Cell Atlas of The Human Fovea and Peripheral Retina

Abstract

Most irreversible blindness results from retinal disease. To advance our understanding of the etiology of blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of ~85,000 cells from the fovea and peripheral retina of seven adult human donors. Utilizing computational methods, we identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, retinal ganglion and non-neuronal cells. Nearly all types are shared between the two retinal regions, but there are notable differences in gene expression and proportions between foveal and peripheral cohorts of shared types. We then used the human retinal atlas to map expression of 636 genes implicated as causes of or risk factors for blinding diseases. Many are expressed in striking cell class-, type-, or region-specific patterns. Finally, we compared gene expression signatures of cell types between human and the cynomolgus macaque monkey, Macaca fascicularis . We show that over 90% of human types correspond transcriptomically to those previously identified in macaque, and that expression of disease-related genes is largely conserved between the two species. These results validate the use of the macaque for modeling blinding disease, and provide a foundation for investigating molecular mechanisms underlying visual processing.

References 71

1. 1.

   Khandhadia S, Cherry J, Lotery AJ. Age-related macular degeneration. Adv. Exp. Med. Biol. 2012;724:15-36.

   Not in our library10.1007/978-1-4614-0653-2_2

2. 2.

   Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: a review. JAMA. 2014;311:1901-1911.

   Not in our library10.1001/jama.2014.3192

3. 3.

   Duh, E. J., Sun, J. K. & Stitt, A. W. Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. JCI Insight2, 10.1172/jci.insight.93751 (2017).

   Not in our library10.1172/jci.insight.93751

4. 4.

   Grassi MA, et al. Genome-wide meta-analysis for severe diabetic retinopathy. Hum. Mol. Genet. 2011;20:2472-2481.

   Not in our library10.1093/hmg/ddr121

5. 5.

   Fritsche LG, et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 2016;48:134-143.

   Not in our library10.1038/ng.3448

6. 6.
   A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci · 2018

   No notice
7. 7.

   Ferris FL, Patz A. Macular edema. A complication of diabetic retinopathy. Surv. Ophthalmol. 1984;28(Suppl):452-461.

   Not in our library10.1016/0039-6257(84)90227-3

8. 8.

   O’Brien, K. M. B. in Visual Transduction and Non-Visual Light Perception (eds Joyce Tombran-Tink & Colin J. Barnstable) 17-33 (Humana Press (2008).

   Not in our library

9. 9.

   Crawford ML, Harwerth RS, Smith EL, Shen F, Carter-Dawson L. Glaucoma in primates: cytochrome oxidase reactivity in parvo- and magnocellular pathways. Invest. Ophthalmol. Vis. Sci. 2000;41:1791-1802.

   Not in our library

10. 10.

    Umeda S, et al. Molecular composition of drusen and possible involvement of anti-retinal autoimmunity in two different forms of macular degeneration in cynomolgus monkey (Macaca fascicularis). FASEB J. 2005;19:1683-1685.

    Not in our library10.1096/fj.04-3525fje

11. 11.

    Picaud, S. et al. The primate model for understanding and restoring vision. Proc Natl Acad Sci USA, 10.1073/pnas.1902292116 (2019).

    Not in our library10.1073/pnas.1902292116

12. 12.

    Peng YR, et al. Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina. Cell. 2019;176:1222-1237 e1222.

    Not in our library10.1016/j.cell.2019.01.004

13. 13.
    Massively parallel digital transcriptional profiling of single cells · 2017

    No notice
14. 14.

    Chen, T. & Guestrin, C. in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785-794 (ACM).

    Not in our library

15. 15.

    Chan TL, Grunert U. Horizontal cell connections with short wavelength-sensitive cones in the retina: a comparison between New World and Old World primates. J. Comp. Neurol. 1998;393:196-209.

    Not in our library10.1002/(sici)1096-9861(19980406)393:2<196::aid-cne5>3.0.co;2-y

16. 16.

    Ahnelt P, Kolb H. Horizontal cells and cone photoreceptors in human retina: a Golgi-electron microscopic study of spectral connectivity. J. Comp. Neurol. 1994;343:406-427.

    Not in our library10.1002/cne.903430306

17. 17.

    Kolb H, et al. Are there three types of horizontal cell in the human retina? J. Comp. Neurol. 1994;343:370-386.

    Not in our library10.1002/cne.903430304

18. 18.

    Greferath U, Grunert U, Wassle H. Rod bipolar cells in the mammalian retina show protein kinase C-like immunoreactivity. J. Comp. Neurol. 1990;301:433-442.

    Not in our library10.1002/cne.903010308

19. 19.

    Boycott BB, Wassle H. Morphological Classification of Bipolar Cells of the Primate Retina. Eur. J. Neurosci. 1991;3:1069-1088.

    Not in our library10.1111/j.1460-9568.1991.tb00043.x

20. 20.

    Crooks J, Kolb H. Localization of GABA, glycine, glutamate and tyrosine hydroxylase in the human retina. J. Comp. Neurol. 1992;315:287-302.

    Not in our library10.1002/cne.903150305

21. 21.

    Kay JN, Voinescu PE, Chu MW, Sanes JR. Neurod6 expression defines new retinal amacrine cell subtypes and regulates their fate. Nat. Neurosci. 2011;14:965.

    Not in our library10.1038/nn.2859

22. 22.

    Yan., W., Laboulaye, M.A., Tran, N.M., Whitney, I.E., Benhar, I., & Sanes, J.R. Mouse retinal cell atlas: molecular identification of over sixty amacrine cell types. J. Neurosci. (in press).

    Not in our library10.1523/jneurosci.0471-20.2020

23. 23.

    Dacey, D. in The Cognitive Neurosciences Iii (ed Michael S. Gazzaniga) 281 (MIT Press (2004).

    Not in our library

24. 24.
    Two Pairs of ON and OFF Retinal Ganglion Cells Are Defined by Intersectional Patterns of Transcription Factor Expression · 2016

    Not checked
25. 25.

    Mure LS, Vinberg F, Hanneken A, Panda S. Functional diversity of human intrinsically photosensitive retinal ganglion cells. Science. 2019;366:1251-1255.

    Not in our library10.1126/science.aaz0898

26. 26.

    Nasir-Ahmad S, Lee SCS, Martin PR, Grunert U. Melanopsin-expressing ganglion cells in human retina: Morphology, distribution, and synaptic connections. J. Comp. Neurol. 2019;527:312-327.

    Not in our library10.1002/cne.24176

27. 27.

    Do MTH. Melanopsin and the Intrinsically Photosensitive Retinal Ganglion Cells: Biophysics to Behavior. Neuron. 2019;104:205-226.

    Not in our library10.1016/j.neuron.2019.07.016

28. 28.

    Curcio CA, et al. Distribution and morphology of human cone photoreceptors stained with anti-blue opsin. J. Comp. Neurol. 1991;312:610-624.

    Not in our library10.1002/cne.903120411

29. 29.

    Calkins DJ. Seeing with S cones. Prog. Retin. Eye Res. 2001;20:255-287.

    Not in our library10.1016/s1350-9462(00)00026-4

30. 30.

    Ikui H, Uga S, Kohno T. Electron Microscope Study on Astrocytes in the Human Retina Using Ruthenium Red. Ophthalmic Res. 1976;8:100-110.

    Not in our library10.1159/000264803

31. 31.

    Distler C, Weigel H, Hoffmann KP. Glia cells of the monkey retina. I. Astrocytes. J. Comp. Neurol. 1993;333:134-147.

    Not in our library10.1002/cne.903330111

32. 32.

    Lee SCS, Martin PR, Grunert U. Topography of Neurons in the Rod Pathway of Human Retina. Invest. Ophthalmol. Vis. Sci. 2019;60:2848-2859.

    Not in our library10.1167/iovs.19-27217

33. 33.

    Berger W, Kloeckener-Gruissem B, Neidhardt J. The molecular basis of human retinal and vitreoretinal diseases. Prog. Retin. Eye Res. 2010;29:335-375.

    Not in our library10.1016/j.preteyeres.2010.03.004

34. 34.

    Michaelides M, Hardcastle AJ, Hunt DM, Moore AT. Progressive cone and cone-rod dystrophies: phenotypes and underlying molecular genetic basis. Surv. Ophthalmol. 2006;51:232-258.

    Not in our library10.1016/j.survophthal.2006.02.007

35. 35.

    den Hollander AI, Roepman R, Koenekoop RK, Cremers FP. Leber congenital amaurosis: genes, proteins and disease mechanisms. Prog. Retin. Eye Res. 2008;27:391-419.

    Not in our library10.1016/j.preteyeres.2008.05.003

36. 36.

    Bech-Hansen NT, et al. Mutations in NYX, encoding the leucine-rich proteoglycan nyctalopin, cause X-linked complete congenital stationary night blindness. Nat. Genet. 2000;26:319-323.

    Not in our library10.1038/81619

37. 37.

    Hasan, N. et al. LRIT3 is required for Nyctalopin expression and normal ON and OFF pathway signaling in the retina. eneuro, ENEURO.0002-0020.2020, 10.1523/eneuro.0002-20.2020 (2020).

    Not in our library10.1523/eneuro.0002-20.2020

38. 38.

    Michaelides M, Hunt DM, Moore AT. The genetics of inherited macular dystrophies. J. Med. Genet. 2003;40:641-650.

    Not in our library10.1136/jmg.40.9.641

39. 39.

    Yu-Wai-Man P, et al. OPA1 mutations cause cytochrome c oxidase deficiency due to loss of wild-type mtDNA molecules. Hum. Mol. Genet. 2010;19:3043-3052.

    Not in our library10.1093/hmg/ddq209

40. 40.

    Delettre C, Lenaers G, Pelloquin L, Belenguer P, Hamel CP. OPA1 (Kjer type) dominant optic atrophy: a novel mitochondrial disease. Mol. Genet. Metab. 2002;75:97-107.

    Not in our library10.1006/mgme.2001.3278

41. 41.

    Ye X, Smallwood P, Nathans J. Expression of the Norrie disease gene (Ndp) in developing and adult mouse eye, ear, and brain. Gene Expr. Patterns. 2011;11:151-155.

    Not in our library10.1016/j.gep.2010.10.007

42. 42.

    Miller SJ, et al. Molecularly defined cortical astroglia subpopulation modulates neurons via secretion of Norrin. Nat. Neurosci. 2019;22:741-752.

    Not in our library10.1038/s41593-019-0366-7

43. 43.

    Mitchell P, Liew G, Gopinath B, Wong TY. Age-related macular degeneration. Lancet. 2018;392:1147-1159.

    Not in our library10.1016/s0140-6736(18)31550-2

44. 44.

    Solomon SD, et al. Diabetic Retinopathy: A Position Statement by the American Diabetes Association. Diabetes Care. 2017;40:412-418.

    Not in our library10.2337/dc16-2641

45. 45.

    Huang YC, et al. Genome-wide association study of diabetic retinopathy in a Taiwanese population. Ophthalmology. 2011;118:642-648.

    Not in our library10.1016/j.ophtha.2010.07.020

46. 46.

    Pollack S, et al. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes. 2019;68:441-456.

    Not in our library10.2337/db18-0567

47. 47.

    Clemons TE, et al. Medical characteristics of patients with macular telangiectasia type 2 (MacTel Type 2) MacTel project report no. 3. Ophthalmic Epidemiol. 2013;20:109-113.

    Not in our library10.3109/09286586.2013.766757

48. 48.

    Wu L, Evans T, Arevalo JF. Idiopathic macular telangiectasia type 2 (idiopathic juxtafoveolar retinal telangiectasis type 2A, Mac Tel 2). Surv. Ophthalmol. 2013;58:536-559.

    Not in our library10.1016/j.survophthal.2012.11.007

49. 49.

    Scerri TS, et al. Genome-wide analyses identify common variants associated with macular telangiectasia type 2. Nat. Genet. 2017;49:559-567.

    Not in our library10.1038/ng.3799

50. 50.

    Bonelli, R. et al. Genetic Disruption of Serine Biosynthesis is a Key Driver of Macular Telangiectasia Type 2 Etiology and Progression. bioRxiv, 2020.2002.2004.934356, 10.1101/2020.02.04.934356 (2020).

    Not in our library10.1186/s13073-021-00848-4

51. 51.

    Friedman DS, Wilson MR, Liebmann JM, Fechtner RD, Weinreb RN. An evidence-based assessment of risk factors for the progression of ocular hypertension and glaucoma. Am. J. Ophthalmol. 2004;138:S19-31.

    Not in our library10.1016/j.ajo.2004.04.058

52. 52.

    Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 2006;90:262-267.

    Not in our library10.1136/bjo.2005.081224

53. 53.

    van Zyl, T. et al. Cell Atlas of Aqueous Humor Outflow Pathways in Eyes of Humans and Four Model Species Provides Insights into Glaucoma Pathogenesis. Proc Natl Acad Sci 117:10339-10349(2020).

    Not in our library10.1073/pnas.2001250117

54. 54.

    Graham PS, et al. Genome-wide association studies for diabetic macular edema and proliferative diabetic retinopathy. BMC Med. Genet. 2018;19:71.

    Not in our library10.1186/s12881-018-0587-8

55. 55.

    Lukowski SW, et al. A single-cell transcriptome atlas of the adult human retina. EMBO J. 2019;38:e100811.

    Not in our library10.15252/embj.2018100811

56. 56.
    Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis · 2019

    No notice
57. 57.

    Liang Q, et al. Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling. Nat. Commun. 2019;10:5743.

    Not in our library10.1038/s41467-019-12917-9

58. 58.
    Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration · 2019

    No notice
59. 59.

    Voigt AP, et al. Molecular characterization of foveal versus peripheral human retina by single-cell RNA sequencing. Exp. Eye Res. 2019;184:234-242.

    Not in our library10.1016/j.exer.2019.05.001

60. 60.

    Orozco LD, et al. Integration of eQTL and a Single-Cell Atlas in the Human Eye Identifies Causal Genes for Age-Related Macular Degeneration. Cell Rep. 2020;30:1246-1259 e1246.

    Not in our library10.1016/j.celrep.2019.12.082

61. 61.

    Sridhar A, et al. Single-Cell Transcriptomic Comparison of Human Fetal Retina, hPSC-Derived Retinal Organoids, and Long-Term Retinal Cultures. Cell Rep. 2020;30:1644-1659 e1644.

    Not in our library10.1016/j.celrep.2020.01.007

62. 62.

    Provis JM, Dubis AM, Maddess T, Carroll J. Adaptation of the central retina for high acuity vision: cones, the fovea and the avascular zone. Prog. retinal eye Res. 2013;35:63-81.

    Not in our library10.1016/j.preteyeres.2013.01.005

63. 63.

    Bringmann A, et al. The primate fovea: Structure, function and development. Prog. Retin. Eye Res. 2018;66:49-84.

    Not in our library10.1016/j.preteyeres.2018.03.006

64. 64.

    Della Santina L, Ou Y. Who’s lost first? Susceptibility of retinal ganglion cell types in experimental glaucoma. Exp. Eye Res. 2017;158:43-50.

    Not in our library10.1016/j.exer.2016.06.006

65. 65.

    Shekhar K, et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell. 2016;166:1308-1323. e1330.

    Not in our library10.1016/j.cell.2016.07.054

66. 66.

    Pandey S, Shekhar K, Regev A, Schier AF. Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-seq. Curr. Biol. 2018;28:1052-1065. e1057.

    Not in our library10.1016/j.cub.2018.02.040

67. 67.
    Population Structure and Eigenanalysis · 2006

    No notice
68. 68.

    Welch JD, et al. Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity. Cell. 2019;177:1873-1887 e1817.

    Not in our library10.1016/j.cell.2019.05.006

69. 69.
    MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data · 2015

    No notice
70. 70.

    Peng YR, et al. Satb1 Regulates Contactin 5 to Pattern Dendrites of a Mammalian Retinal Ganglion Cell. Neuron. 2017;95:869-883 e866.

    Not in our library10.1016/j.neuron.2017.07.019

71. 71.
    The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 · 2018

    No notice

Cited by papers we hold 4

• Solving neurodegeneration: common mechanisms and strategies for new treatments · 2022
  No notice
• In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy · 2021
  Not checked
• Retinal Ganglion Cells—Diversity of Cell Types and Clinical Relevance · 2021
  No notice
• A cell atlas of the chick retina based on single-cell transcriptomics · 2021
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.