MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Second-Strand Synthesis-Based Massively Parallel scRNA-Seq Reveals Cellular States and Molecular Features of Human Inflammatory Skin Pathologies

Author(s)
Hughes, Travis K; Wadsworth, Marc H; Gierahn, Todd M; Do, Tran; Weiss, David; Andrade, Priscila R; Ma, Feiyang; de Andrade Silva, Bruno J; Shao, Shuai; Tsoi, Lam C; Ordovas-Montanes, Jose; Gudjonsson, Johann E; Modlin, Robert L; Love, J Christopher; Shalek, Alex K; ... Show more Show less
Thumbnail
DownloadPublished version (7.017Mb)
Publisher with Creative Commons License

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
Metadata
Show full item record
Abstract
© 2020 The Authors High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S3 (“Second-Strand Synthesis”), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S3 increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S3 to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.
Date issued
2020
URI
https://hdl.handle.net/1721.1/135291
Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science; Ragon Institute of MGH, MIT and Harvard; Koch Institute for Integrative Cancer Research at MIT; Massachusetts Institute of Technology. Department of Chemical Engineering; Massachusetts Institute of Technology. Department of Chemistry
Journal
Immunity
Publisher
Elsevier BV

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.