SnapShot: Chromosome organization.

Chromosomes in higher eukaryotes are folded at different length scales into loop extrusion domains, spatial compartments, and chromosome territories and exhibit interactions with nuclear structures such as the lamina. Microscopic methods can probe this structure by measuring positions of chromosomes in the nuclear space in individual cells, while sequencing-based contact capture approaches can report the frequency of contacts of different regions within these structural layers. To view this SnapShot, open or download the PDF.

Voices on technology: The molecular biologists' ever-expanding toy box.

With the focus on technology for this issue of Molecular Cell, a group of scientists working in different areas of molecular biology provide their perspective on the most recent important technological advance in their field, where the field is lacking, and their wish list for future technology development.

Chromosome Conformation Capture and Beyond: Toward an Integrative View of Chromosome Structure and Function.

Rapidly developing technologies have recently fueled an exciting era of discovery in the field of chromosome structure and nuclear organization. In addition to chromosome conformation capture (3C) methods, new alternative techniques have emerged to study genome architecture and biological processes in the nucleus, often in single or living cells. This sets an unprecedented stage for exploring the mechanisms that link chromosome structure and biological function. Here we review popular as well as emerging approaches to study chromosome organization, focusing on the contribution of complementary methodologies to our understanding of structures revealed by 3C methods and their biological implications, and discuss the next technical and conceptual frontiers.

Spatial organization of the mouse genome and its role in recurrent chromosomal translocations.

The extent to which the three-dimensional organization of the genome contributes to chromosomal translocations is an important question in cancer genomics. We generated a high-resolution Hi-C spatial organization map of the G1-arrested mouse pro-B cell genome and used high-throughput genome-wide translocation sequencing to map translocations from target DNA double-strand breaks (DSBs) within it. RAG endonuclease-cleaved antigen-receptor loci are dominant translocation partners for target DSBs regardless of genomic position, reflecting high-frequency DSBs at these loci and their colocalization in a fraction of cells. To directly assess spatial proximity contributions, we normalized genomic DSBs via ionizing radiation. Under these conditions, translocations were highly enriched in cis along single chromosomes containing target DSBs and within other chromosomes and subchromosomal domains in a manner directly related to pre-existing spatial proximity. By combining two high-throughput genomic methods in a genetically tractable system, we provide a new lens for viewing cancer genomes.

Translocation mapping exposes the risky lifestyle of B cells.

Recurrent chromosomal translocations can drive oncogenesis, but how they form has remained elusive. Now, Chiarle et al. (2011) and Klein et al. (2011) characterize the genome-wide spectrum of translocations that form from a single double-stranded break, revealing that specific loci have an intrinsic predisposition for frequent chromosomal rearrangements.

Constricted migration is associated with stable 3D genome structure differences in cancer cells.

To spread from a localized tumor, metastatic cancer cells must squeeze through constrictions that cause major nuclear deformations. Since chromosome structure affects nucleus stiffness, gene regulation, and DNA repair, here, we investigate the relationship between 3D genome structure and constricted migration in cancer cells. Using melanoma (A375) cells, we identify phenotypic differences in cells that have undergone multiple rounds of constricted migration. These cells display a stably higher migration efficiency, elongated morphology, and differences in the distribution of Lamin A/C and heterochromatin. Hi-C experiments reveal differences in chromosome spatial compartmentalization specific to cells that have passed through constrictions and related alterations in expression of genes associated with migration and metastasis. Certain features of the 3D genome structure changes, such as a loss of B compartment interaction strength, are consistently observed after constricted migration in clonal populations of A375 cells and in MDA-MB-231 breast cancer cells. Our observations suggest that consistent types of chromosome structure changes are induced or selected by passage through constrictions and that these may epigenetically encode stable differences in gene expression and cellular migration phenotype.

SMILE: mutual information learning for integration of single-cell omics data.

MOTIVATION: Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single-cell omics data to be integrated across sources, types and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). RESULTS: Using a unique cell-pairing design, SMILE successfully integrates multisource single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint-profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome-wide peaks for ATAC-seq. Integrated representations learned from joint-profiling technologies can then be used as a framework for comparing independent single source data. AVAILABILITY AND IMPLEMENTATION: The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE, implemented in Python. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment.

Inside the nucleus, chromosomes are subjected to direct physical interaction between different components, active forces, and thermal noise, leading to the formation of an ensemble of three-dimensional structures. However, it is still not well understood to what extent and how the structural ensemble varies from one chromosome region or cell-type to another. We designed a statistical analysis technique and applied it to single-cell chromosome imaging data to reveal the heterogeneity of individual chromosome structures. By analyzing the resulting structural landscape, we find that the largest dynamic variation is the overall radius of gyration of the chromatin region, followed by domain reorganization within the region. By comparing different human cell-lines and experimental perturbation data using this statistical analysis technique and a network-based similarity quantification approach, we identify both cell-type and condition-specific features of the structural landscapes. We identify a relationship between epigenetic state and the properties of chromosome structure fluctuation and validate this relationship through polymer simulations. Overall, our study suggests that the types of variation in a chromosome structure ensemble are cell-type as well as region-specific and can be attributed to constraints placed on the structure by factors such as variation in epigenetic state.

CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis.

BACKGROUND: The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships across cell types in a tissue. However, many current analysis methods do not take full advantage of the spatial organization of the data, instead treating pixels as independent features. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering. RESULTS: By analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes broader spatial patterns rather than pixel-level correlation. CoSTA provides a quantitative measure of expression pattern similarity between each pair of genes rather than only classifying genes into categories. We find that CoSTA identifies narrower, but biologically relevant, sets of significantly related genes as compared to other approaches. CONCLUSIONS: The deep learning CoSTA approach provides a different angle to spatial transcriptomics analysis by focusing on the shape of expression patterns, using more information about the positions of neighboring pixels than would an overlap or pixel correlation approach. CoSTA can be applied to any spatial transcriptomics data represented in matrix form and may have future applications to datasets such as histology in which images of different genes are from similar but not identical biological sections.

Condensin-driven remodelling of X chromosome topology during dosage compensation.

The three-dimensional organization of a genome plays a critical role in regulating gene expression, yet little is known about the machinery and mechanisms that determine higher-order chromosome structure. Here we perform genome-wide chromosome conformation capture analysis, fluorescent in situ hybridization (FISH), and RNA-seq to obtain comprehensive three-dimensional (3D) maps of the Caenorhabditis elegans genome and to dissect X chromosome dosage compensation, which balances gene expression between XX hermaphrodites and XO males. The dosage compensation complex (DCC), a condensin complex, binds to both hermaphrodite X chromosomes via sequence-specific recruitment elements on X (rex sites) to reduce chromosome-wide gene expression by half. Most DCC condensin subunits also act in other condensin complexes to control the compaction and resolution of all mitotic and meiotic chromosomes. By comparing chromosome structure in wild-type and DCC-defective embryos, we show that the DCC remodels hermaphrodite X chromosomes into a sex-specific spatial conformation distinct from autosomes. Dosage-compensated X chromosomes consist of self-interacting domains (∼1 Mb) resembling mammalian topologically associating domains (TADs). TADs on X chromosomes have stronger boundaries and more regular spacing than on autosomes. Many TAD boundaries on X chromosomes coincide with the highest-affinity rex sites and become diminished or lost in DCC-defective mutants, thereby converting the topology of X to a conformation resembling autosomes. rex sites engage in DCC-dependent long-range interactions, with the most frequent interactions occurring between rex sites at DCC-dependent TAD boundaries. These results imply that the DCC reshapes the topology of X chromosomes by forming new TAD boundaries and reinforcing weak boundaries through interactions between its highest-affinity binding sites. As this model predicts, deletion of an endogenous rex site at a DCC-dependent TAD boundary using CRISPR/Cas9 greatly diminished the boundary. Thus, the DCC imposes a distinct higher-order structure onto X chromosomes while regulating gene expression chromosome-wide.

Inferring chromosome radial organization from Hi-C data.

BACKGROUND: The 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. RESULTS: A 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. CONCLUSIONS: Our 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.

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework.

Conformational ensembles of biopolymers, whether proteins or chromosomes, can be described using contact matrices. Principal component analysis (PCA) on the contact data has been used to interrogate both protein and chromosome structures and/or dynamics. However, as these fields have developed separately, variants of PCA have emerged. Previously, a variant we hereby term Implicit-PCA (I-PCA) has been applied to chromosome contact matrices and revealed the spatial segregation of active and inactive chromatin. Separately, Explicit-PCA (E-PCA) has previously been applied to proteins and characterized their correlated structure fluctuations. Here, we swapped analysis methods (I-PCA and E-PCA), applying each to a different biopolymer type (chromosome or protein) than the one for which they were initially developed. We find that applying E-PCA to chromosome distance matrices derived from microscopy data can reveal the dominant motion (concerted fluctuation) of these chromosomes. Further, by applying E-PCA to Hi-C data across the human blood cell lineage, we isolated the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we applied I-PCA to simulation snapshots of proteins, the major component reported the consensus features of the structure, making this a promising approach for future analysis of semi-structured proteins.

Iteratively improving Hi-C experiments one step at a time.

The 3D organization of eukaryotic chromosomes affects key processes such as gene expression, DNA replication, cell division, and response to DNA damage. The genome-wide chromosome conformation capture (Hi-C) approach can characterize the landscape of 3D genome organization by measuring interaction frequencies between all genomic regions. Hi-C protocol improvements and rapid advances in DNA sequencing power have made Hi-C useful to study diverse biological systems, not only to elucidate the role of 3D genome structure in proper cellular function, but also to characterize genomic rearrangements, assemble new genomes, and consider chromatin interactions as potential biomarkers for diseases. Yet, the Hi-C protocol is still complex and subject to variations at numerous steps that can affect the resulting data. Thus, there is still a need for better understanding and control of factors that contribute to Hi-C experiment success and data quality. Here, we evaluate recently proposed Hi-C protocol modifications as well as often overlooked variables in sample preparation and examine their effects on Hi-C data quality. We examine artifacts that can occur during Hi-C library preparation, including microhomology-based artificial template copying and chimera formation that can add noise to the downstream data. Exploring the mechanisms underlying Hi-C artifacts pinpoints steps that should be further optimized in the future. To improve the utility of Hi-C in characterizing the 3D genome of specialized populations of cells or small samples of primary tissue, we identify steps prone to DNA loss which should be considered to adapt Hi-C to lower cell numbers.

3D Genome Organization Influences the Chromosome Translocation Pattern.

Recent imaging, molecular, and computational modeling studies have greatly enhanced our knowledge of how eukaryotic chromosomes are folded in the nuclear space. This work has begun to reveal how 3D genome structure contributes to various DNA-mediated metabolic activities such as replication, transcription, recombination, and repair. Failure of proper DNA repair can lead to the chromosomal translocations observed in human cancers and other diseases. Questions about the role of 3D genome structure in translocation mechanisms have interested scientists for decades. Recent applications of imaging and Chromosome Conformation Capture approaches have clarified the influence of proximal positioning of chromosomal domains and gene loci on the formation of chromosomal translocations. These approaches have revealed the importance of 3D genome structure not only in translocation partner selection, but also in repair efficiency, likelihood of DNA damage, and the biological implications of translocations. This chapter focuses on our current understanding of the role of 3D genome structure in chromosome translocation formation and its potential implications in disease outcome.

RUNX1 contributes to higher-order chromatin organization and gene regulation in breast cancer cells.

RUNX1 is a transcription factor functioning both as an oncogene and a tumor suppressor in breast cancer. RUNX1 alters chromatin structure in cooperation with chromatin modifier and remodeling enzymes. In this study, we examined the relationship between RUNX1-mediated transcription and genome organization. We characterized genome-wide RUNX1 localization and performed RNA-seq and Hi-C in RUNX1-depleted and control MCF-7 breast cancer cells. RNA-seq analysis showed that RUNX1 depletion led to up-regulation of genes associated with chromatin structure and down-regulation of genes related to extracellular matrix biology, as well as NEAT1 and MALAT1 lncRNAs. Our ChIP-Seq analysis supports a prominent role for RUNX1 in transcriptional activation. About 30% of all RUNX1 binding sites were intergenic, indicating diverse roles in promoter and enhancer regulation and suggesting additional functions for RUNX1. Hi-C analysis of RUNX1-depleted cells demonstrated that overall three-dimensional genome organization is largely intact, but indicated enhanced association of RUNX1 near Topologically Associating Domain (TAD) boundaries and alterations in long-range interactions. These results suggest an architectural role for RUNX1 in fine-tuning local interactions rather than in global organization. Our results provide novel insight into RUNX1-mediated perturbations of higher-order genome organization that are functionally linked with RUNX1-dependent compromised gene expression in breast cancer cells.

Correlated alterations in genome organization, histone methylation, and DNA-lamin A/C interactions in Hutchinson-Gilford progeria syndrome.

Hutchinson-Gilford progeria syndrome (HGPS) is a premature aging disease that is frequently caused by a de novo point mutation at position 1824 in LMNA. This mutation activates a cryptic splice donor site in exon 11, and leads to an in-frame deletion within the prelamin A mRNA and the production of a dominant-negative lamin A protein, known as progerin. Here we show that primary HGPS skin fibroblasts experience genome-wide correlated alterations in patterns of H3K27me3 deposition, DNA-lamin A/C associations, and, at late passages, genome-wide loss of spatial compartmentalization of active and inactive chromatin domains. We further demonstrate that the H3K27me3 changes associate with gene expression alterations in HGPS cells. Our results support a model that the accumulation of progerin in the nuclear lamina leads to altered H3K27me3 marks in heterochromatin, possibly through the down-regulation of EZH2, and disrupts heterochromatin-lamina interactions. These changes may result in transcriptional misregulation and eventually trigger the global loss of spatial chromatin compartmentalization in late passage HGPS fibroblasts.

Cohesin-based chromatin interactions enable regulated gene expression within preexisting architectural compartments.

Chromosome conformation capture approaches have shown that interphase chromatin is partitioned into spatially segregated Mb-sized compartments and sub-Mb-sized topological domains. This compartmentalization is thought to facilitate the matching of genes and regulatory elements, but its precise function and mechanistic basis remain unknown. Cohesin controls chromosome topology to enable DNA repair and chromosome segregation in cycling cells. In addition, cohesin associates with active enhancers and promoters and with CTCF to form long-range interactions important for gene regulation. Although these findings suggest an important role for cohesin in genome organization, this role has not been assessed on a global scale. Unexpectedly, we find that architectural compartments are maintained in noncycling mouse thymocytes after genetic depletion of cohesin in vivo. Cohesin was, however, required for specific long-range interactions within compartments where cohesin-regulated genes reside. Cohesin depletion diminished interactions between cohesin-bound sites, whereas alternative interactions between chromatin features associated with transcriptional activation and repression became more prominent, with corresponding changes in gene expression. Our findings indicate that cohesin-mediated long-range interactions facilitate discrete gene expression states within preexisting chromosomal compartments.

Iterative correction of Hi-C data reveals hallmarks of chromosome organization.

Extracting biologically meaningful information from chromosomal interactions obtained with genome-wide chromosome conformation capture (3C) analyses requires the elimination of systematic biases. We present a computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. We validate this ICE (iterative correction and eigenvector decomposition) technique on published data obtained by the high-throughput 3C method Hi-C, and we demonstrate that eigenvector decomposition of the obtained maps provides insights into local chromatin states, global patterns of chromosomal interactions, and the conserved organization of human and mouse chromosomes.

Yeast one-hybrid assays for gene-centered human gene regulatory network mapping.

Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA to protein) approach to identify transcription factors that can bind a DNA sequence of interest. We present Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, that can be used to detect both known and new interactions between human DNA regions and transcription factors.