Key Points
-
Advances in DNA sequencing enable the analysis of the genomes and transcriptomes of single cells and will soon enable single-cell epigenomic and proteomic analyses.
-
Single-cell genomic analysis can reveal genomic variability among individual cells, which can be used to reconstruct cellular ancestries in the form of a lineage tree.
-
Single-cell transcriptome analysis can be used to study the functional states of individual cells and to infer and discover cell types in an unbiased manner.
-
Future integrated single-cell analyses based on high-throughput sequencing will enable the simultaneous analysis of genomic, transcriptomic and epigenomic states of cells. Such data will reveal the ancestries of cells, their types, their current functional states and may be used to infer the types and functional states of their ancestors.
-
Integrated single-cell analyses will shed light on fundamental questions of biology and medicine, including questions of the origin and development of cancer, the number of and relationship between human cell types, and the rate and structure of cell turnover in regenerating tissues.
Abstract
The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Single-cell gene and isoform expression analysis reveals signatures of ageing in haematopoietic stem and progenitor cells
Communications Biology Open Access 24 May 2023
-
Single-cell RNA sequencing in orthopedic research
Bone Research Open Access 24 February 2023
-
Comprehensive single-shot biophysical cytometry using simultaneous quantitative phase imaging and Brillouin spectroscopy
Scientific Reports Open Access 31 October 2022
Access options
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout



References
Wetterstrand, K. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program [online] (2013).
Walker, T. M. et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect. Dis. 13, 137–146 (2013).
Lander, E. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).
The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621–628 (2008).
Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nature Protoc. 6, 468–481 (2011).
Darmanis, S. et al. ProteinSeq: high-performance proteomic analyses by proximity ligation and next generation sequencing. PLoS ONE 6, e25583 (2011).
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–104 (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).
Kalisky, T., Blainey, P. & Quake, S. R. Genomic analysis at the single-cell level. Annu. Rev. Genet. 45, 431–445 (2011).
Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotech. 30, 777–782 (2012). This paper described the first single-cell RNA-seq method to achieve near full-length coverage of transcripts, and demonstrated transcriptome sequencing from single circulating tumour cells.
Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nature Biotech. 29, 1120–1127 (2011).
Cristofanilli, M. et al. Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J. Clin. Oncol. 23, 1420–1430 (2005).
Blainey, P. C. The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol. Rev. 37, 407–427 (2013). A review of single-cell genomics of microorganisms, including currently available WGA techniques.
Gundry, M., Li, W., Maqbool, S. B. & Vijg, J. Direct, genome-wide assessment of DNA mutations in single cells. Nucleic Acids Res. 40, 2032–2040 (2012).
Frumkin, D., Wasserstrom, A., Kaplan, S., Feige, U. & Shapiro, E. Genomic variability within an organism exposes its cell lineage tree. PLoS Computat. Biol. 1, 382–394 (2005). A conceptual and theoretical basis for organism cell lineage tree reconstruction using the genomic variability among organismal cells. It is also a preliminary proof-of-concept demonstration of reconstructing cell lineage trees using somatic mutations in a small panel of microsatellites.
Kurimoto, K., Yabuta, Y., Ohinata, Y. & Saitou, M. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nature Protoc. 2, 739–752 (2007).
Reizel, Y. et al. Colon stem cell and crypt dynamics exposed by cell lineage reconstruction. Plos Genet. 7, e1002192 (2011).
Shlush, L. I. et al. Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and miscrosatellite instability. Blood 120, 603–612 (2012).
Choi, J. H. et al. Development and optimization of a process for automated recovery of single cells identified by microengraving. Biotechnol. Prog. 26, 888–895 (2010).
Zhang, H. & Liu, K. K. Optical tweezers for single cells. J. R. Soc. Interface 5, 671–690 (2008).
Frumkin, D. et al. Amplification of multiple genomic loci from single cells isolated by laser micro-dissection of tissues. BMC Biotechnol. 8, 17 (2008).
Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).
Bhattacherjee, V. et al. Laser capture microdissection of fluorescently labeled embryonic cranial neural crest cells. Genesis 39, 58–64 (2004).
Guo, M. T., Rotem, A., Heyman, J. A. & Weitz, D. A. Droplet microfluidics for high-throughput biological assays. Lab. Chip 12, 2146–2155 (2012).
Fan, H., Wang, J., Potanina, A. & Quake, S. Whole-genome molecular haplotyping of single cells. Nature Biotech. 29, 51–57 (2011).
Wang, J., Fan, H. C., Behr, B. & Quake, S. R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150, 402–412 (2012).
White, A. et al. High-throughput microfluidic single-cell RT-qPCR. Proc. Natl Acad. Sci. USA 108, 13999–14004 (2011).
Lecault, V., White, A. K., Singhal, A. & Hansen, C. L. Microfluidic single cell analysis: from promise to practice. Curr. Opin. Chem. Biol. 16, 381–390 (2012).
Schatz, D. G. & Swanson, P. C. V(D)J recombination: mechanisms of initiation. Annu. Rev. Genet. 45, 167–202 (2011).
Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nature Rev. Genet. 13, 795–806 (2012).
Reizel, Y. et al. Cell lineage analysis of the mammalian female germline. PLoS Genet. 8, e1002477 (2012).
Szabat, M. et al. Maintenance of β-cell maturity and plasticity in the adult pancreas: developmental biology concepts in adult physiology. Diabetes 61, 1365–1371 (2012).
Ming, G. & Song, H. Adult neurogenesis in the mammalian brain: significant answers and significant questions. Neuron 70, 687–702 (2011).
Chojnacki, A. K., Mak, G. K. & Weiss, S. Identity crisis for adult periventricular neural stem cells: subventricular zone astrocytes, ependymal cells or both? Nature Rev. Neurosci. 10, 153–163 (2009).
Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).
Schepers, A. G. et al. Lineage tracing reveals Lgr5+ stem cell activity in mouse intestinal adenomas. Science 337, 730–735 (2012).
Carlson, C. et al. Decoding cell lineage from acquired mutations using arbitrary deep sequencing. Nature Methods 9, 78–80 (2012).
Ellegren, H. Microsatellites: Simple sequences with complex evolution. Nature Rev. Genet. 5, 435–445 (2004).
Salipante, S. & Horwitz, M. Phylogenetic fate mapping. Proc. Natl Acad. Sci. USA 103, 5448–5453 (2006).
Tsao, J. et al. Colorectal adenoma and cancer divergence - evidence of multilineage progression. Am. J. Pathol. 154, 1815–1824 (1999).
Zhou, W. et al. Use of somatic mutations to quantify random contributions to mouse development. BMC Genomics 14, 39 (2013).
Vilkki, S. et al. Extensive somatic microsatellite mutations in normal human tissue. Cancer Res. 61, 4541–4544 (2001).
Wasserstrom, A. et al. Reconstruction of cell lineage trees in mice. PLoS ONE 3, e1939 (2008).
Wasserstrom, A. et al. Estimating cell depth from somatic mutations. PLoS Computat. Biol. 4, e1000058 (2008).
Segev, E. et al. Muscle-bound primordial stem cells give rise to myofiber-associated myogenic and non-myogenic progenitors. PLoS ONE 6, e25605 (2011).
Ross, M. G. et al. Characterizing and measuring bias in sequence data. Genome Biol. 14, R51 (2013).
Fidler, I. & Kripke, M. Metastasis results from preexisting variant cells within a malignant-tumor. Science 197, 893–895 (1977).
Kim, M. Y. et al. Tumor self-seeding by circulating cancer cells. Cell 139, 1315–1326 (2009).
Fidler, I. Critical determinants of metastasis. Seminars Cancer Biol. 12, 89–96 (2002).
Eaves, C. J. Cancer stem cells: here, there, everywhere? Nature 456, 581–582 (2008).
Frank, N. Y., Schatton, T. & Frank, M. H. The therapeutic promise of the cancer stem cell concept. J. Clin. Invest. 120, 41–50 (2010).
Pawelek, J. M. & Chakraborty, A. K. Fusion of tumour cells with bone marrow-derived cells: a unifying explanation for metastasis. Nature Rev. Cancer 8, 377–386 (2008).
Lazova, R. et al. A melanoma brain metastasis with a donor-patient hybrid genome following bone marrow transplantation: first evidence for fusion in human cancer. PLoS ONE 8, e66731 (2013).
Blagosklonny, M. V. Target for cancer therapy: proliferating cells or stem cells. Leukemia 20, 385–391 (2006).
Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).
Anderson, K. et al. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 469, 356–361 (2011).
Baslan, T. et al. Genome-wide copy number analysis of single cells. Nature Protoc. 7, 1024–1041 (2012).
Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012).
Jan, M. et al. Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia. Sci. Transl Med. 4, 149ra118 (2012).
Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012). An exposition of the heterogeneity within different regions in a single tumour, demonstrating the importance of the integration of several analysis methods including DNA and RNA sequencing.
Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
Cheung, V. & Nelson, S. Whole genome amplification using a degenerate oligonucleotide primer allows hundreds of genotypes to be performed on less than one nanogram of genomic DNA. Proc. Natl Acad. Sci. USA 93, 14676–14679 (1996).
Arneson, N., Hughes, S., Houlston, R. & Done, S. Whole-genome amplification by improved primer extension preamplification PCR (I-PEP-PCR). CSH Protoc. 2008, pdb.prot4921 (2008).
Klein, C. A. et al. Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis of single cells. Proc. Natl Acad. Sci. USA 96, 4494–4499 (1999).
Dean, F. et al. Comprehensive human genome amplification using multiple displacement amplification. Proc. Natl Acad. Sci. USA 99, 5261–5266 (2002).
Lu, S. et al. Probing meiotic recombination and aneuploidy of single sperm cells by whole-genome sequencing. Science 338, 1627–1630 (2012).
Peng, W., Takabayashi, H. & Ikawa, K. Whole genome amplification from single cells in preimplantation genetic diagnosis and prenatal diagnosis. Eur. J. Obstet. Gynecol. Reprod. Biol. 131, 13–20 (2007).
Salipante, S. J., Kas, A., McMonagle, E. & Horwitz, M. S. Phylogenetic analysis of developmental and postnatal mouse cell lineages. Evol. Dev. 12, 84–94 (2010).
Zaretsky, I. et al. Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays. Lab. Chip 12, 5007–5015 (2012).
Harris, T. D. et al. Single-molecule DNA sequencing of a viral genome. Science 320, 106–109 (2008).
Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).
Schadt, E., Turner, S. & Kasarskis, A. A window into third-generation sequencing. Hum. Mol. Genet. 19, R227–R240 (2010).
Xu, M., Fujita, D. & Hanagata, N. Perspectives and challenges of emerging single-molecule DNA sequencing technologies. Small 5, 2638–2649 (2009).
Ng, S. et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).
Teer, J. & Mullikin, J. Exome sequencing: the sweet spot before whole genomes. Hum. Mol. Genet. 19, R145–R151 (2010).
Giulino-Roth, L. et al. Targeted genomic sequencing of pediatric Burkitt lymphoma identifies recurrent alterations in antiapoptotic and chromatin-remodeling genes. Blood 120, 5181–5184 (2012).
Valencia, C. A. et al. Comprehensive mutation analysis for congenital muscular dystrophy: a clinical PCR-based enrichment and next-generation sequencing panel. PLoS ONE 8, e53083 (2013).
Hollants, S., Redeker, E. & Matthijs, G. Microfluidic amplification as a tool for massive parallel sequencing of the familial hypercholesterolemia genes. Clin. Chem. 58, 717–724 (2012).
Tewhey, R. et al. Microdroplet-based PCR enrichment for large-scale targeted sequencing. Nature Biotech. 27, 1025–1031 (2009).
Li, J. et al. Multiplex padlock targeted sequencing reveals human hypermutable CpG variations. Genome Res. 19, 1606–1615 (2009).
Johansson, H. et al. Targeted resequencing of candidate genes using selector probes. Nucleic Acids Res. 39, e8 (2011).
Diaz-Horta, O. et al. Whole-exome sequencing efficiently detects rare mutations in autosomal recessive nonsyndromic hearing loss. PLoS ONE 7, e50628 (2012).
Arendt, D. The evolution of cell types in animals: emerging principles from molecular studies. Nature Rev. Genet. 9, 868–882 (2008). In this Review, the author discusses the origin and evolution of diverse cell types in animals, an issue that has been curiously neglected by biologists.
Vickaryous, M. K. & Hall, B. K. Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest. Biol. Rev. Cambridge Philos. Soc. 81, 425–455 (2006). This paper is a careful review of all human cell types that have been given names in the literature, which is a useful starting point for future cell-type discovery experiments.
Gehlenborg, N. et al. Visualization of omics data for systems biology. Nature Methods 7, S56–S68 (2010).
Johnston, I. G. et al. Mitochondrial variability as a source of extrinsic cellular noise. PLoS Computat. Biol. 8, e1002416 (2012).
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006). This study using single-molecule imaging of mRNAs shows that mRNA abundances vary tremendously within putatively homogenous cell populations, and provides initial estimates of transcriptional burst kinetics in mammalian cells.
Raj, A. & Vanoudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).
Endele, M. & Schroeder, T. Molecular live cell bioimaging in stem cell research. Ann. NY Acad. Sci. 1266, 18–27 (2012).
Ozsolak, F. et al. Direct RNA sequencing. Nature 461, 814–818 (2009).
Casbon, J. A., Osborne, R. J., Brenner, S. & Lichtenstein, C. P. A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids Res. 39, e81 (2011).
Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods 9, 72–74 (2011).
Shiroguchi, K., Jia, T. Z., Sims, P. A. & Xie, X. S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc. Natl Acad. Sci. USA 109, 1347–1352 (2012).
Fu, G. K., Hu, J., Wang, P. H. & Fodor, S. P. Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc. Natl Acad. Sci. USA 108, 9026–9031 (2011).
Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W. & Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl Acad. Sci. USA 108, 9530–9535 (2011).
Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).
Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).
Klein, C. A. et al. Combined transcriptome and genome analysis of single micrometastatic cells. Nature Biotech. 20, 387–392 (2002). This study reported a simultaneous genomic and transcriptomic analysis of individual cells using a microarray readout. This is a first example of an integrated single-cell analysis.
Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377–382 (2009). The first demonstration of single-cell RNA-seq with accurate detection of alternatively spliced transcripts in single mouse oocytes.
Maleszka, R. & Stange, G. Molecular cloning, by a novel approach, of a cDNA encoding a putative olfactory protein in the labial palps of the moth Cactoblastis cactorum. Gene 202, 39–43 (1997).
Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011). The first demonstration of highly multiplexed single-cell RNA-seq showing that cell types can be distinguished in an unbiased manner on the basis of unfiltered single-cell gene expression profiles.
Arand, J. et al. In vivo control of CpG and non-CpG DNA methylation by DNA methyltransferases. PLoS Genet. 8, e1002750 (2012).
Taylor, K. H. et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer Res. 67, 8511–8518 (2007).
Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nature Genet. 44, 1207–1214 (2012).
Jothi, R., Cuddapah, S., Barski, A., Cui, K. & Zhao, K. Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res. 36, 5221–5231 (2008).
Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).
van de Werken, H. J. et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nature Methods 9, 969–972 (2012).
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Kantlehner, M. et al. A high-throughput DNA methylation analysis of a single cell. Nucleic Acids Res. 39, e44 (2011).
Denomme, M. M., Zhang, L. & Mann, M. R. Single oocyte bisulfite mutagenesis. J. Vis. Exp. 64, e4046 (2012).
Hayashi-Takanaka, Y. et al. Tracking epigenetic histone modifications in single cells using Fab-based live endogenous modification labeling. Nucleic Acids Res. 39, 6475–6488 (2011).
Tsao, J. L. et al. Genetic reconstruction of individual colorectal tumor histories. Proc. Natl Acad. Sci. USA 97, 1236–1241 (2000).
Siegmund, K., Marjoram, P., Woo, Y., Tavare, S. & Shibata, D. Inferring clonal expansion and cancer stem cell dynamics from DNA methylation patterns in colorectal cancers. Proc. Natl Acad. Sci. USA 106, 4828–4833 (2009).
Nicolas, P., Kim, K., Shibata, D. & Tavare, S. The stem cell population of the human colon crypt: analysis via methylation patterns. PLoS Computat. Biol. 3, 364–374 (2007).
Yatabe, Y., Tavaré, S. & Shibata, D. Investigating stem cells in human colon by using methylation patterns. Proc. Natl Acad. Sci. USA 98, 10839–10844 (2001).
Kim, K. M. & Shibata, D. Methylation reveals a niche: stem cell succession in human colon crypts. Oncogene 21, 5441–5449 (2002).
Hodgkinson, V., ElFadl, D., Drew, P., Lind, M. & Cawkwell, L. Repeatedly identified differentially expressed proteins (RIDEPs) from antibody microarray proteomic analysis. J. Proteom. 74, 698–703 (2011).
Bendall, S. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Lee, H. W. et al. Real-time single-molecule co-immunoprecipitation analyses reveal cancer-specific Ras signalling dynamics. Nature Commun. 4, 1505 (2013).
Jain, A. et al. Probing cellular protein complexes using single-molecule pull-down. Nature 473, 484–488 (2011).
Keshishian, H. et al. Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell Proteom. 8, 2339–2349 (2009).
Niemeyer, C., Adler, M. & Wacker, R. Detecting antigens by quantitative immuno-PCR. Nature Protoc. 2, 1918–1930 (2007).
Fredriksson, S. et al. Multiplexed protein detection by proximity ligation for cancer biomarker validation. Nature Methods 4, 327–329 (2007).
Turner, D. J. et al. Toward clinical proteomics on a next-generation sequencing platform. Anal. Chem. 83, 666–670 (2011).
Salehi-Reyhani, A. et al. A first step towards practical single cell proteomics: a microfluidic antibody capture chip with TIRF detection. Lab. Chip 11, 1256–1261 (2011).
Shi, Q. et al. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109, 419–424 (2012).
Li, G. W. & Xie, X. S. Central dogma at the single-molecule level in living cells. Nature 475, 308–315 (2011). A review of the central dogma of molecular biology in terms of stochastic kinetics in single cells and of imaging-based methods for single-cell and single-molecule analysis.
Sulston, J. E., Schierenberg, E., White, J. G. & Thomson, J. N. The embryonic cell lineage of the nematode Caenorhabditis elegans. Dev. Biol. 100, 64–119 (1983).
Sulston, J. E. & Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977). The first reconstruction of a complete organism cell lineage, of the C. elegans nematode, published almost four decades ago. Complete cell lineage trees of higher organisms are yet to be reconstructed.
Noctor, S. C., Martinez-Cerdeno, V., Ivic, L. & Kriegstein, A. R. Cortical neurons arise in symmetric and asymmetric division zones and migrate through specific phases. Nature Neurosci. 7, 136–144 (2004).
Murray, J. et al. Automated analysis of embryonic gene expression with cellular resolution in C. elegans. Nature Methods 5, 703–709 (2008).
Murray, J. et al. Multidimensional regulation of gene expression in the C. elegans embryo. Genome Res. 22, 1282–1294 (2012).
DeKosky, B. J. et al. High-throughput sequencing of the paired human immunoglobulin heavy and light chain repertoire. Nature Biotech. 31, 166–169 (2013).
Peters, B. et al. Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells. Nature 487, 190–195 (2012).
Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).
Timmermann, B. et al. Somatic mutation profiles of MSI and MSS colorectal cancer identified by whole exome next generation sequencing and bioinformatics analysis. PLoS ONE 5, e15661 (2010).
Diep, D. et al. Library-free methylation sequencing with bisulfite padlock probes. Nature Methods 9, 270–272 (2012).
Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).
Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods 5, 613–619 (2008).
Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA-Seq reveals non-genetic gene expression heterogeneity. Genome Biol. 14, R31 (2013).
Acknowledgements
The work of S.L. was supported by grant 261063 from the European Research Council and by the Swedish Research Council STARGET consortium. The work of E.S. and T.B. was supported by The European Union FP7-ERC-AdG grant and by a grant from the Kenneth and Sally Leafman Appelbaum Discovery Fund. E.S. is the Incumbent of The Harry Weinrebe Professorial Chair of Computer Science and Biology. The contribution of E.S. to this Review was inspired by a research proposal prepared by E.S. in collaboration with I. Amit, A. Tanay and M. Schwarz.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Related links
Glossary
- Next-generation sequencing
-
(NGS). High-throughput DNA sequencing of a large number of DNA molecules in parallel. There is a trade-off between read length and throughput that depends on the sequencing technology, run time and quality.
- Organismal cell lineage tree
-
A mathematical entity capturing all cell division and death events in the life of an organism up to a particular time point. The tree consists of labelled nodes, which represent all organismal cells, and directed edges, which represent progeny relationships among them. A reconstructed tree describes lineage relationships among cells sampled from an organism, and is precise only if it is a subtree of the (true) organismal cell lineage tree.
- Cell type
-
A classification of cells by morphology, genotype, phenotype or developmental origin. There is no consensus on which properties are necessary and sufficient for this classification, nor is there general agreement on the actual number of cell types or their proper classification in any higher organism, including in humans.
- Fluorescence-activated cell sorting
-
(FACS). A tool that enables high-speed counting and/or sorting of cells according to features detected by fluorescence.
- Laser-capture microdissection
-
(LCM). A method that combines high-resolution microscopy and the accurate isolation of user-defined regions of a tissue slice for downstream analysis. Typically, a powerful laser is used to cut an outline of the target region, which can then be ejected into a sample tube.
- Microsatellites
-
Repetitive elements in the genome that consist of basic units 1–6 bp long that are repeated from a few to a few dozen times. Microsatellites occupy 3% of the human genome.
- Cell depth
-
The number of divisions a cell underwent since the zygote.
- Sequencing depth
-
The total amount of raw sequence mapped to a reference genome, divided by the length of the genome.
- Whole-genome amplification
-
(WGA). Refers to methods that are used to amplify the genomic DNA of single cells to increase the number of copies of DNA for downstream processing.
- Clonal expansion
-
A method to retrieve representative DNA from a single cell following its proliferation. A single cell is isolated, cultured ex vivo, and allowed to divide several times. DNA is isolated from the bulk cell population using standard DNA extraction techniques that do not involve amplification.
- Single-nucleotide polymorphism calls
-
(SNP calls). Following sequencing read assembly, this is the identification of single nucleotides that are different from the nucleotide at the same position in a specific reference genome. This process requires high-quality sequencing and adequate sequencing depth for statistical significance.
- Sequencing coverage
-
In a sequencing experiment, the number of reads covering a specific nucleotide position is the coverage of that position. Increasing read depth leads to increasing coverage, and to increasing accuracy of the base calls.
- Amplicons
-
DNA products of PCR amplifications.
- Higher moments
-
Measures of the shape of a statistical distribution beyond mean and variance, such as skewness and kurtosis.
Rights and permissions
About this article
Cite this article
Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14, 618–630 (2013). https://doi.org/10.1038/nrg3542
Published:
Issue Date:
DOI: https://doi.org/10.1038/nrg3542
This article is cited by
-
Single-cell RNA sequencing in orthopedic research
Bone Research (2023)
-
Methodological concerns and lack of evidence for single-synapse RNA-seq
Nature Biotechnology (2023)
-
Single-cell gene and isoform expression analysis reveals signatures of ageing in haematopoietic stem and progenitor cells
Communications Biology (2023)
-
Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D
Genome Biology (2022)
-
Evolutionarily conservative and non-conservative regulatory networks during primate interneuron development revealed by single-cell RNA and ATAC sequencing
Cell Research (2022)