Leveraging single cell multiomic analyses to identify gene regulatory networks that drive human articular cartilage cell fate

利用单细胞多组学分析来识别驱动人类关节软骨细胞命运的基因调控网络

阅读:1

Abstract

OBJECTIVE: Compromises to the integrity of articular cartilage, whether genetic, post-traumatic or age-related, lead to debilitating chronic degenerative diseases including osteoarthritis. In this study, our objective was to leverage multiomic and spatial transcriptomic datasets to identify gene regulatory networks that drive human articular cartilage cell fate, and build a foundation from which novel therapeutics to overcome degenerative disease could be developed. DESIGN: We jointly profiled the transcriptome and open chromatin regions in individual nuclei recovered from distal femora at 2 fetal timepoints and performed high-definition spatial transcriptomics at an additional timepoint. We established a human pluripotent stem cell platform to interrogate the function of computationally predicted transcription factors during human chondrocyte differentiation. RESULTS: We computationally predicted gene regulatory networks governing chondrocyte subsets comprising the human distal femur during development. Following functional analysis of two transcription factors predicted to function in the superficial zone, CREB5 and NFATC2, using our in vitro experimental platform, we found both have the potential to reprogram growth plate cartilage and induce features of articular cartilage. We further identified new biological roles for CREB5 related to ECM organization and taxis. CONCLUSIONS: We expect new regulatory networks we uncovered to be important for promoting cartilage health and treating disease, and our platform to be a useful tool for studying cartilage development and homeostasis in vitro. The ability to reprogram chondrocytes toward an articular-like fate has significant therapeutic potential to treat degenerative joint diseases.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。