Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed...

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Veröffentlicht in:	Nature reviews. Genetics Jg. 22; H. 10; S. 627 - 644
Hauptverfasser:	Longo, Sophia K., Guo, Margaret G., Ji, Andrew L., Khavari, Paul A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 01.10.2021 Nature Publishing Group
Schlagworte:	631/1647/1513 631/1647/2217/2018 631/1647/48 631/208/199 631/208/212/2019 Agriculture Algorithms Animal Genetics and Genomics Animals Bar codes Biomedical and Life Sciences Biomedicine Cancer Research Cell Communication Cell interaction Cell populations Cell research Communication Composition Computational biology Computational Biology - methods Computer applications Disease Gene expression Gene Function Homeostasis Human Genetics Humans Hybridization Identification and classification Ligands Methods Physiological aspects Review Article Ribonucleic acid RNA RNA sequencing Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software Spatial data Spatial distribution Tissues Transcription Transcriptome Transcriptomes Transcriptomics United States
ISSN:	1471-0056, 1471-0064, 1471-0064
Online-Zugang:	Volltext
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Zusammenfassung:	Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies. Combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics can localize transcriptionally characterized single cells within their native tissue context. This Review discusses methodologies and tools to integrate scRNA-seq with spatial transcriptomics approaches, and illustrates the types of insights that can be gained.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 Author contributions S.K.L. researched the literature and wrote the article. M.G.G., A.L.J. and P.A.K. contributed substantially to discussions of the content. All authors reviewed and/or edited the manuscript.
ISSN:	1471-0056 1471-0064 1471-0064
DOI:	10.1038/s41576-021-00370-8