Zhou Huabin, Hutchings Joshua, Shiozaki Momoko, Zhao Xiaowei, Doolittle Lynda K, Yang Shixin, Yan Rui, Jean Nikki, Riggi Margot, Yu Zhiheng, Villa Elizabeth, Rosen Michael K
Department of Biophysics, Howard Hughes Medical Institute, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
School of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
bioRxiv. 2024 Dec 31:2024.12.01.626131. doi: 10.1101/2024.12.01.626131.
Phase separation is an important mechanism to generate certain biomolecular condensates and organize the cell interior. Condensate formation and function remain incompletely understood due to difficulties in visualizing the condensate interior at high resolution. Here we analyzed the structure of biochemically reconstituted chromatin condensates through cryo-electron tomography. We found that traditional blotting methods of sample preparation were inadequate, and high-pressure freezing plus focused ion beam milling was essential to maintain condensate integrity. To identify densely packed molecules within the condensate, we integrated deep learning-based segmentation with novel context-aware template matching. Our approaches were developed on chromatin condensates, and were also effective on condensed regions of in situ native chromatin. Using these methods, we determined the average structure of nucleosomes to 6.1 and 12 Å resolution in reconstituted and native systems, respectively, and found that nucleosomes form heterogeneous interaction networks in both cases. Our methods should be applicable to diverse biochemically reconstituted biomolecular condensates and to some condensates in cells.
相分离是产生某些生物分子凝聚物并组织细胞内部的重要机制。由于难以在高分辨率下可视化凝聚物内部,凝聚物的形成和功能仍未被完全理解。在这里,我们通过冷冻电子断层扫描分析了生化重构的染色质凝聚物的结构。我们发现传统的样本制备印迹方法并不充分,高压冷冻加聚焦离子束铣削对于维持凝聚物的完整性至关重要。为了识别凝聚物内紧密堆积的分子,我们将基于深度学习的分割与新颖的上下文感知模板匹配相结合。我们的方法是在染色质凝聚物上开发的,对原位天然染色质的凝聚区域也有效。使用这些方法,我们分别在重构系统和天然系统中以6.1 Å和12 Å的分辨率确定了核小体的平均结构,并发现核小体在两种情况下都形成了异质相互作用网络。我们的方法应该适用于各种生化重构的生物分子凝聚物以及细胞中的一些凝聚物。