Short Communication - Journal of RNA and Genomics (2018) Volume 14, Issue 1
Single-cell RNA Sequencing to Investigate Human DiseaseEuan J Rodge1,2*
- *Corresponding Author:
- Euan Rodger
E-mail: [email protected]
Tel: +64 3 470 3455
Received Date: 22 January 2018; Accepted Date: 26 January 2018; Published Date: 31 January 2018
© Copyright The Author(s). First Published by Allied Academies. This is an open access article, published under the terms of theCreative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0). This license permitsnon-commercial use, distribution and reproduction of the article, provided the original work is appropriately acknowledged with correct citation details.
The transcription of DNA into single-stranded RNA molecules defines the biological activity and phenotype of a cell. At any given time, the total amount of synthesized RNA in a cell is referred to as the transcriptome , changes in which are likely to have functional consequences. Therefore, studying gene expression is crucial to understanding altered phenotypes and properties of a cell in development and disease.
The last two decades has seen the continual improvement of profiling gene expression at a genome-scale using hybridizationbased microarrays  and, more recently, RNA sequencing (RNA-Seq) , a technique for unbiased sequencing of expressed genomic loci at high coverage. Regarded as the industry standard for gene expression profiling via measurement of messenger RNA (mRNA), RNA-Seq also allows for analysis of non-coding RNA classes [1,3]. However, this technique conventionally requires millions of cells (∼1 μg of total mRNA) and therefore the output for each gene is an average expression level across the population of input cells . Now often referred to as ‘bulk’ RNA-Seq, it does not account for the stochastic nature of gene expression, cellular diversity (i.e. differences between cells of the same ‘type’), or cellular heterogeneity (i.e. different cell types within the same tissue/cell population).
In recent years, technological advances in next generation sequencing have allowed for unbiased profiling of single cells at multiple layers (i.e. the genome, epigenome and transcriptome) . Although single-cell RNA-Seq (scRNASeq) was first published by Tang et al. in 2009, it only started to gain widespread popularity several years later following lower sequencing costs and refinement of protocols [6,7]. Earlier scRNA-Seq approaches such as Smart-Seq , MARS-Seq  and Fluidigm C1 , were well-based, but recent droplet-based approaches such as Drop-Seq , in Drop  and Chromium  have significantly increased the number of cells that can be profiled in parallel for a single experiment. So far, scRNASeq has already yielded insight into a number of different areas that could not be achieved using bulk transcriptome profiling including, for example, the stochastic nature of gene expression [14-17]. To reveal complexity in the brain, studies in the central nervous system have successfully mapped cellular diversity and have even identified novel cellular subtypes [18-21]. Similarly, studies in embryonic and immune cells have also revealed new levels of heterogeneity [9,22,23]. In a scRNA-Seq analysis of ~2400 immune cells, a subpopulation of dendritic cells were identified that could potently stimulate T-cell activity , which has therapeutic implications against cancer. In several different contexts, scRNA-Seq has been used to infer cellular lineages and developmental relationships [25-28]. This approach has also been used in cancer to investigate the cellular heterogeneity in the tumour microenvironment [29-31] and for profiling individual circulating tumour cells . These are a just few examples of how single cell analysis, in particular scRNA-Seq, is transforming how we perform genomic profiling. The future looks bright for this emerging technology in investigating human disease, alone or in combination with other –omics analysis. For example, as scRNA-Seq can resolve each clone within a tumour, it could potentially be used for longitudinal monitoring of tumour relapse, reveal subsets refractory to therapy, and be used in a clinical setting for detection of rare disease-associated cells.
Ozsolak F and Milos PM. 2011. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet, 12, 87-98.
Schena M, Shalon D, Davis RW, et al. 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 467-467.
Wang Z, Mark G, Snyder M, et al. 2009. RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet, 10, 57-63.
Wilhelm BT and Landry JR. 2009. RNA-Seq?quantitative measurement of expression through massively parallel RNA-sequencing. Methods, 48, 249-57.
Linnarsson S and Teichmann. 2016. Single-cell genomics: coming of age. Genome Biol, 17, 97.
Tang F, Barbacioru C, Wang Y, et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat methods, 6, 377-382. 2014. Method of the year. Nat Methods, 12.
Ramskold D, Luo S, Yu-Chieh W, et al. 2012.Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumour cells. Nat Biotechnol, 30, 777-782.
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, 776-779.
Xin Y, Kim J, Wei Y, et al. 2016. Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc Natl Acad Sci U S A, 113, 3293-3298.
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.
Klein AM, Mazutis L, Akartuna I, et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell, 161, 1187-1201.
Zheng GX, Terry JM, Belgrader P, et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat commun, 8, 14049.
Shalek AK, Satija R, Shuqa J, et al. 2014. Single-cell RNA-Seq reveals dynamic paracrine control of cellular variation. Nature, 510, 363-369.
Shalek AK, Satija R, Adiconics X, et al. 2013. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature, 498, 236-240.
Kim JK, Kolodziejczyk AA, Ilicic T, et al. 2015. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat commun, 6, 8687.
Kar G, Kim JK, Kolodziejczyk AA, et al. 2017. Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression. Nat Commun, 8, 36.
Zeisel A, Muñoz-Manchado AB, Codeluppi S, et al. 2015. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347, 1138-1142.
Földy C, Darmanis S, Aoto J, et al .2016. Single-cell RNAseq reveals cell adhesion molecule profiles in electro physiologically defined neurons. Proc Natl Acad Sci U S A, 113, E5222-E5231.
La MG, Gyllborg D, Codeluppi S, et al .2016. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell, 167, 566-580.
Lake BB, Ai R , Kaeser GE, et al. 2016.Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science, 352, 1586-1590.
Deng Q, Ramsköld D, Reinius B, Sandberg R. 2014. Single-cell RNA-Seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science, 343, 193-196.
Yan L, Yang M, Guo H, et al. 2013. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol, 20, 1131-1139.
Villani AC, Satija R, Reynolds G, et al. 2017. Single-cell RNA-Seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science, 356, 4573.
Treutlein B, Brownfield DG, Wu AR, et al. 2014. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature, 509, 371-375.
Blakeley P, Fogarty NM, Del Valle I, et al. 2015. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq. Dev, 142, 3151-3165.
Petropoulos S, Edsgärd D, Reinius B, et al. 2016. Single-Cell RNA-Seq Reveals Lineage and X chromosome Dynamics in Human Preimplantation Embryos. Cell, 167.
Venteicher AS, Tirosh I, Hebert C, et al. 2017. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science, 355.
Patel AP, Trombetta JJ, Tirosh I, et al. 2014. Single-cell RNA-Seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344,1396-1401.
Tirosh I, Izar B, Prakadan SM, et al. 2016. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science.352, 189-196.
Puram SV, Tirosh I, Parikh AS, et al. 2017. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumour Ecosystems in Head and Neck Cancer. Cell, 171, 1611-1624.
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.