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.
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Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
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).
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).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
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.
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, eaam8999 (2018).
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).
Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, 1127 (2017).
Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
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).
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
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).
Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).
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.
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 161, 1202 (2017).
Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).
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).
Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).
Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 1–6 (2017).
Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).
Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).
McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).
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).
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
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.
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.
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).
Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).
Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).
Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
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).
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).
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.
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).
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).
Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).
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).
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.
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).
Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380–389 (2016).
Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 1–15 (2016).
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).
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1873 (2016).
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
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.
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).
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).
Liu, X. S. et al. Editing DNA methylation in the mammalian genome. Cell 167, 233–247 (2016).
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).
Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–178 (2018).
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).
Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
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).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
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).
Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
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.
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
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.
Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).
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).
Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).
Argelaguet, R. et al. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
Colomé-Tatché, M. & Theis, F. J. Statistical single cell multi-omics integration. Curr. Opin. Syst. Biol. 7, 54–59 (2018).
Leek, J. T. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).
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.
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.
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).
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).
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).
Barkas, N. et al. Wiring together large single-cell RNA-seq sample collections. Preprint at bioRxiv https://doi.org/10.1101/460246 (2018).
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).
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).
Stuart, T. et al. Comprehensive integration of single cell data. Preprint at bioRxiv https://doi.org/10.1101/460147 (2018).
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).
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.
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).
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).
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).
Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).
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).
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).
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).
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.
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Scott, M. P. & Carroll, S. B. The segmentation and homeotic gene network in early Drosophila development. Cell 51, 689–698 (1987).
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).
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).
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).
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).
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).
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).
Moffitt, J. R. et al. Molecular, spatial and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).
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).
Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
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).
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).
Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).
Rand, A. C. et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat. Methods 14, 411–413 (2017).
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).
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.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 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.
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.
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.
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.
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Stuart, T., Satija, R. Integrative single-cell analysis. Nat Rev Genet 20, 257–272 (2019). https://doi.org/10.1038/s41576-019-0093-7
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