Inter-chromosomal contacts demarcate genome topology along a spatial gradient

Non-homologous chromosomal contacts (NHCCs) between different chromosomes participate considerably in gene and genome regulation. However, due to analytical challenges, NHCCs are currently considered as singular, stochastic events, and their extent and fundamental principles across cell types and sexes remain controversial. To determine the fundamental properties of NHCCs, we developed a supervised and unsupervised learning algorithm, termed Signature. Signature revealed 40,282 NHCCs and their properties across 62 Hi-C datasets of 53 diploid human cell types. Genomic regions of NHCCs are gene-dense, highly expressed, and harbor genes for cell-specific and sex-specific functions. Extensive inter-telomeric and inter-centromeric clustering occurs across cell types and 61 NHCCs are consistently found at the nuclear speckles. These constitutive 'anchor loci' facilitate an axis of genome activity whilst cell-type-specific NHCCs act in discrete hubs. Our results suggest that non-random chromosome positioning is supported by constitutive NHCCs that shape genome topology along an off-centered spatial gradient of genome activity. Overall design: Omni-C datasets of chondrogenic differentiation, CHLA-9, TC-32, and TeloHAEC cells. Each dataset is derived from two biological replicates, each with three technical replicates. Omni-C libraries were sequenced as paired-end runs with 2x150 bp on S4 flowcells of NovaSeq6000 with > 2 billion reads per sample.

非同源染色体接触（Non-homologous chromosomal contacts, NHCCs）在不同染色体间广泛存在，并在基因与基因组调控过程中发挥关键作用。然而受限于分析技术瓶颈，目前学界仅将NHCCs视为单一的随机事件，其在不同细胞类型与性别中的分布范围及核心调控原理仍存在较大争议。
为阐明NHCCs的基本特性，我们开发了一套集成监督与无监督学习的算法，将其命名为Signature。通过该算法，我们在涵盖53种二倍体人类细胞类型的62组Hi-C（高通量染色体构象捕获）数据集中共鉴定出40282个NHCCs，并解析了其相关特征。
NHCCs所覆盖的基因组区域基因密度高、表达水平显著，且富集有介导细胞特异性与性别特异性功能的基因。跨细胞类型的分析显示，端粒间与着丝粒间存在广泛的聚类现象，且有61个NHCCs始终定位于核斑点区域。
这些组成型“锚定位点”构建了基因组活性的调控轴，而细胞类型特异性的NHCCs则在离散的调控枢纽中发挥功能。我们的研究结果表明，非随机的染色体定位由组成型NHCCs所维持，这类NHCCs沿着基因组活性的偏心空间梯度塑造了基因组拓扑结构。
整体实验设计：本研究涵盖软骨细胞分化、CHLA-9、TC-32及TeloHAEC细胞的Omni-C数据集。每组数据集均来自两份生物学重复，每份生物学重复包含三份技术重复。Omni-C文库采用NovaSeq6000的S4流动槽进行2×150 bp双端测序，每个样本的测序reads数超过20亿。