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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Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Inference and analysis of cell-cell communication using CellChat
Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572

Show all 183 references

Research works printed but not linked: 3

Cho, C.-S. et al. Seq-Scope: submicrometer-resolution spatial transcriptomics for single cell and subcellular studies. bioRxiv https://doi.org/10.1101/2021.01.25.427807 (2021). · DOI
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015). · DOI · Paper page
Bishop, C. M. Pattern Recognition and Machine Learning. Oxidation Communications Vol. 27 (Springer, 2004).

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Forjaz, A., Vaz, E., Matos-Romero, V., Joshi, S., Queiroga, V., Braxton, A. M., Jiang, A. C., Fujikura, K., Cornish, T. C., Hong, S., et al. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101075 · Cited by 31
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Li, R., Li, N., Yang, Q., Tong, X., Wang, W. J., Li, C., Zhao, J., Jiang, D., Huang, H., Chen, F., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01422-4 · Cited by 33
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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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