Cited by 1,059Single-cell and spatial transcriptomicsBiotin and Related StudiesGenomics and Chromatin Dynamics

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

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Longo, Sophia K., Guo, Margaret G., Ji, Andrew L., Khavari, Paul A.·2021·Nature Reviews Genetics

DOI 10.1038/s41576-021-00370-8PMC PMC9888017

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Abstract

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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Authors: Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A.
Affiliations: Stanford Cancer Institute (US)
VA Palo Alto Health Care System (US)
Corresponding author: Khavari, Paul A.
ORCID: Longo, Sophia K.
Guo, Margaret G.
Ji, Andrew L.
Khavari, Paul A.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2021-06-18
Volume: 22
Issue: 10
Pages: 627-644
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/s41576-021-00370-8
PMC: PMC9888017
OpenAlex: W3171692328
Cited by: 1,059
References: 183
Citations by year: 2026: 233 · 2025: 203 · 2024: 242 · 2023: 196 · 2022: 157 · 2021: 27
FWCI: 16.92
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute (R01CA142635)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR049737, R01AR043799, R01AR076965)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords: single-cell RNA sequencing, spatial transcriptomics, data integration, intercellular communication, tissue dynamics, in situ sequencing, cell localization
MeSH terms: Animals, Cell Communication, Humans, Software, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis, Transcriptome

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

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