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 Vol. 22; no. 10; pp. 627 - 644
Main Authors: Longo, Sophia K, Guo, Margaret G, Ji, Andrew L, Khavari, Paul A
Format: Journal Article
Language:English
Published: England Nature Publishing Group 01.10.2021
Subjects:
Algorithms
Animals
Bar codes
Cell Communication
Communication
Computational Biology - methods
Computer applications
Disease
Gene expression
Homeostasis
Humans
Hybridization
Ligands
Ribonucleic acid
RNA
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Software
Spatial data
Spatial distribution
Transcription
Transcriptome
Transcriptomes
Transcriptomics
ISSN:1471-0056, 1471-0064, 1471-0064
Online Access:Get full text
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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.
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ISSN:1471-0056
1471-0064
1471-0064
DOI:10.1038/s41576-021-00370-8