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Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution
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Abstract
Human organoids recapitulating the cell-type diversity and function of their target organ are valuable for basic and translational research. We developed light-sensitive human retinal organoids with multiple nuclear and synaptic layers and functional synapses. We sequenced the RNA of 285,441 single cells from these organoids at seven developmental time points and from the periphery, fovea, pigment epithelium and choroid of light-responsive adult human retinas, and performed histochemistry. Cell types in organoids matured in vitro to a stable "developed" state at a rate similar to human retina development in vivo. Transcriptomes of organoid cell types converged toward the transcriptomes of adult peripheral retinal cell types. Expression of disease-associated genes was cell-type-specific in adult retina, and cell-type specificity was retained in organoids. We implicate unexpected cell types in diseases such as macular degeneration. This resource identifies cellular targets for studying disease mechanisms in organoids and for targeted repair in human retinas.
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“ABCA4 is also present at extremely low levels in the retinal pigment epithelium (RPE) where it has been proposed to enhance endolysosomal function (2, 3).”
From Retinal gene therapy for Stargardt disease with dual AAV intein vectors is both safe and effective in large animal models (2025) · cites this paper as [2]
Experimental — accuracy not yet measuredIn the citing paper
“The physiological similarities between human and macaque retina (Cowan et al., 2019; Kling et al., 2020; Soto et al., 2020; Rodieck, 1998), including their responses to electrical stimulation (Madugula et al., 2022), suggest that the benefits of the present stimulation approach could translate from the ex vivo lab prototype with healthy macaque retina to an in vivo implant in the degenerated human”
From Precise control of neural activity using dynamically optimized electrical stimulation (2024) · cites this paper as Cowan et al., 2019
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Modeling Development and Disease with Organoids
Clevers, H. (2016) · Cell · doi:10.1016/j.cell.2016.05.082 · Cited by 3,196
Organogenesis in a dish: Modeling development and disease using organoid technologies
Lancaster, M. A., & Knoblich, J. A. (2014) · Science · doi:10.1126/science.1247125 · Cited by 2,931
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
Nickla, D., & Wallman, J. (2009) · Progress in retinal and eye research · doi:10.1016/j.preteyeres.2009.12.002 · Cited by 1,921
Generation of cerebral organoids from human pluripotent stem cells
Lancaster, M. A., & Knoblich, J. A. (2014) · Nature Protocols · doi:10.1038/nprot.2014.158 · Cited by 1,823
Chan, K. Y., Jang, M. J., Yoo, B. B., Greenbaum, A., Ravi, N., Wu, W.-L., Sánchez-Guardado, L., Lois, C., Mazmanian, S. K., Deverman, B. E., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4593 · Cited by 1,578
Fritsche, L. G., Igl, W., Bailey, J. N. C., Grassmann, F., Sengupta, S., Bragg-Gresham, J. L., Burdon, K. P., Hebbring, S. J., Wen, C., Gorski, M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3448 · Cited by 1,544
Self-citationSelf-formation of optic cups and storable stratified neural retina from human ESCs
Nakano, T., Ando, S., Takata, N., Kawada, M., Muguruma, K., Sekiguchi, K., Saito, K., Yonemura, S., Eiraku, M., & Sasai, Y. (2012) · Cell stem cell · doi:10.1016/j.stem.2012.05.009 · Cited by 1,447
The Neuronal Organization of the Retina
Masland, R. H. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.002 · Cited by 1,245
High-performance calcium sensors for imaging activity in neuronal populations and microcompartments
Dana, H., Sun, Y., Mohar, B., Hulse, B. K., Kerlin, A., Hasseman, J. P., Tsegaye, G., Tsang, A., Wong, A. M., Patel, R. H., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0435-6 · Cited by 1,202
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176
Multivariate Density Estimation: Theory, Practice, and Visualization
Scott, D. W. (2015) · Wiley Series in Probability and Statistics · doi:10.1002/9781118575574 · Cited by 1,165
The Retina: An Approachable Part of the Brain
(1989) · Archives of Ophthalmology · doi:10.1001/archopht.1989.01070020357018 · Cited by 1,124
Individual brain organoids reproducibly form cell diversity of the human cerebral cortex
Velasco, S., Kedaigle, A. J., Simmons, S. K., Nash, A., Rocha, M., Quadrato, G., Paulsen, B., Nguyen, L., Adiconis, X., Regev, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1289-x · Cited by 1,094
Retinal ischemia: mechanisms of damage and potential therapeutic strategies
Osborne, N. N., Casson, R. J., Wood, J. P. M., Chidlow, G., Graham, M., & Melena, J. (2004) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2003.12.001 · Cited by 1,072
The functional diversity of retinal ganglion cells in the mouse
Baden, T., Berens, P., Franke, K., Román Rosón, M., Bethge, M., & Euler, T. (2016) · Nature · doi:10.1038/nature16468 · Cited by 999
CaImAn an open source tool for scalable calcium imaging data analysis
Giovannucci, A., Friedrich, J. H., Gunn, P., Kalfon, J., Brown, B., Koay, S. A., Taxidis, J., Najafi, F., Gauthier, J. L., Zhou, P., et al. (2019) · eLife · doi:10.7554/elife.38173 · Cited by 989
Generation of three-dimensional retinal tissue with functional photoreceptors from human iPSCs
Zhong, X., Gutierrez, C., Xue, T., Hampton, C., Vergara, M. N., Cao, L.-H., Peters, A., Park, T. S., Zambidis, E. T., Meyer, J. S., et al. (2014) · Nature Communications · doi:10.1038/ncomms5047 · Cited by 970
Retinitis Pigmentosa: Genes and Disease Mechanisms
Parmeggiani, F., Sorrentino, F. S., Ponzin, D., Barbaro, V., Ferrari, S., & Iorio, E. D. (2011) · Current Genomics · doi:10.2174/138920211795860107 · Cited by 663
The Retinal Pigment Epithelium in Health and Disease
Sparrrow, J. R., Hicks, D., & Hamel, C. (2010) · Current Molecular Medicine · doi:10.2174/156652410793937813 · Cited by 656
Cell stress in cortical organoids impairs molecular subtype specification
Bhaduri, A., Andrews, M. G., Mancia Leon, W., Jung, D., Shin, D., Allen, D., Jung, D., Schmunk, G., Haeussler, M., Salma, J., et al. (2020) · Nature · doi:10.1038/s41586-020-1962-0 · Cited by 624
Multilineage communication regulates human liver bud development from pluripotency
Camp, J. G., Sekine, K., Gerber, T., Loeffler-Wirth, H., Binder, H., Gac, M., Kanton, S., Kageyama, J., Damm, G., Seehofer, D., et al. (2017) · Nature · doi:10.1038/nature22796 · Cited by 621
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586
Multivariate Density Estimation: Theory, Practice, and Visualization.
Weiss, R. E., & Scott, D. W. (1994) · Journal of the American Statistical Association · doi:10.2307/2291242 · Cited by 523
Vertical interactions across ten parallel, stacked representations in the mammalian retina
Roska, B., & Werblin, F. S. (2001) · Nature · doi:10.1038/35069068 · Cited by 507
Self-citationAurnhammer, C., Haase, M., Müther, N., Mück-Häusl, M., Rauschhuber, C., Huber, I., Nitschko, H., Busch, U., Sing, A., Ehrhardt, A., et al. (2012) · Human Gene Therapy Methods · doi:10.1089/hgtb.2011.034 · Cited by 492
Meyer, J. S., Howden, S. E., Wallace, K. A., Verhoeven, A. D., Wright, L. S., Capowski, E. E., Pinilla, I., Martin, J. M., Tian, S., Stewart, R. M., et al. (2011) · Stem Cells · doi:10.1002/stem.674 · Cited by 479
Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina
Peng, Y., Shekhar, K., Yan, W., Herrmann, D., Sappington, A., Bryman, G. S., van Zyl, T. A., Do, M. T. H., Regev, A., & Sanes, J. R. (2019) · Cell · doi:10.1016/j.cell.2019.01.004 · Cited by 470
Visualizing Large-scale and High-dimensional Data
Tang, J., Liu, J., Zhang, M., & Mei, Q. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883041 · Cited by 404
Disease modelling in human organoids
Lancaster, M. A., & Huch, M. (2019) · Disease Models & Mechanisms · doi:10.1242/dmm.039347 · Cited by 390
Information Processing in the Primate Retina: Circuitry and Coding
Field, G. D., & Chichilnisky, E. J. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.30.051606.094252 · Cited by 378
The primate fovea: Structure, function and development
Bringmann, A., Syrbe, S., Görner, K., Kacza, J., Francke, M., Wiedemann, P., & Reichenbach, A. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.03.006 · Cited by 355
Generation of a ciliary margin-like stem cell niche from self-organizing human retinal tissue
Kuwahara, A., Ozone, C., Nakano, T., Saito, K., Eiraku, M., & Sasai, Y. (2015) · Nature Communications · doi:10.1038/ncomms7286 · Cited by 353
Otani, T., Marchetto, M. C., Gage, F. H., Simons, B. D., & Livesey, F. J. (2016) · Cell Stem Cell · doi:10.1016/j.stem.2016.03.003 · Cited by 353
Usher syndrome: Hearing loss, retinal degeneration and associated abnormalities
Mathur, P. D., & Yang, J. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.11.020 · Cited by 351
A single‐cell transcriptome atlas of the adult human retina
Lukowski, S. W., Lo, C. Y.-W., Sharov, A. A., Nguyen, Q. H., Fang, L., Hung, S. S. C., Zhu, L., Zhang, T., Grünert, U., Nguyen, T., et al. (2019) · The EMBO Journal · doi:10.15252/embj.2018100811 · Cited by 347
InfoVAE: Information Maximizing Variational AutoencodersPreprint
Zhao, S., Song, J., & Ermon, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02262 · Cited by 341
Rivera, A., White, K., Stöhr, H., Steiner, K., Hemmrich, N., Grimm, T., Jurklies, B., Lorenz, B., Scholl, H. P. N., Apfelstedt-Sylla, E., et al. (2000) · The American Journal of Human Genetics · doi:10.1086/303090 · Cited by 340
Self-citationInterpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337
Menon, M., Mohammadi, S., Davila-Velderrain, J., Goods, B. A., Cadwell, T. D., Xing, Y., Stemmer-Rachamimov, A., Shalek, A. K., Love, J. C., Kellis, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12780-8 · Cited by 331
High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels
Müller, J., Ballini, M., Livi, P., Chen, Y., Radivojević, M., Shadmani, A., Viswam, V., Jones, I. L., Michele, F., Diggelmann, R., et al. (2015) · Lab on a Chip · doi:10.1039/c5lc00133a · Cited by 329
Self-citationFireworks in the Primate Retina
Dacey, D. M., Peterson, B. B., Robinson, F. R., & Gamlin, P. D. (2003) · Neuron · doi:10.1016/s0896-6273(02)01143-1 · Cited by 321
Capowski, E. E., Samimi, K., Mayerl, S. J., Phillips, M. J., Pinilla, I., Howden, S. E., Saha, J., Jansen, A. D., Edwards, K. L., Jager, L. D., et al. (2018) · Development · doi:10.1242/dev.171686 · Cited by 311
Sridhar, A., Hoshino, A., Finkbeiner, C., Chitsazan, A., Dai, L., Haugan, A. K., Eschenbacher, K. M., Jackson, D. L., Trapnell, C., Bermingham‐McDonogh, O., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.01.007 · Cited by 291
Lu, Y., Shiau, F., Yi, W., Lü, S., Wu, Q., Pearson, J. D., Kallman, A., Zhong, S., Hoang, T. V., Zuo, Z., et al. (2019) · Developmental Cell · doi:10.1016/j.devcel.2020.04.009 · Cited by 284
Błasiak, J., Petrovski, G., Veréb, Z., Facskó, A., & Kaarniranta, K. (2014) · BioMed Research International · doi:10.1155/2014/768026 · Cited by 264
Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology
Bae, J. A., Mu, S., Kim, J. S., Turner, N. L., Tartavull, I., Kemnitz, N., Jordan, C. S., Norton, A. D., Silversmith, W. M., Prentki, R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.04.040 · Cited by 263
Molecular Anatomy of the Developing Human Retina
Hoshino, A., Ratnapriya, R., Brooks, M. J., Chaitankar, V., Wilken, M. S., Zhang, C., Starostik, M. R., Gieser, L. M., Torre, A. L., Nishio, M., et al. (2017) · Developmental Cell · doi:10.1016/j.devcel.2017.10.029 · Cited by 254
Biology and therapy of inherited retinal degenerative disease: insights from mouse models
Veleri, S., Lazar, C. H., Chang, B., Sieving, P. A., Banin, E., & Swaroop, A. (2015) · Disease Models & Mechanisms · doi:10.1242/dmm.017913 · Cited by 253
Transcriptional code and disease map for adult retinal cell types
Siegert, S., Cabuy, E., Scherf, B. G., Kohler, H., Panda, S., Le, Y.-Z., Fehling, H. J., Gaidatzis, D., Stadler, M. B., & Roska, B. (2012) · Nature Neuroscience · doi:10.1038/nn.3032 · Cited by 253
Self-citationThe Retina: An Approachable Part of the Brain
COGAN, D. G. (1988) · Archives of Ophthalmology · doi:10.1001/archopht.1988.01060130648016 · Cited by 252
Synthetic Analyses of Single-Cell Transcriptomes from Multiple Brain Organoids and Fetal Brain
Tanaka, Y., Çakır, B., Xiang, Y., Sullivan, G. J., & Park, I. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.01.038 · Cited by 244
Single-cell–initiated monosynaptic tracing reveals layer-specific cortical network modules
Wertz, A., Trenholm, S., Yonehara, K., Hillier, D., Raics, Z., Leinweber, M., Szalay, G., Ghanem, A., Keller, G. B., Rózsa, B. J., et al. (2015) · Science · doi:10.1126/science.aab1687 · Cited by 237
Self-citationFewer permutations, more accurate P -values
Knijnenburg, T., Wessels, L. F. A., Reinders, M. J. T., & Shmulevich, I. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp211 · Cited by 225
Jüttner, J., Szabo, A., Gross-Scherf, B., Morikawa, R. K., Rompani, S. B., Hantz, P., Szikra, T., Esposti, F., Cowan, C. S., Bharioke, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0431-2 · Cited by 218
Self-citationSubramanian, A., Sidhom, E.-H., Emani, M., Vernon, K., Sahakian, N., Zhou, Y., Kost-Alimova, M., Slyper, M., Waldman, J., Dionne, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13382-0 · Cited by 211
Baden, T., Schubert, T., Chang, L., Wei, T., Zaichuk, M., Wissinger, B., & Euler, T. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.030 · Cited by 194
Hallam, D., Hilgen, G., Dorgau, B., Zhu, L., Yu, M., Bojić, S., Hewitt, P. G., Schmitt, M. W., Uteng, M., Kustermann, S., et al. (2018) · Stem Cells · doi:10.1002/stem.2883 · Cited by 192
Localization of Usher 1 proteins to the photoreceptor calyceal processes, which are absent from mice
Sahly, I., Dufour, E., Schietroma, C., Michel, V., Bahloul, A., Perfettini, I., Pepermans, E., Singh‐Estivalet, A., Carette, D., Aghaie, A., et al. (2012) · The Journal of Cell Biology · doi:10.1083/jcb.201202012 · Cited by 191
Cronin, T., Vandenberghe, L. H., Hantz, P., Jüttner, J., Reimann, A., Kacsó, Á., Huckfeldt, R. M., Busskamp, V., Kohler, H., Lagali, P. S., et al. (2014) · EMBO Molecular Medicine · doi:10.15252/emmm.201404077 · Cited by 184
Self-citationHypoxia and Oxidative Stress in the Causation of Diabetic Retinopathy
Arden, G. B., & Sivaprasad, S. (2011) · Current Diabetes Reviews · doi:10.2174/157339911797415620 · Cited by 180
Generation, transcriptome profiling, and functional validation of cone-rich human retinal organoids
Kim, S., Lowe, A., Dharmat, R., Lee, S., Owen, L. A., Wang, J., Shakoor, A., Li, Y., Morgan, D. J., Hejazi, A. A., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1901572116 · Cited by 178
Cellular and Circuit Mechanisms Shaping the Perceptual Properties of the Primate Fovea
Sinha, R., Hoon, M., Baudin, J., Okawa, H., Wong, R., & Rieke, F. M. (2017) · Cell · doi:10.1016/j.cell.2017.01.005 · Cited by 170
Lenis, T. L., Hu, J., Ng, S. Y., Jiang, Z., Sarfare, S., Lloyd, M. B., Esposito, N. J., Samuel, W., Jaworski, C., Bok, D., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802519115 · Cited by 158
Pluripotent stem cell-derived retinal organoids for disease modeling and development of therapies
Kruczek, K., & Swaroop, A. (2020) · Stem Cells · doi:10.1002/stem.3239 · Cited by 148
Molecular characterization of foveal versus peripheral human retina by single-cell RNA sequencing
Voigt, A. P., Whitmore, S. S., Flamme‐Wiese, M. J., Riker, M. J., Wiley, L. A., Tucker, B. A., Stone, E. M., Mullins, R. F., & Scheetz, T. E. (2019) · Experimental Eye Research · doi:10.1016/j.exer.2019.05.001 · Cited by 147
Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling
Liang, Q., Dharmat, R., Owen, L. A., Shakoor, A., Li, Y., Kim, S., Vitale, A. T., Kim, I. K., Morgan, D. J., Liang, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12917-9 · Cited by 121
Variability of human pluripotent stem cell lines
Ortmann, D., & Vallier, L. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.07.004 · Cited by 117
Collin, J., Queen, R. A., Zerti, D., Dorgau, B., Hussain, R., Coxhead, J. M., Cockell, S., & Lako, M. (2018) · Stem Cells · doi:10.1002/stem.2963 · Cited by 111
da Silva, S., & Cepko, C. L. (2017) · Developmental Cell · doi:10.1016/j.devcel.2017.05.024 · Cited by 105
High-throughput single-cell transcriptomics on organoids
Brazovskaja, A., Treutlein, B., & Camp, J. G. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.11.002 · Cited by 95
The physics of organoids: a biophysical approach to understanding organogenesis
Dahl-Jensen, S., & Grapin-Botton, A. (2017) · Development · doi:10.1242/dev.143693 · Cited by 81
Chen, H. Y., Kaya, K. D., Dong, L., & Swaroop, A. (2016) · Europe PMC (PubMed Central) · Cited by 80
Ran, X., Cai, W.-J., Huang, X., Liu, Q., Lü, F., Qu, J., Wu, J., & Jin, Z. (2014) · Database · doi:10.1093/database/bau047 · Cited by 61
Efficient Coding by Midget and Parasol Ganglion Cells in the Human Retina
Soto, F., Hsiang, J.-C., Rajagopal, R., Piggott, K., Harocopos, G. J., Couch, S. M., Custer, P. L., Morgan, J., & Kerschensteiner, D. (2020) · Neuron · doi:10.1016/j.neuron.2020.05.030 · Cited by 55
Automatic spike sorting for high-density microelectrode arrays
Diggelmann, R., Michele, F., Hierlemann, A. R., & Franke, F. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00803.2017 · Cited by 40
Self-citationPatient-derived induced pluripotent stem cells for modelling genetic retinal dystrophies
Foltz, L. P., & Clegg, D. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.09.002 · Cited by 39
Luo, Z., Zhong, X., Li, K., Xie, B., Liu, Y., Ye, M., Li, K. V., Xu, C., & Ge, J. (2018) · Stem Cells · doi:10.1002/stem.2890 · Cited by 39
Two-photon microperimetry: sensitivity of human photoreceptors to infrared light
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Metadata
- Authors
- Cowan, C. S., Renner, M., De Gennaro, M., Gross-Scherf, B., Goldblum, D., Hou, Y., Munz, M., Rodrigues, T. M., Krol, J., Szikra, T., Cuttat, R., Waldt, A., Papasaikas, P., Diggelmann, R., Patino-Alvarez, C. P., Galliker, P., Spirig, S. E., Pavlinic, D., Gerber-Hollbach, N., Schuierer, S., Srdanovic, A., Balogh, M., Panero, R., Kusnyerik, A., Szabo, A., Stadler, M. B., Orgül, S., Picelli, S., Hasler, P. W., Hierlemann, A., Scholl, H. P. N., Roma, G., Nigsch, F., & Roska, B.
- Affiliations
- Institute of Molecular and Clinical Ophthalmology Basel (CH)
- University of Basel (CH)
- Novartis Institutes for BioMedical Research (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- ETH Zurich (CH)
- Friedrich Miescher Institute (CH)
- Johns Hopkins University (US)
- Novartis (Switzerland) (CH)
- Corresponding author
- Roma, Guglielmo
- Nigsch, Florian
- Roska, Botond
- ORCID
- Cowan, Cameron S.
- Renner, Magdalena
- Goldblum, David
- Hou, Yanyan
- Munz, Martin
- Rodrigues, Tiago M.
- Cuttat, Rachel
- Waldt, Annick
- Papasaikas, Panagiotis
- Diggelmann, Roland
- Spirig, Stefan E.
- Schuierer, Sven
- Srdanovic, Aldin
- Panero, Riccardo
- Stadler, Michael B.
- Orgül, Selim
- Picelli, Simone
- Hasler, Pascal W.
- Hierlemann, Andreas
- Scholl, Hendrik P.N.
- Roma, Guglielmo
- Nigsch, Florian
- Roska, Botond
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 182
- Issue
- 6
- Pages
- 1623-1640.e34
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7505495
- OpenAlex
- W3087102910
- Cited by
- 654
- References
- 94
- Citations by year
- 2026: 79 · 2025: 118 · 2024: 118 · 2023: 113 · 2022: 123 · 2021: 74 · 2020: 23
- FWCI
- 26.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The Novartis Repository (Novartis) (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- European Research Council
- Topics
- Health Sciences › Medicine › Ophthalmology › Retinal Development and Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- retinal cell types, single-cell transcriptomics, retinal development, photosensitivity, retinal pigment epithelium, choroid, fovea, macular degeneration, cell type specificity, retinal repair
- MeSH terms
- Cell Differentiation, Cell Line, Electrophysiology, Female, Multigene Family, Humans, Microscopy, Electron, Naphthoquinones, Organoids, Retina, Synapses, In Situ Hybridization, Gene Expression Regulation, Developmental, Cell Culture Techniques, Genetic Predisposition to Disease, Induced Pluripotent Stem Cells, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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