Single-Cell Multiomics: Multiple Measurements from Single Cells
Authors: Iain C. Macaulay, Chris P. Ponting, Thierry Voet
Journal: Trends in Genetics (2017)

Single-cell sequencing provides information that is not confounded by genotypic or phenotypic heterogeneity of bulk samples. Sequencing of one molecular type (RNA, methylated DNA or open chromatin) in a single cell, furthermore, provides insights into the cell's phenotype and links to its genotype. Nevertheless, only by taking measurements of these phenotypes and genotypes from the same single cells can such inferences be made unambiguously. In this review, we survey the first experimental approaches that assay, in parallel, multiple molecular types from the same single cell, before considering the challenges and opportunities afforded by these and future technologies. Single-cell sequencing provides information that is not confounded by genotypic or phenotypic heterogeneity of bulk samples. Sequencing of one molecular type (RNA, methylated DNA or open chromatin) in a single cell, furthermore, provides insights into the cell's phenotype and links to its genotype. Nevertheless, only by taking measurements of these phenotypes and genotypes from the same single cells can such inferences be made unambiguously. In this review, we survey the first experimental approaches that assay, in parallel, multiple molecular types from the same single cell, before considering the challenges and opportunities afforded by these and future technologies. Unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell.Estimating RNA and DNA copy number abundance in single cells is now possible using a variety of experimental approaches.Parallel measurement of single-cell epigenomes and transcriptomes provides further insight into the regulation of cellular identity and phenotypes.Parallel measurement of single-cell transcriptomes and protein abundance (by FACS, proximity ligation assays/PEA or mass cytometry) allows insight into expression dynamics.Our understanding of cancer progression and diversity is likely to be advanced greatly by the multiomics investigation of single cells, as is our understanding of normal developmental and other disease processes.Ongoing technological advances will see improvements in the coverage, sensitivity of multiomics approaches, as well the number of analytes that can be surveyed in parallel. Unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell. Estimating RNA and DNA copy number abundance in single cells is now possible using a variety of experimental approaches. Parallel measurement of single-cell epigenomes and transcriptomes provides further insight into the regulation of cellular identity and phenotypes. Parallel measurement of single-cell transcriptomes and protein abundance (by FACS, proximity ligation assays/PEA or mass cytometry) allows insight into expression dynamics. Our understanding of cancer progression and diversity is likely to be advanced greatly by the multiomics investigation of single cells, as is our understanding of normal developmental and other disease processes. Ongoing technological advances will see improvements in the coverage, sensitivity of multiomics approaches, as well the number of analytes that can be surveyed in parallel. The cell is a natural unit of biology, whose type and state can vary according to external influences or to internal processes. In multicellular organisms, all cells are derived from a single zygote which, through regulated programmes of proliferation and differentiation, generates all of the diverse cell types that populate the organism. Dysregulation of these programmes in single 'renegade' cells can lead to diseases such as cancers [1Yates L.R. Campbell P.J. Evolution of the cancer genome.Nat. Rev. Genet. 2012; 13: 795-806Crossref PubMed Scopus (412) Google Scholar], neurological disorders [2Poduri A. et al.Somatic mutation, genomic variation, and neurological disease.Science. 2013; 341: 1237758Crossref PubMed Scopus (404) Google Scholar] and developmental disorders [3Biesecker L.G. Spinner N.B. A genomic view of mosaicism and human disease.Nat. Rev. Genet. 2013; 14: 307-320Crossref PubMed Scopus (432) Google Scholar]. Sequencing technologies now permit genome [4Gawad C. et al.Single-cell genome sequencing: current state of the science.Nat. Rev. Genet. 2016; 17: 175-188Crossref PubMed Scopus (800) Google Scholar], epigenome [5Schwartzman O. Tanay A. Single-cell epigenomics: techniques and emerging applications.Nat. Rev. Genet. 2015; 16: 716-726Crossref PubMed Scopus (178) Google Scholar], transcriptome [6Stegle O. et al.Computational and analytical challenges in single-cell transcriptomics.Nat. Rev. Genet. 2015; 16: 133-145Crossref PubMed Scopus (696) Google Scholar], or protein [7Wu M. Singh A.K. Single-cell protein analysis.Curr. Opin. Biotechnol. 2012; 23: 83-88Crossref PubMed Scopus (130) Google Scholar] profiling of single cells sampled from heterogeneous cell types and different cellular states, thereby enabling normal development and disease processes to be studied and dissected at cellular resolution. However, the sampling of just one molecular type from individual cells provides only incomplete information because a cell's state is determined by the complex interplay of molecules within its genome, epigenome, transcriptome and proteome. To more comprehensively understand and model cellular processes, new technologies are required to simultaneously assay different types of molecules, such as DNA and RNA or RNA and protein, to survey as much of the cellular state as possible. Such multiomics approaches will enable, amongst other things, the generation of mechanistic models relating (epi)genomic variation and transcript/protein expression dynamics, which in turn should allow a more detailed exploration of cellular behaviour in health and disease. In this review, we discuss the developments, opportunities and challenges of sequencing technologies, which have enabled single-cell multiomics, and provide an outlook on future research and technological directions. The ability to survey both the genome and the transcriptome of the same single cell in parallel will offer a number of unique experimental opportunities. Primarily, it would directly link the wild-type or modified genotype of a cell to its transcriptomic phenotype, which reflects, in turn, its functional state. Genomic variation in a population of cells could be associated with transcriptional variation, and molecular mechanisms that are causal of cellular phenotypic variation could be deduced without the potentially confounding effects of cell type heterogeneity. Second, single-cell genome sequences could be used to reconstruct a cell lineage tree that captures the genealogical record of acquired DNA mutations in the cells' genomes over time; in parallel, the RNA sequences of these same cells would reflect the types and states of the cells. These phenotypically annotated lineage trees should enhance our understanding of the cellular properties and population architectures of heterogeneous tissues in health and disease. Direct measurement of multiple molecular types in the same cell offers substantial advantage over the separate measurement of each molecular type in different cells. This is because relating molecules, for example, RNA in one cell versus DNA in another (or in a population of cells), is confounded by the cells' potential differences in genotype (e.g., somatic variation in cancer), phenotype (e.g., cell cycle) or environment (e.g., cell–cell interactions). Consequently, although a single cell's genomic copy number can be inferred indirectly from single-cell RNA-sequencing (scRNA-seq) data [8Patel A.P. et al.Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.Science. 2014; 344: 1396-1401Crossref PubMed Scopus (2482) Google Scholar, 9Tirosh I. et al.Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.Science. 2016; 352: 189-196Crossref PubMed Scopus (2033) Google Scholar], only by applying multiomics approaches to one cell can its genotype–phenotype relationships be determined unambiguously. Two complementary strategies have been developed that permit both genome and transcriptome sequencing from single cells (Figure 1, see Box 1 for information about single-cell isolation). In the first approach, gDNA–mRNA sequencing (DR-seq) [10Dey S.S. et al.Integrated genome and transcriptome sequencing of the same cell.Nat. Biotechnol. 2015; 33: 285-289Crossref PubMed Scopus (317) Google Scholar] (Figure 1A), genomic DNA (gDNA) and mRNA present in a single cell's lysate are preamplified simultaneously before splitting the reaction in two for parallel gDNA [using a modified multiple annealing and looping-based amplification cycles (MALBAC) [11Zong C. et al.Genome-wide detection of single-nucleotide and copy-number variations of a single human cell.Science. 2012; 338: 1622-1626Crossref PubMed Scopus (755) Google Scholar] approach] and mRNA library preparation (using a modified CEL-seq [12Hashimshony T. et al.CEL-seq: single-cell RNA-seq by multiplexed linear amplification.Cell Rep. 2012; 2: 666-673Abstract Full Text Full Text PDF PubMed Scopus (752) Google Scholar] approach) and subsequent sequencing. In the other approach, exemplified by genome and transcriptome sequencing (G&T-seq) [13Macaulay I.C. et al.G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.Nat. Methods. 2015; 12: 519-522Crossref PubMed Scopus (443) Google Scholar, 14Macaulay I.C. et al.Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq.Nat. Protoc. 2016; 11: 2081-2103Crossref PubMed Scopus (92) Google Scholar] (Figure 1A), mRNA is physically separated from gDNA using oligo-dT-coated beads to capture and isolate the polyadenylated mRNA molecules from a fully lysed single cell. The mRNA is then amplified using a modified Smart-seq2 protocol [15Picelli S. et al.Smart-seq2 for sensitive full-length transcriptome profiling in single cells.Nat. Methods. 2013; 10: 1096-1098Crossref PubMed Scopus (1266) Google Scholar, 16Picelli S. et al.Full-length RNA-seq from single cells using Smart-seq2.Nat. Protoc. 2014; 9: 171-181Crossref PubMed Scopus (1958) Google Scholar], while the gDNA can be amplified and sequenced by a variety of methods [13Macaulay I.C. et al.G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.Nat. Methods. 2015; 12: 519-522Crossref PubMed Scopus (443) Google Scholar, 14Macaulay I.C. et al.Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq.Nat. Protoc. 2016; 11: 2081-2103Crossref PubMed Scopus (92) Google Scholar]. The transcriptogenomics method [17Li W. et al.Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles.Mutat. Res. 2015; 772: 55-62Crossref PubMed Scopus (17) Google Scholar] is based upon a similar principle of separation and parallel amplification. Separation of genome and transcriptome can also be accomplished using more gentle cell lysis procedures that dismantle the cellular but not the nuclear membrane (Figure 1B), allowing the intact nucleus to be separated from the cytoplasmic lysate; the nucleus can be used as a substrate for genomic [18Han L. et al.Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform.Sci. Rep. 2014; 4: 6485Crossref PubMed Scopus (49) Google Scholar] and epigenomic analysis [19Hou Y. et al.Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.Cell. Res. 2016; 26: 304-319Crossref PubMed Scopus (359) Google Scholar, 20Hu Y. et al.Simultaneous profiling of transcriptome and DNA methylome from a single cell.Genome Biol. 2016; 17: 88Crossref PubMed Scopus (166) Google Scholar], while the cytoplasmic lysate can be used to perform mRNA profiling of the single cell. In addition to these methods, which apply microliter volume reactions, a microfluidic platform method using nanolitre reaction chambers that physically separates cytoplasmic mRNA from nuclear gDNA of the same single cell was described [14Macaulay I.C. et al.Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq.Nat. Protoc. 2016; 11: 2081-2103Crossref PubMed Scopus (92) Google Scholar], which can be used for targeted amplicon sequencing of both molecular types.Box 1Isolation of Single CellsEnsuring that a sample contains only a single cell remains technically challenging. The first key step is to generate a single-cell suspension. This varies considerably between tissue types and optimisation is required to ensure analysis of a viable, unbiased, cell population. When tissue complexity or handling prohibits intact cell isolation, suspensions of single nuclei can be prepared [68Baslan T. et al.Genome-wide copy number analysis of single cells.Nat. Protoc. 2012; 7: 1024-1041Crossref PubMed Scopus (248) Google Scholar, 69Habib N. et al.Div-seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.Science. 2016; 353: 925-928Crossref PubMed Scopus (293) Google Scholar]. Single nucleus (epi)genomic and transcriptomic analyses have been demonstrated [19Hou Y. et al.Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.Cell. Res. 2016; 26: 304-319Crossref PubMed Scopus (359) Google Scholar, 68Baslan T. et al.Genome-wide copy number analysis of single cells.Nat. Protoc. 2012; 7: 1024-1041Crossref PubMed Scopus (248) Google Scholar, 69Habib N. et al.Div-seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.Science. 2016; 353: 925-928Crossref PubMed Scopus (293) Google Scholar], and thus in principle solely nuclei may be used as input for multiomics approaches.There are various approaches for isolating single cells from a suspension. Manual isolation – either using specialised pipettes or micromanipulation equipment – notably allows a single cell to be directly visualised during isolation. When all of a small number of cells are to be analysed – for example, daughter cells from a single cell division – this is often the most suitable option [70Voet T. et al.Single-cell paired-end genome sequencing reveals structural variation per cell cycle.Nucleic Acids Res. 2013; 41: 6119-6138Crossref PubMed Scopus (120) Google Scholar]. Nevertheless, it is by necessity low throughput.FACS allows phenotypically distinct cells, and even nuclei, to be sorted into user-defined vessels and lysis buffers, thus enabling diverse single-cell and single-nuclei protocols to be applied at significantly higher throughput [68Baslan T. et al.Genome-wide copy number analysis of single cells.Nat. Protoc. 2012; 7: 1024-1041Crossref PubMed Scopus (248) Google Scholar]. Index sorting [71Osborne G.W. Recent advances in flow cytometric cell sorting.Methods Cell. Biol. 2011; 102: 533-556Crossref PubMed Scopus (29) Google Scholar] additionally allows direct linking of a single cell's phenotype (e.g., surface marker expression, DNA content) with multiomics analysis. However, large numbers of cells are required as input, and because the platform currently offers no opportunity to visualise sorted cells, care must be taken to identify and exclude cell doublets.Microfluidics technologies that isolate single cells in individual capture sites and initiate nucleic acid amplification in nanolitre volumes have been widely applied in single-cell omics studies (e.g., Fluidigm C1 [72Pollen A.A. et al.Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.Nat. Biotechnol. 2014; 32: 1053-1058Crossref PubMed Scopus (585) Google Scholar]). Once captured, cells can be visualised on the chip, confirming the presence of a single cell.Advances in microfluidics approaches in which single cells are encapsulated within individual droplets prior to barcoded sequence library preparation (e.g., Drop-seq [73Macosko E.Z. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3656) Google Scholar], inDrop [74Zilionis R. et al.Single-cell barcoding and sequencing using droplet microfluidics.Nat. Protoc. 2017; 12: 44-73Crossref PubMed Scopus (390) Google Scholar, 75Klein A.M. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1870) Google Scholar]) allow tens of thousands of single cells to be investigated in parallel. However, these approaches rely on limiting dilution Poisson statistics for cell isolation, which result in a doublet rate dependent on the concentration of cells in the input material. Visual validation is not currently a component of these protocols.Single cells can also be isolated using laser capture microdissection [76Boone D.R. et al.Laser capture microdissection of enriched populations of neurons or single neurons for gene expression analysis after traumatic brain injury.J. Vis. Exp. 2013; : e50308Google Scholar], which offers a unique opportunity to study cells in their topological context, although this has not yet been applied widely to multiomics analysis. Ensuring that a sample contains only a single cell remains technically challenging. The first key step is to generate a single-cell suspension. This varies considerably between tissue types and optimisation is required to ensure analysis of a viable, unbiased, cell population. When tissue complexity or handling prohibits intact cell isolation, suspensions of single nuclei can be prepared [68Baslan T. et al.Genome-wide copy number analysis of single cells.Nat. Protoc. 2012; 7: 1024-1041Crossref PubMed Scopus (248) Google Scholar, 69Habib N. et al.Div-seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.Science. 2016; 353: 925-928Crossref PubMed Scopus (293) Google Scholar]. Single nucleus (epi)genomic and transcriptomic analyses have been demonstrated [19Hou Y. et al.Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.Cell. Res. 2016; 26: 304-319Crossref PubMed Scopus (359) Google Scholar, 68Baslan T. et al.Genome-wide copy number analysis of single cells.Nat. Protoc. 2012; 7: 1024-1041Crossref PubMed Scopus (248) Google Scholar, 69Habib N. et al.Div-seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.Science. 2016; 353: 925-928Crossref PubMed Scopus (293) Google Scholar], and thus in principle solely nuclei may be used as input for multiomics approaches. There are various approaches for isolating single cells from a suspension. Manual isolation – either using specialised pipettes or micromanipulation equipment – notably allows a single cell to be directly visualised during isolation. When all of a small number of cells are to be analysed – for example, daughter cells from a single cell division – this is often the most suitable option [70Voet T. et al.Single-cell paired-end genome sequencing reveals structural variation per cell cycle.Nucleic Acids Res. 2013; 41: 6119-6138Crossref PubMed Scopus (120) Google Scholar]. Nevertheless, it is by necessity low throughput. FACS allows phenotypically distinct cells, and even nuclei, to be sorted into user-defined vessels and lysis buffers, thus enabling diverse single-cell and single-nuclei protocols to be applied at significantly higher throughput [68Baslan T. et al.Genome-wide copy number analysis of single cells.Nat. Protoc. 2012; 7: 1024-1041Crossref PubMed Scopus (248) Google Scholar]. Index sorting [71Osborne G.W. Recent advances in flow cytometric cell sorting.Methods Cell. Biol. 2011; 102: 533-556Crossref PubMed Scopus (29) Google Scholar] additionally allows direct linking of a single cell's phenotype (e.g., surface marker expression, DNA content) with multiomics analysis. However, large numbers of cells are required as input, and because the platform currently offers no opportunity to visualise sorted cells, care must be taken to identify and exclude cell doublets. Microfluidics technologies that isolate single cells in individual capture sites and initiate nucleic acid amplification in nanolitre volumes have been widely applied in single-cell omics studies (e.g., Fluidigm C1 [72Pollen A.A. et al.Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.Nat. Biotechnol. 2014; 32: 1053-1058Crossref PubMed Scopus (585) Google Scholar]). Once captured, cells can be visualised on the chip, confirming the presence of a single cell. Advances in microfluidics approaches in which single cells are encapsulated within individual droplets prior to barcoded sequence library preparation (e.g., Drop-seq [73Macosko E.Z. et al.Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3656) Google Scholar], inDrop [74Zilionis R. et al.Single-cell barcoding and sequencing using droplet microfluidics.Nat. Protoc. 2017; 12: 44-73Crossref PubMed Scopus (390) Google Scholar, 75Klein A.M. et al.Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1870) Google Scholar]) allow tens of thousands of single cells to be investigated in parallel. However, these approaches rely on limiting dilution Poisson statistics for cell isolation, which result in a doublet rate dependent on the concentration of cells in the input material. Visual validation is not currently a component of these protocols. Single cells can also be isolated using laser capture microdissection [76Boone D.R. et al.Laser capture microdissection of enriched populations of neurons or single neurons for gene expression analysis after traumatic brain injury.J. Vis. Exp. 2013; : e50308Google Scholar], which offers a unique opportunity to study cells in their topological context, although this has not yet been applied widely to multiomics analysis. To achieve success, single-cell protocols need to maximise accuracy, uniformity and coverage when sampling a cell's available molecules. Minimising the loss, while maintaining the diversity and fidelity of information from a single cell, is a critical challenge in the development of multiomics approaches. The major advantage of avoiding a priori separation, as in DR-seq, is that it minimises the risk of losing minute quantities of the cell's genomic/transcriptomic material during any transfer steps, whereas the advantage of physical separation is that the cell's gDNA and mRNA are amenable to independent protocols of choice for further amplification and sequencing (Figure 1C). However, protocols that rely on physical separation of nucleus and cytoplasm [19Hou Y. et al.Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.Cell. Res. 2016; 26: 304-319Crossref PubMed Scopus (359) Google Scholar, 20Hu Y. et al.Simultaneous profiling of transcriptome and DNA methylome from a single cell.Genome Biol. 2016; 17: 88Crossref PubMed Scopus (166) Google Scholar] are often dependent on manual isolation of the nucleus from each single cell and thus such methods, unless transferred to a microfluidics platform [18Han L. et al.Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform.Sci. Rep. 2014; 4: 6485Crossref PubMed Scopus (49) Google Scholar], may only be applicable in low-throughput settings. The first-generation methods for multiomics single-cell sequencing – DR-seq and G&T-seq in particular – demonstrated how genomic variation among a population of single cells can explain transcriptomic variation. Both methods were applied to reveal, for the first time, the direct association between (sub)chromosomal copy number and gene expression in the same single cell (Figure 2A) . DR-seq demonstrated a positive correlation between large-scale DNA copy number variation in the genome and gene expression levels in individual cells. Furthermore, these data indicated that genes with low DNA copy number tend to generate transcripts with noisier expression levels [10Dey S.S. et al.Integrated genome and transcriptome sequencing of the same cell.Nat. Biotechnol. 2015; 33: 285-289Crossref PubMed Scopus (317) Google Scholar]. G&T-seq was applied to human breast cancer and matched normal lymphoblastoid cell lines, as well as to primary cells from eight-cell stage mouse embryos and human inducible pluripotent stem cell-derived neurons derived from individuals with either a disomy or trisomy for chromosome 21. Data from these G&T-seq experiments further confirmed the relationship between (sub)chromosomal copy number and expression level of genes located within DNA copy number variable regions in single cells [13Macaulay I.C. et al.G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.Nat. Methods. 2015; 12: 519-522Crossref PubMed Scopus (443) Google Scholar]. These approaches also allow the functional consequences of de novo structural variants to be investigated in single cells. In cancer, structural DNA rearrangements can translocate gene regulatory elements to the vicinity of other genes thereby perturbing their expression, or may result in novel fusion genes, which contribute to the overall progression of the disease. With G&T-seq, the full length of the mRNA molecule is preserved during amplification (Figure 1C), which enables the detection of expressed fusion transcripts either by assembling Illumina short reads or as long reads using the Pacific Biosciences RSII sequencer [13Macaulay I.C. et al.G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.Nat. Methods. 2015; 12: 519-522Crossref PubMed Scopus (443) Google Scholar]. The concurrent availability of a matched genome sequence from the same single cell allows the causal genomic fusion to be validated and mapped to single base resolution, in parallel with the ability to detect genome-wide dysregulation of gene expression associated with a structural rearrangement (Figure 2B). DR-seq [10Dey S.S. et al.Integrated genome and transcriptome sequencing of the same cell.Nat. Biotechnol. 2015; 33: 285-289Crossref PubMed Scopus (317) Google Scholar], G&T-seq [13Macaulay I.C. et al.G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.Nat. Methods. 2015; 12: 519-522Crossref PubMed Scopus (443) Google Scholar] and the method described by Li et al. [17Li W. et al.Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles.Mutat. Res. 2015; 772: 55-62Crossref PubMed Scopus (17) Google Scholar] all have potential to detect single nucleotide variants (SNVs) in matched single-cell genomes and transcriptomes. This enables, if the transcript carrying the variant allele is expressed, confirmation of the detection of SNVs in two readouts from the same cell. Where genome coverage is sufficient to detect both alleles of an expressed gene, it would also be possible to extend this analysis to consider allele-specific expression, with the cell's own genome as a reference. Furthermore, the comparative analysis of genome and transcriptome sequencing data from the same single cell should enable the detection of RNA editing events, again using the cell's own genome as a reference (Figure 2C). The availability of both DNA and RNA sequencing data from the same cell also has clear potential to enable the detection of expressed, coding mutations in populations of single cells (Figure 2D, upper and middle panels) as well as the study of acquired expression quantitative trait loci, whereby de novo genetic variants in, for instance, gene regulatory elements of single cells may affect the expression of the gene(s) under the control of this element, altering the cell's transcriptomic cell state (Figure 2D, lower panel), or how newly acquired genomic variants may alter the splicing or reading frame of a transcript in a cell. However, limitations in whole-genome amplification mean that detection of all classes of variants currently cannot be achieved comprehensively and with complete accuracy in every single cell [4Gawad C. et al.Single-cell genome sequencing: current state of the science.Nat. Rev. Genet. 2016; 17: 175-188Crossref PubMed Scopus (800) Google Scholar, 21Macaulay I.C. Voet T. Single cell genomics: advances and future perspectives.PLoS Genet. 2014; 10: e1004126Crossref PubMed Scopus (274) Google Scholar]. All whole-genome amplification approaches result in frequent allelic and locus dropouts – in which, respectively, either one or both alleles of a sequence are not detected leading to false-negative calls and it is likely that physical separation or manipulation of gDNA in multiomic assays can exacerbate the levels of dropout observed. Furthermore, all polymerases have a baseline error rate, and thus base misincorporation errors occur during amplification of both DNA and RNA leading to false-positive SNV calls. Additional limitations exist in whole-transcriptome amplification approaches. Reverse transcriptase and subsequent polymerase-based amplification steps also have potential to introduce biases in representation in the data. In single-cell whole-transcriptome amplification, it is estimated that only 10–40% of the original mRNA molecules from a cell are represented in the final sequencing library [22Islam S. et al.Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing.Nat. Protoc. 2012; 7: