Single-cell genomics and single-cell transcriptomics have emerged as powerful tools to study the biology of single cells at a genome-wide scale. However, a major challenge is to sequence both genomic DNA and mRNA from the same cell, which would allow direct comparison of genomic variation and transcriptome heterogeneity. We describe a quasilinear amplification strategy to quantify genomic DNA and mRNA from the same cell without physically separating the nucleic acids before amplification. We show that the efficiency of our integrated approach is similar to existing methods for single-cell sequencing of either genomic DNA or mRNA. Further, we find that genes with high cell-to-cell variability in transcript numbers generally have lower genomic copy numbers, and vice versa, suggesting that copy number variations may drive variability in gene expression among individual cells. Applications of our integrated sequencing approach could range from gaining insights into cancer evolution and heterogeneity to understanding the transcriptional consequences of copy number variations in healthy and diseased tissues.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 25 June 2022
Genome Biology Open Access 09 May 2022
Stem Cell Reviews and Reports Open Access 22 March 2022
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Gene Expression Omnibus
Stranger, B.E. et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007).
Conrad, D.F. et al. Origins and functional impact of copy number variation in the human genome. Nature 464, 704–712 (2010).
Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).
Sheltzer, J.M., Torres, E.M., Dunham, M.J. & Amon, A. Transcriptional consequences of aneuploidy. Proc. Natl. Acad. Sci. USA 109, 12644–12649 (2012).
Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
Zong, C., Lu, S., Chapman, A.R. & Xie, X.S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).
Falconer, E. et al. DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution. Nat. Methods 9, 1107–1112 (2012).
Hou, Y. et al. Genome analyses of single human oocytes. Cell 155, 1492–1506 (2013).
Evrony, G.D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).
McConnell, M.J. et al. Mosaic copy number variation in human neurons. Science 342, 632–637 (2013).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Reports 2, 666–673 (2012).
Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).
Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593–597 (2013).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).
Wu, A.R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).
Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).
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).
Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).
Junker, J.P. & van Oudenaarden, A. Every cell is special: genome-wide studies add a new dimension to single-cell biology. Cell 157, 8–11 (2014).
Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).
Baker, S.C. et al. The External RNA Controls Consortium. A progress report. Nat. Methods 2, 731–734 (2005).
Zhang, C. et al. A single cell level based method for copy number variation analysis by low coverage massively parallel sequencing. PLoS ONE 8, e54236 (2013).
Venkatraman, E.S. & Olshen, A.B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007).
Bienko, M. et al. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH. Nat. Methods 10, 122–124 (2013).
We would like to thank C. Zong (Harvard University) for sharing details of MALBAC and D. Grün (Hubrecht Institute) for assistance in data analysis. We would also like to thank N. Crosetto (Hubrecht Institute) for assistance in preparing DNA FISH probes. This work was supported by the European Research Council Advanced grant (ERC-AdG 294325-GeneNoiseControl), the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Vici award and by the Human Frontier Science Program (to M.B.).
The authors declare no competing financial interests.
About this article
Cite this article
Dey, S., Kester, L., Spanjaard, B. et al. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol 33, 285–289 (2015). https://doi.org/10.1038/nbt.3129
CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing
BMC Bioinformatics (2022)
Genome Biology (2022)
Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application
Molecular Neurodegeneration (2022)
British Journal of Cancer (2022)
Nature Communications (2022)