or negative by RNA-ISH. Source data are provided as a Source data file.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 263

Related

See all

Metadata

Authors
Desai, N., Neyaz, A., Szabolcs, A., Shih, A. R., Chen, J. H., Thapar, V., Nieman, L. T., Solovyov, A., Mehta, A., Lieb, D. J., Kulkarni, A. S., Jaicks, C., Xu, K. H., Raabe, M. J., Pinto, C. J., Juric, D., Chebib, I., Colvin, R. B., Kim, A. Y., Monroe, R., Warren, S. E., Danaher, P., Reeves, J. W., Gong, J., Rueckert, E. H., Greenbaum, B. D., Hacohen, N., Lagana, S. M., Rivera, M. N., Sholl, L. M., Stone, J. R., Ting, D. T., & Deshpande, V.
Affiliations
  • Massachusetts General Hospital (US)
  • Memorial Sloan Kettering Cancer Center (US)
  • Broad Institute (US)
  • Advanced Cell Diagnostics (United States) (US)
  • Nanostring Technologies (United States) (US)
  • Columbia University Irving Medical Center (US)
  • Brigham and Women's Hospital (US)
  • Harvard University (US)
Corresponding author
  • Stone, James R.
  • Ting, David T.
  • Deshpande, Vikram
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-12-09
Volume
11
Issue
1
Pages
6319-6319
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-20139-7
PMC
PMC7725958
OpenAlex
W3112860625
Cited by
245
References
51
Citations by year
2026: 17 · 2025: 9 · 2024: 27 · 2023: 45 · 2022: 70 · 2021: 70 · 2020: 7
FWCI
6.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (R01CA240924, 7R01AI081848-04)
  • ACD/Biotechne, The MGH Research Scholars Program, The Pershing Square Sohn Prize—Mark Foundation Fellow, The V Foundation, SU2C-Lustgarten
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Life Sciences › Immunology and Microbiology › Immunology › interferon and immune responses
Keywords
SARS-CoV-2, COVID-19 pandemic, pulmonary infection, interferon pathway, M1 macrophages, spatial transcriptomics, digital spatial profiling, Napsin A, mucin genes, disease severity, autopsy specimens

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review