The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions
Open accessAbstract
In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin–angiotensin–aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.
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.
Integrity signals
Registry
Cites
Statistics
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.
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
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
Katoh, K., & Standley, D. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst010 · Cited by 46,209
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
lmerTest Package: Tests in Linear Mixed Effects Models
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Vincent, J. L., Moreno, R., Takala, J., Willatts, S., De Mendonça, A., Bruining, H., Reinhart, C. K., Suter, P. M., & Thijs, L. G. (1996) · Intensive Care Medicine · doi:10.1007/bf01709751 · Cited by 11,674
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2004973 · Cited by 9,951
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
Cited by
Museum of spatial transcriptomics
Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255
Museum of Spatial Transcriptomics
Moses, L., & Pachter, L. (2021) · doi:10.1101/2021.05.11.443152 · Cited by 1,255
Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing
Riva, L., Yuan, S., Yin, X., Martin-Sancho, L., Matsunaga, N., Pache, L., Burgstaller-Muehlbacher, S., De Jesus, P. D., Teriete, P., Hull, M. V., et al. (2020) · Nature · doi:10.1038/s41586-020-2577-1 · Cited by 760
Introductions and early spread of SARS-CoV-2 in the New York City area
Gonzalez-Reiche, A. S., Hernandez, M. M., Sullivan, M. J., Ciferri, B., Alshammary, H., Obla, A., Fabre, S., Kleiner, G., Polanco, J., Khan, Z., et al. (2020) · Science · doi:10.1126/science.abc1917 · Cited by 384
Towards population-scale long-read sequencing
De Coster, W., Weissensteiner, M. H., & Sedlazeck, F. J. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00367-3 · Cited by 323
Self-citation
Metadata
- Authors
- Butler, D., Mozsary, C., Meydan, C., Foox, J., Rosiene, J., Shaiber, A., Danko, D., Afshinnekoo, E., MacKay, M., Sedlazeck, F. J., Ivanov, N. A., Sierra, M., Pohle, D., Zietz, M., Gisladottir, U., Ramlall, V., Sholle, E. T., Schenck, E. J., Westover, C. D., Hassan, C., Ryon, K., Young, B., Bhattacharya, C., Ng, D. L., Granados, A. C., Santos, Y. A., Servellita, V., Federman, S., Ruggiero, P., Fungtammasan, A., Chin, C.-S., Pearson, N. M., Langhorst, B. W., Tanner, N. A., Kim, Y., Reeves, J. W., Hether, T. D., Warren, S. E., Bailey, M., Gawrys, J., Meleshko, D., Xu, D., Couto-Rodriguez, M., Nagy-Szakal, D., Barrows, J., Wells, H., O’Hara, N. B., Rosenfeld, J. A., Chen, Y., Steel, P. A. D., Shemesh, A. J., Xiang, J., Thierry-Mieg, J., Thierry-Mieg, D., Iftner, A., Bezdan, D., Sanchez, E., Campion, T. R., Sipley, J., Cong, L., Craney, A., Velu, P., Melnick, A. M., Shapira, S., Hajirasouliha, I., Borczuk, A., Iftner, T., Salvatore, M., Loda, M., Westblade, L. F., Cushing, M., Wu, S., Levy, S., Chiu, C., Schwartz, R. E., Tatonetti, N., Rennert, H., Imielinski, M., & Mason, C. E.
- Venue
- Nature Communications
- Published
- 2021-03-12
- Volume
- 12
- Issue
- 1
- Pages
- 1660-1660
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3133845106
- Cited by
- 200
- References
- 31
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. https://app.cochat.ai/papers/10.1038/s41467-021-21361-7
- The paper
- Butler, D., Mozsary, C., Meydan, C., Foox, J., Rosiene, J., Shaiber, A., Danko, D., Afshinnekoo, E., MacKay, M., Sedlazeck, F. J., Ivanov, N. A., Sierra, M., Pohle, D., Zietz, M., Gisladottir, U., Ramlall, V., Sholle, E. T., Schenck, E. J., Westover, C. D., . . . Mason, C. E. (2021). Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. Nature Communications. https://doi.org/10.1038/s41467-021-21361-7
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.