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An Efficient Direct Conversion Strategy to Generate Functional Astrocytes from Human Adult Fibroblasts

GSE239320 Homo sapiens Expression profiling by high throughput sequencing 9 samples Submitted 2024/08/31 Platform GPL24676
Summary
Direct reprogramming approaches offer an attractive alternative to stem-cell-derived models, allowing the retention of epigenetic information and age-associated cellular phenotypes. To explore such age-related phenotypes, several groups have generated multiple neuronal subtypes, neural progenitor cells, oligodendrocytes, and other cell types directly from fibroblasts. Other groups have had success at the efficient conversion of embryonic fibroblasts to astrocytes but have not yet achieved similar conversion efficiency for adult human fibroblasts. In order to generate astrocytes for the study of age-related diseases, we developed an improved direct conversion strategy employing a combination of small molecules to activate specific pathways that induce trans-differentiation of human adult fibroblasts to astrocytes. We demonstrate that this method produces mature GFAP+/S100β+ cells at high efficiency (40-45%), comparable to previous studies utilizing embryonic fibroblasts. Further, Fibroblast-derived induced Astrocytes (FdiAs) are enriched for markers of astrocyte functionality, including ion-channel buffering, gap-junction communication, and glutamate uptake; and exhibit astrocyte-like calcium signaling and neuroinflammatory phenotypes. RNA-Seq analysis indicates an adult rather than fetal astrocytic gene expression signature, with a greater correlation to temporal lobe astrocytes. Fibroblast-derived induced astrocytes provide a useful tool in understanding age-associated disease processes and complement existing in vitro models of induced neurons (iNs), providing an additional platform to study late-stage brain disorders.
Published in
An Efficient Direct Conversion Strategy to Generate Functional Astrocytes from Human Adult Fibroblasts
Bhaskar U, Shrimpton E, Ayo J et al. · bioRxiv : the preprint server for biology 2024 · PMID 39282386 · doi:10.1101/2024.09.02.610876
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Also filed as BioProject PRJNA998809 and SRA study SRP451575. Searching any of these in the dataset finder brings you back here.

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