five

Giadone et al CMap SMA RNA-seq data

收藏
Figshare2025-12-17 更新2026-04-08 收录
下载链接:
https://figshare.com/articles/dataset/Giadone_et_al_CMap_SMA_RNA-seq_data/30753266/1
下载链接
链接失效反馈
官方服务:
资源简介:
<br>Richard M. Giadone*, Kristina M. Holton*, Xiaoyu Hu*, Ted Natoli*, Sabrina Ghosh, Stanley P. Gill, Aravind Subramanian, Lee L. Rubin.An induced pluripotent stem cell-based chemical genetic approach for studying spinal muscular atrophy.Spinal muscular atrophy (SMA) is a genetic disease characterized by degeneration of spinal cord motor neurons and neuromuscular junctions. Despite recent development in therapies for SMA, treatment efficacy largely relies on administration of drugs early in disease progression and is impacted by underlying patient genetics. Drug discovery for other diseases of the central nervous system (CNS) has also been hindered by heterogeneity in patient genetics and clinical presentations, as well as the need for early intervention. To address these hurdles, we utilized a chemical genetic-based screening approach to adapt the Connectivity Map (CMAP)/L1000 platform to study SMA. To do this, we differentiated moderate and severe SMA patient-specific induced pluripotent stem cells into neuronal cells utilizing a forward programming differentiation protocol, exposed each to 360 neuroactive or CNS disease-related compounds, and interrogated resulting changes in expression of &gt;400 neural genes in a platform we term CMAPneuro. In doing so, we generated 4,559 transcriptional profiles identifying stimuli that modulate gene expression differences across SMA neurons. Finally, we make these data queryable, allowing the research community to 1.) identify CNS disease-related perturbagens that mimic or reverse differentially expressed genes, or 2.) explore the transcriptional response of a given perturbation in diverse SMA neuronal cells. Taken together, CMAPneuro represents a novel tool to identify candidate stimuli for follow-up investigation into the biology of SMA and related disorders.<br>
提供机构:
Giadone, Richard M.; Rubin, Lee L.; Holton, Kristina; Hu, Xiaoyu; Natoli, Ted; Subramanian, Aravind
创建时间:
2025-12-17
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