Advances in single-cell RNA sequencing in the retina_Chinese Journal of Ocular Fundus Diseases

Authors：

Zhu He , Zhou Xiangtian ,  Zhao Fuxin

Eye Hospital of Wenzhou Medical University, State Key Laboratory of Optometry, Ophthalmology and Vision Science, Wenzhou 325027, China;

Corresponding author：

Zhao Fuxin, Email: striveswzmc@163.com

Keywords：

Single-cell RNA sequencing; Retina; Retinal development; Retinal degeneration; Review

DOI：

10.3760/cma.j.cn511434-20220117-00030

Video：

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Abstract Full text Figures/Tables Video References Cited by

Retina is composed of a heterogeneous population of cell types, each with a unique biological function. Even if the same type of cells, due to genetic heterogeneity will lead to cell function differences. In the past, traditional molecular biological methods cannot resolve variations in their functional roles that arise from these differences, and some cells are difficult to define due to the lack of specific molecular markers or the scarcity of numbers, which hindered the understanding and research of these cells. With the development of biotechnology, single-cell RNA sequencing can analyze and resolve differences in single-cell transcriptome expression profiles, characterize intracellular population heterogeneity, identify new and rare cell subtypes, and more definitely define the characteristics of each cell type. It clarifies the origin, function, and variations in cell phenotypes. Other attributes include pinpointing both disease-related characteristics of cell subtypes and specific differential gene expression patterns, to deepen our understanding of the causes and progression of diseases, as well as to aid clinical diagnosis and targeted therapy.

Citation： Zhu He, Zhou Xiangtian, Zhao Fuxin. Advances in single-cell RNA sequencing in the retina. Chinese Journal of Ocular Fundus Diseases, 2023, 39(1): 73-77. doi: 10.3760/cma.j.cn511434-20220117-00030 Copy

1.	Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell[J]. Nat Methods, 2009, 6(5): 377-382. DOI: 10.1038/nmeth.1315.
2.	Gomis S, Labib M, Coles BLK, et al. Single-cell tumbling enables high-resolution size profiling of retinal stem cells[J]. ACS Appl Mater Interfaces, 2018, 10(41): 34811-34816. DOI: 10.1021/acsami.8b10513.
3.	Phillips MJ, Jiang P, Howden S, et al. A novel approach to single cell RNA-sequence analysis facilitates in silico gene reporting of human pluripotent stem cell-derived retinal cell types[J]. Stem Cells, 2018, 36(3): 313-324. DOI: 10.1002/stem.2755.
4.	Herms J, Schön C. In vivo imaging of retinal neurodegeneration at the single cell level in humans[J]. Brain, 2017, 140(6): 1542-1543. DOI: 10.1093/brain/awx100.
5.	Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells[J]. Nat Biotechnol, 2012, 30(8): 777-782. DOI: 10.1038/nbt.2282.
6.	Picelli S, Björklund ÅK, Faridani OR, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells[J]. Nat Methods, 2013, 10(11): 1096-1098. DOI: 10.1038/nmeth.2639.
7.	Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets[J]. Cell, 2015, 161(5): 1202-1214. DOI: 10.1016/j.cell.2015.05.002.
8.	Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells[J]. Cell, 2015, 161(5): 1187-1201. DOI: 10.1016/j.cell.2015.04.044.
9.	Gierahn TM, Wadsworth MH, 2nd, Hughes TK, et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput[J]. Nat Methods, 2017, 14(4): 395-398. DOI: 10.1038/nmeth.4179.
10.	Chen G, Ning B, Shi T. Single-cell RNA-Seq technologies and related computational data analysis[J/OL]. Front Genet, 2019, 10: 317[2019-04-05]. https://pubmed.ncbi.nlm.nih.gov/31024627/. DOI: 10.3389/fgene.2019.00317.
11.	Han X, Wang R, Zhou Y, et al. Mapping the mouse cell atlas by microwell-seq[J/OL]. Cell, 2018, 173(5): 1307[2018-05-17]. https://pubmed.ncbi.nlm.nih.gov/29775597/. DOI: 10.1016/j.cell.2018.05.012.
12.	Han X, Zhou Z, Fei L, et al. Construction of a human cell landscape at single-cell level[J]. Nature, 2020, 581(7808): 303-309. DOI: 10.1038/s41586-020-2157-4.
13.	Jeon CJ, Strettoi E, Masland RH. The major cell populations of the mouse retina[J]. J Neurosci, 1998, 18(21): 8936-8946. DOI: 10.1523/JNEUROSCI.18-21-08936.1998.
14.	Breuninger T, Puller C, Haverkamp S, et al. Chromatic bipolar cell pathways in the mouse retina[J]. J Neurosci, 2011, 31(17): 6504-6517. DOI: 10.1523/JNEUROSCI.0616-11.2011.
15.	Hoon M, Okawa H, Della Santina L, et al. Functional architecture of the retina: development and disease[J]. Prog Retin Eye Res, 2014, 42: 44-84. DOI: 10.1016/j.preteyeres.2014.06.003.
16.	Shekhar K, Lapan SW, Whitney IE, et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics[J]. Cell, 2016, 166(5): 1308-1323. DOI: 10.1016/j.cell.2016.07.054.
17.	Yan W, Laboulaye MA, Tran NM, et al. Mouse retinal cell atlas: molecular identification of over sixty amacrine cell types[J]. J Neurosci, 2020, 40(27): 5177-5195. DOI: 10.1523/JNEUROSCI.0471-20.2020.
18.	Rheaume BA, Jereen A, Bolisetty M, et al. Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes[J/OL]. Nat Commun, 2018, 9(1): 2759[2018-07-17]. https://pubmed.ncbi.nlm.nih.gov/30018341/. DOI: 10.1038/s41467-018-05134-3.
19.	Tran NM, Shekhar K, Whitney IE, et al. Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes[J]. Neuron, 2019, 104(6): 1039-1055. DOI: 10.1016/j.neuron.2019.11.006.
20.	Bae JA, Mu S, Kim JS, et al. Digital museum of retinal ganglion cells with dense anatomy and physiology[J]. Cell, 2018, 173(5): 1293-1306. DOI: 10.1016/j.cell.2018.04.040.
21.	Wässle H, Puller C, Müller F, et al. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina[J]. J Neurosci, 2009, 29(1): 106-117. DOI: 10.1523/JNEUROSCI.4442-08.2009.
22.	Masland RH. The neuronal organization of the retina[J]. Neuron, 2012, 76(2): 266-280. DOI: 10.1016/j.neuron.2012.10.002.
23.	Lukowski SW, Lo CY, Sharov AA, et al. A single-cell transcriptome atlas of the adult human retina[J/OL]. EMBO J, 2019, 38(18): e100811[2019-09-16]. https://pubmed.ncbi.nlm.nih.gov/31436334/. DOI: 10.15252/embj.2018100811.
24.	Yi W, Lu Y, Zhong S, et al. A single-cell transcriptome atlas of the aging human and macaque retina[J/OL]. Natl Sci Rev, 2021, 8(4): nwaa179[2020-08-25]. https://pubmed.ncbi.nlm.nih.gov/34691611/. DOI: 10.1093/nsr/nwaa179.
25.	Daniszewski M, Senabouth A, Nguyen QH, et al. Single cell RNA sequencing of stem cell-derived retinal ganglion cells[J/OL]. Sci Data, 2018, 5: 180013[2018-02-13]. https://pubmed.ncbi.nlm.nih.gov/29437159/. DOI: 10.1038/sdata.2018.13.
26.	Lu Y, Shiau F, Yi W, et al. Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development[J]. Dev Cell, 2020, 53(4): 473-491. DOI: 10.1016/j.devcel.2020.04.009.
27.	Hu Y, Wang X, Hu B, et al. Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis[J/OL]. PLoS Biol, 2019, 17(7): e3000365[2019-07-03]. https://pubmed.ncbi.nlm.nih.gov/31269016/. DOI: 10.1371/journal.pbio.3000365.
28.	Wang S, Zheng Y, Li Q, et al. Deciphering primate retinal aging at single-cell resolution[J]. Protein Cell, 2021, 12(11): 889-898. DOI: 10.1007/s13238-020-00791-x.
29.	Clark BS, Stein-O'Brien GL, Shiau F, et al. Single-cell RNA-Seq analysis of retinal development identifies NFI factors as regulating mitotic exit and late-born cell specification[J]. Neuron, 2019, 102(6): 1111-1126. DOI: 10.1016/j.neuron.2019.04.010.
30.	Voigt AP, Binkley E, Flamme-Wiese MJ, et al. Single-cell RNA sequencing in human retinal degeneration reveals distinct glial cell populations[J]. Cells, 2020, 9(2): 438. DOI: 10.3390/cells9020438.
31.	Yu Y, Liu P. Usage of single-cell RNA sequencing to unveil immune lymphoid cell precursors[J]. Methods Mol Biol, 2019, 1884: 73-86. DOI: 10.1007/978-1-4939-8885-3_5.
32.	Ronning KE, Karlen SJ, Miller EB, et al. Molecular profiling of resident and infiltrating mononuclear phagocytes during rapid adult retinal degeneration using single-cell RNA sequencing[J/OL]. Sci Rep, 2019, 9(1): 4858[2019-03-19]. https://pubmed.ncbi.nlm.nih.gov/30890724/. DOI: 10.1038/s41598-019-41141-0.
33.	Yu C, Saban DR. Identification of a unique subretinal microglia type in retinal degeneration using single cell RNA-Seq[J]. Adv Exp Med Biol, 2019, 1185: 181-186. DOI: 10.1007/978-3-030-27378-1_30.
34.	Voigt AP, Mulfaul K, Mullin NK, et al. Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration[J]. Proc Natl Acad Sci USA, 2019, 116(48): 24100-24107. DOI: 10.1073/pnas.1914143116.
35.	Menon M, Mohammadi S, Davila-Velderrain J, et al. Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration[J/OL]. Nat Commun, 2019, 10(1): 4902[2019-10-25]. https://pubmed.ncbi.nlm.nih.gov/31653841/. DOI: 10.1038/s41467-019-12780-8.
36.	Luthert PJ, Kiel C. Combining gene-disease associations with single-cell gene expression data provides anatomy-specific subnetworks in age-related macular degeneration[J]. Netw Syst Med, 2020, 3(1): 105-121. DOI: 10.1089/nsm.2020.0005.
37.	Zhang Z, Xu Z, Yuan F, et al. Retinal organoid technology: where are we now?[J/OL]. Int J Mol Sci, 2021, 22(19): 10244[2021-09-23]. https://pubmed.ncbi.nlm.nih.gov/34638582/. DOI: 10.3390/ijms221910244.
38.	Wang S, Poli S, Liang X, et al. Longitudinal single-cell RNA-seq of hESCs-derived retinal organoids[J]. Sci China Life Sci, 2021, 64(10): 1661-1676. DOI: 10.1007/s11427-020-1836-7.
39.	You M, Rong R, Zeng Z, et al. Single-cell RNA sequencing: a new opportunity for retinal research[J/OL]. Wiley Interdiscip Rev RNA, 2021, 12(5): e1652[2021-03-22]. https://pubmed.ncbi.nlm.nih.gov/33754496/. DOI: 10.1002/wrna.1652.
40.	Ying P, Huang C, Wang Y, et al. Single-cell RNA sequencing of retina: new looks for gene marker and old diseases[J/OL]. Front Mol Biosci, 2021, 8: 699906[2021-07-30]. https://pubmed.ncbi.nlm.nih.gov/34395530/. DOI: 10.3389/fmolb.2021.699906.
41.	Sun L, Wang R, Hu G, et al. Single cell RNA sequencing (scRNA-Seq) deciphering pathological alterations in streptozotocin-induced diabetic retinas[J/OL]. Exp Eye Res, 2021, 210: 108718[2021-08-06]. https://pubmed.ncbi.nlm.nih.gov/34364890/. DOI: 10.1016/j.exer.2021.108718.
42.	Voigt AP, Mullin NK, Stone EM, et al. Single-cell RNA sequencing in vision research: insights into human retinal health and disease[J/OL]. Prog Retin Eye Res, 2021, 83: 100934[2020-12-28]. https://pubmed.ncbi.nlm.nih.gov/33383180/. DOI: 10.1016/j.preteyeres.2020.100934.
43.	Shiau F, Ruzycki PA, Clark BS. A single-cell guide to retinal development: cell fate decisions of multipotent retinal progenitors in scRNA-seq[J]. Dev Biol, 2021, 478: 41-58. DOI: 10.1016/j.ydbio.2021.06.005.
44.	Rossin EJ, Sobrin L, Kim LA. Single-cell RNA sequencing: an overview for the ophthalmologist[J]. Semin Ophthalmol, 2021, 36(4): 191-197. DOI: 10.1080/08820538.2021.1889615.
45.	Xiao Y, Hu X, Fan S, et al. Single-cell transcriptome profiling reveals the suppressive role of retinal neurons in microglia activation under diabetes mellitus[J/OL]. Front Cell Dev Biol, 2021, 9: 680947[2021-08-09]. https://pubmed.ncbi.nlm.nih.gov/34434927/. DOI: 10.3389/fcell.2021.680947.
46.	Niu T, Fang J, Shi X, et al. Pathogenesis study based on high-throughput single-cell sequencing analysis reveals novel transcriptional landscape and heterogeneity of retinal cells in type 2 diabetic mice[J]. Diabetes, 2021, 70(5): 1185-1197. DOI: 10.2337/db20-0839.
47.	Voigt AP, Whitmore SS, Lessing ND, et al. Spectacle: an interactive resource for ocular single-cell RNA sequencing data analysis[J/OL]. Exp Eye Res, 2020, 200: 108204[2020-09-07]. https://pubmed.ncbi.nlm.nih.gov/32910939/. DOI: 10.1016/j.exer.2020.108204.
48.	Swamy VS, Fufa TD, Hufnagel RB, et al. Building the mega single-cell transcriptome ocular meta-atlas[J/OL]. Gigascience, 2021, 10(10): giab061[2021-10-13]. https://pubmed.ncbi.nlm.nih.gov/34651173/. DOI: 10.1093/gigascience/giab061.

1. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell[J]. Nat Methods, 2009, 6(5): 377-382. DOI: 10.1038/nmeth.1315.
2. Gomis S, Labib M, Coles BLK, et al. Single-cell tumbling enables high-resolution size profiling of retinal stem cells[J]. ACS Appl Mater Interfaces, 2018, 10(41): 34811-34816. DOI: 10.1021/acsami.8b10513.
3. Phillips MJ, Jiang P, Howden S, et al. A novel approach to single cell RNA-sequence analysis facilitates in silico gene reporting of human pluripotent stem cell-derived retinal cell types[J]. Stem Cells, 2018, 36(3): 313-324. DOI: 10.1002/stem.2755.
4. Herms J, Schön C. In vivo imaging of retinal neurodegeneration at the single cell level in humans[J]. Brain, 2017, 140(6): 1542-1543. DOI: 10.1093/brain/awx100.
5. Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells[J]. Nat Biotechnol, 2012, 30(8): 777-782. DOI: 10.1038/nbt.2282.
6. Picelli S, Björklund ÅK, Faridani OR, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells[J]. Nat Methods, 2013, 10(11): 1096-1098. DOI: 10.1038/nmeth.2639.
7. Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets[J]. Cell, 2015, 161(5): 1202-1214. DOI: 10.1016/j.cell.2015.05.002.
8. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells[J]. Cell, 2015, 161(5): 1187-1201. DOI: 10.1016/j.cell.2015.04.044.
9. Gierahn TM, Wadsworth MH, 2nd, Hughes TK, et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput[J]. Nat Methods, 2017, 14(4): 395-398. DOI: 10.1038/nmeth.4179.
10. Chen G, Ning B, Shi T. Single-cell RNA-Seq technologies and related computational data analysis[J/OL]. Front Genet, 2019, 10: 317[2019-04-05]. https://pubmed.ncbi.nlm.nih.gov/31024627/. DOI: 10.3389/fgene.2019.00317.
11. Han X, Wang R, Zhou Y, et al. Mapping the mouse cell atlas by microwell-seq[J/OL]. Cell, 2018, 173(5): 1307[2018-05-17]. https://pubmed.ncbi.nlm.nih.gov/29775597/. DOI: 10.1016/j.cell.2018.05.012.
12. Han X, Zhou Z, Fei L, et al. Construction of a human cell landscape at single-cell level[J]. Nature, 2020, 581(7808): 303-309. DOI: 10.1038/s41586-020-2157-4.
13. Jeon CJ, Strettoi E, Masland RH. The major cell populations of the mouse retina[J]. J Neurosci, 1998, 18(21): 8936-8946. DOI: 10.1523/JNEUROSCI.18-21-08936.1998.
14. Breuninger T, Puller C, Haverkamp S, et al. Chromatic bipolar cell pathways in the mouse retina[J]. J Neurosci, 2011, 31(17): 6504-6517. DOI: 10.1523/JNEUROSCI.0616-11.2011.
15. Hoon M, Okawa H, Della Santina L, et al. Functional architecture of the retina: development and disease[J]. Prog Retin Eye Res, 2014, 42: 44-84. DOI: 10.1016/j.preteyeres.2014.06.003.
16. Shekhar K, Lapan SW, Whitney IE, et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics[J]. Cell, 2016, 166(5): 1308-1323. DOI: 10.1016/j.cell.2016.07.054.
17. Yan W, Laboulaye MA, Tran NM, et al. Mouse retinal cell atlas: molecular identification of over sixty amacrine cell types[J]. J Neurosci, 2020, 40(27): 5177-5195. DOI: 10.1523/JNEUROSCI.0471-20.2020.
18. Rheaume BA, Jereen A, Bolisetty M, et al. Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes[J/OL]. Nat Commun, 2018, 9(1): 2759[2018-07-17]. https://pubmed.ncbi.nlm.nih.gov/30018341/. DOI: 10.1038/s41467-018-05134-3.
19. Tran NM, Shekhar K, Whitney IE, et al. Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes[J]. Neuron, 2019, 104(6): 1039-1055. DOI: 10.1016/j.neuron.2019.11.006.
20. Bae JA, Mu S, Kim JS, et al. Digital museum of retinal ganglion cells with dense anatomy and physiology[J]. Cell, 2018, 173(5): 1293-1306. DOI: 10.1016/j.cell.2018.04.040.
21. Wässle H, Puller C, Müller F, et al. Cone contacts, mosaics, and territories of bipolar cells in the mouse retina[J]. J Neurosci, 2009, 29(1): 106-117. DOI: 10.1523/JNEUROSCI.4442-08.2009.
22. Masland RH. The neuronal organization of the retina[J]. Neuron, 2012, 76(2): 266-280. DOI: 10.1016/j.neuron.2012.10.002.
23. Lukowski SW, Lo CY, Sharov AA, et al. A single-cell transcriptome atlas of the adult human retina[J/OL]. EMBO J, 2019, 38(18): e100811[2019-09-16]. https://pubmed.ncbi.nlm.nih.gov/31436334/. DOI: 10.15252/embj.2018100811.
24. Yi W, Lu Y, Zhong S, et al. A single-cell transcriptome atlas of the aging human and macaque retina[J/OL]. Natl Sci Rev, 2021, 8(4): nwaa179[2020-08-25]. https://pubmed.ncbi.nlm.nih.gov/34691611/. DOI: 10.1093/nsr/nwaa179.
25. Daniszewski M, Senabouth A, Nguyen QH, et al. Single cell RNA sequencing of stem cell-derived retinal ganglion cells[J/OL]. Sci Data, 2018, 5: 180013[2018-02-13]. https://pubmed.ncbi.nlm.nih.gov/29437159/. DOI: 10.1038/sdata.2018.13.
26. Lu Y, Shiau F, Yi W, et al. Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development[J]. Dev Cell, 2020, 53(4): 473-491. DOI: 10.1016/j.devcel.2020.04.009.
27. Hu Y, Wang X, Hu B, et al. Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis[J/OL]. PLoS Biol, 2019, 17(7): e3000365[2019-07-03]. https://pubmed.ncbi.nlm.nih.gov/31269016/. DOI: 10.1371/journal.pbio.3000365.
28. Wang S, Zheng Y, Li Q, et al. Deciphering primate retinal aging at single-cell resolution[J]. Protein Cell, 2021, 12(11): 889-898. DOI: 10.1007/s13238-020-00791-x.
29. Clark BS, Stein-O'Brien GL, Shiau F, et al. Single-cell RNA-Seq analysis of retinal development identifies NFI factors as regulating mitotic exit and late-born cell specification[J]. Neuron, 2019, 102(6): 1111-1126. DOI: 10.1016/j.neuron.2019.04.010.
30. Voigt AP, Binkley E, Flamme-Wiese MJ, et al. Single-cell RNA sequencing in human retinal degeneration reveals distinct glial cell populations[J]. Cells, 2020, 9(2): 438. DOI: 10.3390/cells9020438.
31. Yu Y, Liu P. Usage of single-cell RNA sequencing to unveil immune lymphoid cell precursors[J]. Methods Mol Biol, 2019, 1884: 73-86. DOI: 10.1007/978-1-4939-8885-3_5.
32. Ronning KE, Karlen SJ, Miller EB, et al. Molecular profiling of resident and infiltrating mononuclear phagocytes during rapid adult retinal degeneration using single-cell RNA sequencing[J/OL]. Sci Rep, 2019, 9(1): 4858[2019-03-19]. https://pubmed.ncbi.nlm.nih.gov/30890724/. DOI: 10.1038/s41598-019-41141-0.
33. Yu C, Saban DR. Identification of a unique subretinal microglia type in retinal degeneration using single cell RNA-Seq[J]. Adv Exp Med Biol, 2019, 1185: 181-186. DOI: 10.1007/978-3-030-27378-1_30.
34. Voigt AP, Mulfaul K, Mullin NK, et al. Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration[J]. Proc Natl Acad Sci USA, 2019, 116(48): 24100-24107. DOI: 10.1073/pnas.1914143116.
35. Menon M, Mohammadi S, Davila-Velderrain J, et al. Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration[J/OL]. Nat Commun, 2019, 10(1): 4902[2019-10-25]. https://pubmed.ncbi.nlm.nih.gov/31653841/. DOI: 10.1038/s41467-019-12780-8.
36. Luthert PJ, Kiel C. Combining gene-disease associations with single-cell gene expression data provides anatomy-specific subnetworks in age-related macular degeneration[J]. Netw Syst Med, 2020, 3(1): 105-121. DOI: 10.1089/nsm.2020.0005.
37. Zhang Z, Xu Z, Yuan F, et al. Retinal organoid technology: where are we now?[J/OL]. Int J Mol Sci, 2021, 22(19): 10244[2021-09-23]. https://pubmed.ncbi.nlm.nih.gov/34638582/. DOI: 10.3390/ijms221910244.
38. Wang S, Poli S, Liang X, et al. Longitudinal single-cell RNA-seq of hESCs-derived retinal organoids[J]. Sci China Life Sci, 2021, 64(10): 1661-1676. DOI: 10.1007/s11427-020-1836-7.
39. You M, Rong R, Zeng Z, et al. Single-cell RNA sequencing: a new opportunity for retinal research[J/OL]. Wiley Interdiscip Rev RNA, 2021, 12(5): e1652[2021-03-22]. https://pubmed.ncbi.nlm.nih.gov/33754496/. DOI: 10.1002/wrna.1652.
40. Ying P, Huang C, Wang Y, et al. Single-cell RNA sequencing of retina: new looks for gene marker and old diseases[J/OL]. Front Mol Biosci, 2021, 8: 699906[2021-07-30]. https://pubmed.ncbi.nlm.nih.gov/34395530/. DOI: 10.3389/fmolb.2021.699906.
41. Sun L, Wang R, Hu G, et al. Single cell RNA sequencing (scRNA-Seq) deciphering pathological alterations in streptozotocin-induced diabetic retinas[J/OL]. Exp Eye Res, 2021, 210: 108718[2021-08-06]. https://pubmed.ncbi.nlm.nih.gov/34364890/. DOI: 10.1016/j.exer.2021.108718.
42. Voigt AP, Mullin NK, Stone EM, et al. Single-cell RNA sequencing in vision research: insights into human retinal health and disease[J/OL]. Prog Retin Eye Res, 2021, 83: 100934[2020-12-28]. https://pubmed.ncbi.nlm.nih.gov/33383180/. DOI: 10.1016/j.preteyeres.2020.100934.
43. Shiau F, Ruzycki PA, Clark BS. A single-cell guide to retinal development: cell fate decisions of multipotent retinal progenitors in scRNA-seq[J]. Dev Biol, 2021, 478: 41-58. DOI: 10.1016/j.ydbio.2021.06.005.
44. Rossin EJ, Sobrin L, Kim LA. Single-cell RNA sequencing: an overview for the ophthalmologist[J]. Semin Ophthalmol, 2021, 36(4): 191-197. DOI: 10.1080/08820538.2021.1889615.
45. Xiao Y, Hu X, Fan S, et al. Single-cell transcriptome profiling reveals the suppressive role of retinal neurons in microglia activation under diabetes mellitus[J/OL]. Front Cell Dev Biol, 2021, 9: 680947[2021-08-09]. https://pubmed.ncbi.nlm.nih.gov/34434927/. DOI: 10.3389/fcell.2021.680947.
46. Niu T, Fang J, Shi X, et al. Pathogenesis study based on high-throughput single-cell sequencing analysis reveals novel transcriptional landscape and heterogeneity of retinal cells in type 2 diabetic mice[J]. Diabetes, 2021, 70(5): 1185-1197. DOI: 10.2337/db20-0839.
47. Voigt AP, Whitmore SS, Lessing ND, et al. Spectacle: an interactive resource for ocular single-cell RNA sequencing data analysis[J/OL]. Exp Eye Res, 2020, 200: 108204[2020-09-07]. https://pubmed.ncbi.nlm.nih.gov/32910939/. DOI: 10.1016/j.exer.2020.108204.
48. Swamy VS, Fufa TD, Hufnagel RB, et al. Building the mega single-cell transcriptome ocular meta-atlas[J/OL]. Gigascience, 2021, 10(10): giab061[2021-10-13]. https://pubmed.ncbi.nlm.nih.gov/34651173/. DOI: 10.1093/gigascience/giab061.

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