Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2(+)TMPRSS2(+) cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Cyclooxygenase-2 controls energy homeostasis in mice by de novo recruitment of brown adipocytes.

Obesity results from chronic energy surplus and excess lipid storage in white adipose tissue (WAT). In contrast, brown adipose tissue (BAT) efficiently burns lipids through adaptive thermogenesis. Studying mouse models, we show that cyclooxygenase (COX)-2, a rate-limiting enzyme in prostaglandin (PG) synthesis, is a downstream effector of beta-adrenergic signaling in WAT and is required for the induction of BAT in WAT depots. PG shifted the differentiation of defined mesenchymal progenitors toward a brown adipocyte phenotype. Overexpression of COX-2 in WAT induced de novo BAT recruitment in WAT, increased systemic energy expenditure, and protected mice against high-fat diet-induced obesity. Thus, COX-2 appears integral to de novo BAT recruitment, which suggests that the PG pathway regulates systemic energy homeostasis

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention

An integrated cell atlas of the lung in health and disease.

Acknowledgements: This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications/). This work was supported by the Chan Zuckerberg Initiative (CZI; LLC Seed Network grant CZF2019-002438 (Lung Cell Atlas 1.0) to P.B., M.D.L., A.V.M., M.C.N., D.P.S., J.R., P.R.T., K.B.M., F.J.T. and H.B.S.); National Institutes of Health (NIH; R01HL145372) and Department of Defense (W81XWH-19-1-0416) (to J.A.K. and N.E.B.); Fondation pour la Recherche Médicale (DEQ20180339158), Conseil Départemental des Alpes Maritimes (2016-294DGADSH-CV), Inserm Cross-cutting Scientific Program HuDeCA 2018, Agence Nationale de la Recherche SAHARRA (ANR-19-CE14-0027), ANR-19-P3IA-0002-3IA, National Infrastructure France Génomique (ANR-10-INBS-09-03) and PPIA 4D-OMICS (21-ESRE-0052) (to P.B.); H2020-SC1-BHC-2018-2020 Discovair (grant agreement 874656) (to P.B., K.B.M., S.A.T., M.C.N., F.J.T., M.P., H.B.S. and J.L.); NIH 1U54HL145608-01 (to M.D.L., K.Z., X.S., J.S.H. and G.P.); Wellcome (WT211276/Z/18/Z) and Sanger core grant WT206194 (to K.B.M. and S.A.T.); ESPOD fellowship of the European Molecular Biology Laboratory European Bioinformatics Institute and Sanger Institute (to E.M.); R01 HL153312, U19 AI135964, P01 AG049665, R01 HL158139, R01 ES034350 and U54 AG079754 (to A.V.M.); Lung Foundation Netherlands project numbers 5.1.14.020 and 4.1.18.226 (to M.C.N.); NIH grants R01HL146557 and R01HL153375 (to P.R.T.); German Center for Lung Research and Helmholtz Association (to H.B.S.); Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (ZT-I-PF-5-01) and Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association ForInter (Interaction of Human Brain Cells) (to F.J.T.); Doris Duke Charitable Foundation (to J.A.K.); Joachim Herz Foundation (to L.D.); Ministry of Economic Affairs and Climate Policy by means of the Public–Private Partnership (to T.M.K.); 3IA Cote d’Azur PhD program (to A.C.); R01 HL135156, R01 MD010443, R01 HL128439, P01 HL132821, P01 HL107202, R01 HL117004 and Department of Defense grant W81WH-16-2-0018 (to M.A.S.); HL142568 and HL14507 from the NHLBI (to D.S.); P50 AR060780-06A1 (to R.L. and T.T.); Medical Research Council Clinician Scientist Fellowship (MR/W00111X/1) (to M.Z.N.); Jikei University School of Medicine (to M.Y.); University College London Birkbeck Medical Research Council Doctoral Training Programme (to K.B.W.); CZI (to J.W., Y.X. and N.K.); 5U01HL148856 (to J.W. and Y.X.); R01 HL153045 (to Y.X.); R01HL127349, R01HL141852 and U01HL145567 (to N.K.); 2R01HL068702 (to D.P.S. and J.R.); 5R01HL14254903 and 4UH3CA25513503 (to T.J.D.); R21HL156124, R56HL157632 and R21HL161760 (to A.M.T.); NIH U54 AG075931 and 5R01 HL146519 (to O.E.); Swedish Research Council and Cancerfonden (to C.S.); CZI Deep Visual Proteomics (to P.H.); U01HL148861-03 (to G.P.); CZI 2021-237918 (to J.S.H., P.R.T., H.B.S. and F.J.T.); CZIF2022-007488 from the CZI Foundation (F.J.T., S.A.T., M.D.L. and K.B.M.); European Respiratory Society and European Union’s Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant agreement number 847462 (to J.G.-S. and A.J.O.); and Fondation de l’Institut Universitaire de Cardiologie et de Pneumologie de Québec (to Y.B.). We thank E. Spiegel from the Core Facility Statistical Consulting at the Helmholtz Center Munich Institute of Computational Biology for statistical consulting.Funder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” NIH 1U54HL145608-01 CZIF2022-007488 from the Chan Zuckerberg Initiative Foundation CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: ESPOD fellowship of EMBL-EBI and Sanger InstituteFunder: 3IA Cote d’Azur PhD programFunder: The Ministry of Economic Affairs and Climate Policy by means of the PPPFunder: Joachim Herz Stiftung (Joachim Herz Foundation); doi: https://doi.org/10.13039/100008662Funder: P50 AR060780-06A1Funder: University College London, Birkbeck MRC Doctoral Training ProgrammeFunder: Jikei University School of Medicine (Jikei University); doi: https://doi.org/10.13039/501100007962Funder: 5R01HL14254903, 4UH3CA25513503Funder: R01HL127349, R01HL141852, U01HL145567 and CZIFunder: MRC Clinician Scientist Fellowship (MR/W00111X/1)Funder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” 2R01HL068702Funder: R01 HL135156, R01 MD010443, R01 HL128439, P01 HL132821, P01 HL107202, R01 HL117004, and DOD Grant W81WH-16-2-0018Funder: HL142568 and HL14507 from the NHLBIFunder: Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0”, 2R01HL068702Funder: Wellcome (WT211276/Z/18/Z) Sanger core grant WT206194 CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: R21HL156124, R56HL157632, and R21HL161760Funder: CZI, 5U01HL148856Funder: CZI, 5U01HL148856, R01 HL153045Funder: The National Institute of Health R01HL145372Funder: Inserm Cross-cutting Scientific Program HuDeCA 2018, ANR SAHARRA (ANR-19-CE14–0027), ANR-19-P3IA-0002–3IA, the National Infrastructure France Génomique (ANR-10-INBS-09-03), PPIA 4D-OMICS (21-ESRE-0052), and the Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0”.Funder: Sanger core grant WT206194 Chan Zuckerberg Initiative, LLC Seed Network grant (CZF2019-002438) “Lung Cell Atlas 1.0” CZIF2022-007488 from the Chan Zuckerberg Initiative FoundationFunder: Doris Duke Charitable Foundation (DDCF); doi: https://doi.org/10.13039/100000862Funder: The National Institute of Health R01HL145372 Department of Defense W81XWH-19-1-0416Funder: The National Institute of Health R01HL146557 and R01HL153375 and funds from Chan Zuckerberg Initiative - Human Lung Cell Atlas-pilot awardFunder: 1U54HL145608-01Funder: CZI Deep Visual ProteomicsFunder: 1U54HL145608-01, U01HL148861-03Funder: 1) the Chan Zuckerberg Initiative, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”; 2) R01 HL153312; 3) U19 AI135964; 4) P01 AG049665Funder: Netherlands Lung Foundation project nos. 5.1.14.020 and 4.1.18.226, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”Funder: grant number 2019-002438 from the Chan Zuckerberg Foundation, by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [ZT-I-PF-5-01] and by the Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association “ForInter” (Interaction of human brain cells)Funder: 1 U01 HL14555-01, R01 HL123766-04Funder: NIH U54 AG075931, 5R01 HL146519Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1(+) profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.A single-cell atlas of the human lungs, integrating data from 2.4 million cells from 486 individuals and including samples from healthy and diseased lungs, provides a roadmap for the generation of organ-scale cell atlases.Peer reviewe