Cell
Volume 184, Issue 13, 24 June 2021, Pages 3573-3587.e29
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Integrated analysis of multimodal single-cell data

https://doi.org/10.1016/j.cell.2021.04.048Get rights and content
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Highlights

  • “Weighted nearest neighbor” analysis integrates multimodal single-cell data

  • A multimodal reference “atlas” of the circulating human immune system

  • Identification and validation of novel sources of lymphoid heterogeneity

  • “Reference-based” mapping of query datasets onto a multimodal atlas

Summary

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

single cell genomics
multimodal analysis
CITE-seq
immune system
T cell
reference mapping
COVID-19

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