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Epigenetic determinants of muscle stem cell quiescence and chronological aging (ChIP-seq)

GSE47362 Mus musculus Genome binding/occupancy profiling by high throughput sequencing 11 samples Submitted 2013/08/13 Platform GPL11002
Summary
Skeletal muscle is a post-mitotic tissue that exhibits an extremely low turnover in the absence of disease or injury. At the same time, muscle possesses remarkable regenerative capacity mediated by satellite cells (SCs) that reside in close association with individual myofibers, underneath the fiber’s basal lamina. Consistent with the low turnover of the muscle, SCs in adult animals are mitotically quiescent and therefore provide an excellent model to study stem cell quiescence. As an organism grows older, the resident stem cells are exposed to a deteriorating environment and experience chronological aging. In stem cells with high turnover, the effects of chronological aging are superimposed upon the effects of the replicative aging that results from DNA replication and cell division. On the contrary, SCs experience minimal replicative aging due to their low turnover. They are thus a good model to study the consequence of chronological aging of quiescent stem cells. We have developed an isolation protocol to selectively enrich SCs by FACS from adult mice and applied the ChIP-seq technology to obtain H3K4me3, H3K27me3 and H3K36me3 from quiescent and activated SCs from young mice and from quiescent SCs from old mice. Our analysis aims to understand the chromatin features underlying stem cell properties such as quiecence and lineage-potency, and to understand how the chromatin structure of a quiescent stem cell pouplation changes with age.
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Direct links to NCBI, no account and no request form: the whole study as GSE47362_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 11 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA205299 and SRA study SRP023153. Searching any of these in the dataset finder brings you back here.

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