Retinal Development pp 159-186 | Cite as
Single-Cell Capture, RNA-seq, and Transcriptome Analysis from the Neural Retina
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Abstract
Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can address the challenge of cellular heterogeneity. In the last decade, the cost per cell has been dramatically reduced, and the throughput has been increased by 104-fold. Like many other tissues, the retina is highly heterogeneous with an estimated of over 100 subtypes of neuronal cells. Here, we describe the current techniques to perform scRNA-seq on the adult retinal tissue including retinal dissection, retinal dissociation, assessment of cell population, cDNA synthesis, library construction, and next-generation sequencing. In addition, we introduce a workflow of scRNA-seq data analysis using open-source tools.
Key words
Retinal dissection and dissociation Single-cell RNA-seq (scRNA-seq) Single-nuclei RNA-seq (snRNA-seq) ICELL8 10× Chromium Poly-(A)+ transcriptome amplification ScRNA-seq analysisReferences
- 1.Huang S (2009) Non-genetic heterogeneity of cells in development: more than just noise. Development 136(23):3853–3862. https://doi.org/10.1242/dev.035139CrossRefPubMedPubMedCentralGoogle Scholar
- 2.Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, Chen P, Gertner RS, Gaublomme JT, Yosef N, Schwartz S, Fowler B, Weaver S, Wang J, Wang X, Ding R, Raychowdhury R, Friedman N, Hacohen N, Park H, May AP, Regev A (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510(7505):363–369. https://doi.org/10.1038/nature13437CrossRefPubMedPubMedCentralGoogle Scholar
- 3.Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525(7568):251–255. https://doi.org/10.1038/nature14966CrossRefPubMedGoogle Scholar
- 4.Rizvi AH, Camara PG, Kandror EK, Roberts TJ, Schieren I, Maniatis T, Rabadan R (2017) Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nat Biotechnol 35(6):551–560. https://doi.org/10.1038/nbt.3854CrossRefPubMedPubMedCentralGoogle Scholar
- 5.Treutlein B, Lee QY, Camp JG, Mall M, Koh W, Shariati SA, Sim S, Neff NF, Skotheim JM, Wernig M, Quake SR (2016) Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature 534(7607):391–395. https://doi.org/10.1038/nature18323CrossRefPubMedPubMedCentralGoogle Scholar
- 6.Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H, David E, Salame TM, Tanay A, van Oudenaarden A, Amit I (2016) Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell 167(7):1883–1896. e1815. https://doi.org/10.1016/j.cell.2016.11.039CrossRefPubMedGoogle Scholar
- 7.Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA (2015) Highly parallel genome-wide expression profiling of individual cells using Nanoliter droplets. Cell 161(5):1202–1214. https://doi.org/10.1016/j.cell.2015.05.002CrossRefPubMedPubMedCentralGoogle Scholar
- 8.Masland RH (2012) The neuronal organization of the retina. Neuron 76(2):266–280. https://doi.org/10.1016/j.neuron.2012.10.002CrossRefPubMedPubMedCentralGoogle Scholar
- 9.Rheaume BA, Jereen A, Bolisetty M, Sajid MS, Yang Y, Renna K, Sun L, Robson P, Trakhtenberg EF (2018) Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes. Nat Commun 9(1):2759. https://doi.org/10.1038/s41467-018-05134-3CrossRefPubMedPubMedCentralGoogle Scholar
- 10.Shekhar K, Lapan SW, Whitney IE, Tran NM, Macosko EZ, Kowalczyk M, Adiconis X, Levin JZ, Nemesh J, Goldman M, McCarroll SA, Cepko CL, Regev A, Sanes JR (2016) Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166(5):1308–1323. e1330. https://doi.org/10.1016/j.cell.2016.07.054CrossRefPubMedPubMedCentralGoogle Scholar
- 11.Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382. https://doi.org/10.1038/nmeth.1315CrossRefPubMedGoogle Scholar
- 12.Hedlund E, Deng Q (2018) Single-cell RNA sequencing: technical advancements and biological applications. Mol Asp Med 59:36–46. https://doi.org/10.1016/j.mam.2017.07.003CrossRefGoogle Scholar
- 13.Gao R, Kim C, Sei E, Foukakis T, Crosetto N, Chan LK, Srinivasan M, Zhang H, Meric-Bernstam F, Navin N (2017) Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer. Nat Commun 8(1):228. https://doi.org/10.1038/s41467-017-00244-wCrossRefPubMedPubMedCentralGoogle Scholar
- 14.Goldstein LD, Chen YJ, Dunne J, Mir A, Hubschle H, Guillory J, Yuan W, Zhang J, Stinson J, Jaiswal B, Pahuja KB, Mann I, Schaal T, Chan L, Anandakrishnan S, Lin CW, Espinoza P, Husain S, Shapiro H, Swaminathan K, Wei S, Srinivasan M, Seshagiri S, Modrusan Z (2017) Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18(1):519. https://doi.org/10.1186/s12864-017-3893-1CrossRefPubMedPubMedCentralGoogle Scholar
- 15.Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. https://doi.org/10.1038/ncomms14049CrossRefPubMedPubMedCentralGoogle Scholar
- 16.Zappia L, Phipson B, Oshlack A (2018) Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput Biol 14(6):e1006245. https://doi.org/10.1371/journal.pcbi.1006245CrossRefPubMedPubMedCentralGoogle Scholar
- 17.Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145. https://doi.org/10.1038/nrg3833CrossRefPubMedGoogle Scholar
- 18.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635CrossRefGoogle Scholar
- 19.Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12(4):357–360. https://doi.org/10.1038/nmeth.3317CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. https://doi.org/10.1186/gb-2009-10-3-r25CrossRefPubMedPubMedCentralGoogle Scholar
- 21.Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, Teichmann SA (2016) Classification of low quality cells from single-cell RNA-seq data. Genome Biol 17:29. https://doi.org/10.1186/s13059-016-0888-1CrossRefPubMedPubMedCentralGoogle Scholar