Si-C is a method for inferring super-resolution intact genome structure from single-cell Hi-C data.
Ontology highlight
ABSTRACT:
SUBMITTER: Meng L
PROVIDER: S-EPMC8285481 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:MotivationAccurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types.ResultsHere, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains.Availability and implementationhttps://github.com/BlanchetteLab/RefHiC.
| S-EPMC10311349 | biostudies-literature
Project description:MotivationHi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data.ResultsWe proposed hicGAN, an open-sourced framework, for inferring high resolution Hi-C data from low resolution Hi-C data with generative adversarial networks (GANs). To the best of our knowledge, this is the first study to apply GANs to 3D genome analysis. We demonstrate that hicGAN effectively enhances the resolution of low resolution Hi-C data by generating matrices that are highly consistent with the original high resolution Hi-C matrices. A typical scenario of usage for our approach is to enhance low resolution Hi-C data in new cell types, especially where the high resolution Hi-C data are not available. Our study not only presents a novel approach for enhancing Hi-C data resolution, but also provides fascinating insights into disclosing complex mechanism underlying the formation of chromatin contacts.Availability and implementationWe release hicGAN as an open-sourced software at https://github.com/kimmo1019/hicGAN.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6612845 | biostudies-literature
Project description:Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information.
| S-EPMC3358321 | biostudies-literature
Project description:BackgroundThe nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations.ResultsA number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromosome ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells.ConclusionsOur analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demonstrate which stages of the approach can extract meaningful information, and also describe limitations of pairwise contacts alone to predict absolute 3D positions.
| S-EPMC7654587 | biostudies-literature
Project description:Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data.
| S-EPMC5226817 | biostudies-literature
Project description:X-ray diffraction plays a pivotal role in the understanding of biological systems by revealing atomic structures of proteins, nucleic acids and their complexes, with much recent interest in very large assemblies like the ribosome. As crystals of such large assemblies often diffract weakly (resolution worse than 4 A), we need methods that work at such low resolution. In macromolecular assemblies, some of the components may be known at high resolution, whereas others are unknown: current refinement methods fail as they require a high-resolution starting structure for the entire complex. Determining the structure of such complexes, which are often of key biological importance, should be possible in principle as the number of independent diffraction intensities at a resolution better than 5 A generally exceeds the number of degrees of freedom. Here we introduce a method that adds specific information from known homologous structures but allows global and local deformations of these homology models. Our approach uses the observation that local protein structure tends to be conserved as sequence and function evolve. Cross-validation with R(free) (the free R-factor) determines the optimum deformation and influence of the homology model. For test cases at 3.5-5 A resolution with known structures at high resolution, our method gives significant improvements over conventional refinement in the model as monitored by coordinate accuracy, the definition of secondary structure and the quality of electron density maps. For re-refinements of a representative set of 19 low-resolution crystal structures from the Protein Data Bank, we find similar improvements. Thus, a structure derived from low-resolution diffraction data can have quality similar to a high-resolution structure. Our method is applicable to the study of weakly diffracting crystals using X-ray micro-diffraction as well as data from new X-ray light sources. Use of homology information is not restricted to X-ray crystallography and cryo-electron microscopy: as optical imaging advances to subnanometre resolution, it can use similar tools.
| S-EPMC2859093 | biostudies-literature
Project description:Super-resolution fluorescence microscopy has revolutionized multicolor imaging of nuclear structures due to the combination of high labeling specificity and high resolution. Here we expanded the recently developed fBALM (DNA structure fluctuation-assisted binding activated localization microscopy) method by developing a stable methodological sequence that enables dual-color imaging of high-resolution genomic DNA together with an immunofluorescently labeled intranuclear protein. Our measurements of the nuclear periphery, imaging DNA and LaminB1 in biologically relevant samples, show that this novel dual-color imaging method is feasible for further quantitative evaluations. We were able to study the relative spatial signal organization between DNA and LaminB1 by means of highly specific colocalization measurements at nanometer resolution. Measurements were performed with and without the antifade embedding medium ProLong Gold, which proved to be essential for imaging of LaminB1, but not for imaging of SytoxOrange labeled DNA. The localization precision was used to differentiate between localizations with higher and lower amounts of emitting photons. We interpret high intensity localizations to be renatured DNA sections in which a high amount of Sytox Orange molecules were bound. This could give insight into the denaturation kinetics of DNA during fBALM. These results were further complemented by measurements of γH2AX and H3K9me3 signal organization to demonstrate differences within the chromatin landscape, which were quantified with image processing methods such as Voronoi segmentation.
| S-EPMC9813922 | biostudies-literature
Project description:The successful application of gene therapy relies on the development of safe and efficient delivery vectors. Cationic polymers such as cell-penetrating peptides (CPPs) can condense genetic material into nanoscale particles, called polyplexes, and induce cellular uptake. With respect to this point, several aspects of the nanoscale structure of polyplexes have remained elusive because of the difficulty in visualizing the molecular arrangement of the two components with nanometer resolution. This limitation has hampered the rational design of polyplexes based on direct structural information. Here, we used super-resolution imaging to study the structure and molecular composition of individual CPP-mRNA polyplexes with nanometer accuracy. We use two-color direct stochastic optical reconstruction microscopy (dSTORM) to unveil the impact of peptide stoichiometry on polyplex structure and composition and to assess their destabilization in blood serum. Our method provides information about the size and composition of individual polyplexes, allowing the study of such properties on a single polyplex basis. Furthermore, the differences in stoichiometry readily explain the differences in cellular uptake behavior. Thus, quantitative dSTORM of polyplexes is complementary to the currently used characterization techniques for understanding the determinants of polyplex activity in vitro and inside cells.
| S-EPMC6509642 | biostudies-literature
Project description:The problem of three-dimensional (3D) chromosome structure inference from Hi-C data sets is important and challenging. While bulk Hi-C data sets contain contact information derived from millions of cells and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single-cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named Structural Inference via Multiscale Bayesian Approach, to infer 3D structures of chromosomes from single-cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C data set as a function of prior strength and demonstrate clustering of solutions using data from the same cell.
| S-EPMC6856950 | biostudies-literature
Project description:SummaryIdentification of functional transcriptional regulators associated with chromatin in-teractions is an important problem in studies of 3-dimensional genome organization and gene regulation. Direct inference of TR binding has been limited by the resolu-tion of Hi-C data. Here, we present BART3D, a computational method for inferring TRs associated with genome-wide differential chromatin interactions by comparing Hi-C maps from two states, leveraging public ChIP-seq data for human and mouse. We demonstrate that BART3D can detect relevant TRs from dynamic Hi-C profiles with TR perturbation or cell differentiation. BART3D can be a useful tool in 3D genome data analysis and functional genomics research.Availability and implementationBART3D is implemented in Python and the source code is available at https://github.com/zanglab/bart3d.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8479658 | biostudies-literature