Single-cell RNA sequencing to explore immune cell heterogeneity
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) have allowed for comprehensive analysis of the immune system. In this Review, we briefly describe the available scRNA-seq technologies together with their corresponding strengths and weaknesses. We discuss in depth how scRNA-seq can be used to deconvolve immune system heterogeneity by identifying novel distinct immune cell subsets in health and disease, characterizing stochastic heterogeneity within a cell population and building developmental 'trajectories' for immune cells. Finally, we discuss future directions of the field and present integrated approaches to complement molecular information from a single cell with studies of the environment, epigenetic state and cell lineage.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
scRNA-seq has been used to deconvolve immune cell type heterogeneity by identifying novel distinct immune cell subsets in health and disease.
“scRNA-seq has been used to deconvolve immune cell type heterogeneity by identifying novel distinct immune cell subsets in health and disease”
From Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program (2019) · cites this paper as Papalexi and Satija, 2018
Venus protein expression correlates mostly with RNA findings, except weak acinar expression around E18.5 which appears underestimated in scRNA sequencing data.
“These patterns correlate mostly with the RNA findings, except the weak but robust acinar expression around E18.5 which appears underestimated in scRNA sequencing data potentially attributed to technical limitations for weakly expressed genes [50–54].”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [54]
Cites
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
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 · see citing works
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 · see citing works
Self-citationSmart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works
Seventeen-colour flow cytometry: unravelling the immune system
Perfetto, S. P., Chattopadhyay, P. K., & Roederer, M. (2004) · Nature Reviews Immunology · doi:10.1038/nri1416 · Cited by 1,019 · see citing works
Human bone marrow hematopoietic stem cells are increased in frequency and myeloid-biased with age
Pang, W. W., Price, E. A., Sahoo, D., Beerman, I., Maloney, W. J., Rossi, D. J., Schrier, S. L., & Weissman, I. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116110108 · Cited by 991 · see citing works
Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis
Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., et al. (2011) · Cell · doi:10.1016/j.cell.2011.01.004 · Cited by 942 · see citing works
Single-cell RNA-seq: advances and future challenges
Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847 · see citing works
A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B. (2016) · Blood · doi:10.1182/blood-2016-05-716480 · Cited by 768 · see citing works
Gury-BenAri, M., Thaiss, C. A., Serafini, N., Winter, D. R., Giladi, A., Lara-Astiaso, D., Levy, M., Salame, T. M., Weiner, A., David, E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.043 · Cited by 587 · see citing works
Integrated genome and transcriptome sequencing of the same cell
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works
Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3154 · Cited by 380 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Self-citationSingle cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations
MacParland, S. A., Liu, J. C., Ma, X.-Z., Innes, B. T., Bartczak, A. M., Gage, B. K., Manuel, J., Khuu, N., Echeverri, J., Linares, I., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06318-7 · Cited by 1,552 · see citing works
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works
Lei, X., Lei, Y., Li, J.-K., Du, W.-X., Li, R.-G., Yang, J., Li, J., Li, F., & Tan, H.-B. (2020) · Cancer Letters · doi:10.1016/j.canlet.2019.11.009 · Cited by 1,470 · see citing works
Li, Q., Cheng, Z., Zhou, L., Darmanis, S., Neff, N. F., Okamoto, J., Gulati, G., Bennett, M. L., Sun, L. O., Clarke, L. E., et al. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.006 · Cited by 1,129 · see citing works
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works
Szabo, P. A., Levitin, H. M., Miron, M., Snyder, M. E., Senda, T., Yuan, J., Cheng, Y. L., Bush, E. C., Dogra, P., Thapa, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12464-3 · Cited by 756 · see citing works
Joint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592 · see citing works
Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program
Geirsdottir, L., David, E., Keren-Shaul, H., Weiner, A., Bohlen, S. C., Neuber, J., Balic, A., Giladi, A., Sheban, F., Dutertre, C.-A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.010 · Cited by 461 · see citing works
Marsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 432 · see citing works
Single-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works
An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells
Vinogradova, E. V., Zhang, X., Remillard, D., Lazar, D. C., Suciu, R. M., Wang, Y., Bianco, G., Yamashita, Y., Crowley, V. M., Schafroth, M. A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.001 · Cited by 351 · see citing works
Caloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging
Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., Ji, Q., Zou, Z., Wang, S., He, X., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.008 · Cited by 349 · see citing works
Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians
Hashimoto, K., Kouno, T., Ikawa, T., Hayatsu, N., Miyajima, Y., Yabukami, H., Terooatea, T., Sasaki, T., Suzuki, T., Valentine, M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907883116 · Cited by 343 · see citing works
Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry
Oetjen, K. A., Lindblad, K. E., Goswami, M., Gui, G., Dagur, P. K., Lai, C., Dillon, L. W., McCoy, J. P., & Hourigan, C. S. (2018) · JCI Insight · doi:10.1172/jci.insight.124928 · Cited by 272 · see citing works
Singh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308 · see citing works
scSLAM-seq reveals core features of transcription dynamics in single cells
Erhard, F., Baptista, M. A. P., Krammer, T., Hennig, T., Lange, M., Arampatzi, P., Jürges, C. S., Theis, F. J., Saliba, A.-E., & Dölken, L. (2019) · Nature · doi:10.1038/s41586-019-1369-y · Cited by 233 · see citing works
Microglia and Astrocytes in Disease: Dynamic Duo or Partners in Crime?
Liddelow, S. A., Marsh, S. E., & Stevens, B. (2020) · Trends in Immunology · doi:10.1016/j.it.2020.07.006 · Cited by 226 · see citing works
Song, G., Shi, Y., Zhang, M., Goswami, S., Afridi, S., Meng, L., Ma, J., Chen, Y., Lin, Y., Zhang, J., et al. (2020) · Cell Discovery · doi:10.1038/s41421-020-00214-5 · Cited by 175 · see citing works
Senescent immune cells release grancalcin to promote skeletal aging
Li, C.-J., Xiao, Y., Sun, Y.-C., He, W.-Z., Liu, L., Huang, M., He, C., Huang, M., Chen, K.-X., Hou, J., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.08.009 · Cited by 190 · see citing works
Xu, Y., Su, G.-H., Ma, D., Xiao, Y., Shao, Z.-M., & Jiang, Y.-Z. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00729-7 · Cited by 163 · see citing works
Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
Giladi, A., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2017.11.011 · Cited by 168 · see citing works
Huang, M.-Y., Jiang, X.-M., Wang, B.-L., Sun, Y., & Lu, J.-J. (2021) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107694 · Cited by 136 · see citing works
TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures
Tu, A. A., Gierahn, T. M., Monian, B., Morgan, D. M., Mehta, N. K., Ruiter, B., Shreffler, W. G., Shalek, A. K., & Love, J. C. (2019) · Nature Immunology · doi:10.1038/s41590-019-0544-5 · Cited by 128 · see citing works
Wohnhaas, C. T., Leparc, G. G., Fernandez-Albert, F., Kind, D., Gantner, F., Viollet, C., Hildebrandt, T., & Baum, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-46932-z · Cited by 105 · see citing works
The role of biomarkers in personalized immunotherapy
Sankar, K., Ye, J. C., Li, Z., Zheng, L., Song, W., & Hu-Lieskovan, S. (2022) · Biomarker Research · doi:10.1186/s40364-022-00378-0 · Cited by 111 · see citing works
Droplet-based single cell RNAseq tools: a practical guide
Salomon, R., Kaczorowski, D., Valdes-Mora, F., Nordon, R. E., Neild, A., Farbehi, N., Bartonicek, N., & Gallego-Ortega, D. (2019) · Lab on a Chip · doi:10.1039/c8lc01239c · Cited by 109 · see citing works
Trzupek, D., Dunstan, M., Cutler, A. J., Lee, M., Godfrey, L., Jarvis, L., Rainbow, D. B., Aschenbrenner, D., Jones, J. L., Uhlig, H. H., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00756-z · Cited by 105 · see citing works
Using single‐cell multiple omics approaches to resolve tumor heterogeneity
Ortega, M. A., Poirion, O., Zhu, X., Huang, S., Wolfgruber, T. K., Sebra, R., & Garmire, L. X. (2017) · Clinical and Translational Medicine · doi:10.1186/s40169-017-0177-y · Cited by 81 · see citing works
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works
Taking Systems Medicine to Heart
Trachana, K., Bargaje, R., Glusman, G., Price, N. D., Huang, S., & Hood, L. E. (2018) · Circulation Research · doi:10.1161/circresaha.117.310999 · Cited by 41 · see citing works
Tong, R., Luo, L., Zhao, Y., Sun, M., Li, R., Zhong, J., Chen, Y., Hu, L., Li, Z., Shi, J., et al. (2023) · Emerging Microbes & Infections · doi:10.1080/22221751.2023.2187245 · Cited by 21 · see citing works
Yu, W., Zhao, X., Jalloh, A. S., Li, Y., Zhao, Y., Dinner, B., Yang, Y., Ouyang, S., Tian, T., Zhao, Z., et al. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c02602 · Cited by 21 · see citing works
Intercellular signaling dynamics from a single cell atlas of the biomaterials response
Cherry, C., Maestas, D. R., Han, J., Andorko, J. I., Cahan, P., Fertig, E. J., Garmire, L. X., & Elisseeff, J. H. (2020) · doi:10.1101/2020.07.24.218537
Abrams, D., Kumar, P., Karuturi, R. K. M., & George, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2817-2 · Cited by 13 · see citing works
Wang, J., Sun, H., Mou, L., Lu, Y., Wu, Z., Pu, Z., & Yang, M.-M. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1382896 · Cited by 13 · see citing works
Lu, J., Duan, Y., Liu, P., He, X., Yang, Y., Zhang, R., & Weng, L. (2023) · The Journal of Pathology · doi:10.1002/path.6015 · Cited by 6 · see citing works
TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration
Melzer, M. K., Schirge, S., Gout, J., Arnold, F., Srinivasan, D., Burtscher, I., Allgöwer, C., Mulaw, M., Zengerling, F., Günes, C., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01553-x · Cited by 5 · see citing works
Progress in single-cell sequencing of retinal vein occlusion or ischemic hypoxic retinopathy
Feng, Y., Wu, Y., Zhu, Y., He, Y., & Weng, W. (2025) · Experimental Eye Research · doi:10.1016/j.exer.2025.110436 · Cited by 3 · see citing works
- Authors
- Papalexi, E., & Satija, R.
- Affiliations
- New York Genome Center (US)
- Corresponding author
- Satija, Rahul
- Venue
- Nature Reviews Immunology
- Published
- 2017-08-07
- Volume
- 18
- Issue
- 1
- Pages
- 35-45
- ISSN
- 1474-1733 (ISSN-L)
- 1474-1741
- OpenAlex
- W2743416243
- Cited by
- 1,806
- References
- 14
- Citations by year
- 2026: 243 · 2025: 274 · 2024: 269 · 2023: 244 · 2022: 225 · 2021: 192 · 2020: 171 · 2019: 110 · 2018: 66 · 2017: 9 · 2016: 1 · 2015: 1 · 2012: 1
- FWCI
- 9.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Immunology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- single-cell RNA sequencing, immune cell heterogeneity, immune cell subsets, stochastic heterogeneity, cell lineage, epigenetic states, single-cell multi-omics
- MeSH terms
- Animals, B-Lymphocytes, Cell Differentiation, Dendritic Cells, Humans, Killer Cells, Natural, Lymphocytes, Macrophages, Monocytes, T-Lymphocytes, Sequence Analysis, RNA, Cell Lineage, Gene Expression Profiling, Epigenomics, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
