Integrated analysis of multimodal single-cell data.

Details

Serval ID
serval:BIB_C664D84081D5
Type
Article: article from journal or magazin.
Collection
Publications
Title
Integrated analysis of multimodal single-cell data.
Journal
Cell
Author(s)
Hao Y., Hao S., Andersen-Nissen E., Mauck W.M., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., Hoffman P., Stoeckius M., Papalexi E., Mimitou E.P., Jain J., Srivastava A., Stuart T., Fleming L.M., Yeung B., Rogers A.J., McElrath J.M., Blish C.A., Gottardo R., Smibert P., Satija R.
ISSN
1097-4172 (Electronic)
ISSN-L
0092-8674
Publication state
Published
Issued date
24/06/2021
Peer-reviewed
Oui
Volume
184
Number
13
Pages
3573-3587.e29
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
Keywords
3T3 Cells, Animals, COVID-19/immunology, Cell Line, Gene Expression Profiling/methods, Humans, Immunity/immunology, Leukocytes, Mononuclear/immunology, Lymphocytes/immunology, Mice, SARS-CoV-2/immunology, Sequence Analysis, RNA/methods, Single-Cell Analysis/methods, Transcriptome/immunology, Vaccination, CITE-seq, COVID-19, T cell, immune system, multimodal analysis, reference mapping, single cell genomics
Pubmed
Web of science
Open Access
Yes
Create date
28/02/2022 12:45
Last modification date
23/03/2024 8:24
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