The future of cerebral organoids in drug discovery
Abstract
Until the discovery of human embryonic stem cells and human induced pluripotent stem cells, biotechnology companies were severely limited in the number of human tissues that they could model in large-scale in vitro studies. Until this point, companies have been limited to immortalized cancer lines or a small number of primary cell types that could be extracted and expanded. Nowadays, protocols continue to be developed in the stem cell field, enabling researchers to model an ever-growing library of cell types in controlled, large-scale screens. One differentiation method in particular- cerebral organoids- shows substantial potential in the field of neuroscience and developmental neurobiology. Cerebral organoid technology is still in an early phase of development, and there are several challenges that are currently being addressed by academic and industrial researchers alike. Here we briefly describe some of the early adopters of cerebral organoids, several of the challenges that they are likely facing, and various technologies that are currently being implemented to overcome them.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cerebral organoids derived from AD patient-specific iPSCs can recapture patient-specific phenotypes and hence serve as excellent disease models for AD research and drug development.
“Cerebral organoids derived from AD patient-specific iPSCs can recapture patient-specific phenotypes and hence serve as excellent disease models for AD research and drug development [51–53].”
From CI-994 is a dual modulator of class I HDACs and Wnt/β-catenin signaling for the treatment of Alzheimer's disease (2026) · cites this paper as [53]
Cites
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
Three-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620 · see citing works
Eiraku, M., Watanabe, K., Matsuo-Takasaki, M., Kawada, M., Yonemura, S., Matsumura, M., Wataya, T., Nishiyama, A., Muguruma, K., & Sasai, Y. (2008) · Cell Stem Cell · doi:10.1016/j.stem.2008.09.002 · Cited by 1,522 · see citing works
Deep profiling of mouse splenic architecture with CODEX multiplexed imaging
Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., Black, S., & Nolan, G. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.010 · Cited by 1,501 · see citing works
Cell diversity and network dynamics in photosensitive human brain organoids
Quadrato, G., Nguyen, T., Macosko, E. Z., Sherwood, J. L., Min Yang, S., Berger, D. R., Maria, N., Scholvin, J., Goldman, M., Kinney, J. P., et al. (2017) · Nature · doi:10.1038/nature22047 · Cited by 1,321 · see citing works
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · see citing works
Kadoshima, T., Sakaguchi, H., Nakano, T., Soen, M., Ando, S., Eiraku, M., & Sasai, Y. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315710110 · Cited by 999 · see citing works
Dense connectomic reconstruction in layer 4 of the somatosensory cortex
Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., & Helmstaedter, M. (2019) · Science · doi:10.1126/science.aay3134 · Cited by 333 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Revolutionizing Disease Modeling: The Emergence of Organoids in Cellular Systems
Silva-Pedrosa, R., Salgado, A. J., & Ferreira, P. E. (2023) · Cells · doi:10.3390/cells12060930 · Cited by 84 · see citing works
Marangon, D., Caporale, N., Boccazzi, M., Abbracchio, M. P., Testa, G., & Lecca, D. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.748849 · Cited by 34 · see citing works
Lu, W., Kawatani, K., Ren, Y., Nambara, T., Jia, L., Jeevaratnam, S., Lee, E., Martinez, P. R., Izhar, T., Wang, N., et al. (2026) · Alzheimer's Research & Therapy · doi:10.1186/s13195-026-01982-0 · Cited by 4 · see citing works
- Authors
- Salick, M. R., Lubeck, E., Riesselman, A., & Kaykas, A.
- Affiliations
- insitro, inc. (United States) (US)
- Corresponding author
- Kaykas, Ajamete
- Venue
- Seminars in Cell & Developmental Biology
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 111
- Pages
- 67-73
- ISSN
- 1084-9521 (ISSN-L)
- OpenAlex
- W3040839760
- Cited by
- 29
- References
- 9
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 5 · 2020: 3
- FWCI
- 0.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Seminars in Cell and Developmental Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Physical Sciences › Engineering › Biomedical Engineering › 3D Printing in Biomedical Research
- Keywords
- brain organoids, drug discovery, human induced pluripotent stem cells, human embryonic stem cells, in vitro disease model, stem cell differentiation, developmental neuroscience
- MeSH terms
- Machine Learning, Cell Differentiation, Cerebral Cortex, Humans, Models, Biological, Mutation, Nerve Tissue Proteins, Neurons, Organoids, Drugs, Investigational, Sequence Analysis, RNA, Neuroprotective Agents, Neurodegenerative Diseases, Drug Discovery, Induced Pluripotent Stem Cells, Single-Cell Analysis, CRISPR-Cas Systems
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 ›
Are you an author, or do you see a problem with this page? Request a review