1932

Abstract

It has been known for over a century that the basic organization of the retina is conserved across vertebrates. It has been equally clear that retinal cells can be classified into numerous types, but only recently have methods been devised to explore this diversity in unbiased, scalable, and comprehensive ways. Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have played a pivotal role in this effort. In this article, we outline the experimental and computational components of scRNA-seq and review studies that have used them to generate retinal atlases of cell types in several vertebrate species. These atlases have enabled studies of retinal development, responses of retinal cells to injury, expression patterns of genes implicated in retinal disease, and the evolution of cell types. Recently, the inquiry has expanded to include the entire eye and visual centers in the brain. These studies have enhanced our understanding of retinal function and dysfunction and provided tools and insights for exploring neural diversity throughout the brain.

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2021-09-15
2024-03-28
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Literature Cited

  1. Baden T, Berens P, Franke K, Rosón MR, Bethge M, Euler T. 2016. The functional diversity of retinal ganglion cells in the mouse. Nature 529:345–50
    [Google Scholar]
  2. Baden T, Euler T, Berens P. 2020. Understanding the retinal basis of vision across species. Nat. Rev. Neurosci 21:5–20
    [Google Scholar]
  3. Bae JA, Mu S, Kim JS, Turner NL, Tartavull I et al. 2018. Digital museum of retinal ganglion cells with dense anatomy and physiology. Cell 173:1293–306
    [Google Scholar]
  4. Bakken TE, van Velthoven CT, Menon V, Hodge RD, Yao Z et al. 2020. Single-cell RNA-seq uncovers shared and distinct axes of variation in dorsal LGN neurons in mice, non-human primates and humans. bioRxiv 367482. https://doi.org/10.1101/2020.11.05.367482
  5. Barlow HB, Hill RM. 1963. Selective sensitivity to direction of movement in ganglion cells of the rabbit retina. Science 139:412–14
    [Google Scholar]
  6. Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR. 2017. Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat. Methods 14:135–39
    [Google Scholar]
  7. Bassett EA, Wallace VA. 2012. Cell fate determination in the vertebrate retina. Trends Neurosci 35:565–73
    [Google Scholar]
  8. Bell OH, Copland DA, Ward A, Nicholson LB, Lange CA et al. 2020. Single eye mRNA-seq reveals normalisation of the retinal microglial transcriptome following acute inflammation. Front. Immunol 10:3033
    [Google Scholar]
  9. Bhat SP, Gangalum RK, Kim D, Mangul S, Kashyap RK et al. 2019. Transcriptional profiling of single fiber cells in a transgenic paradigm of an inherited childhood cataract reveals absence of molecular heterogeneity. J. Biol. Chem. 294:13530–44
    [Google Scholar]
  10. Blackshaw S, Harpavat S, Trimarchi J, Cai L, Huang H et al. 2004. Genomic analysis of mouse retinal development. PLOS Biol 2:e247
    [Google Scholar]
  11. Brady G, Barbara M, Iscove NN. 1990. Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol. Cell Biol. 2:17–25
    [Google Scholar]
  12. Bray ER, Yungher BJ, Levay K, Ribeiro M, Dvoryanchikov G et al. 2019. Thrombospondin-1 mediates axon regeneration in retinal ganglion cells. Neuron 103:642–57
    [Google Scholar]
  13. Breiman L. 2001. Random forests. Mach. Learn 45:5–32
    [Google Scholar]
  14. Bringmann A, Syrbe S, Görner K, Kacza J, Francke M et al. 2018. The primate fovea: structure, function and development. Prog. Retin. Eye Res 66:49–84
    [Google Scholar]
  15. Buenaventura DF, Corseri A, Emerson MM. 2019. Identification of genes with enriched expression in early developing mouse cone photoreceptors. Investig. Ophthalmol. Vis. Sci 60:2787–99
    [Google Scholar]
  16. Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–67
    [Google Scholar]
  17. Cembrowski MS, Spruston N. 2019. Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons. Nat. Rev. Neurosci. 20:193–204
    [Google Scholar]
  18. Cepko C. 2014. Intrinsically different retinal progenitor cells produce specific types of progeny. Nat. Rev. Neurosci. 15:615–27
    [Google Scholar]
  19. Chen T, Guestrin C. 2016. Xgboost: a scalable tree boosting system. KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining785–94 New York: ACM
  20. Cherry TJ, Trimarchi JM, Stadler MB, Cepko CL 2009. Development and diversification of retinal amacrine interneurons at single cell resolution. PNAS 106:9495–500
    [Google Scholar]
  21. Cherry TJ, Yang MG, Harmin DA, Tao P, Timms AE et al. 2020. Mapping the cis-regulatory architecture of the human retina reveals noncoding genetic variation in disease. PNAS 117:9001–12
    [Google Scholar]
  22. Clark BS, Stein-O'Brien GL, Shiau F, Cannon GH, Davis-Marcisak E et al. 2019. Single-cell RNA-seq analysis of retinal development identifies NFI factors as regulating mitotic exit and late-born cell specification. Neuron 102:1111–26
    [Google Scholar]
  23. Collin J, Queen R, Zerti D, Dorgau B, Hussain R et al. 2019. Deconstructing retinal organoids: single cell RNA-seq reveals the cellular components of human pluripotent stem cell-derived retina. Stem Cells 37:593–98
    [Google Scholar]
  24. Cowan CS, Renner M, De Gennaro M, Gross-Scherf B, Goldblum D et al. 2020. Cell types of the human retina and its organoids at single-cell resolution. Cell 182:1623–40
    [Google Scholar]
  25. Craig JE, Han X, Qassim A, Hassall M, Cooke Bailey JN et al. 2020. Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression. Nat. Genet. 52:160–66
    [Google Scholar]
  26. de Sevilla Müller LP, Sargoy A, Rodriguez AR, Brecha NC. 2014. Melanopsin ganglion cells are the most resistant retinal ganglion cell type to axonal injury in the rat retina. PLOS ONE 9:e93274
    [Google Scholar]
  27. Del Dosso A, Urenda JP, Nguyen T, Quadrato G. 2020. Upgrading the physiological relevance of human brain organoids. Neuron 107:1014–28
    [Google Scholar]
  28. Della Santina L, Kuo SP, Yoshimatsu T, Okawa H, Suzuki SC, Hoon M et al. 2016. Glutamatergic monopolar interneurons provide a novel pathway of excitation in the mouse retina. Curr. Biol. 26:2070–77
    [Google Scholar]
  29. DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire M-D et al. 2012. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28:1530–32
    [Google Scholar]
  30. Dhande OS, Stafford BK, Lim J-HA, Huberman AD. 2015. Contributions of retinal ganglion cells to subcortical visual processing and behaviors. Annu. Rev. Vis. Sci 1:291–328
    [Google Scholar]
  31. Dixit A, Parnas O, Li B, Chen J, Fulco CP et al. 2016. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853–66
    [Google Scholar]
  32. Dowling JE. 2012. The Retina: An Approachable Part of the Brain Cambridge, MA: Harvard Univ. Press
  33. Duan X, Qiao M, Bei F, Kim I-J, He Z, Sanes JR. 2015. Subtype-specific regeneration of retinal ganglion cells following axotomy: effects of osteopontin and mTOR signaling. Neuron 85:1244–56
    [Google Scholar]
  34. Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S et al. 1992. Analysis of gene expression in single live neurons. PNAS 89:3010–14
    [Google Scholar]
  35. Eiraku M, Takata N, Ishibashi H, Kawada M, Sakakura E et al. 2011. Self-organizing optic-cup morphogenesis in three-dimensional culture. Nature 472:51–56
    [Google Scholar]
  36. Eng C-HL, Lawson M, Zhu Q, Dries R, Koulena N et al. 2019. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568:235–39
    [Google Scholar]
  37. Euler T, Haverkamp S, Schubert T, Baden T. 2014. Retinal bipolar cells: elementary building blocks of vision. Nat. Rev. Neurosci. 15:507–19
    [Google Scholar]
  38. Finak G, McDavid A, Yajima M, Deng J, Gersuk V et al. 2015. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16:278
    [Google Scholar]
  39. Franke K, Berens P, Schubert T, Bethge M, Euler T, Baden T. 2017. Inhibition decorrelates visual feature representations in the inner retina. Nature 542:439–44
    [Google Scholar]
  40. Fritsche LG, Igl W, Bailey JN, Grassmann F, Sengupta S et al. 2016. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48:134–43
    [Google Scholar]
  41. Fruchterman TM, Reingold EM. 1991. Graph drawing by force-directed placement. Softw. Pract. Exp 21:111129–64
    [Google Scholar]
  42. Fuzik J, Zeisel A, Máté Z, Calvigioni D, Yanagawa Y et al. 2016. Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat. Biotechnol 34:175–83
    [Google Scholar]
  43. Gierahn TM, Wadsworth MH, Hughes TK, Bryson BD, Butler A et al. 2017. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14:395–98
    [Google Scholar]
  44. Giudice QL, Leleu M, La Manno G, Fabre PJ 2019. Single-cell transcriptional logic of cell-fate specification and axon guidance in early-born retinal neurons. Development 146:dev178103
    [Google Scholar]
  45. Gollisch T, Meister M. 2010. Eye smarter than scientists believes: neural computations in circuits of the retina. Neuron 65:150–64
    [Google Scholar]
  46. Gouwens NW, Sorensen SA, Baftizadeh F, Budzillo A, Lee BR et al. 2020. Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell 183:935–53.e19
    [Google Scholar]
  47. Gouwens NW, Sorensen SA, Berg J, Lee C, Jarsky T et al. 2019. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat. Neurosci 22:1182–95
    [Google Scholar]
  48. Granit R. 1952. Aspects of excitation and inhibition in the retina. Proc. R. Soc. Lond. B 140:191–99
    [Google Scholar]
  49. Greene MJ, Kim JS, Seung HS. 2016. Analogous convergence of sustained and transient inputs in parallel on and off pathways for retinal motion computation. Cell Rep 14:1892–900
    [Google Scholar]
  50. Habib N, Li Y, Heidenreich M, Swiech L, Avraham-Davidi I et al. 2016. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353:925–28
    [Google Scholar]
  51. Hamashima K, Gautam P, Lau KA, Khiong CW, Blenkinsop TA et al. 2020. Potential modes of COVID-19 transmission from human eye revealed by single-cell atlas. bioRxiv 085613. https://doi.org/10.1101/2020.05.09.085613
    [Google Scholar]
  52. He J, Zhang G, Almeida AD, Cayouette M, Simons BD, Harris WA. 2012. How variable clones build an invariant retina. Neuron 75:786–98
    [Google Scholar]
  53. Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W. 2013. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500:168–74
    [Google Scholar]
  54. Hie B, Peters J, Nyquist SK, Shalek AK, Berger B, Bryson BD. 2020. Computational methods for single-cell RNA sequencing. Annu. Rev. Biomed. Data Sci. 3:339–64
    [Google Scholar]
  55. Hoang T, Wang J, Boyd P, Wang F, Santiago C et al. 2020. Gene regulatory networks controlling vertebrate retinal regeneration. Science 370:eabb8598
    [Google Scholar]
  56. Hoshino A, Ratnapriya R, Brooks MJ, Chaitankar V, Wilken MS et al. 2017. Molecular anatomy of the developing human retina. Dev. Cell 43:763–79
    [Google Scholar]
  57. Hu Y, Wang X, Hu B, Mao Y, Chen Y et al. 2019. Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis. PLOS Biol 17:e3000365
    [Google Scholar]
  58. Huang L, Kebschull JM, Fürth D, Musall S, Kaufman MT et al. 2020. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 182:177–88
    [Google Scholar]
  59. Huberman AD, Manu M, Koch SM, Susman MW, Lutz AB et al. 2008. Architecture and activity-mediated refinement of axonal projections from a mosaic of genetically identified retinal ganglion cells. Neuron 59:425–38
    [Google Scholar]
  60. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F et al. 2014. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776–79
    [Google Scholar]
  61. Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H et al. 2016. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167:1883–96
    [Google Scholar]
  62. Jeon C-J, Strettoi E, Masland RH. 1998. The major cell populations of the mouse retina. J. Neurosci. 18:8936–46
    [Google Scholar]
  63. Kalish BT, Cheadle L, Hrvatin S, Nagy MA, Rivera S et al. 2018. Single-cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement. PNAS 115:E1051–60
    [Google Scholar]
  64. Kaplan N, Wang J, Wray B, Patel P, Yang W et al. 2019. Single-cell RNA transcriptome helps define the limbal/corneal epithelial stem/early transit amplifying cells and how autophagy affects this population. Investig. Ophthalmol. Vis. Sci 60:3570–83
    [Google Scholar]
  65. Kay JN, Chu MW, Sanes JR. 2012. MEGF10 and MEGF11 mediate homotypic interactions required for mosaic spacing of retinal neurons. Nature 483:465–69
    [Google Scholar]
  66. Kay JN, Voinescu PE, Chu MW, Sanes JR. 2011. Neurod6 expression defines new retinal amacrine cell subtypes and regulates their fate. Nat. Neurosci 14:965–72
    [Google Scholar]
  67. Kebschull JM, da Silva PG, Reid AP, Peikon ID, Albeanu DF, Zador AM. 2016. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91:975–87
    [Google Scholar]
  68. Kim EJ, Zhang Z, Huang L, Ito-Cole T, Jacobs MW et al. 2020. Extraction of distinct neuronal cell types from within a genetically continuous population. Neuron 107:274–82
    [Google Scholar]
  69. Kim I-J, Zhang Y, Yamagata M, Meister M, Sanes JR. 2008. Molecular identification of a retinal cell type that responds to upward motion. Nature 452:478–82
    [Google Scholar]
  70. Kim S, Lowe A, Dharmat R, Lee S, Owen LA et al. 2019. Generation, transcriptome profiling, and functional validation of cone-rich human retinal organoids. PNAS 116:10824–33
    [Google Scholar]
  71. Kiselev VY, Andrews TS, Hemberg M. 2019. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20:273–82
    [Google Scholar]
  72. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–201
    [Google Scholar]
  73. Kobak D, Berens P. 2019. The art of using t-SNE for single-cell transcriptomics. Nat. Commun 10:5416
    [Google Scholar]
  74. Kölsch Y, Hahn J, Sappington A, Stemmer M, Fernandes AM et al. 2021. Molecular classification of zebrafish retinal ganglion cells links genes to cell types to behavior. Neuron 109:645–62
    [Google Scholar]
  75. Krienen FM, Goldman M, Zhang Q, Del Rosario RC, Florio M et al. 2020. Innovations present in the primate interneuron repertoire. Nature 586:262–69
    [Google Scholar]
  76. Kuffler SW. 1953. Discharge patterns and functional organization of mammalian retina. J. Neurophysiol 16:37–68
    [Google Scholar]
  77. Laboissonniere LA, Martin GM, Goetz JJ, Bi R, Pope B et al. 2017. Single cell transcriptome profiling of developing chick retinal cells. J. Comp. Neurol. 525:2735–81
    [Google Scholar]
  78. Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC et al. 2020. Eleven grand challenges in single-cell data science. Genome Biol 21:31
    [Google Scholar]
  79. Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC et al. 2016. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352:1586–90
    [Google Scholar]
  80. Lake BB, Codeluppi S, Yung YC, Gao D, Chun J et al. 2017. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci. Rep 7:6031
    [Google Scholar]
  81. Lamb TD, Collin SP, Pugh EN. 2007. Evolution of the vertebrate eye: opsins, photoreceptors, retina and eye cup. Nat. Rev. Neurosci. 8:960–76
    [Google Scholar]
  82. Lareau CA, Ludwig LS, Muus C, Gohil SH, Zhao T et al. 2021. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. 39:451–61
    [Google Scholar]
  83. Lehmann GL, Hanke-Gogokhia C, Hu Y, Bareja R, Salfati Z et al. 2020. Single-cell profiling reveals an endothelium-mediated immunomodulatory pathway in the eye choroid. J. Exp. Med. 217:e20190730
    [Google Scholar]
  84. Lettvin J, Maturana H, McCulloch W, Pitts W. 1959. What the frog's eye tells the frog's brain. Proc. IRE 47:1940–51
    [Google Scholar]
  85. Liang Q, Dharmat R, Owen L, Shakoor A, Li Y et al. 2019. Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling. Nat. Commun 10:5743
    [Google Scholar]
  86. Liu J, Reggiani JD, Laboulaye MA, Pandey S, Chen B et al. 2018. Tbr1 instructs laminar patterning of retinal ganglion cell dendrites. Nat. Neurosci 21:659–70
    [Google Scholar]
  87. Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15:1053–58
    [Google Scholar]
  88. Lu Y, Shiau F, Yi W, Lu S, Wu Q et al. 2020. Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development. Dev. Cell 53:473–91
    [Google Scholar]
  89. Luecken MD, Theis FJ. 2019. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol 15:e8746
    [Google Scholar]
  90. Lukowski SW, Lo CY, Sharov AA, Nguyen Q, Fang L et al. 2019. A single-cell transcriptome atlas of the adult human retina. EMBO J 38:e100811
    [Google Scholar]
  91. Lyu J, Mu X. 2021. Genetic control of retinal ganglion cell genesis. Cell. Mol. Life Sci. 78:4417–33
    [Google Scholar]
  92. Lyu Y, Zauhar R, Dana N, Strang CE, Wang K et al. 2019. Integrative single-cell and bulk RNA-seq analysis in human retina identified cell type-specific composition and gene expression changes for age-related macular degeneration. bioRxiv 768143. https://doi.org/10.1101/768143
  93. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–14
    [Google Scholar]
  94. Mao CA, Li H, Zhang Z, Kiyama T, Panda S et al. 2014. T-box transcription regulator Tbr2 is essential for the formation and maintenance of Opn4/melanopsin-expressing intrinsically photosensitive retinal ganglion cells. J. Neurosci. 34:13083–95
    [Google Scholar]
  95. Mao X, An Q, Xi H, Yang X-J, Zhang X et al. 2019. Single-cell RNA sequencing of hESC-derived 3D retinal organoids reveals novel genes regulating RPC commitment in early human retinogenesis. Stem Cell Rep 13:747–60
    [Google Scholar]
  96. Marc RE, Jones BW, Watt CB, Anderson JR, Sigulinsky C, Lauritzen S 2013. Retinal connectomics: towards complete, accurate networks. Prog. Retin. Eye Res 37:141–62
    [Google Scholar]
  97. Marioni JC, Arendt D. 2017. How single-cell genomics is changing evolutionary and developmental biology. Annu. Rev. Cell Dev. Biol 33:537–53
    [Google Scholar]
  98. Martersteck EM, Hirokawa KE, Evarts M, Bernard A, Duan X et al. 2017. Diverse central projection patterns of retinal ganglion cells. Cell Rep 18:2058–72
    [Google Scholar]
  99. Masland RH. 2012. The neuronal organization of the retina. Neuron 76:266–80
    [Google Scholar]
  100. McInnes L, Healy J, Melville J. 2018. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]
  101. Mellough CB, Bauer R, Collin J, Dorgau B, Zerti D et al. 2019. An integrated transcriptional analysis of the developing human retina. Development 146:dev169474
    [Google Scholar]
  102. Menon M, Mohammadi S, Davila-Velderrain J, Goods BA, Cadwell TD et al. 2019. Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration. Nat. Commun 10:4902
    [Google Scholar]
  103. Mereu E, Lafzi A, Moutinho C, Ziegenhain C, McCarthy DJ et al. 2020. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat. Biotechnol. 38:747–55
    [Google Scholar]
  104. Moffitt JR, Bambah-Mukku D, Eichhorn SW, Vaughn E, Shekhar K et al. 2018. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362:eaau5324
    [Google Scholar]
  105. Ocko S, Lindsey J, Ganguli S, Deny S 2018. The emergence of multiple retinal cell types through efficient coding of natural movies. Advances in Neural Information Processing Systems 31 (NeurIPS 2018)9389–400 N.p.: NeurIPS
    [Google Scholar]
  106. O'Hara-Wright M, Gonzalez-Cordero A. 2020. Retinal organoids: a window into human retinal development. Development 147:dev.189746
    [Google Scholar]
  107. Orozco LD, Chen H-H, Cox C, Katschke KJ Jr., Arceo R et al. 2020. Integration of eQTL and single-cell atlas in the human eye identifies causal genes for age-related macular degeneration. Cell Rep 30:1246–59
    [Google Scholar]
  108. Patel G, Fury W, Yang H, Gomez-Caraballo M, Bai Y et al. 2020. Molecular taxonomy of human ocular outflow tissues defined by single-cell transcriptomics. PNAS 117:12856–67
    [Google Scholar]
  109. Pauly D, Agarwal D, Dana N, Schäfer N, Biber J et al. 2019. Cell-type-specific complement expression in the healthy and diseased retina. Cell Rep 29:2835–48
    [Google Scholar]
  110. Peng Y-R, Shekhar K, Yan W, Herrmann D, Sappington A et al. 2019. Molecular classification and comparative taxonomics of foveal and peripheral cells in primate retina. Cell 176:1222–37
    [Google Scholar]
  111. Peng Y-R, Tran NM, Krishnaswamy A, Kostadinov D, Martersteck EM, Sanes JR. 2017. Satb1 regulates contactin 5 to pattern dendrites of a mammalian retinal ganglion cell. Neuron 95:869–83
    [Google Scholar]
  112. Pollack S, Igo RP, Jensen RA, Christiansen M, Li X et al. 2019. Multiethnic genome-wide association study of diabetic retinopathy using liability threshold modeling of duration of diabetes and glycemic control. Diabetes 68:441–56
    [Google Scholar]
  113. Polyak S. 1957. The Vertebrate Visual System H Klüver Chicago: Univ. Chicago Press
  114. Raj B, Wagner DE, McKenna A, Pandey S, Klein AM et al. 2018. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol 36:442–50
    [Google Scholar]
  115. Ramani V, Deng X, Qiu R, Gunderson KL, Steemers FJ et al. 2017. Massively multiplex single-cell Hi-C. Nat. Methods 14:263–66
    [Google Scholar]
  116. Ramón y Cajal S. 1892. La retine des vertebres. Cellule 9:119–257
    [Google Scholar]
  117. Rheaume BA, Jereen A, Bolisetty M, Sajid MS, Yang Y et al. 2018. Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes. Nat. Commun 9:2759
    [Google Scholar]
  118. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E et al. 2019. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363:1463–67
    [Google Scholar]
  119. Roesch K, Jadhav AP, Trimarchi JM, Stadler MB, Roska B et al. 2008. The transcriptome of retinal Müller glial cells. J. Comp. Neurol. 509:225–38
    [Google Scholar]
  120. Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–82
    [Google Scholar]
  121. Saelens W, Cannoodt R, Todorov H, Saeys Y. 2019. A comparison of single-cell trajectory inference methods. Nat. Biotechnol 37:547–54
    [Google Scholar]
  122. Sajgo S, Ghinia MG, Brooks M, Kretschmer F, Chuang K et al. 2017. Molecular codes for cell type specification in Brn3 retinal ganglion cells. PNAS 114:E3974–83
    [Google Scholar]
  123. Sanes JR, Masland RH. 2015. The types of retinal ganglion cells: current status and implications for neuronal classification. Annu. Rev. Neurosci 38:221–46
    [Google Scholar]
  124. Sanes JR, Zipursky SL. 2010. Design principles of insect and vertebrate visual systems. Neuron 66:15–36
    [Google Scholar]
  125. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. 2015. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol 33:495–502
    [Google Scholar]
  126. Shekhar K, Lapan SW, Whitney IE, Tran NM, Macosko EZ et al. 2016. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166:1308–23
    [Google Scholar]
  127. Slyper M, Porter CB, Ashenberg O, Waldman J, Drokhlyansky E et al. 2020. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med 26:792–802
    [Google Scholar]
  128. Soneson C, Robinson MD. 2018. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15:255–61
    [Google Scholar]
  129. Sridhar A, Hoshino A, Finkbeiner CR, Chitsazan A, Dai L et al. 2020. Single-cell transcriptomic comparison of human fetal retina, hPSC-derived retinal organoids, and long-term retinal cultures. Cell Rep 30:1644–59
    [Google Scholar]
  130. Srivatsan SR, McFaline-Figueroa JL, Ramani V, Saunders L, Cao J et al. 2020. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367:45–51
    [Google Scholar]
  131. Stanley G, Gokce O, Malenka RC, Südhof TC, Quake SR. 2020. Continuous and discrete neuron types of the adult murine striatum. Neuron 105:688–99
    [Google Scholar]
  132. Stegle O, Teichmann SA, Marioni JC. 2015. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16:133–45
    [Google Scholar]
  133. Stein-O'Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q et al. 2019. Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species. Cell Syst 8:395–411
    [Google Scholar]
  134. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:865–68
    [Google Scholar]
  135. Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung BZ et al. 2018. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol 19:244
    [Google Scholar]
  136. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2019. Comprehensive integration of single-cell data. Cell 177:1888–902
    [Google Scholar]
  137. Stuart T, Satija R. 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
    [Google Scholar]
  138. Svensson V, Vento-Tormo R, Teichmann SA. 2018. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc 13:599–604
    [Google Scholar]
  139. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:377–82
    [Google Scholar]
  140. Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN et al. 2018. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563:72–78
    [Google Scholar]
  141. Tietjen I, Rihel JM, Cao Y, Koentges G, Zakhary L, Dulac C. 2003. Single-cell transcriptional analysis of neuronal progenitors. Neuron 38:161–75
    [Google Scholar]
  142. Tran NM, Shekhar K, Whitney IE, Jacobi A, Benhar I et al. 2019. Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes. Neuron 104:1039–55
    [Google Scholar]
  143. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol 32:381–86
    [Google Scholar]
  144. Trimarchi JM, Stadler MB, Cepko CL. 2008. Individual retinal progenitor cells display extensive heterogeneity of gene expression. PLOS ONE 3:e1588
    [Google Scholar]
  145. Trimarchi JM, Stadler MB, Roska B, Billings N, Sun B et al. 2007. Molecular heterogeneity of developing retinal ganglion and amacrine cells revealed through single cell gene expression profiling. J. Comp. Neurol. 502:1047–65
    [Google Scholar]
  146. Van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:112579–605
    [Google Scholar]
  147. Van Hove I, De Groef L, Boeckx B, Modave E, Hu T-T et al. 2020. Single-cell transcriptome analysis of the Akimba mouse retina reveals cell-type-specific insights into the pathobiology of diabetic retinopathy. Diabetologia 63:2235–48
    [Google Scholar]
  148. van Zyl T, Yan W, McAdams A, Peng Y-R, Shekhar K et al. 2020. Cell atlas of aqueous humor outflow pathways in eyes of humans and four model species provides insight into glaucoma pathogenesis. PNAS 117:10339–49
    [Google Scholar]
  149. Vickovic S, Eraslan G, Salmén F, Klughammer J, Stenbeck L et al. 2019. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16:987–90
    [Google Scholar]
  150. Vlasits AL, Euler T, Franke K. 2019. Function first: classifying cell types and circuits of the retina. Curr. Opin. Neurobiol 56:8–15
    [Google Scholar]
  151. Voigt AP, Binkley E, Flamme-Wiese MJ, Zeng S, DeLuca AP et al. 2020a. Single-cell RNA sequencing in human retinal degeneration reveals distinct glial cell populations. Cells 9:438
    [Google Scholar]
  152. Voigt AP, Mulfaul K, Mullin NK, Flamme-Wiese MJ, Giacalone JC et al. 2019a. Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration. PNAS 116:24100–7
    [Google Scholar]
  153. Voigt AP, Whitmore SS, Flamme-Wiese M, Riker M, Wiley L et al. 2019b. Molecular characterization of foveal versus peripheral human retina by single-cell RNA sequencing. Exp. Eye Res 184:234–42
    [Google Scholar]
  154. Voigt AP, Whitmore SS, Mulfaul K, Chirco KR, Giacalone JC et al. 2020b. Bulk and single-cell gene expression analyses reveal aging human choriocapillaris has pro-inflammatory phenotype. Microvasc. Res. 131:104031
    [Google Scholar]
  155. Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691
    [Google Scholar]
  156. Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet 10:57–63
    [Google Scholar]
  157. Wässle H, Puller C, Müller F, Haverkamp S. 2009. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina. J. Neurosci. 29:106–17
    [Google Scholar]
  158. Wattenberg M, Viégas F, Johnson I 2016. How to use t-SNE effectively. Distill 1:e2
    [Google Scholar]
  159. Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ. 2019. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177:1873–87
    [Google Scholar]
  160. Williams PR, Benowitz LI, Goldberg JL, He Z. 2020. Axon regeneration in the mammalian optic nerve. Annu. Rev. Vis. Sci. 6:195–213
    [Google Scholar]
  161. Wolf FA, Angerer P, Theis FJ. 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15
    [Google Scholar]
  162. Wu F, Bard JE, Kann J, Yergeau D, Sapkota D et al. 2021. Single cell transcriptomics reveals lineage trajectory of retinal ganglion cells in wild-type and Atoh7-null retinas. Nat. Commun. 12:1465
    [Google Scholar]
  163. Yamagata M, Yan W, Sanes JR 2021. Cell atlas of the chick retina: single cell profiling identifies 136 cell types. eLife 10:e63907
    [Google Scholar]
  164. Yan W, Laboulaye MA, Tran NM, Whitney IE, Benhar I, Sanes JR. 2020a. Mouse retinal cell atlas: molecular identification of over sixty amacrine cell types. J. Neurosci. 40:5177–95
    [Google Scholar]
  165. Yan W, Peng Y-R, van Zyl T, Regev A, Shekhar K et al. 2020b. Cell atlas of the human fovea and peripheral retina. Sci. Rep. 10: 9802.
    [Google Scholar]
  166. Yi W, Lu Y, Zhong S, Zhang M, Sun L et al. 2020. A single-cell transcriptomic atlas of the aging human and macaque retina. Nat. Sci. Rev. 8:nwaa179
    [Google Scholar]
  167. Young RW. 1985. Cell differentiation in the retina of the mouse. Anat. Rec. 212:199–205
    [Google Scholar]
  168. Yu C, Saban DR. 2019. Identification of a unique subretinal microglia type in retinal degeneration using single cell RNA-seq. Adv. Exp. Med. Biol. 1185:181–86
    [Google Scholar]
  169. Yuste R, Hawrylycz M, Aalling N, Aguilar-Valles A, Arendt D et al. 2020. A community-based transcriptomics classification and nomenclature of neocortical cell types. Nat. Neurosci 23:1456–68
    [Google Scholar]
  170. Zeng H, Sanes JR. 2017. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18:530–46
    [Google Scholar]
  171. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun 8:14049
    [Google Scholar]
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