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:

Single-cell RNA sequencing for the study of development, physiology and disease

Abstract

An ongoing technological revolution is continually improving our ability to carry out very high-resolution studies of gene expression patterns. Current technology enables the global gene expression profiles of single cells to be defined, facilitating dissection of heterogeneity in cell populations that was previously hidden. In contrast to gene expression studies that use bulk RNA samples and provide only a virtual average of the diverse constituent cells, single-cell studies enable the molecular distinction of all cell types within a complex population mix, such as a tumour or developing organ. For instance, single-cell gene expression profiling has contributed to improved understanding of how histologically identical, adjacent cells make different differentiation decisions during development. Beyond development, single-cell gene expression studies have enabled the characteristics of previously known cell types to be more fully defined and facilitated the identification of novel categories of cells, contributing to improvements in our understanding of both normal and disease-related physiological processes and leading to the identification of new treatment approaches. Although limitations remain to be overcome, technology for the analysis of single-cell gene expression patterns is improving rapidly and beginning to provide a detailed atlas of the gene expression patterns of all cell types in the human body.

Key points

  • RNA sequencing of single cells (scRNA-seq) enables the global gene expression patterns of individual cells to be defined.

  • Almost all tissues and organs include a heterogeneous mix of cell types; the heterogeneity of these cell populations can be defined through the use of scRNA-seq.

  • scRNA-seq can fully define the expression of transcription factors, growth factors, receptors, solute transporters and other proteins for each cell type present, providing insights into cell function and cell–cell crosstalk.

  • scRNA-seq is an increasingly powerful tool for the analysis of development as well as normal and disease processes.

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: General strategy for scRNA-seq.
Fig. 2: Microdroplet-based scRNA-seq.
Fig. 3: Creation of a single-cell-resolution virtual organ.
Fig. 4: Use of cluster and combine methodology to define cell types.
Fig. 5: Use of cluster and subcluster methodology to define cell subtypes.
Fig. 6: Multilineage priming.

Similar content being viewed by others

References

  1. Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49, 708–718 (2017).

    Article  PubMed  CAS  Google Scholar 

  2. Tang, Q. et al. Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single-cell resolution using RNA sequencing. J. Exp. Med. 214, 2875–2887 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360.e4 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  6. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. DeLaughter, D. M. et al. Single-cell resolution of temporal gene expression during heart development. Dev. Cell 39, 480–490 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  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  PubMed  CAS  Google Scholar 

  13. 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  PubMed  CAS  Google Scholar 

  14. 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  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  16. Hochgerner, H. et al. STRT-seq-2i: dual-index 5’ single cell and nucleus RNA-seq on an addressable microwell array. Sci. Rep. 7, 16327 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  19. 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  PubMed  CAS  PubMed Central  Google Scholar 

  20. Wang, Y. & Navin, N. E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Kuppers, R., Zhao, M., Hansmann, M. L. & Rajewsky, K. Tracing B cell development in human germinal centres by molecular analysis of single cells picked from histological sections. EMBO J. 12, 4955–4967 (1993).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  23. Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593–597 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Kamme, F. et al. Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J. Neurosci. 23, 3607–3615 (2003).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Frumkin, D. et al. Amplification of multiple genomic loci from single cells isolated by laser micro-dissection of tissues. BMC Biotechnol. 8, 17 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    Article  PubMed  CAS  Google Scholar 

  27. Adam, M., Potter, A. S. & Potter, S. S. Psychrophilic proteases dramatically reduce single cell RNA-seq artifacts: a molecular atlas of kidney development. Development 144, 3625–3632 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  28. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. See, K. et al. Single cardiomyocyte nuclear transcriptomes reveal a lincRNA-regulated de-differentiation and cell cycle stress-response in vivo. Nat. Commun. 8, 225 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Gao, R. et al. Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer. Nat. Commun. 8, 228 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Habib, N. et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. van Velthoven, C. T. J., de Morree, A., Egner, I. M., Brett, J. O. & Rando, T. A. Transcriptional profiling of quiescent muscle stem cells in vivo. Cell Rep. 21, 1994–2004 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Bensaude, O. Inhibiting eukaryotic transcription: which compound to choose? How to evaluate its activity? Transcription 2, 103–108 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Letschert, K., Faulstich, H., Keller, D. & Keppler, D. Molecular characterization and inhibition of amanitin uptake into human hepatocytes. Toxicol. Sci. 91, 140–149 (2006).

    Article  PubMed  CAS  Google Scholar 

  36. Ross, I. L., Browne, C. M. & Hume, D. A. Transcription of individual genes in eukaryotic cells occurs randomly and infrequently. Immunol. Cell Biol. 72, 177–185 (1994).

    Article  PubMed  CAS  Google Scholar 

  37. Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).

    Article  PubMed  CAS  Google Scholar 

  38. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Kim, J. K., Kolodziejczyk, A. A., Ilicic, T., Teichmann, S. A. & Marioni, J. C. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Usoskin, D. et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015).

    Article  PubMed  CAS  Google Scholar 

  42. Hyvarinen, A. & Oja, E. Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000).

    Article  PubMed  CAS  Google Scholar 

  43. Pierson, E. & Yau, C. ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 16, 241 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Bacher, R. & Kendziorski, C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 17, 63 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    Article  PubMed  CAS  Google Scholar 

  46. Guo, M., Wang, H., Potter, S. S., Whitsett, J. A. & Xu, Y. SINCERA: a pipeline for single-cell RNA-seq profiling analysis. PLoS Comput. Biol. 11, e1004575 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  PubMed  CAS  Google Scholar 

  50. Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

    Article  PubMed  CAS  Google Scholar 

  52. Zhu, X., Ching, T., Pan, X., Weissman, S. M. & Garmire, L. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization. PeerJ 5, e2888 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Grun, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Diaz, A. et al. SCell: integrated analysis of single-cell RNA-seq data. Bioinformatics 32, 2219–2220 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  PubMed  PubMed Central  CAS  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  PubMed  PubMed Central  CAS  Google Scholar 

  59. 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  PubMed  CAS  Google Scholar 

  60. Herring, C. A. et al. Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternative tuft cell origins in the gut. Cell Syst. 6, 37–51.e9 (2018).

    Article  PubMed  CAS  Google Scholar 

  61. Shin, J. et al. Single-cell RNA-seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  62. Takasato, M. et al. Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526, 564–568 (2015).

    Article  PubMed  CAS  Google Scholar 

  63. Taguchi, A. et al. Redefining the in vivo origin of metanephric nephron progenitors enables generation of complex kidney structures from pluripotent stem cells. Cell Stem Cell 14, 53–67 (2014).

    Article  PubMed  CAS  Google Scholar 

  64. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

    Article  PubMed  CAS  Google Scholar 

  65. Chiang, M. K. & Melton, D. A. Single-cell transcript analysis of pancreas development. Dev. Cell 4, 383–393 (2003).

    Article  PubMed  CAS  Google Scholar 

  66. Miyamoto, T. et al. Myeloid or lymphoid promiscuity as a critical step in hematopoietic lineage commitment. Dev. Cell 3, 137–147 (2002).

    Article  PubMed  CAS  Google Scholar 

  67. Ohnishi, Y. et al. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat. Cell Biol. 16, 27–37 (2014).

    Article  PubMed  CAS  Google Scholar 

  68. McMahon, A. P. Development of the mammalian kidney. Curr. Top. Dev. Biol. 117, 31–64 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Brunskill, E. W. et al. Single cell dissection of early kidney development: multilineage priming. Development 141, 3093–3101 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    Article  PubMed  CAS  Google Scholar 

  72. Costantini, F. & Shakya, R. GDNF/Ret signaling and the development of the kidney. Bioessays 28, 117–127 (2006).

    Article  PubMed  CAS  Google Scholar 

  73. Skinner, M. A., Safford, S. D., Reeves, J. G., Jackson, M. E. & Freemerman, A. J. Renal aplasia in humans is associated with RET mutations. Am. J. Hum. Genet. 82, 344–351 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Dressler, G. R. Advances in early kidney specification, development and patterning. Development 136, 3863–3874 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Ardini-Poleske, M. E. et al. LungMAP: The molecular atlas of lung development program. Am. J. Physiol. Lung Cell. Mol. Physiol. 313, L733–L740 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Du, Y. et al. Lung Gene Expression Analysis (LGEA): an integrative web portal for comprehensive gene expression data analysis in lung development. Thorax 72, 481–484 (2017).

    Article  PubMed  Google Scholar 

  77. Zepp, J. A. et al. Distinct mesenchymal lineages and niches promote epithelial self-renewal and myofibrogenesis in the lung. Cell 170, 1134–1148.e10 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  78. Takebe, T. et al. Vascularized and functional human liver from an iPSC-derived organ bud transplant. Nature 499, 481–484 (2013).

    Article  PubMed  CAS  Google Scholar 

  79. Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017).

    PubMed  CAS  Google Scholar 

  80. Balkwill, F., Charles, K. A. & Mantovani, A. Smoldering and polarized inflammation in the initiation and promotion of malignant disease. Cancer Cell 7, 211–217 (2005).

    Article  PubMed  CAS  Google Scholar 

  81. Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).

    Article  PubMed  CAS  Google Scholar 

  82. Finak, G. et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat. Med. 14, 518–527 (2008).

    Article  PubMed  CAS  Google Scholar 

  83. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

    Article  PubMed  CAS  Google Scholar 

  84. Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Chung, W. et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8, 15081 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Kim, K. T. et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 17, 80 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Lu, Y. et al. Genome-wide identification of genes essential for podocyte cytoskeletons based on single-cell RNA sequencing. Kidney Int. 92, 1119–1129 (2017).

    Article  PubMed  CAS  Google Scholar 

  90. Lu, Y., Ye, Y., Yang, Q. & Shi, S. Single-cell RNA-sequence analysis of mouse glomerular mesangial cells uncovers mesangial cell essential genes. Kidney Int. 92, 504–513 (2017).

    Article  PubMed  CAS  Google Scholar 

  91. Chen, L. et al. Transcriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq. Proc. Natl Acad. Sci. USA 114, E9989–E9998 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  92. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science. https://doi.org/10.1126/science.aar2131 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  93. Der, E. et al. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight 2, 93009 (2017).

    Article  PubMed  Google Scholar 

Download references

Reviewer information

Nature Reviews Nephrology thanks L. Oxburgh, K. Kiryluk and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Steven Potter.

Ethics declarations

Competing interests

The author declares no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Chan Zuckerberg Initiative Atlas Project: https://www.chanzuckerberg.com/human-cell-atlas

Human BioMolecular Atlas Program: https://commonfund.nih.gov/hubmap

LGEA (Lung Gene Expression Analysis) Web Portal: https://research.cchmc.org/pbge/lunggens/mainportal.html

lungMAP consortium: https://www.lungmap.net/

Glossary

Splicing patterns

Sequences recognized by RNA-processing enzymes of the spliceosome, which splice out introns. Introns almost always begin with the bases GU and terminate with AG, but additional sequences around splice sites are required to provide sufficient specificity.

Early response genes

Genes that are activated rapidly in response to a variety of stimuli, including stress and growth factors. About 40 immediate early response genes exist, including members of the FOS and JUN families.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Potter, S.S. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol 14, 479–492 (2018). https://doi.org/10.1038/s41581-018-0021-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41581-018-0021-7

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer