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Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
Open accessAbstract
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 analysis of over 100 single-cell and single-nucleus transcriptomics studies illustrates severe acute respiratory syndrome coronavirus 2 viral entry gene coexpression patterns across different human tissues, and shows association of age, smoking status and sex with viral entry gene expression in respiratory cell populations.
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- Muus, C., Luecken, M. D., Eraslan, G., Sikkema, L., Waghray, A., Heimberg, G., Kobayashi, Y., Vaishnav, E. D., Subramanian, A., Smillie, C., Jagadeesh, K. A., Duong, E. T., Fiskin, E., Torlai Triglia, E., Ansari, M., Cai, P., Lin, B., Buchanan, J., Chen, S., Shu, J., Haber, A. L., Chung, H., Montoro, D. T., Adams, T., Aliee, H., Allon, S. J., Andrusivova, Z., Angelidis, I., Ashenberg, O., Bassler, K., Bécavin, C., Benhar, I., Bergenstråhle, J., Bergenstråhle, L., Bolt, L., Braun, E., Bui, L. T., Callori, S., Chaffin, M., Chichelnitskiy, E., Chiou, J., Conlon, T. M., Cuoco, M. S., Cuomo, A. S. E., Deprez, M., Duclos, G., Fine, D., Fischer, D. S., Ghazanfar, S., Gillich, A., Giotti, B., Gould, J., Guo, M., Gutierrez, A. J., Habermann, A. C., Harvey, T., He, P., Hou, X., Hu, L., Hu, Y., Jaiswal, A., Ji, L., Jiang, P., Kapellos, T. S., Kuo, C. S., Larsson, L., Leney-Greene, M. A., Lim, K., Litviňuková, M., Ludwig, L. S., Lukassen, S., Luo, W., Maatz, H., Madissoon, E., Mamanova, L., Manakongtreecheep, K., Leroy, S., Mayr, C. H., Mbano, I. M., McAdams, A. M., Nabhan, A. N., Nyquist, S. K., Penland, L., Poirion, O. B., Poli, S., Qi, C., Queen, R., Reichart, D., Rosas, I., Schupp, J. C., Shea, C. V., Shi, X., Sinha, R., Sit, R. V., Slowikowski, K., Slyper, M., Smith, N. P., Sountoulidis, A., Strunz, M., Sullivan, T. B., Sun, D., Talavera-López, C., Tan, P., Tantivit, J., Travaglini, K. J., Tucker, N. R., Vernon, K. A., Wadsworth, M. H., Waldman, J., Wang, X., Xu, K., Yan, W., Zhao, W., Ziegler, C. G. K., The NHLBI LungMap Consortium, Deutsch, G. H., Dutra, J., Gaulton, K. J., Holden-Wiltse, J., Huyck, H. L., Mariani, T. J., Misra, R. S., Poole, C., Preissl, S., Pryhuber, G. S., Rogers, L., Sun, X., Wang, A., Whitsett, J. A., Xu, Y., The Human Cell Atlas Lung Biological Network, Alladina, J., Banovich, N. E., Barbry, P., Beane, J. E., Bhattacharyya, R. P., Black, K. E., Brazma, A., Campbell, J. D., Cho, J. L., Collin, J., Conrad, C., de Jong, K., Desai, T., Ding, D. Z., Eickelberg, O., Eils, R., Ellinor, P. T., Faiz, A., Falk, C. S., Farzan, M., Gellman, A., Getz, G., Glass, I. A., Greka, A., Haniffa, M., Hariri, L. P., Hennon, M. W., Horvath, P., Hübner, N., Hung, D. T., Huyck, H. L., Janssen, W. J., Juric, D., Kaminski, N., Koenigshoff, M., Koppelman, G. H., Krasnow, M. A., Kropski, J. A., Kuhnemund, M., Lafyatis, R., Lako, M., Lander, E. S., Lee, H., Lenburg, M. E., Marquette, C.-H., Metzger, R. J., Linnarsson, S., Liu, G., Lo, Y. M. D., Lundeberg, J., Marioni, J. C., Mazzilli, S. A., Medoff, B. D., Meyer, K. B., Miao, Z., Misharin, A. V., Nawijn, M. C., Nikolić, M. Z., Noseda, M., Ordovas-Montanes, J., Oudit, G. Y., Pe’er, D., Powell, J. E., Quake, S. R., Rajagopal, J., Tata, P. R., Rawlins, E. L., Regev, A., Reid, M. E., Reyfman, P. A., Rieger-Christ, K. M., Rojas, M., Rozenblatt-Rosen, O., Saeb-Parsy, K., Samakovlis, C., Sanes, J. R., Schiller, H. B., Schultze, J. L., Schwarz, R. F., Segre, A. V., Seibold, M. A., Seidman, C. E., Seidman, J. G., Shalek, A. K., Shepherd, D. P., Sinha, R., Spence, J. R., Spira, A., Sun, X., Sundström, E., Teichmann, S. A., Theis, F. J., Tsankov, A. M., Vallier, L., van den Berge, M., Van Zyl, T. A., Villani, A.-C., Weins, A., Xavier, R. J., Yildirim, A. Ö., Zaragosi, L.-E., Zerti, D., Zhang, H., Zhang, K., & Zhang, X.
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- 2021-03
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- Issue
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- 1546-170X
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- W3134181354
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- 10.08
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- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.