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Cited by 377

The DOI matches its Crossref record; 167 of the 167 references it cites were found in a registry or an index.

Disease modelling in human organoids

Open access
Lancaster, Madeline A., Huch, Meritxell·2019·Disease Models & Mechanisms

Abstract

The past decade has seen an explosion in the field of in vitro disease modelling, in particular the development of organoids. These self-organizing tissues derived from stem cells provide a unique system to examine mechanisms ranging from organ development to homeostasis and disease. Because organoids develop according to intrinsic developmental programmes, the resultant tissue morphology recapitulates organ architecture with remarkable fidelity. Furthermore, the fact that these tissues can be derived from human progenitors allows for the study of uniquely human processes and disorders. This article and accompanying poster highlight the currently available methods, particularly those aimed at modelling human biology, and provide an overview of their capabilities and limitations. We also speculate on possible future technological advances that have the potential for great strides in both disease modelling and future regenerative strategies.

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Metadata

Authors
Lancaster, M. A., & Huch, M.
Venue
Disease Models & Mechanisms
Published
2019-07-01
Volume
12
Issue
7
ISSN
  • 1754-8403 (ISSN-L)
  • 1754-8411
OpenAlex
W2964772567
Cited by
377
References
167
FWCI
15.76
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Disease modelling in human organoids. https://app.cochat.ai/papers/10.1242/dmm.039347
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Lancaster, M. A., & Huch, M. (2019). Disease modelling in human organoids. Disease Models & Mechanisms. https://doi.org/10.1242/dmm.039347

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.