The statistical-mechanics of chromosome conformation capture

Multiplex-GAM: genome-wide identification of chromatin contacts yields insights overlooked by Hi-C

Technology for measuring 3D genome topology is increasingly important for studying gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of genome architecture mapping (GAM), a ligation-free technique that maps chromatin contacts genome-wide. We perform a detailed comparison of multiplex-GAM and Hi-C using mouse embryonic stem cells. When examining the strongest contacts detected by either method, we find that only one-third of these are shared. The strongest contacts specifically found in GAM often involve 'active' regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain 'inactive' regions. Our work shows that active genomic regions are involved in extensive complex contacts that are currently underestimated in ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

Long range inter-chromosomal interaction of Oct4 distal enhancer loci regulates ESCs pluripotency

Nuclear architecture underlies the transcriptional programs within the cell to establish cell identity. As previously demonstrated, long-range chromatin interactions of the Oct4 distal enhancer (DE) are correlated with active transcription in naïve state embryonic stem cells. Here, we identify and characterize extreme long-range interactions of the Oct4 DE through a novel CRISPR labeling technique we developed and chromosome conformation capture to identify lethal giant larvae 2 (Llgl2) and growth factor receptor-bound protein 7 (Grb7) as putative functional interacting target genes in different chromosomes. We show that the Oct4 DE directly regulates expression of Llgl2 and Grb7 in addition to Oct4. Expression of Llgl2 and Grb7 closely correlates with the pluripotent state, where knock down of either result in loss of pluripotency, and overexpression enhances somatic cell reprogramming. We demonstrated that biologically important interactions of the Oct4 DE can occur at extreme distances that are necessary for the maintenance of the pluripotent state.

Cell type-specific chromatin topology and gene regulation

Chromatin structure is critically involved in gene regulation and cell fate determination. How this structure is established and maintained in distinct, terminally differentiated cells remains elusive. Winick-Ng et al. address this puzzle by applying immunoGAM in different brain cell types and reveal cell type-specific chromatin topologies, long gene decompaction, and the involvement of transcription factors (TFs).

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Single-cell measurement of higher-order 3D genome organization with scSPRITE

Although three-dimensional (3D) genome organization is central to many aspects of nuclear function, it has been difficult to measure at the single-cell level. To address this, we developed 'single-cell split-pool recognition of interactions by tag extension' (scSPRITE). scSPRITE uses split-and-pool barcoding to tag DNA fragments in the same nucleus and their 3D spatial arrangement. Because scSPRITE measures multiway DNA contacts, it generates higher-resolution maps within an individual cell than can be achieved by proximity ligation. We applied scSPRITE to thousands of mouse embryonic stem cells and detected known genome structures, including chromosome territories, active and inactive compartments, and topologically associating domains (TADs) as well as long-range inter-chromosomal structures organized around various nuclear bodies. We observe that these structures exhibit different levels of heterogeneity across the population, with TADs representing dynamic units of genome organization across cells. We expect that scSPRITE will be a critical tool for studying genome structure within heterogeneous populations.

Transcriptional enhancers: from prediction to functional assessment on a genome-wide scale

Enhancers are cis-regulatory sequences located distally to target genes. These sequences consolidate developmental and environmental cues to coordinate gene expression in a tissue-specific manner. Enhancer function and tissue specificity depend on the expressed set of transcription factors, which recognize binding sites and recruit cofactors that regulate local chromatin organization and gene transcription. Unlike other genomic elements, enhancers are challenging to identify because they function independently of orientation, are often distant from their promoters, have poorly defined boundaries, and display no reading frame. In addition, there are no defined genetic or epigenetic features that are unambiguously associated with enhancer activity. Over recent years there have been developments in both empirical assays and computational methods for enhancer prediction. We review genome-wide tools, CRISPR advancements, and high-throughput screening approaches that have improved our ability to both observe and manipulate enhancers in vitro at the level of primary genetic sequences, chromatin states, and spatial interactions. We also highlight contemporary animal models and their importance to enhancer validation. Together, these experimental systems and techniques complement one another and broaden our understanding of enhancer function in development, evolution, and disease.

SpectralTAD: an R package for defining a hierarchy of topologically associated domains using spectral clustering

BACKGROUND

The three-dimensional (3D) structure of the genome plays a crucial role in gene expression regulation. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), sub-TADs, and chromatin loops. Identifying such hierarchical structures is a critical step in understanding genome regulation. Existing tools for TAD calling are frequently sensitive to biases in Hi-C data, depend on tunable parameters, and are computationally inefficient.

METHODS

To address these challenges, we developed a novel sliding window-based spectral clustering framework that uses gaps between consecutive eigenvectors for TAD boundary identification.

RESULTS

Our method, implemented in an R package, SpectralTAD, detects hierarchical, biologically relevant TADs, has automatic parameter selection, is robust to sequencing depth, resolution, and sparsity of Hi-C data. SpectralTAD outperforms four state-of-the-art TAD callers in simulated and experimental settings. We demonstrate that TAD boundaries shared among multiple levels of the TAD hierarchy were more enriched in classical boundary marks and more conserved across cell lines and tissues. In contrast, boundaries of TADs that cannot be split into sub-TADs showed less enrichment and conservation, suggesting their more dynamic role in genome regulation.

CONCLUSION

SpectralTAD is available on Bioconductor, http://bioconductor.org/packages/SpectralTAD/ .

multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

MOTIVATION

With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets.

RESULTS

Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights.

AVAILABILITY AND IMPLEMENTATION

multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolving methodologies and concepts in 4D nucleome research

The genome requires tight regulation in space and time to maintain viable cell functions. Advances in our understanding of the 3D genome show a complex hierarchical network of structures, involving compartments, membraneless bodies, topologically associating domains, lamina associated domains, protein- or RNA-mediated loops, enhancer-promoter contacts, and accessible chromatin regions, with chromatin state regulation through epigenetic and transcriptional mechanisms. Further technology developments are poised to increase genomic resolution, dissect single-cell behaviors, including in vivo dynamics of genome folding, and provide mechanistic perspectives that identify further 3D genome players by integrating multiomics information. We highlight recent key developments in 4D nucleome methodologies and give a perspective on their future directions.

Bayesian inference of chromatin structure ensembles from population-averaged contact data

Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation. Chromosome conformation capture techniques allow us to characterize chromatin structure by mapping contacts between chromosomal loci on a genome-wide scale. The most widespread modality is to measure contact frequencies averaged over a population of cells. Single-cell variants exist, but suffer from low contact numbers and have not yet gained the same resolution as population methods. While intriguing biological insights have already been garnered from ensemble-averaged data, information about three-dimensional (3D) genome organization in the underlying individual cells remains largely obscured because the contact maps show only an average over a huge population of cells. Moreover, computational methods for structure modeling of chromatin have mostly focused on fitting a single consensus structure, thereby ignoring any cell-to-cell variability in the model itself. Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way. We illustrate our approach on simulated data and compute multistate models of chromatin from chromosome conformation capture carbon copy (5C) data. Comparison with independent data suggests that the inferred ensembles represent the underlying sample population faithfully. Harnessing the rich information contained in multistate models, we investigate cell-to-cell variability of chromatin organization into topologically associating domains, thus highlighting the ability of our approach to deliver insights into chromatin organization of great biological relevance.

Methods for mapping 3D chromosome architecture

Determining how chromosomes are positioned and folded within the nucleus is critical to understanding the role of chromatin topology in gene regulation. Several methods are available for studying chromosome architecture, each with different strengths and limitations. Established imaging approaches and proximity ligation-based chromosome conformation capture (3C) techniques (such as DNA-FISH and Hi-C, respectively) have revealed the existence of chromosome territories, functional nuclear landmarks (such as splicing speckles and the nuclear lamina) and topologically associating domains. Improvements to these methods and the recent development of ligation-free approaches, including GAM, SPRITE and ChIA-Drop, are now helping to uncover new aspects of 3D genome topology that confirm the nucleus to be a complex, highly organized organelle.

Disentangling chromatin architecture to gain insights into the etiology of brain disorders

Chromatin organization, together with DNA and histone modifications, is directly linked to the spatiotemporal control of gene expression that specifies and maintains cell type-specific functions. This is particularly important in the brain where hundreds of cell types with distinct functions reside. Recent advances in molecular and computational technologies have enabled the query of chromatin architecture at unprecedented resolution and detail. Here, we review recent studies on the emerging importance of chromatin architecture in the pathogenesis of brain disorders, with emphasis on schizophrenia, autism spectrum disorders (ASD), and unstable repeat expansion disorders. These studies provide molecular insights into how these brain disorders arise at the level of chromatin architecture and implicate new therapeutic directions.

Selfish: discovery of differential chromatin interactions via a self-similarity measure

MOTIVATION

High-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.

RESULTS

We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ucrbioinfo/Selfish.

R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets

The three-dimensional (3D) interactions of chromatin regulate cell-type-specific gene expression, recombination, X-chromosome inactivation, and many other genomic processes. High-throughput chromatin conformation capture (Hi-C) technologies capture the structure of the chromatin on a global scale by measuring all-vs.-all interactions and can provide new insights into genomic regulation. The workflow presented here describes how to analyze and interpret a comparative Hi-C experiment. We describe the process of obtaining Hi-C data from public repositories and give suggestions for pre-processing pipelines for users who intend to analyze their own raw data. We then describe the data normalization and comparative analysis process. We present three protocols describing the use of the multiHiCcompare, diffHic, and FIND R packages, respectively, to perform a comparative analysis of Hi-C experiments. Finally, visualization of the results and downstream interpretation of the differentially interacting regions are discussed. The bulk of this tutorial uses the R programming environment, and the processes described can be performed with most operating systems and a single computer. © 2019 by John Wiley & Sons, Inc.

Extensive Heterogeneity and Intrinsic Variation in Spatial Genome Organization

Several general principles of global 3D genome organization have recently been established, including non-random positioning of chromosomes and genes in the cell nucleus, distinct chromatin compartments, and topologically associating domains (TADs). However, the extent and nature of cell-to-cell and cell-intrinsic variability in genome architecture are still poorly characterized. Here, we systematically probe heterogeneity in genome organization. High-throughput optical mapping of several hundred intra-chromosomal interactions in individual human fibroblasts demonstrates low association frequencies, which are determined by genomic distance, higher-order chromatin architecture, and chromatin environment. The structure of TADs is variable between individual cells, and inter-TAD associations are common. Furthermore, single-cell analysis reveals independent behavior of individual alleles in single nuclei. Our observations reveal extensive variability and heterogeneity in genome organization at the level of individual alleles and demonstrate the coexistence of a broad spectrum of genome configurations in a cell population.

Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus

Eukaryotic genomes are packaged into a 3-dimensional structure in the nucleus. Current methods for studying genome-wide structure are based on proximity ligation. However, this approach can fail to detect known structures, such as interactions with nuclear bodies, because these DNA regions can be too far apart to directly ligate. Accordingly, our overall understanding of genome organization remains incomplete. Here, we develop split-pool recognition of interactions by tag extension (SPRITE), a method that enables genome-wide detection of higher-order interactions within the nucleus. Using SPRITE, we recapitulate known structures identified by proximity ligation and identify additional interactions occurring across larger distances, including two hubs of inter-chromosomal interactions that are arranged around the nucleolus and nuclear speckles. We show that a substantial fraction of the genome exhibits preferential organization relative to these nuclear bodies. Our results generate a global model whereby nuclear bodies act as inter-chromosomal hubs that shape the overall packaging of DNA in the nucleus.

A cautionary note on the use of chromosome conformation capture in plants

BACKGROUND

The chromosome conformation capture (3C) technique is a method to study chromatin interactions at specific genomic loci. Initially established for yeast the 3C technique has been adapted to plants in recent years in order to study chromatin interactions and their role in transcriptional gene regulation. As the plant scientific community continues to implement this technology, a discussion on critical controls, validations steps and interpretation of 3C data is essential to fully benefit from 3C in plants.

RESULTS

Here we assess the reliability and robustness of the 3C technique for the detection of chromatin interactions in Arabidopsis. As a case study, we applied this methodology to the genomic locus of a floral integrator gene SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1), and demonstrate the need of several controls and standard validation steps to allow a meaningful interpretation of 3C data. The intricacies of this promising but challenging technique are discussed in depth.

CONCLUSIONS

The 3C technique offers an interesting opportunity to study chromatin interactions at a resolution infeasible by microscopy. However, for interpretation of 3C interaction data and identification of true interactions, 3C technology demands a stringent experimental setup and extreme caution.

Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction

Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D chromatin units and looping paradigm is the basic principle of gene regulation. To better interpret the 3D data dramatically accumulating in past five years and to gain deep biological insights, huge efforts have been made in developing novel quantitative analysis methods. However, the full understanding of genome regulation requires thorough knowledge in both genomic technologies and their related data analyses. We summarize the recent advances in genomic technologies in identifying the 3D chromatin structure and interaction, and illustrate the quantitative analysis methods to infer functional domains and chromatin interactions, and further elucidate the emerging single-cell Hi-C technique and its computational analysis, and finally discuss the future directions such as advances of 3D chromatin techniques in diseases.

Complex multi-enhancer contacts captured by Genome Architecture Mapping (GAM)

The organization of the genome in the nucleus and the interactions of genes with their regulatory elements are key features of transcriptional control and their disruption can cause disease. We developed a novel genome-wide method, Genome Architecture Mapping (GAM), for measuring chromatin contacts, and other features of three-dimensional chromatin topology, based on sequencing DNA from a large collection of thin nuclear sections. We apply GAM to mouse embryonic stem cells and identify an enrichment for specific interactions between active genes and enhancers across very large genomic distances, using a mathematical model ‘SLICE’ (Statistical Inference of Co-segregation). GAM also reveals an abundance of three-way contacts genome-wide, especially between regions that are highly transcribed or contain super-enhancers, highlighting a previously inaccessible complexity in genome architecture and a major role for gene-expression specific contacts in organizing the genome in mammalian nuclei.

The nuclear higher-order structure defined by the set of topological relationships between DNA and the nuclear matrix is species-specific in hepatocytes

During the interphase the nuclear DNA of metazoan cells is organized in supercoiled loops anchored to constituents of a nuclear substructure or compartment known as the nuclear matrix. The stable interactions between DNA and the nuclear matrix (NM) correspond to a set of topological relationships that define a nuclear higher-order structure (NHOS). Current evidence suggests that the NHOS is cell-type-specific. Biophysical evidence and theoretical models suggest that thermodynamic and structural constraints drive the actualization of DNA-NM interactions. However, if the topological relationships between DNA and the NM were the subject of any biological constraint with functional significance then they must be adaptive and thus be positively selected by natural selection and they should be reasonably conserved, at least within closely related species. We carried out a coarse-grained, comparative evaluation of the DNA-NM topological relationships in primary hepatocytes from two closely related mammals: rat and mouse, by determining the relative position to the NM of a limited set of target sequences corresponding to highly-conserved genomic regions that also represent a sample of distinct chromosome territories within the interphase nucleus. Our results indicate that the pattern of topological relationships between DNA and the NM is not conserved between the hepatocytes of the two closely related species, suggesting that the NHOS, like the karyotype, is species-specific.

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

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.

4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?

Genome-wide sequencing technologies enable investigations of the structural properties of the genome in various spatial dimensions. Here, we review computational techniques developed to model the three-dimensional genome in single cells versus ensembles of cells and assess their underlying assumptions. We further address approaches to study the spatio-temporal aspects of genome organization from single-cell data.

The interphase mammalian chromosome as a structural system based on tensegrity

Each mammalian chromosome is constituted by a DNA fiber of macroscopic length that needs to be fitted in a microscopic nucleus. The DNA fiber is subjected at physiological temperature to random thermal bending and looping that must be constrained so as achieve structural stability thus avoiding spontaneous rupturing of the fiber. Standard textbooks assume that chromatin proteins are primarily responsible for the packaging of DNA and so of its protection against spontaneous breakage. Yet the dynamic nature of the interactions between chromatin proteins and DNA is unlikely to provide the necessary long-term structural stability for the chromosomal DNA. On the other hand, longstanding evidence indicates that stable interactions between DNA and constituents of a nuclear compartment commonly known as the nuclear matrix organize the chromosomal DNA as a series of topologically constrained, supercoiled loops during interphase. This results in a primary level of DNA condensation and packaging within the nucleus, as well as in protection against spontaneous DNA breakage, independently of chromatin proteins which nevertheless increase and dynamically modulate the degree of DNA packaging and its role in the regulation of DNA function. Thus current evidence, presented hereunder, supports a model for the organization of the interphase chromosome as resilient system that satisfies the principles of structural tensegrity.

In the loop: promoter-enhancer interactions and bioinformatics

Enhancer-promoter regulation is a fundamental mechanism underlying differential transcriptional regulation. Spatial chromatin organization brings remote enhancers in contact with target promoters in cis to regulate gene expression. There is considerable evidence for promoter-enhancer interactions (PEIs). In the recent years, genome-wide analyses have identified signatures and mapped novel enhancers; however, being able to precisely identify their target gene(s) requires massive biological and bioinformatics efforts. In this review, we give a short overview of the chromatin landscape and transcriptional regulation. We discuss some key concepts and problems related to chromatin interaction detection technologies, and emerging knowledge from genome-wide chromatin interaction data sets. Then, we critically review different types of bioinformatics analysis methods and tools related to representation and visualization of PEI data, raw data processing and PEI prediction. Lastly, we provide specific examples of how PEIs have been used to elucidate a functional role of non-coding single-nucleotide polymorphisms. The topic is at the forefront of epigenetic research, and by highlighting some future bioinformatics challenges in the field, this review provides a comprehensive background for future PEI studies.

Manifold Based Optimization for Single-Cell 3D Genome Reconstruction

The three-dimensional (3D) structure of the genome is important for orchestration of gene expression and cell differentiation. While mapping genomes in 3D has for a long time been elusive, recent adaptations of high-throughput sequencing to chromosome conformation capture (3C) techniques, allows for genome-wide structural characterization for the first time. However, reconstruction of "consensus" 3D genomes from 3C-based data is a challenging problem, since the data are aggregated over millions of cells. Recent single-cell adaptations to the 3C-technique, however, allow for non-aggregated structural assessment of genome structure, but data suffer from sparse and noisy interaction sampling. We present a manifold based optimization (MBO) approach for the reconstruction of 3D genome structure from chromosomal contact data. We show that MBO is able to reconstruct 3D structures based on the chromosomal contacts, imposing fewer structural violations than comparable methods. Additionally, MBO is suitable for efficient high-throughput reconstruction of large systems, such as entire genomes, allowing for comparative studies of genomic structure across cell-lines and different species.

Understanding how the genome is folded in three-dimensional (3D) space is crucial for unravelling the complex regulatory mechanisms underlying the differentiation and proliferation of cells. With recent high-throughput adaptations of chromosome conformation capture in techniques such as single-cell Hi-C, it is now possible to probe 3D information of chromosomes genome-wide. Such experiments, however, only provide sparse information about contacts between regions in the genome. We have developed a tool, based on manifold based optimization (MBO), that reconstructs 3D structures from such contact information. We show that MBO allows for reconstruction of 3D genomes more consistent with the original contact map, and with fewer structural violations compared to other, related methods. Since MBO is also computationally fast, it can be used for high-throughput and large-scale 3D reconstruction of entire genomes.

Topology, structures, and energy landscapes of human chromosomes

Chromosome conformation capture experiments provide a rich set of data concerning the spatial organization of the genome. We use these data along with a maximum entropy approach to derive a least-biased effective energy landscape for the chromosome. Simulations of the ensemble of chromosome conformations based on the resulting information theoretic landscape not only accurately reproduce experimental contact probabilities, but also provide a picture of chromosome dynamics and topology. The topology of the simulated chromosomes is probed by computing the distribution of their knot invariants. The simulated chromosome structures are largely free of knots. Topologically associating domains are shown to be crucial for establishing these knotless structures. The simulated chromosome conformations exhibit a tendency to form fibril-like structures like those observed via light microscopy. The topologically associating domains of the interphase chromosome exhibit multistability with varying liquid crystalline ordering that may allow discrete unfolding events and the landscape is locally funneled toward "ideal" chromosome structures that represent hierarchical fibrils of fibrils.

Three-dimensional genome architecture: players and mechanisms

The different cell types of an organism share the same DNA, but during cell differentiation their genomes undergo diverse structural and organizational changes that affect gene expression and other cellular functions. These can range from large-scale folding of whole chromosomes or of smaller genomic regions, to the re-organization of local interactions between enhancers and promoters, mediated by the binding of transcription factors and chromatin looping. The higher-order organization of chromatin is also influenced by the specificity of the contacts that it makes with nuclear structures such as the lamina. Sophisticated methods for mapping chromatin contacts are generating genome-wide data that provide deep insights into the formation of chromatin interactions, and into their roles in the organization and function of the eukaryotic cell nucleus.

3D imaging of Sox2 enhancer clusters in embryonic stem cells

Combinatorial cis-regulatory networks encoded in animal genomes represent the foundational gene expression mechanism for directing cell-fate commitment and maintenance of cell identity by transcription factors (TFs). However, the 3D spatial organization of cis-elements and how such sub-nuclear structures influence TF activity remain poorly understood. Here, we combine lattice light-sheet imaging, single-molecule tracking, numerical simulations, and ChIP-exo mapping to localize and functionally probe Sox2 enhancer-organization in living embryonic stem cells. Sox2 enhancers form 3D-clusters that are segregated from heterochromatin but overlap with a subset of Pol II enriched regions. Sox2 searches for specific binding targets via a 3D-diffusion dominant mode when shuttling long-distances between clusters while chromatin-bound states predominate within individual clusters. Thus, enhancer clustering may reduce global search efficiency but enables rapid local fine-tuning of TF search parameters. Our results suggest an integrated model linking cis-element 3D spatial distribution to local-versus-global target search modalities essential for regulating eukaryotic gene transcription.

Models of chromosome structure

Understanding the mechanisms that control chromosome folding in the nucleus of eukaryotes and their contribution to gene regulation is a key open issue in molecular biology. Microscopy and chromatin-capture techniques have shown that chromatin has a complex organization, which dynamically changes across organisms and cell types. The need to make sense of such a fascinating complexity has prompted the development of quantitative models from physics, to find the principles of chromosome folding, its origin and function. Here, we concisely review recent advances in chromosome modeling, focusing on a recently proposed framework, the Strings & Binders Switch (SBS) model, which recapitulates key features of chromosome organization in space and time.

The missing story behind Genome Wide Association Studies: single nucleotide polymorphisms in gene deserts have a story to tell

Genome wide association studies are central to the evolution of personalized medicine. However, the propensity for single nucleotide polymorphisms (SNPs) to fall outside of genes means that understanding how these polymorphisms alter cellular function requires an expanded view of human genetics. Integrating the study of genome structure (chromosome conformation capture) into its function opens up new avenues of exploration. Changes in the epigenome associated with SNPs in gene deserts will allow us to define complex diseases in a much clearer manner, and usher in a new era of disease pathway exploration.