一种精确捕获真核细胞群体的3D基因组构象及其系综分布的新技术和新算法

Eukaryotic genomes have complex conformation forms in the nucleus, and many cellular processes in the nucleus depends on the correct folding. With the rapid development of high-throughput sequencing technology, a series of chromatin conformation capture (3C) based new technologies advanced our understanding of the genome spatial structure. However, limited by the technologies per se, an array of questions cannot be fully addressed. In order to solve a key limitation of the current technologies, i.e unable to obtain the distribution of chromatin structure ensemble in a cell population, this project will develop Mi-C, a new chromatin conformation capture technology. By introducing more bridge linkers, Mi-C aimed to join multiple intra-complex interactions at the loop hub region simultaneously. The Multi-connection can get inside information of the cell, and captures the combination of interactions in situ. Based on the information, we will develop a new data model and a new computational method to improve the accuracy of spatial conformation reconstruction, moreover, we will be able to exploring the distribution of conformation ensemble in the cell population with Mi-C. Finally, we plan to study the chromatin conformation difference between two hepatocellular carcinoma cell lines with diverged metastasis capacity (HCC-LM3/97L). After we identified the key structural difference between the two cell line, Crispr/CAS9 experiments will be conducted to knockout the key regulator/element to exam if the metastasis capacity can be altered. Mi-C will be not only at a very low cost to complete the chromatin conformation reconstruction with high accuracy, and for the first time, it going to make the study of chromatin conformation distribution in a large cell population become possible. This, in turn, will help the expansion of three-dimensional genome research to broad fields.

真核生物的诸多生命过程都依赖其基因组在细胞核内的空间构象。随着测序技术的发展，基于染色质构象捕获的各种技术提高了我们对核内空间构象的认识。然而当前的技术还仍然存在分辨率过低和难以获得染色质构象在细胞群体中分布的难题。本课题拟开发新的染色质构象捕获技术Mi-C。通过引入多分子连接和多桥接头，Mi-C富集相互作用热点，并捕获细胞内原位相互作用组合的信息。同时，我们将开发针对Mi-C的新数据模型和计算方法。在大幅提高分辨率的同时，我们可以初步探索空间构象在细胞群体中的分布。最后，我们拟以肝癌转移细胞模型HCC-LM3/97L为材料，研究它们染色质空间构象的异同，并通过Crispr/CAS9靶向敲除实验来检验转移能力和染色质构象的关联。Mi-C在以低成本完成高精度染色质构象重建的同时，第一次使得研究染色质构象在细胞群体中的分布成为可能。Mi-C技术将为三维基因组的研究扩展到更多的领域奠定基础。

真核生物的诸多生命过程都依赖其基因组在细胞核内的空间构象。随着测序技术的发展，基于染色质构象捕获的各种技术提高了我们对核内空间构象的认识。然而当前的技术还仍然存在分辨率过低和难以获得染色质构象在细胞群体中分布的难题。本课题开发了新的染色质构象捕获技术Mi-C。通过引入多分子连接和多桥接头，Mi-C富集相互作用热点，并捕获细胞内原位相互作用组合的信息。同时，我们开发了新染色质构象分析算法CISD/CISD_loop, deDoc, deNOPA,大幅提高在极低数据量下的分辨率水平。最后我们应用我们开发的新算法在猪的早期胚胎发育中，发现在猪胚胎的染色质高级结构重编程过程中特异性的超级结构域和在合子基因组激活阶段的去区室效应。Mi-C技术将为三维基因组的研究扩展到更多的领域奠定基础。