five

Induced human forebrain neurons overexpressing MAPT demonstrate chromatin structure alterations

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97409
下载链接
链接失效反馈
官方服务:
资源简介:
We assessed whether forebrain neurons from human induced pluripotent stem cells (iNs) could recapitulate the tau-related epigenomic changes that we found in human brain H3K9ac data (Synapse ID: syn4896408). We used an existing iN model system whereby we can virally transduce NGN2-induced neurons with lentivirus to modulate expression of specific genes. Here, we overexpressed the 4R isoform of human MAPT, which induces AD-related features such as the intracellular accumulation of phosphorylated tau. To characterize the epigenomic changes in our model system, we used the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) to map genome accessibility. Finally, we utilized the iN model system to assess whether a drug compound (17-DMAG) might attenuate the tau effect on chromatin organization. We generated n=9 MAPT OE and n=9 control iNs in 3 batches of independent differentiations. The second and third batch of experiments contained additional iNs treated with 1, 3, or 10 nM 17-DMAG for 24 hours prior to collection. Further, the third batch contained additional control iNs cultured in concentration-matched DMSO solution for 24 hours prior to collection. The main purpose of the study was to detect domains (or peaks) that were different between the n=9 MAPT OE iNs and n=9 control iNs. Therefore, a set of common peaks was defined in the n=2x9 iNs samples, then the numbers of reads in these peaks were counted, and finally, peaks were tested for differences between MAPT OE iNs and control iNs. The processed data provided in this series consist of a bed file with peak locations and a matrix file with read counts for each peak and sample. While the peaks were defined on the initial n=2x9 iNs, the matrix file also contains read counts for the 17-DMAG treated iNs.
创建时间:
2019-05-15
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作