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:

Integrative single-cell analysis

Nature Reviews Genetics volume 20, pages 257–272 (2019)Cite this article

Subjects

Abstract

The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Multimodal and integrative methods for single-cell analyses.
Fig. 2: Experimental methods for performing single-cell multimodal measurements.
Fig. 3: Computational methods for the analysis of multimodal single-cell data.
Fig. 4: Computational approaches for integrating multiple single-cell data sets.
Fig. 5: Clustering and classification of cells.
Fig. 6: Integration of spatial single-cell data.

Similar content being viewed by others

References

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

    CAS  PubMed  Google Scholar 

  2. 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).

    PubMed  PubMed Central  Google Scholar 

  3. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). References 4 and 5 are two of the first published high-cell-throughput droplet-based methods for scRNA-seq.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, eaam8999 (2018).

    Google Scholar 

  9. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, 1127 (2017).

    Google Scholar 

  12. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    CAS  PubMed  Google Scholar 

  16. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 9, 2579 (2017). This study presents a method for simultaneously measuring gene expression and proteins in single cells through an innovative barcoding strategy.

    Google Scholar 

  21. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 161, 1202 (2017).

    Google Scholar 

  22. Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

    CAS  PubMed  Google Scholar 

  23. Gomez, D., Shankman, L. S., Nguyen, A. T. & Owens, G. K. Detection of histone modifications at specific gene loci in single cells in histological sections. Nat. Methods 10, 171–177 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 1–6 (2017).

    Google Scholar 

  26. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    CAS  PubMed  Google Scholar 

  27. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    CAS  PubMed  Google Scholar 

  28. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    PubMed  PubMed Central  Google Scholar 

  29. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018). This study greatly increases the number of genes able to be spatially profiled in a single experiment through the development of combinatorial smFISH indexing and tissue clearing methods.

    PubMed  PubMed Central  Google Scholar 

  32. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018). This is one of the first studies to simultaneously measure the transcriptome and cell lineage relationships.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).

    CAS  PubMed  Google Scholar 

  34. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    CAS  PubMed  Google Scholar 

  36. Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  39. Hayashi, T. et al. Single-cell gene profiling of planarian stem cells using fluorescent activated cell sorting and its ‘index sorting’ function for stem cell research. Dev. Growth Differ. 52, 131–144 (2010).

    CAS  PubMed  Google Scholar 

  40. Wilson, N. K. et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712–724 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015). This study performs index sorting coupled to scRNA-seq on myeloid progenitor cells and identifies transcriptional heterogeneity within sorted populations.

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 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).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  45. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016). This study performs parallel DNA methylome and transcriptome sequencing in the same cell and examines the relationships between DNA methylation and gene expression.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Google Scholar 

  48. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).

    CAS  PubMed  Google Scholar 

  50. Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 1–15 (2016).

    Google Scholar 

  51. Di Giusto, D. A., Wlassoff, W. A., Gooding, J. J., Messerle, B. A. & King, G. C. Proximity extension of circular DNA aptamers with real-time protein detection. Nucleic Acids Res. 33, e64 (2005).

    Google Scholar 

  52. Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1873 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR- pooled screens with single-cell RNA-Seq. Cell 167, 1883–1888 (2016). References 52–55 are the first to perform pooled genetic screens using CRISPR–Cas9 coupled to scRNA-seq to infer causal relationships in gene regulatory networks.

    CAS  PubMed  Google Scholar 

  56. Klann, T. S. et al. CRISPR–Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome. Nat. Biotechnol. 35, 561 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Thakore, P. I., Black, J. B., Hilton, I. B. & Gersbach, C. A. Editing the epigenome: technologies for programmable transcription and epigenetic modulation. Nat. Methods 13, 127–137 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Liu, X. S. et al. Editing DNA methylation in the mammalian genome. Cell 167, 233–247 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Hilton, I. B. et al. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

    CAS  PubMed  Google Scholar 

  61. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–178 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Schmidt, S. T., Zimmerman, S. M., Wang, J., Kim, S. K. & Quake, S. R. Quantitative analysis of synthetic cell lineage tracing using nuclease barcoding. ACS Synth. Biol. 6, 936–942 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    CAS  PubMed  Google Scholar 

  68. van der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).

    PubMed  PubMed Central  Google Scholar 

  69. Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Google Scholar 

  70. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). This study develops a method of deriving the rate of change in gene expression from scRNA-seq data through the measurement of intronic RNA read abundance in each cell.

    PubMed  PubMed Central  Google Scholar 

  71. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  73. 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). This study introduces the first method to order individual cells along a pseudotime trajectory.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M. & Klein, A. M. Fundamental limits on dynamic inference from single-cell snapshots. Proc. Natl Acad. Sci. USA 115, E2467–E2476 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Argelaguet, R. et al. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).

    PubMed  PubMed Central  Google Scholar 

  78. Colomé-Tatché, M. & Theis, F. J. Statistical single cell multi-omics integration. Curr. Opin. Syst. Biol. 7, 54–59 (2018).

    Google Scholar 

  79. Leek, J. T. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).

    PubMed Central  Google Scholar 

  80. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018). This study pioneers the use of CCA to jointly reduce dimensionality for a pair of scRNA-seq data sets, allowing common cell states to be identified across data sets.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018). This study introduces the concept of using MNNs as a method for identifying equivalent cell states across data sets.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Dekel, T., Oron, S., Rubinstein, M., Avidan, S. & Freeman, W. T. in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition 2021–2029 (IEEE, 2015).

  84. Hie, B. L., Bryson, B. & Berger, B. Panoramic stitching of heterogeneous single-cell transcriptomic data. Preprint at bioRxiv https://doi.org/10.1101/371179 (2018).

    Article  Google Scholar 

  85. Barkas, N. et al. Wiring together large single-cell RNA-seq sample collections. Preprint at bioRxiv https://doi.org/10.1101/460246 (2018).

    Article  Google Scholar 

  86. Park, J.-E., Polanski, K., Meyer, K. & Teichmann, S. A. Fast batch alignment of single cell transcriptomes unifies multiple mouse cell atlases into an integrated landscape. Preprint at bioRxiv https://doi.org/10.1101/397042 (2018).

    Article  Google Scholar 

  87. Korsunsky, I. et al. Fast, sensitive, and flexible integration of single cell data with Harmony. Preprint at bioRxiv https://doi.org/10.1101/461954 (2018).

    Article  Google Scholar 

  88. Stuart, T. et al. Comprehensive integration of single cell data. Preprint at bioRxiv https://doi.org/10.1101/460147 (2018).

    Article  Google Scholar 

  89. Welch, J. et al. Integrative inference of brain cell similarities and differences from single-cell genomics. Preprint at bioRxiv https://doi.org/10.1101/459891 (2018).

    Article  Google Scholar 

  90. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–198 (2017). This study combines scRNA-seq and in situ hybridization data to predict spatial patterns of gene expression in the Drosophila embryo.

    Google Scholar 

  91. 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).

    CAS  PubMed  Google Scholar 

  92. 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 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Alpert, A., Moore, L. S., Dubovik, T. & Shen-Orr, S. S. Alignment of single-cell trajectories to compare cellular expression dynamics. Nat. Methods 15, 267–270 (2018).

    CAS  PubMed  Google Scholar 

  94. Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).

    PubMed  PubMed Central  Google Scholar 

  95. Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).

    CAS  PubMed  Google Scholar 

  96. Alquicira-Hernandez, J., Nguyen, Q. & Powell, J. E. scPred: single cell prediction using singular value decomposition and machine learning classification. Preprint at bioRxiv https://doi.org/10.1101/369538 (2018).

    Article  Google Scholar 

  97. Boufea, K., Seth, S. & Batada, N. N. Mapping transcriptionally equivalent populations across single cell RNA-seq datasets. Preprint at bioRxiv https://doi.org/10.1101/470203 (2018).

    Article  Google Scholar 

  98. Wagner, F. & Yanai, I. Moana: a robust and scalable cell type classification framework for single-cell RNA-Seq data. Preprint at bioRxiv https://doi.org/10.1101/456129 (2018).

    Article  Google Scholar 

  99. Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017). This study presents a method of aligning pseudotime trajectories developed from different data modalities as a way to compare pseudotemporal changes in each modality.

    PubMed  PubMed Central  Google Scholar 

  100. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Scott, M. P. & Carroll, S. B. The segmentation and homeotic gene network in early Drosophila development. Cell 51, 689–698 (1987).

    CAS  PubMed  Google Scholar 

  102. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127–1133 (2013).

    CAS  PubMed  Google Scholar 

  104. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed  PubMed Central  Google Scholar 

  105. Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758 (2017).

    CAS  PubMed  Google Scholar 

  106. Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl Acad. Sci. USA 113, 14456–14461 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    PubMed  PubMed Central  Google Scholar 

  109. Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).

    CAS  PubMed  Google Scholar 

  110. Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    CAS  PubMed  Google Scholar 

  111. Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  113. Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Pandey, S., Shekhar, K., Regev, A. & Schier, A. F. Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-Seq. Curr. Biol. 28, 1052–1065 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).

    CAS  PubMed  Google Scholar 

  119. Rand, A. C. et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat. Methods 14, 411–413 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Preprint at bioRxiv. https://doi.org/10.1101/459529 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the US National Institutes of Health through a New Innovator Award (1DP2HG009623-01) and an R01 (5R01MH071679-12) to R.S.

Author information

Authors and Affiliations

  1. New York Genome Center, New York, NY, USA

    Tim Stuart & Rahul Satija

  2. Center for Genomics and Systems Biology, New York University, New York, NY, USA

    Rahul Satija

Authors
  1. Tim Stuart
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul Satija
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors contributed to all aspects of the manuscript.

Corresponding author

Correspondence to Rahul Satija.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Glossary

Single-cell RNA sequencing

(scRNA-seq). Sequencing of cDNAs derived from RNA molecules (usually polyadenylated mRNAs) from a single cell. It is typically performed for many hundreds to thousands of cells in a single experiment.

Multimodal

Data of multiple types, for example, of RNA and protein.

Index sorting

Fluorescence-activated sorting of cells into known plate locations.

In vitro transcription

Transcription of a DNA sequence in vitro using the T7 RNA polymerase.

CITE-seq and REAP-seq

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and RNA expression and protein sequencing assay (REAP-seq) are methods that are capable of detecting cell surface protein abundance and gene expression within the same single cell. They achieve this through the use of barcoded antibodies captured alongside mRNA transcripts in single-cell RNA sequencing (scRNA-seq) experiments.

CRISPR–Cas9

A protein–RNA complex that allows targeted mutation or binding of DNA sequences as determined by a guide RNA sequence.

Pooled genetic screens

Screening experiments in which each individual cell may receive a different perturbation at random without prior separation of groups of cells and perturbation treatments.

Lineage tracing

The identification of lineage relationships between groups of cells through shared DNA mutations.

Single-molecule fluorescence in situ hybridization

(smFISH). A fluorescence in situ hybridization method capable of detecting the presence of a single molecule (usually RNA) through the recruitment of many fluorophores to the same area. It enables a quantitative readout of the number of molecules present in a cell.

Expression quantitative trait loci

(eQTLs). Genomic loci that explain variation in the RNA expression levels of genes.

Intron retention

The presence of intronic RNA bases in an RNA transcript. These bases are usually removed by RNA splicing shortly after or during transcription.

Pseudotime

The ordering of cells along a one-dimensional axis describing a continuous differentiation process.

Joint clustering

Grouping cells on the basis of measurements from multiple data modalities.

Canonical correlation analysis

(CCA). A statistical method for investigating relationships between two data sets. CCA aims to identify shared sources of variation in a pair of data sets.

Dynamic time warping

A method for locally stretching or compressing two one-dimensional vectors to correct for lag in one vector relative to another.

Mutual nearest neighbours

(MNNs). Cells that are mutually nearest to one another in normalized gene expression space.

Cell-type classifications

Biologically meaningful labels given to groups of cells on the basis of common molecular profiles and prior knowledge of the cell types.

Gradient boosting

A statistical method that produces a prediction model for classification or regression on the basis of an ensemble of weaker prediction models.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stuart, T., Satija, R. Integrative single-cell analysis. Nat Rev Genet 20, 257–272 (2019). https://doi.org/10.1038/s41576-019-0093-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-019-0093-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing