Zhan Yuxiang, Musella Francesco, Alber Frank
Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, California, United States of America.
Institute of Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS Comput Biol. 2025 May 23;21(5):e1013114. doi: 10.1371/journal.pcbi.1013114. eCollection 2025 May.
The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.
基因组被组织成至少两个主要类别的不同染色质区室，即转录活跃的A区室和不活跃的B区室，大致对应于常染色质和异染色质。同一区室内的染色质区域比相反区室内的区域更倾向于相互作用。传统上，A/B区室是通过对协方差矩阵进行主成分分析，从整体Hi-C接触频率矩阵中识别出来的。然而，从稀疏的单细胞Hi-C数据中在单细胞水平上定义区室具有挑战性，特别是因为同源拷贝往往无法分辨。为了解决这个问题，我们提出了MaxComp，一种无监督方法，用于基于单细胞染色体结构中的3D几何考虑来推断单细胞A/B区室，这些结构来自多重FISH-组学成像或从Hi-C数据推导的3D结构模型。通过将每个3D染色体结构表示为一个无向图，其边权重编码结构信息，MaxComp将区室预测重新表述为最大割问题的一个变体，使用半定图规划（SPD）求解，以将图最优地划分为两个结构区室。我们的结果表明，MaxComp单细胞区室注释的群体平均值与从整体Hi-C主成分分析得出的注释紧密匹配，这表明仅使用染色质区域的3D坐标和核微环境，就可以仅从几何原理中恢复区室化。我们的方法揭示了区室组织中广泛的细胞间变异性，基因组位点存在大量异质性。当应用于多重FISH成像数据时，MaxComp还揭示了单细胞水平上区室注释与转录活性之间的关系。总之，MaxComp为理解单细胞中的染色质区室化提供了一个新框架，将3D基因组结构、转录活性与染色质区室的细胞间变异联系起来。
