ArticleStem cells translational medicine2025
Modeling rare genetic disease with gene-edited induced pluripotent stem cells: relevance of the starting stock line.
Article in Stem cells translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Changes in Purine Metabolism During Differentiation of Dopamine Neurons from Human Induced Pluripotent Stem Cells.Cellular and molecular neurobiology · 2026Article
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9 authors.
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Abstract
Induced pluripotent stem cells (iPSCs) are commonly used to model human genetic diseases. Two main strategies are used. The first involves making iPSC lines from individual cases with a disease, and the second involves making disease-relevant gene edits in established iPSC lines. Because generating gene-edited lines is time consuming and expensive, most studies begin with one starting iPSC stock line and evaluate several gene-edited sublines. The current studies focus on gene-editing to model Lesch-Nyhan disease (LND), which is caused by mutations in the HPRT1 gene. The same pathogenic c.508C>T edit was made in four well-established stock lines, and three gene-edited lines were isolated from each. RNA sequencing (RNAseq) was, then, used to evaluate the impact of the gene edit. Gene-edited lines were compared to their corresponding stock lines, as well as to each other. An aggregate analysis of all lines combined was also conducted to determine the most robust findings across all lines. Results from gene editing were further compared with iPSC lines derived from individual cases with LND, to determine how closely findings from gene editing match results obtained with case-derived lines. There were two main findings. First, the same gene edit has a different impact on gene expression when starting with different starting stock lines. Second, the gene editing strategy does not produce the same results as the case-derived strategy. Potential explanations for these differences are addressed, along with the relevance of these two different strategies for disease modeling.
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