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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Bibliographic Details
Published in:	Nature reviews. Genetics 2021-10, Vol.22 (10), p.627-644
Main Authors:	Longo, Sophia K, Guo, Margaret G, Ji, Andrew L, Khavari, Paul A
Format:	Article
Language:	eng
Subjects:	Algorithms Animals Bar codes Cell Communication Cell interaction Cell populations Cell research Communication Composition Computational biology Computational Biology - methods Computer applications Disease Gene expression Homeostasis Humans Hybridization Identification and classification Ligands Methods Physiological aspects Ribonucleic acid RNA RNA sequencing Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software Spatial data Spatial distribution Subpopulations Tissues Transcription Transcriptome Transcriptomes Transcriptomics
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Summary:	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.
ISSN:	1471-0056 1471-0064