Department of Mathematics, KU Leuven, Celestijnenlaan 200B, 3001, Leuven, Belgium.
LPTMC, Sorbonne Université, CNRS, F-75005, Paris, France.
Nat Commun. 2024 Sep 4;15(1):7670. doi: 10.1038/s41467-024-51650-w.
Involved in mitotic condensation, interaction of transcriptional regulatory elements and isolation of structural domains, loop formation has become a paradigm in the deciphering of chromatin architecture and its functional role. Despite the emergence of increasingly powerful genome visualization techniques, the high variability in cell populations and the randomness of conformations still make loop detection a challenge. We introduce an approach for determining the presence and frequency of loops in a collection of experimental conformations obtained by multiplexed super-resolution imaging. Based on a spectral approach, in conjunction with neural networks, this method offers a powerful tool to detect loops in large experimental data sets, both at the population and single-cell levels. The method's performance is confirmed on experimental FISH data where Hi-C and other loop detection results are available. The method is then applied to recently published experimental data, where it provides a detailed and statistically quantified description of the global architecture of the chromosomal region under study.
参与有丝分裂浓缩、转录调控元件相互作用和结构域隔离，环形成已成为解析染色质结构及其功能作用的范例。尽管越来越强大的基因组可视化技术不断涌现，但细胞群体的高度可变性和构象的随机性仍然使环检测成为一项挑战。我们介绍了一种方法，用于确定通过多路复用超分辨率成像获得的实验构象集合中环的存在和频率。基于光谱方法，结合神经网络，该方法为在群体和单细胞水平上检测大实验数据集的环提供了强大的工具。该方法在具有 Hi-C 和其他环检测结果的实验 FISH 数据上得到了验证。然后将该方法应用于最近发表的实验数据，其中提供了所研究染色体区域的全局结构的详细和统计量化描述。
