The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data

Unveiling Multi-Scale Architectural Features in Single-Cell Hi-C Data Using scCAFE

Single-cell Hi-C (scHi-C) has provided unprecedented insights into the heterogeneity of 3D genome organization. However, its sparse and noisy nature poses challenges for computational analyses, such as chromatin architectural feature identification. Here, scCAFE is introduced, which is a deep learning model for the multi-scale detection of architectural features at the single-cell level. scCAFE provides a unified framework for annotating chromatin loops, TAD-like domains (TLDs), and compartments across individual cells. This model outperforms previous scHi-C loop calling methods and delivers accurate predictions of TLDs and compartments that are biologically consistent with previous studies. The resulting single-cell annotations also offer a measure to characterize the heterogeneity of different levels of architectural features across cell types. This heterogeneity is then leveraged to identify a series of marker loop anchors, demontrating the potential of the 3D genome data to annotate cell identities without the aid of simultaneously sequenced omics data. Overall, scCAFE not only serves as a useful tool for analyzing single-cell genomic architecture, but also paves the way for precise cell-type annotations solely based on 3D genome features.

Polymer model integrates imaging and sequencing to reveal how nanoscale heterochromatin domains influence gene expression

Chromatin organization regulates gene expression, with nanoscale heterochromatin domains playing a fundamental role. Their size varies with microenvironmental stiffness and epigenetic interventions, but how these factors regulate their formation and influence transcription remains unclear. To address this, we developed a sequencing-informed copolymer model that simulates chromatin evolution through diffusion and active epigenetic reactions. Our model predicts the formation of nanoscale heterochromatin domains and quantifies how domain size scales with epigenetic reaction rates, showing that epigenetic and compaction changes primarily occur at domain boundaries. We validated these predictions via Hi-C and super-resolution imaging of hyperacetylated melanoma cells and identified differential expression of metastasis-related genes through RNA-seq. We validated our findings in hMSCs, where epigenetic reaction rates respond to microenvironmental stiffness. Conclusively, our simulations reveal that heterochromatin domain boundaries regulate gene expression and epigenetic memory. These findings demonstrate how external cues drive chromatin organization and transcriptional memory in development and disease.

Evolution of 3D Chromatin Folding

Studies examining the evolution of genomes have focused mainly on sequence conservation. However, the inner working of a cell implies tightly regulated crosstalk between complex gene networks controlled by small dispersed regulatory elements of physically contacting DNA regions. How these different levels of chromatin organization crosstalk in different species underpins the potential for genome evolutionary plasticity. We review the evolution of chromatin organization across the Animal Tree of Life. We introduce general aspects of the mode and tempo of genome evolution to later explore the multiple layers of genome organization. We argue that both genome and chromosome size modulate patterns of chromatin folding and that chromatin interactions facilitate the formation of lineage-specific chromosomal reorganizations, especially in germ cells. Overall, analyzing the mechanistic forces involved in the maintenance of chromatin structure and function of the germ line is critical for understanding genome evolution, maintenance, and inheritance.

Mapping the 3D genome architecture

The spatial organization of the genome plays a critical role in regulating gene expression, cellular differentiation, and genome stability. This review provides an in-depth examination of the methodologies, computational tools, and frameworks developed to map the three-dimensional (3D) architecture of the genome, focusing on both ligation-based and ligation-free techniques. We also explore the limitations of these methods, including biases introduced by restriction enzyme digestion and ligation inefficiencies, and compare them to more recent ligation-free approaches such as Genome Architecture Mapping (GAM) and Split-Pool Recognition of Interactions by Tag Extension (SPRITE). These techniques offer unique insights into higher-order chromatin structures by bypassing ligation steps, thus enabling the capture of complex multi-way interactions that are often challenging to resolve with traditional methods. Furthermore, we discuss the integration of chromatin interaction data with other genomic layers through multimodal approaches, including recent advances in single-cell technologies like sci-HiC and scSPRITE, which help unravel the heterogeneity of chromatin architecture in development and disease.

Genome structural dynamics: insights from Gaussian network analysis of Hi-C data

Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type-dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Hi-C and micro-C sequencing have shed light on the profound importance of 3D genome organization in cellular function by probing 3D contact frequencies across the linear genome. The resulting contact matrices are extremely sparse and susceptible to technical- and sequence-based biases, making their comparison challenging. The development of reliable, robust and efficient methods for quantifying similarity between contact matrices is crucial for investigating variations in the 3D genome organization in different cell types or under different conditions, as well as evaluating experimental reproducibility. We present a novel method, ENT3C, which measures the change in pattern complexity in the vicinity of contact matrix diagonals to quantify their similarity. ENT3C provides a robust, user-friendly Hi-C or micro-C contact matrix similarity metric and a characteristic entropy signal that can be used to gain detailed biological insights into 3D genome organization.

Chromatin region binning of gene expression for improving embryo cell subtype identification

Mammalian embryonic development is a complex process, characterized by intricate spatiotemporal dynamics and distinct chromatin preferences. However, the quick diversification in early embryogenesis leads to significant cellular diversity and the sparsity of scRNA-seq data, posing challenges in accurately determining cell fate decisions. In this study, we introduce a chromatin region binning method using scChrBin, designed to identify chromatin regions that elucidate the dynamics of embryonic development and lineage differentiation. This method transforms scRNA-seq data into a chromatin-based matrix, leveraging genomic annotations. Our results showed that the scChrBin method achieves high accuracy, with 98.0% and 89.2% on two single-cell embryonic datasets, demonstrating its effectiveness in analyzing complex developmental processes. We also systematically and comprehensively analysis of these key chromatin binning regions and their associated genes, focusing on their roles in lineage and stage development. The perspective of chromatin region binning method enables a comprehensive analysis of transcriptome data at the chromatin level, allowing us to unveil the dynamic expression of chromatin regions across temporal and spatial development. The tool is available as an application at https://github.com/liameihao/scChrBin.