Biological structures and the epigenome are intertwined. For example, complex tissues are often the combined products of various groups of spatially patterned cell types with distinct epigenetic states. Furthermore, chromatin at various subnuclear locations within a cell often differ in their epigenetic properties. Thus, a systematic understanding of the relationship between the epigenome and its spatial distribution across biological scales would inform tissue and cellular functions as well as gene regulatory mechanisms. Yet, spatially resolved epigenome profiling-particularly at subcellular resolution-remains technically challenging. Here, we present E pigenetic C UT& T ag via H igh-resolution O ptical S election (ECHOS), a platform that combines high-resolution imaging and high-throughput sequencing to enable precise, spatially targeted epigenetic profiling across biological scales. At the cellular scale, ECHOS generates high-quality DNA-binding protein and histone modification datasets that show strong concordances with datasets from ChIP-seq and CUT&Tag experiments. Further optimization of ECHOS (ECHOS+) enables the characterization of the histone modification landscape of chromatin at the sub-micron resolution. Using ECHOS+, we revealed distinct gene regulatory logics at different layers of human ectocervical epithelium. We also showed that micronuclei-small nucleus-like structures formed by mitotic errors-exhibited a different epigenetic state from the same chromosome regions on the intact nuclei. Finally, we found that human aging altered the epigenetic state of the inactive X chromosome located in a subcellular nuclear structure called the Barr body, which may contribute to genes escaping X chromosome inactivation during female aging. Together, ECHOS and ECHOS+ represent a scalable and generalizable framework for spatial epigenomic analyses, with broad potential applications in various domains of biology.
ECHOS enables spatial epigenome profiling at subcellular resolution.
阅读:1
作者:Cao Qiqi, Xu Qianlan, Ueda Yusuke, Rajachandran Shreya, Sharma Manjita, Zhang Xin, Mahendroo Mala, Grow Edward J, Chen Haiqi
| 期刊: | bioRxiv | 影响因子: | 0.000 |
| 时间: | 2026 | 起止号: | 2026 Mar 27 |
| doi: | 10.64898/2026.03.26.714421 | ||
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。
