Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data

pC-SAC: A method for high-resolution 3D genome reconstruction from low-resolution Hi-C data

The three-dimensional (3D) organization of the genome is crucial for gene regulation, with disruptions linked to various diseases. High-throughput Chromosome Conformation Capture (Hi-C) and related technologies have advanced our understanding of 3D genome organization by mapping interactions between distal genomic regions. However, capturing enhancer-promoter interactions at high resolution remains challenging due to the high sequencing depth required. We introduce pC-SAC (probabilistically Constrained Self-Avoiding Chromatin), a novel computational method for producing accurate high-resolution Hi-C matrices from low-resolution data. pC-SAC uses adaptive importance sampling with sequential Monte Carlo to generate ensembles of 3D chromatin chains that satisfy physical constraints derived from low-resolution Hi-C data. Our method achieves over 95% accuracy in reconstructing high-resolution chromatin maps and identifies novel interactions enriched with candidate cis-regulatory elements (cCREs) and expression quantitative trait loci (eQTLs). Benchmarking against state-of-the-art deep learning models demonstrates pC-SAC's performance in both short- and long-range interaction reconstruction. pC-SAC offers a cost-effective solution for enhancing the resolution of Hi-C data, thus enabling deeper insights into 3D genome organization and its role in gene regulation and disease. Our tool can be found at https://github.com/G2Lab/pCSAC.

Phase Space Invaders' podcast episode with Tamar Schlick: a trajectory from mathematics to biology

We present a transcript of the Phase Space Invaders podcast interview, with Tamar Schlick interviewed by Miłosz Wieczór. The conversation covers topics in computational biophysics and beyond: DNA and RNA research from genome organization to viral RNA frameshifting, transitioning from applied math to biology, developing algorithms and their utility in molecular dynamics and complex multiscale systems, the role of computers in biophysical research, writing reviews and books, collaborating in science, and using long-distance running as a template for building supportive communities.

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.

Reconstruction of the real 3D shape of the SARS-CoV-2 virus

The photographs of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus taken by electron transmission microscopy and cryoelectron microscopy provide only a 2D silhouette. The viruses appear to look like distorted circles. The present paper questions the real shape of the SARS-CoV-2 virus and makes an attempt to give an answer. Is this a general ellipsoid, a spheroid with rotational symmetry, a sphere, or something else? The answer requires the application of tools from three different disciplines: structural mechanics, microbiology, and statistics. A total of 590 virus photographs taken from 22 recently published papers were examined. From this experimental data pool, the histogram of diameter ratios was built from the 283 measurements where the virus images could be approximated as ellipses. The curve peaks at the diameter ratio of 1.22. The transformation equation for the spatial shape to the planar shade was derived for a fixed light source of the microscope. This equation involves an unknown orientation of the viruses with respect to the microscope. Two sets of models were developed, one with a uniform distribution of the virus orientation and the other with the orientation defined by the normalized beta distribution. In both sets of models, the unknown diameter ratio of the spheroidal virus was regarded as a random realization from translated gamma distributions. The parameters of the distribution of the kernel functions were determined by minimizing the mean square difference between the predicted and measured 2D histograms. The information included in the measured histograms was found to be insufficient to find an unknown distribution of the virus's orientation. Simply too many unknown parameters render the solution physically unrealistic. The minimization procedure with a uniform probability of virus orientation predicted the peak of the aspect ratio of the 3D spheroid at 1.32. Based on this result, models of the virus will be developed in the continuation of this research for a full dynamic analysis.

Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics

The structure and dynamics of the eukaryotic genome are intimately linked to gene regulation and transcriptional activity. Many chromosome conformation capture experiments like Hi-C have been developed to detect genome-wide contact frequencies and quantify loop/compartment structures for different cellular contexts and time-dependent processes. However, a full understanding of these events requires explicit descriptions of representative chromatin and chromosome configurations. With the exponentially growing amount of data from Hi-C experiments, many methods for deriving 3D structures from contact frequency data have been developed. Yet, most reconstruction methods use polymer models with low resolution to predict overall genome structure. Here we present a Brownian Dynamics (BD) approach termed Hi-BDiSCO for producing 3D genome structures from Hi-C and Micro-C data using our mesoscale-resolution chromatin model based on the Discrete Surface Charge Optimization (DiSCO) model. Our approach integrates reconstruction with chromatin simulations at nucleosome resolution with appropriate biophysical parameters. Following a description of our protocol, we present applications to the NXN, HOXC, HOXA and Fbn2 mouse genes ranging in size from 50 to 100 kb. Such nucleosome-resolution genome structures pave the way for pursuing many biomedical applications related to the epigenomic regulation of chromatin and control of human disease.