ReviewFrontiers in cell and developmental biology2026
Harnessing iPSC technology for population-level human disease/traits modeling -where are we?
Review in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This mini review highlights the major developments in iPSC research and the importance of dissecting the contribution of epigenetic factors in resetting the epigenome and their correlation to the stage-wise transitions to the dedifferentiated state. Since the need of the hour is to identify multiple genetic variants at the population level that are the major contributors to the heterogeneity in the inter-individual response (combination of environmental and/or genetic influences), it is necessary to adopt methodologies (in human iPSC cell lines) including DNA sequencing methods, SNP genotyping arrays, and other variants identification methods to identify, quantify these micro-level changes and correlate them with the disease phenotype. Further, studies (including scRNA-based approaches) need to be performed to compare variants at the transcriptomic level to trace it back to the specific individual/human cohort, thereby unraveling the context-dependent masked effects at the population level and fill in the gaps between the clinical aspects and e-localization signals. Further, village-in-a-dish (co-culture of heterogeneous cell types differentiated from the different iPSC cell lines isolated from human donors) and humanity-in-a dish model strategies will benefit from ongoing methods to generate an atlas of genetic variants that can explain genetic diversity globally. Parallel efforts to improve the scalability, automation and throughput in the development of mature human cells as well as refine strategies to better correlate and perform QTL mapping studies will complement the existing body of clinical and molecular data and unravel the relative role played by environmental factors to the overall disease risk, phenotype and drug response.
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