Abstract
Cellular heterogeneity within a cell population is a common phenomenon in multicellular organisms, tissues, cultured cells, and even FACS-sorted subpopulations. Important information may be masked if the cells are studied as a mass. Transcriptome profiling is a parameter that has been intensively studied, and relatively easier to address than protein composition. To understand the basis and importance of heterogeneity and stochastic aspects of the cell function and its mechanisms, it is essential to examine transcriptomes of a panel of single cells. High-throughput technologies, starting from microarrays and now RNA-seq, provide a full view of the expression of transcriptomes but are limited by the amount of RNA for analysis. Recently, several new approaches for amplification and sequencing the transcriptome of single cells or a limited low number of cells have been developed and applied. In this review, we summarize these major strategies, such as PCR-based methods, IVT-based methods, phi29-DNA polymerase-based methods, and several other methods, including their principles, characteristics, advantages, and limitations, with representative applications in cancer stem cells, early development, and embryonic stem cells. The prospects for development of future technology and application of transcriptome analysis in a single cell are also discussed.
Similar content being viewed by others
References
Irish JM, Kotecha N, Nolan GP (2006) Mapping normal and cancer cell signalling networks: towards single-cell proteomics. Nat Rev Cancer 6(2):146–155
Graf T, Stadtfeld M (2008) Heterogeneity of embryonic and adult stem cells. Cell Stem Cell 3(5):480–483
Huang S (2009) Non-genetic heterogeneity of cells in development: more than just noise. Development 136(23):3853–3862
Shackleton M et al (2009) Heterogeneity in cancer: cancer stem cells versus clonal evolution. Cell 138(5):822–829
Turner NC, Reis-Filho JS (2012) Genetic heterogeneity and cancer drug resistance. Lancet Oncol 13(4):e178–e185
Clarke MF et al (2006) Cancer stem cells—perspectives on current status and future directions: AACR workshop on cancer stem cells. Cancer Res 66(19):9339–9344
Buganim Y et al (2012) Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150(6):1209–1222
Wang D, Bodovitz S (2010) Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol 28(6):281–290
Tang F, Lao K, Surani MA (2011) Development and applications of single-cell transcriptome analysis. Nat Methods 8(4 Suppl):S6–11
Tang F et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63
Warren L et al (2006) Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR. Proc Natl Acad Sci USA 103(47):17807–17812
Raj A et al (2008) Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 5(10):877–879
Wang F et al (2013) Robust measurement of telomere length in single cells. Proc Natl Acad Sci USA 110(21):E1906–E1912
Hou Y et al (2012) Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148(5):873–885
Navin N et al (2011) Tumour evolution inferred by single-cell sequencing. Nature 472(7341):90–94
Xu X et al (2012) Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148(5):886–895
Islam S et al (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21(7):1160–1167
Kurimoto K et al (2006) An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res 34(5):e42
Saitou M, Barton SC, Surani MA (2002) A molecular programme for the specification of germ cell fate in mice. Nature 418(6895):293–300
Tang F et al (2010) RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 5(3):516–535
Tougan T, Okuzaki D, Nojima H (2008) Chum-RNA allows preparation of a high-quality cDNA library from a single-cell quantity of mRNA without PCR amplification. Nucleic Acids Res 36(15):e92
Li L, Clevers H (2010) Coexistence of quiescent and active adult stem cells in mammals. Science 327(5965):542–545
Hood L et al (2004) Systems biology and new technologies enable predictive and preventative medicine. Science 306(5696):640–643
Velculescu VE et al (1997) Characterization of the yeast transcriptome. Cell 88(2):243–251
Royce TE, Rozowsky JS, Gerstein MB (2007) Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification. Nucleic Acids Res 35(15):e99
Bainbridge MN et al (2006) Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach. BMC Genom 7:246
Morin R et al (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45(1):81–94
Sultan M et al (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321(5891):956–960
Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12(2):87–98
Mortazavi A et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628
Wang ET et al (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456(7221):470–476
Eberwine J et al (1992) Analysis of gene expression in single live neurons. Proc Natl Acad Sci USA 89(7):3010–3014
Van Gelder RN et al (1990) Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci USA 87(5):1663–1667
Brady G, Iscove NN (1993) Construction of cDNA libraries from single cells. Methods Enzymol 225:611–623
Dixon AK et al (1998) Expression profiling of single cells using 3 prime end amplification (TPEA) PCR. Nucleic Acids Res 26(19):4426–4431
Islam S et al (2012) Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat Protoc 7(5):813–828
Pan X et al (2013) Two methods for full-length RNA sequencing for low quantities of cells and single cells. Proc Natl Acad Sci USA 110(2):594–599
Iscove NN et al (2002) Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA. Nat Biotechnol 20(9):940–943
Matz M et al (1999) Amplification of cDNA ends based on template-switching effect and step-out PCR. Nucleic Acids Res 27(6):1558–1560
Zhu YY et al (2001) Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30(4):892–897
Kurimoto K et al (2007) Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat Protoc 2(3):739–752
Tang F et al (2010) Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6(5):468–478
Lecault V et al (2012) Microfluidic single cell analysis: from promise to practice. Curr Opin Chem Biol 16(3–4):381–390
Hebenstreit D (2012) Methods, challenges and potentials of single Cell RNA-seq. Biology 1(3):658–667
Ramskold D et al (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30(8):777–782
Qiu S et al (2012) Single-neuron RNA-Seq: technical feasibility and reproducibility. Front Genet 3:124
Picelli S et al (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1098
Patel OV et al (2005) Validation and application of a high-fidelity mRNA linear amplification procedure for profiling gene expression. Vet Immunol Immunopathol 105(3–4):331–342
Hashimshony T et al (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2(3):666–673
Sasagawa Y et al (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol 14(4):R31
Blanco L, Salas M (1984) Characterization and purification of a phage phi 29-encoded DNA polymerase required for the initiation of replication. Proc Natl Acad Sci USA 81(17):5325–5329
Dean FB et al (2002) Comprehensive human genome amplification using multiple displacement amplification. Proc Natl Acad Sci USA 99(8):5261–5266
Kang Y et al (2011) Transcript amplification from single bacterium for transcriptome analysis. Genome Res 21(6):925–935
Dalerba P et al (2011) Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat Biotechnol 29(12):1120–1127
Toloudi M et al (2011) Correlation between cancer stem cells and circulating tumor cells and their value. Case Rep Oncol 4(1):44–54
Cann GM et al (2012) mRNA-Seq of single prostate cancer circulating tumor cells reveals recapitulation of gene expression and pathways found in prostate cancer. PLoS One 7(11):e49144
Xue Z et al (2013) Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500(7464):593–597
Aghajanova L et al (2012) Comparative transcriptome analysis of human trophectoderm and embryonic stem cell-derived trophoblasts reveal key participants in early implantation. Biol Reprod 86(1):1–21
Dobson AT et al (2004) The unique transcriptome through day 3 of human preimplantation development. Hum Mol Genet 13(14):1461–1470
Guo G et al (2010) Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell 18(4):675–685
Tang F et al (2011) Deterministic and stochastic allele-specific gene expression in single mouse blastomeres. PLoS One 6(6):e21208
Yan L et al (2013) Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 20(9):1131–1139
Evans MJ, Kaufman MH (1981) Establishment in culture of pluripotential cells from mouse embryos. Nature 292(5819):154–156
Niwa H (2007) How is pluripotency determined and maintained? Development 134(4):635–646
Zong C et al (2012) Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338(6114):1622–1626
Moroz LL, Kohn AB (2013) Single-neuron transcriptome and methylome sequencing for epigenomic analysis of aging. Methods Mol Biol 1048:323–352
Acknowledgments
We thank Dr. Sherman Weissman for his valuable comments during the composition of this review. This work was supported by China MOST National Major Basic Research Program (973 Program) (2012CB911202), National Natural Science Foundation of China (31000651), China Scholarship Council (201208120050), MOST International Cooperation Grant (2014DFA30450), and National Institutes of Health Grants 1P01GM099130-01.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, N., Liu, L. & Pan, X. Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos. Cell. Mol. Life Sci. 71, 2707–2715 (2014). https://doi.org/10.1007/s00018-014-1601-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00018-014-1601-8