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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Open accessAbstract
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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Cites
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
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
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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Dou, S., Zhang, F., He, Y., Du, P., Deng, Y., Shao, M., Yang, G., Wu, K., Zeng, Y.-Q., Peng, C., et al. (2025) · Journal of Dermatological Science · doi:10.1016/j.jdermsci.2025.05.002 · Cited by 7
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Weiderman, R. M., Hasan, M., & Miller, L. C. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26136163 · Cited by 6
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Sharma, N., Rájová, J., Mermelekas, G., Thrane, K., Lundeberg, J., Shamikh, A., Vikström, S., Babačić, H., Jensdottir, M., Lehtiö, J., et al. (2025) · Translational Oncology · doi:10.1016/j.tranon.2025.102468 · Cited by 6
Du, Y., Xu, K., Zhang, S., Chen, M., Liu, Z., & Xie, L. (2025) · Genes · doi:10.3390/genes16070821 · Cited by 7
Zhang, H., Liu, Y., Liu, W., Lin, A., Fang, Y., Qu, L., Zhang, X., Luo, P., Wang, L., & Jiang, A. (2025) · Biomarker Research · doi:10.1186/s40364-025-00811-0 · Cited by 8
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Shen, R., Cheng, M., Wang, W., Fan, Q., Yan, H., Wen, J., Yuan, Z., Yao, J., Li, Y., & Yuan, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae576 · Cited by 7
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Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach
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Huang, Z., Luo, S., Zhang, Z., Wang, Z., Zhou, T., & Zhang, J. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230529145130 · Cited by 6
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Janíček, T., Hobza, R., & Hudzieczek, V. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3226-0_9 · Cited by 5
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Lin, L., Wang, H., Chen, Y., Wang, Y., Xu, Y., Chen, Z., Yang, Y., Liu, K., & Ma, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae685 · Cited by 5
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Hou, S., Tian, K., Yang, S., Xiang, J., Yang, W., Zhang, J., & Han, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.13.512059 · Cited by 3
Yang, W., Xu, Z., Luo, M., Cai, Y., Xu, C., Wang, P., Wei, S., Xue, G., Jin, X., Cheng, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.11.516061 · Cited by 4
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Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · eLife · doi:10.7554/elife.88431.1 · Cited by 4
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Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissuePreprint
Cheng, Y., Dang, S., Zhang, Y., Chen, Y., Yu, R., Miao, L., Jin, S., Han, A., Katz, S. G., & Wang, S. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.06.641951 · Cited by 4
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Luo, H., Yao, J., & Zhang, R. (2025) · Advanced Biotechnology · doi:10.1007/s44307-025-00063-x · Cited by 3
Ren, L., Yang, Y., Wan, L., Zheng, X., Liu, J., Li, S., Hong, Y., Zhang, Y., Wang, H., Du, G., et al. (2025) · The Innovation · doi:10.1016/j.xinn.2025.101005 · Cited by 3
Wang, R., Qin, G.-H., Jiang, Y., Chen, F., Wang, Z., Ju, L., Chen, L., Fu, D., Liu, E., Zhang, S., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1592416 · Cited by 3
Deng, H., Liu, Q., Tian, Z., Liu, G., Wang, S., Liang, D., Wang, Y., Han, Y., & Xie, S. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1620886 · Cited by 3
Xiao, M., Zhang, W., Huang, X., Zhu, H., Wu, M., Li, X., & Zhou, Y. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3609721 · Cited by 6
Zinc as a Modulator of miRNA Signaling in Obesity
Taslim, N. A., Graciela, A. M., Harbuwono, D. S., Syauki, A. Y., Anthony, A. N., ASHARI, N., Aman, A. M., Tjandrawinata, R. R., Hardinsyah, H., Bukhari, A., et al. (2025) · Nutrients · doi:10.3390/nu17213375 · Cited by 3
Xiu, Z., Zeng, W., Guo, J., Tu, G., Chen, X., Sheng, J., & Chen, H. (2025) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl37633 · Cited by 3
Inflammatory niches as spatial drivers of disease mechanisms and targets for personalized treatment
Jiang, R., Fang, Z., Tsoi, L. C., & Gudjonsson, J. E. (2025) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.70170 · Cited by 3
scGeneBank: cross-species screening of functional gene sets at single-cell resolution
Qin, S., Yang, Y., Peng, L., Xu, L., Deng, Y., Wang, H., Qiu, X., He, Y., Wang, X., Ma, Y., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1137 · Cited by 3
Harnessing spatial transcriptomics to understand host-parasite interactions in plants and animals
Pijnacker, A., Bruggeman, C. W., Korswagen, H. C., Smant, G., & Lozano‐Torres, J. L. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108760 · Cited by 3
Sun, K., Ling, H., Peng, F., Yang, T., Hao, Y., Bai, Q., Sun, Y., Qiu, J., & Xia, Q. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000004094 · Cited by 3
Liu, J., Li, Z., Liu, G., Pei, Z., Liu, W. G., Yang, W., Li, F., & Huo, L. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003966 · Cited by 3
Spatial transcriptomics in epilepsy research: Early successes, opportunities, and challenges
Phillips, D. J., Ivy, A. S., Guzman, S. J., Hazany, S., & Xu, X. (2026) · Epilepsia · doi:10.1002/epi.70079 · Cited by 3
Liu, Z., Jiang, S., Hu, T., Xue, Z., Chen, Y., Ruan, D., Xia, H., Feng, Y., Hao, J., Li, W., et al. (2026) · Patterns · doi:10.1016/j.patter.2025.101471 · Cited by 4
Metadata
- 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.
- Venue
- Nature Reviews Genetics
- Published
- 2021-06-18
- Volume
- 22
- Issue
- 10
- Pages
- 627-644
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- 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
- 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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