HiCMamba: Enhancing Hi-C resolution and identifying 3D genome structures with state space modeling

Studying the three-dimensional (3D) structure of a genome, including chromatin loops and Topologically Associating Domains (TADs), is essential for understanding how the genome is organized, such as gene activation, cell development, protein-protein interaction, etc. Hi-C protocol enables us to study 3D genome structure and organization. Chromatin 3D structure changes dynamically over time, and modeling these continuous changes is crucial for downstream analysis in various domains such as disease diagnosis, vaccine development, etc. The high expense and impracticality of continuous genome sequencing, particularly what evolves between two timestamps, limit the most effective genomic analysis. It is crucial to develop a straightforward and cost-efficient method for constantly generating genomic data between two timestamps in order to address these constraints. In this study, we developed HiCInterpolate, a 4D spatiotemporal interpolation architecture that accepts two timestamp Hi-C contact matrices to interpolate intermediate Hi-C contact matrices at high resolution. HiCInterpolate predicts the intermediate Hi-C contact map using a deep learning-based flow predictor, and a feature encoder and decoder architecture similar to U-Net. In addition, HiCInterpolate supports downstream analysis of multiple 3D genomic features, including A/B compartments, chromatin loops, TADs, and 3D genome structure, through an integrated analysis pipeline. Across multiple evaluation metrics, including PSNR, SSIM, GenomeDISCO, HiCRep, and LPIPS, HiCInterpolate achieved consistently strong performance. Biological validation further demonstrated preservation of key chromatin organization features, such as chromatin loops, A/B compartments, and TADs. Together, these results indicate that HiCInterpolate provides a robust computer vision-based framework for high-resolution interpolation of intermediate Hi-C contact matrices and facilitates biologically meaningful downstream analyses. HiCInterpolate is publicly available at https://github.com/OluwadareLab/HiCInterpolate .

Prostate cancer (PCa) aggressiveness and propensity to metastasize can be influenced by gene expression patterns, genomic aberrations, and overall cellular and nuclear morphology. The 3D folding of chromosomes into loops, domains, and territories as they are packaged into the nucleus plays a key role in all of these processes, affecting proper gene regulation, DNA replication and repair, and nuclear physical properties. However, while nuclear atypia has long been used as a marker of PCa progression, little is known about what changes in 3D genome interactions occur or how these may influence cancer-promoting gene expression profiles or the nuclear malleability necessary for cancer cells to metastasize. Thus, we set out to characterize the role of the 3D genome changes in PCa, analyzing changes in the 3D genome structure of cell lines that model the continuum of disease. Using chromosome conformation capture (Hi-C) we have conducted a systematic comparison of the 3D genome structure of a normal epithelium model cell line (RWPE) to that of adenocarcinoma (LNCaP), bone metastatic (VCaP, PC3, 22RV1, LNCaP-C42B, MDaPCa2a and MDAPCa2b), and atypical metastasis (Brain - DU145) lines. Our study has shown that the open/closed chromatin compartmentalization identity (A vs B compartment) changes with progression. We have characterized a cohort of 316 genes whose compartmentalization changes from the B to the A compartment, suggesting that those genes become poised for transcription. These include androgen receptor, WNT5A and CDK14, as well as genes located in proximity to TMPRSS2, such as BACE2. The positive correlation between switches of compartmentalization identity and transcription activation has been confirmed by microarray. Of these B/A switch genes, 256 are grouped in 48 proximal clusters. We have also identified 84 genes whose compartments switch from the A to B compartment, suggesting a genomic rearrangement that could result in silencing. Such is the case of certain cadherins, annexins and mediators of inflammation. Of these, 64 are grouped in 16 clusters. Finally, we have observed that compartmentalization changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation; one of the main drivers of prostate cancer, present in 40% - 80% of prostate cancer patients. Interestingly, changes in gene transcription are also accompanied by changes in the structure, size, and boundaries of the topologically associating domains (TADs) surrounding the loci of interest, suggesting that 3D genome structure plays a role in the activation of alternate pathways for proliferation and survival. Citation Format: Rebeca San Martin, Priyojit Das, Renata Dos Reis Marques, Rachel P. McCord. Rearrangement of the 3D genome structure in prostate cancer is a potential mechanism for disease exacerbation and classifies cancers according to progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2033.

Topologically associated domains (TADs) represent essential units constituting chromatin’s intricate three-dimensional spatial organization. TADs are stably present across cell types and species, and their influence on vital biological processes, such as gene expression, DNA replication, and chromosomal translocation, underscores their significance. Accordingly, the identification of TADs within the Hi-C interaction matrix is a key point in three-dimensional genomics. TADs manifest as contiguous blocks along the diagonal of the Hi-C interaction matrix, which are characterized by dense interactions within blocks and sparse interactions between blocks. An optimization method is proposed to enhance Hi-C interaction matrix data using the empirical mode decomposition method, which requires no prior knowledge and adaptively decomposes Hi-C data into a sum of multiple eigenmodal functions via exploiting the inherent characteristics of variations in the input Hi-C data. We identify TADs within the optimized data and compared the results with five commonly used TAD detection methods, namely the Directionality Index (DI), Interaction Isolation (IS), HiCKey, HiCDB, and TopDom. The results demonstrate the universality and efficiency of the proposed method, highlighting its potential as a valuable tool in TAD identification.

BackgroundHi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances or the locations of topologically associating domains (TADs).ResultsWe have addressed one of the key issues of using Hi-C data, that is, the unclear relationship between spatial distances and the number of Hi-C contacts, which is crucial to understand significant biological functions, such as the enhancer-promoter interactions. Specifically, we developed a new method to infer this converting parameter and pairwise Euclidean distances based on the topology of the Hi-C complex network (HiCNet). The inferred distances were modeled by clustering coefficient and multiple other types of constraints. We found that our inferred distances between bead-pairs within the same TAD were apparently smaller than those distances between bead-pairs from different TADs. Our inferred distances had a higher correlation with fluorescence in situ hybridization (FISH) data, fitted the localization patterns of Xist transcripts on DNA, and better matched 156 pairs of protein-enabled long-range chromatin interactions detected by ChIA-PET. Using the inferred distances and another round of optimization, we further reconstructed 40 kb high-resolution 3D chromosomal structures of mouse male ES cells. The high-resolution structures successfully illustrate TADs and DNA loops (peaks in Hi-C contact heatmaps) that usually indicate enhancer-promoter interactions.ConclusionsWe developed a novel method to infer the wish distances between DNA bead-pairs from Hi-C contacts. High-resolution 3D structures of chromosomes were built based on the newly-inferred wish distances. This whole process has been implemented as a tool named HiCNet, which is publicly available at http://dna.cs.miami.edu/HiCNet/.

Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.

The eukaryotic chromatin is folded into highly complex three-dimensional (3D) structures, which plays an important role in the precise regulation of gene expression and normal physiological function. During differentiation and terminal maturation, cells usually undergo dramatic morphology and gene expression changes, accompanied by significant changes in the 3D structure of the genome. In this review, we provide a comprehensive view of the spatial hierarchical organization of the genome, including chromosome territories, A/B compartment, topologically associating domains (TADs) and looping, focusing on recent progresses in the dynamic 3D genomic structural changes and functional regulation during cell differentiation and terminal maturation. In the end, we summarize the unsolved issues as well as prospects of the 3D genome research in cell differentiation and maturation.

177 Integrative computational analysis identifies eQTL-lincRNA pairs associated with alopecia areata

The three-dimensional structure of chromosomes plays an important role in gene expression regulation and also influences the repair of radiation-induced DNA damage. Genomic aberrations that disrupt chromosome spatial domains can lead to diseases including cancer, but how the 3D genome structure responds to DNA damage is poorly understood. Here, we investigate the impact of DNA damage response and repair on 3D genome folding using Hi-C experiments on wild type cells and ataxia telangiectasia mutated (ATM) patient cells. We irradiate fibroblasts, lymphoblasts, and ATM-deficient fibroblasts with 5 Gy X-rays and perform Hi-C at 30 minutes, 24 hours, or 5 days after irradiation. We observe that 3D genome changes after irradiation are cell type-specific, with lymphoblastoid cells generally showing more contact changes than irradiated fibroblasts. However, all tested repair-proficient cell types exhibit an increased segregation of topologically associating domains (TADs). This TAD boundary strengthening after irradiation is not observed in ATM deficient fibroblasts and may indicate the presence of a mechanism to protect 3D genome structure integrity during DNA damage repair.

The hierarchical folding dynamics of topologically associating domains are closely related to transcriptional abnormalities in cancers

Hi-C technique is an important tool for the study of 3D genome organization. In the past few years, we have seen an explosion of Hi-C data in a variety of cell/tissue types. While these publicly available data presents an unprecedented opportunity to interrogate chromosomal architecture, how to quantitatively compare Hi-C data from different tissues and identify tissue-specific chromatin interactions remains challenging. Here, we present HiCComp, a comprehensive framework for comparing Hi-C data. HiCComp utilizes convolutional neural networks to extract key features in Hi-C interaction matrices in a fully automatic way. The core component of HiCComp is a triplet network, which contains three identical convolutional neural networks with shared parameters. The inputs to our network are three Hi-C matrices: two of them are biological replicates from the same cell type and the third one is from another cell type. The HiCComp network takes advantages of the two biological replicates to estimate the natural variation in the experiments and further use it to identify significant variations between Hi-C matrices from different cell types. Furthermore, we incorporate systematic occluding method into our framework so that we can identify the dynamic interaction regions from Hi-C maps. Finally, we show that the dynamic regions between two cell types are enriched for transcription factor binding sites and histone modifications that are associated with cis-regulatory functions, suggesting these variations in 3D genome structure are potentially gene regulatory events.

Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks.

Chromosome conformation capture technologies such as Hi-C are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and interchromosomal interactions. We pioneered a probabilistic approach for the generation of a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome's organization in space and time. We provide a user-friendly software package, called PGS, which runs on local machines (for practice runs) and high-performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix, along with information about genome segmentation, and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command-line knowledge is sufficient for using this software. A typical running time of the pipeline is ∼3 d with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at topological-associated domain (TAD)-level resolution.

TADs and Their Borders: Free Movement or Building a Wall?

Animal genomes are organized into topologically associated domains (TADs). TADs are thought to contribute to gene regulation by facilitating enhancer-promoter (E-P) contacts within a TAD and preventing these contacts across TAD borders. However, the absolute difference in contact frequency across TAD boundaries is usually less than 2-fold, even though disruptions of TAD borders can change gene expression by 10-fold. Existing models fail to explain this hypersensitive response. Here, we propose a futile cycle model of enhancer-mediated regulation that can exhibit hypersensitivity through bistability and hysteresis. Consistent with recent experiments, this regulation does not exhibit strong correlation between E-P contact and promoter activity, even though regulation occurs through contact. Through mathematical analysis and stochastic simulation, we show that this system can create an illusion of E-P biochemical specificity and explain the importance of weak TAD boundaries. It also offers a mechanism to reconcile apparently contradictory results from recent global TAD disruption with local TAD boundary deletion experiments. Together, these analyses advance our understanding of cis-regulatory contacts in controlling gene expression and suggest new experimental directions.

It is already more than 10 years after the completion of Human Genome Project. However, the mechanism about how genomic information guides the gene expression in a particular space and time remains to be elucidated. Structure determines function is a consensus that we understand the laws of nature. In biology, this consensus is not only applicable to RNA, protein and other small-scale organic molecules; the same should also apply to macromolecules such as the entire DNA composition of the whole genome. Recent studies have shown that three-dimensional (3D) structure of the genome has an important impact on the regulation of gene transcription and other nuclear functions. Therefore, the study of the 3D structure and function of the whole genome (referred to as 3D genomics) has become a new trend of development in genomics. With the consideration of the genome sequence, gene structure and regulatory elements at the same time, 3D genomics studies the 3D structure of the genome in the nucleus, and its impact on gene transcription, replication, repair, regulation and others biological processes. With the progress of large-scale genome sequencing technology, we already have the relevant technologies such as ChIA-PET and Hi-C for the study of 3D genomics. Preliminary results showed the influence of genome 3D structures to genome functions, which provides the basis for further study of genome structure and function in the spatial nuclear space. This paper summarizes the emergence of 3D genomics, lists the issues and the current progress in this new field, and discusses possible applications of 3D genomics to human health and agriculture researches. We anticipate that 3D genomics will provide novel insights into the genome regulatory functions and provide new opportunities of improving human life qualities.