Abstract
The single-cell transcriptome is the set of messenger RNA (mRNA) molecules expressed in one cell, and it can vary due to external environmental conditions and cellular stage. The transcriptome defines a cell’s function and makes it different from other cells, giving rise to biological heterogeneity. This biological system is comprised of a multitude of different cells with different genotypes and phenotypes that are biochemically and functionally diverse. Traditionally, most genetic studies have analyzed pools of several thousands of cells, which provided information on only average gene expression levels. This approach vastly limits the study of unique cellular signatures of individual cells. Recently, single-cell technologies have been developed, and they have become a powerful approach for a more detailed understanding of biological systems at single-cell resolution. The detection of gene expression variations greatly helped with resolving healthy and diseased cell states. The single-cell transcriptome is currently used in multiple research fields, such as immunology, stem cell research, and cancer biology. This chapter explores the biological applications of the single-cell transcriptome, recent advances in single-cell studies, and the current state of single-cell techniques. Understanding the biological implications of high variability in biological systems will lead to a more precise approach for the development of novel diagnosis and therapy tools.
References
Attar M, Sharma E, Li S et al (2018) A practical solution for preserving single cells for RNA sequencing. Sci Rep 8:1–10. https://doi.org/10.1038/s41598-018-20372-7
Baccelli I, Schneeweiss A, Riethdorf S et al (2013) Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat Biotechnol 31:539–544. https://doi.org/10.1038/nbt.2576
Baslan T, Hicks J (2017) Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer 17:557–569. https://doi.org/10.1038/nrc.2017.58
Björklund AK, Forkel M, Picelli S et al (2016) The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing. Nat Immunol 17:451–460. https://doi.org/10.1038/ni.3368
Gaublomme JT, Yosef N, Lee Y et al (2015) Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163:1400–1412. https://doi.org/10.1016/j.cell.2015.11.009
Giladi A, Amit I (2018) Single-cell genomics: a stepping stone for future immunology discoveries. Cell 172:14–21. https://doi.org/10.1016/j.cell.2017.11.011
Grün D, Lyubimova A, Kester L et al (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251–255. https://doi.org/10.1038/nature14966
Grün D, Muraro MJ, Boisset JC et al (2016) De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19:266–277. https://doi.org/10.1016/j.stem.2016.05.010
Guo G, Huss M, Tong GQ et al (2010) Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell 18:675–685. https://doi.org/10.1016/j.devcel.2010.02.012
Habib N, Li Y, Heidenreich M, Swiech L (2016) Div-seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353:925–928
Han X, Wang R, Zhou Y et al (2018) Mapping the mouse cell atlas by microwell-seq. Cell 172:1091–1097. https://doi.org/10.1016/j.cell.2018.02.001
Haque A, Engel J, Teichmann SA, Lönnberg T (2017) A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med 9:1–12. https://doi.org/10.1186/s13073-017-0467-4
Hashimshony T, Wagner F, Sher N, Yanai I (2012) CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2:666–673. https://doi.org/10.1016/j.celrep.2012.08.003
Hashimshony T, Senderovich N, Avital G et al (2016) CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17:1–7. https://doi.org/10.1186/s13059-016-0938-8
Jaitin DA, Kenigsberg E, Keren-shaul H et al (2014) Massively parallel single-cell RNA-seq for marker free decomposition of tissues into cell types. Science 343(6172):776–779. https://doi.org/10.1126/science.1247651
Jordan NV, Bardia A, Wittner BS et al (2016) HER2 expression identifies dynamic functional states within circulating breast cancer cells. Nature 537:102–106. https://doi.org/10.1038/nature19328
Kalluri R, Zeisberg M (2006) Fibroblasts in cancer. Nat Rev Cancer 6:392–401. https://doi.org/10.1038/nrc1877
Karaiskos N, Wahle P, Alles J et al (2017) The Drosophila embryo at single-cell transcriptome resolution. Science 358:194–199. https://doi.org/10.1126/science.aan3235
Keren-Shaul H, Spinrad A, Weiner A et al (2017) A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169:1276–1290. https://doi.org/10.1016/j.cell.2017.05.018
Kivioja T, Vähärautio A, Karlsson K et al (2012) Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9:72–74. https://doi.org/10.1038/nmeth.1778
Klein AM, Mazutis L, Akartuna I et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201. https://doi.org/10.1016/j.cell.2015.04.044
Lapin M, Tjensvoll K, Oltedal S et al (2017) Single-cell mRNA profiling reveals transcriptional heterogeneity among pancreatic circulating tumour cells. BMC Cancer 17:390–400. https://doi.org/10.1186/s12885-017-3385-3
Lawson DA, Bhakta NR, Kessenbrock K et al (2015) Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature 526:131–135. https://doi.org/10.1038/nature15260
Lee M-CW, Lopez-Diaz FJ, Khan SY et al (2014) Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc Natl Acad Sci 111:4726–4735. https://doi.org/10.1073/pnas.1404656111
Li H, Courtois ET, Sengupta D et al (2017) Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet 49:708–718. https://doi.org/10.1038/ng.3818
Macosko EZ, Basu A, Satija R et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214. https://doi.org/10.1016/j.cell.2015.05.002
Miyamoto DT, Zheng Y, Wittner BS et al (2015) RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349:1351–1356. https://doi.org/10.1126/science.aab0917
Narsinh KH, Sun N, Sanchez-freire V et al (2011) Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells. J Clin Invest 121:1217–1221. https://doi.org/10.1172/JCI44635DS1
Navin N, Hicks J (2011) Future medical applications of single-cell sequencing in cancer. Genome Med 3:1–12. https://doi.org/10.1186/gm247
Nguyen MQ, Wu Y, Bonilla LS et al (2017) Diversity amongst trigeminal neurons revealed by high throughput single cell sequencing. PLoS One 12:e0185543. https://doi.org/10.1371/journal.pone.0185543
Picelli S, Björklund ÅK, Faridani OR et al (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1100. https://doi.org/10.1038/nmeth.2639
Pogorzala LA, Mishra SK, Hoon MA (2013) The cellular code for mammalian thermosensation. J Neurosci 33:5533–5541. https://doi.org/10.1523/JNEUROSCI.5788-12.2013
Poirion OB, Zhu X, Ching T, Garmire L (2016) Single-cell transcriptomics bioinformatics and computational challenges. Front Genet 7:1–11. https://doi.org/10.3389/fgene.2016.00163
Powell AA, Talasaz AAH, Zhang H et al (2012) Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS One 7:e33788. https://doi.org/10.1371/journal.pone.0033788
Puram SV, Tirosh I, Parikh AS et al (2017) Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in Head and Neck Cancer. Cell 171:1611–1624.e24. https://doi.org/10.1016/j.cell.2017.10.044
Ramskold D, Luo S, Wang Y et al (2013) Fulllength mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30:777–782. https://doi.org/10.1038/nbt.2282
Schmidt F, Efferth T (2016) Tumor heterogeneity, single-cell sequencing, and drug resistance. Pharmaceuticals 9. https://doi.org/10.3390/ph9020033
Shah S, Lubeck E, Zhou W, Cai L (2016) In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92:342–357. https://doi.org/10.1016/j.neuron.2016.10.001
Shalek AK, Satija R, Shuga J et al (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510:363–369. https://doi.org/10.1038/nature13437
Skylaki S, Hilsenbeck O, Schroeder T (2016) Challenges in long-term imaging and quantification of single-cell dynamics. Nat Biotechnol 34:1137–1144. https://doi.org/10.1038/nbt.3713
Stratton MR, Campbell PJ, Futreal PA (2009) The cancer genome. Nature 458:719–724. https://doi.org/10.1038/nature07943
Stubbington MJT, Rozenblatt-Rosen O, Regev A, Teichmann SA (2017) Single cell transcriptomics to explore the immune system in health and disease. Science 358:58–63. https://doi.org/10.1126/science.aan6828
Tang F, Barbacioru C, Wang Y et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382. https://doi.org/10.1038/nmeth.1315
Van Den Brink SC, Sage F, Vértesy Á et al (2017) Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods 14:935–936. https://doi.org/10.1038/nmeth.4437
Van der Flier LG, Clevers H (2009) Stem cells, self-renewal, and differentiation in the intestinal epithelium. Annu Rev Physiol 71:241–260. https://doi.org/10.1146/annurev.physiol.010908.163145
Wang J, Song Y (2017) Single cell sequencing: a distinct new field. Clin Transl Med 6:10. https://doi.org/10.1186/s40169-017-0139-4
Wen L, Tang F (2016) Single-cell sequencing in stem cell biology. Genome Biol 17:1–12. https://doi.org/10.1186/s13059-016-0941-0
Yu M, Bardia A, Wittner BS et al (2013) Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339:580–584. https://doi.org/10.1126/science.1228522
Yuan GC, Cai L, Elowitz M et al (2017) Challenges and emerging directions in single-cell analysis. Genome Biol 18:1–8. https://doi.org/10.1186/s13059-017-1218-y
Zheng C, Zheng L, Yoo JK et al (2017) Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169:1342–1356.e16. https://doi.org/10.1016/j.cell.2017.05.035
Zhu S, Qing T, Zheng Y, Shi L (2017) Advances in single-cell RNA sequencing and its applications in cancer research. Oncotarget 8:53763–53779. https://doi.org/10.18632/oncotarget.17893
Ziegenhain C, Vieth B, Parekh S et al (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65:631–643.e4. https://doi.org/10.1016/j.molcel.2017.01.023
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Prieto-Vila, M., Yamamoto, Y., Takahashi, Ru., Ochiya, T. (2018). Single-Cell Transcriptomics. In: Santra, T., Tseng, FG. (eds) Handbook of Single Cell Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-10-4857-9_12-1
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