Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Revolutionizing immunology with single-cell RNA sequencing

Abstract

The immune system is composed of a complex hierarchy of cell types that protect the organism against disease and maintain homeostasis. Identifying heterogeneity of immune cells is the key to understanding the immune system. Advanced single-cell RNA sequencing (scRNA-seq) technologies are revolutionizing our ability to study immunology. By measuring transcriptomes at the single-cell level, scRNA-seq enables identification of cellular heterogeneity in far greater detail than conventional methods. In this review, we introduce the existing scRNA-seq technologies and present their strengths and weaknesses. We also discuss potential applications and future innovations of scRNA-seq in immunology.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1

Similar content being viewed by others

References

  1. Stubbington, M. J. T., Rozenblatt-Rosen, O., Regev, A. & Teichmann, S. A. Single-cell transcriptomics to explore the immune system in health and disease. Science 358, 58–63 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Proserpio, V. & Mahata, B. Single-cell technologies to study the immune system. Immunology 147, 133–140 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  5. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Streets, A. M. et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 111, 7048–7053 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5’ end sequencing. Nat. Protoc. 7, 813–828 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988 e1916 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Han, X. et al. Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing. Genome Biol. 19, 47 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 e634 (2017).

    Article  CAS  PubMed  Google Scholar 

  20. Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091.e17–1107.e17 (2018).

    Article  CAS  Google Scholar 

  21. Gierahn, T. M. et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fan, H. C., Fu, G. K. & Fodor, S. P. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article  PubMed  CAS  Google Scholar 

  23. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Briggs, J. A., et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780 (2018).

  26. Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

  27. Tosches, M. A. et al. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science 360, 881–888 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Plass, M. et al. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360, eaaq1723 (2018).

  29. Fincher, C. T., Wurtzel, O., de Hoog, T., Kravarik, K. M. & Reddien, P. W. Cell type transcriptome atlas for the planarian Schmidtea mediterranea. Science 360, eaaq1736 (2018).

  30. Farrell, J. A. et al Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131 (2018).

  31. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang, X. et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol. Cell 73, 130.e5–142.e5 (2018).

  33. Magella, B. et al. Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf. Dev. Biol. 434, 36–47 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Sugimura, R. et al. Haematopoietic stem and progenitor cells from human pluripotent stem cells. Nature 545, 432–438 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lis, R. et al. Conversion of adult endothelium to immunocompetent haematopoietic stem cells. Nature 545, 439–445 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhou, F. et al. Tracing haematopoietic stem cell formation at single-cell resolution. Nature 533, 487–492 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Fares, I. et al. EPCR expression marks UM171-expanded CD34(+) cord blood stem cells. Blood 129, 3344–3351 (2017).

    CAS  PubMed  Google Scholar 

  40. Martin, G. H. & Park, C. Y. EPCR: a novel marker of cultured cord blood HSCs. Blood 129, 3279–3280 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Baron, C. S. et al. Single-cell transcriptomics reveal the dynamic of haematopoietic stem cell production in the aorta. Nat. Commun. 9, 2517 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535.e16–1548.e16 (2018).

    Article  CAS  Google Scholar 

  43. Karamitros, D. et al. Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells. Nat. Immunol. 19, 85–97 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016).

    Article  PubMed  CAS  Google Scholar 

  49. Haas, S., Trumpp, A. & Milsom, M. D. Causes and consequences of hematopoietic stem cell heterogeneity. Cell Stem Cell 22, 627–638 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Macaulay, I. C. et al. Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep. 14, 966–977 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Athanasiadis, E. I. et al. Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis. Nat. Commun. 8, 2045 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Drissen, R. et al. Distinct myeloid progenitor-differentiation pathways identified through single-cell RNA sequencing. Nat. Immunol. 17, 666–676 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Schlitzer, A. et al. Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow. Nat. Immunol. 16, 718–728 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).

  55. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 e1815 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Olsson, A. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698–702 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kristiansen, T. A. et al. Cellular barcoding links B-1a B cell potential to a fetal hematopoietic stem cell state at the single-cell level. Immunity 45, 346–357 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Yu, Y. et al. Single-cell RNA-seq identifies a PD-1(hi) ILC progenitor and defines its development pathway. Nature 539, 102–106 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Psaila, B. et al. Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways. Genome Biol. 17, 83 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Tusi, B. K. et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555, 54–60 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Grover, A. et al. Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells. Nat. Commun. 7, 11075 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Martinez-Jimenez, C. P. et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355, 1433–1436 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

  66. Gao, B., Jeong, W. I. & Tian, Z. Liver: an organ with predominant innate immunity. Hepatology 47, 729–736 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Mass, E. et al. Specification of tissue-resident macrophages during organogenesis. Science 353, aaf4238 (2016).

  68. De Schepper, S. et al. Self-maintaining gut macrophages are essential for intestinal homeostasis. Cell 175, 400.e13–415.e13 (2018).

  69. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031.e18–1044.e18 (2018).

    Article  CAS  Google Scholar 

  70. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563, 347–353 (2018).

    Article  CAS  PubMed  Google Scholar 

  71. Bjorklund, A. K. et al. The heterogeneity of human CD127(+) innate lymphoid cells revealed by single-cell RNA sequencing. Nat. Immunol. 17, 451–460 (2016).

    Article  PubMed  CAS  Google Scholar 

  72. Gury-BenAri, M. et al. The spectrum and regulatory landscape of intestinal innate lymphoid cells are shaped by the microbiome. Cell 166, 1231.e13–1246.e13 (2016).

    Article  CAS  Google Scholar 

  73. Gaublomme, J. T. et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400–1412 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Karmaus, P. W. F. et al. Metabolic heterogeneity underlies reciprocal fates of Th17 cell stemness and plasticity. Nature 565, 101–105 (2018).

  75. Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

    Article  CAS  PubMed  Google Scholar 

  76. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293.e36–1308.e36 (2018).

    Article  CAS  Google Scholar 

  77. Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 171, 1221–1223 (2017).

    Article  CAS  PubMed  Google Scholar 

  78. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998.e20–1013.e20 (2018).

    Article  CAS  Google Scholar 

  80. Lonnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci. Immunol. 2, eaal2192 (2017).

  81. Avraham, R. et al. Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell 162, 1309–1321 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Saliba, A. E. et al. Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella. Nat. Microbiol. 2, 16206 (2016).

    Article  CAS  PubMed  Google Scholar 

  83. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276.e17–1290.e17 (2017).

    Article  CAS  Google Scholar 

  84. Winkels, H. et al. Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry. Circ. Res. 122, 1675–1688 (2018).

    Article  CAS  PubMed  Google Scholar 

  85. Croote, D., Darmanis, S., Nadeau, K. C. & Quake, S. R. High-affinity allergen-specific human antibodies cloned from single IgE B cell transcriptomes. Science 362, 1306–1309 (2018).

    Article  CAS  PubMed  Google Scholar 

  86. De Simone, M., Rossetti, G. & Pagani, M. Single cell T cell receptor sequencing: techniques and future challenges. Front. Immunol. 9, 1638 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Guo, X. et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24, 978–985 (2018).

  88. Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342.e16–1356.e16 (2017).

    Article  CAS  Google Scholar 

  89. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Article  CAS  PubMed  Google Scholar 

  90. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775.e18–789.e18 (2018).

  91. De Bie, J. et al. Single-cell sequencing reveals the origin and the order of mutation acquisition in T-cell acute lymphoblastic leukemia. Leukemia 32, 1358–1369 (2018).

  92. Giustacchini, A. et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat. Med. 23, 692–702 (2017).

    Article  CAS  PubMed  Google Scholar 

  93. Ledergor, G. et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 24, 1867–1876 (2018).

    Article  CAS  PubMed  Google Scholar 

  94. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  95. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

  96. Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    Article  CAS  PubMed  Google Scholar 

  97. Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018).

    Article  CAS  PubMed  Google Scholar 

  99. Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

Download references

Acknowledgements

We thank Z Tian, X Cao, and F Gao for suggestions. This work was supported by the Natural Science Foundation of China (91842301, 31722027, and 81770188).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guoji Guo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Ye, F. & Guo, G. Revolutionizing immunology with single-cell RNA sequencing. Cell Mol Immunol 16, 242–249 (2019). https://doi.org/10.1038/s41423-019-0214-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41423-019-0214-4

This article is cited by

Search

Quick links