Interpretation of T cell states from single-cell transcriptomics data using reference atlases.

Details

Serval ID
serval:BIB_BE1D257DF142
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Interpretation of T cell states from single-cell transcriptomics data using reference atlases.
Journal
Nature communications
Author(s)
Andreatta M., Corria-Osorio J., Müller S., Cubas R., Coukos G., Carmona S.J.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
20/05/2021
Peer-reviewed
Oui
Volume
12
Number
1
Pages
2965
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Abstract
Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that "deviate" from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.
Keywords
General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry
Pubmed
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 180010
Create date
26/05/2021 19:12
Last modification date
01/06/2021 6:36
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