Search

SummaryThe development of embryonic cell lineages is tightly controlled by transcription factors that regulate gene expression and chromatin organisation. To investigate the specialisation of 3D genome structure in pluripotent or extra-embryonic endoderm lineages, we applied Genome Architecture Mapping (GAM) in embryonic stem (ES) cells, extra-embryonic endoderm (XEN) stem cells, and in theirin vivocounterparts, the epiblast (Epi) and primitive endoderm (PrE) cells, respectively. We discover extensive differences in 3D genome topology including the formation domain boundaries that differ between Epi and PrE lineages, bothin vivoandin vitro, at lineage commitment genes. In ES cells,Sox2contacts other active regions enriched for NANOG and SOX2 binding sites. PrE-specific genes, such asLama1andGata6, form repressive chromatin hubs in ES cells.Lama1activation in XEN or PrE cells coincides with its extensive decondensation. Putative binding sites for OCT4 and SNAIL, or GATA4/6, distinguish chromatin contacts unique to embryonic or extra-embryonic lineages, respectively. Overall, 3D genome folding is highly specialised in early development, especially at genes encoding factors driving lineage identity.HighlightsES and XEN cells have specialised 3D genome structuresGAM applied in the blastocyst distinguishes Epi and PrE genome structuresLineage specific genes establish cell-type specific chromatin contactsSpecific chromatin contacts feature putative bindings sites for GATA4/6 in XEN cells and SNAIL in ES cells

Homologous Recombination Deficiency (HRD) is a predictive biomarker of poly-ADP ribose polymerase 1 inhibitors (PARPi) response. Most HRD detection methods are based on genome wide enumeration of scarring events and require deep genome sequence profiles (> 30x). The cost and workflow-specific biases introduced by these genome profiling methods currently limits clinical adoption of HRD testing.We introduce the Genomic Integrity Index (GII), a Convolutional Neuronal Network, that leverages features from low pass (1x) Whole Genome Sequencing data to distinguish HRD positive and negative samples. In a cohort of 230 ovarian and breast cancer, we found GII supports accurate stratification of samples yielding results that are highly concordant with state-of-the-art HRD detection methods (0.865We conclude that the deep learning framework supporting GII allows accurate detection of HRD from shallow genome profiles, reducing biases and data generation costs making it uniquely suited for clinical applications.

MotivationGenome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM’s ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes.ResultsWe present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data.AvailabilityGAMIBHEAR is available as an R package under the open source GPL-2 license athttps://bitbucket.org/schwarzlab/gamibhearMaintainer:julia.markowski@mdc-berlin.de

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.

Hi-C, split-pool recognition of interactions by tag extension (SPRITE) and genome architecture mapping (GAM) are powerful technologies utilized to probe chromatin interactions genome wide, but how faithfully they capture three-dimensional (3D) contacts and how they perform relative to each other is unclear, as no benchmark exists. Here, we compare these methods in silico in a simplified, yet controlled, framework against known 3D structures of polymer models of murine and human loci, which can recapitulate Hi-C, GAM and SPRITE experiments and multiplexed fluorescence in situ hybridization (FISH) single-molecule conformations. We find that in silico Hi-C, GAM and SPRITE bulk data are faithful to the reference 3D structures whereas single-cell data reflect strong variability among single molecules. The minimal number of cells required in replicate experiments to return statistically similar contacts is different across the technologies, being lowest in SPRITE and highest in GAM under the same conditions. Noise-to-signal levels follow an inverse power law with detection efficiency and grow with genomic distance differently among the three methods, being lowest in GAM for genomic separations >1 Mb. This Analysis reports a computational approach to implement Hi-C, SPRITE and GAM, which allows researchers to assess the performances of the three technologies to capture DNA contacts in chromatin three-dimensional models.

SummaryTechnologies for measuring 3D genome topology are increasingly important for studying mechanisms of 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 to map chromatin contacts genome-wide. We perform a detailed comparison of contacts obtained by multiplex-GAM and Hi-C using mouse embryonic stem (mES) cells. We find that both methods detect similar topologically associating domains (TADs). However, 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 currently go under-estimated in genome-wide ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

DNA accessibility of cis regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many of the genes that regulate embryonic development have been described, the underlying CRE dynamics controlling their expression remain largely unknown. To address this, we applied single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq) to whole 24 hours post fertilization (hpf) stage zebrafish embryos and developed a new computational tool, ScregSeg, that selects informative genome segments and classifies complex accessibility dynamics. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization, expanding knowledge of regulatory principles between chromatin modalities. Sci-ATAC-seq profiling ofnpas4l/clochemutant embryos revealed novel cellular roles for this hemato-vascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work constitutes a valuable resource for future studies in developmental, molecular, and computational biology.

Neurons and oligodendrocytes are terminally differentiated cells that perform highly specialized functions, which depend on cascades of gene activation and repression to retain homeostatic control over a lifespan. Gene expression is regulated by three-dimensional (3D) genome organisation, from local levels of chromatin compaction to the organisation of topological domains and chromosome compartments. Whereas our understanding of 3D genome architecture has vastly increased in the past decade, it remains difficult to study specialized cells in their native environment without disturbing their activity. To develop the application of Genome Architecture Mapping (GAM) in small numbers of specialized cells in complex tissues, we combined GAM with immunoselection. We applied immunoGAM to map the genome architecture of specific cell populations in the juvenile/adult mouse brain: dopaminergic neurons (DNs) from the midbrain, pyramidal glutamatergic neurons (PGNs) from the hippocampus, and oligodendrocyte lineage cells (OLGs) from the cortex. We integrate 3D genome organisation with single-cell transcriptomics data, and find specific chromatin structures that relate with cell-type specific patterns of gene expression. We discover abundant changes in compartment organisation, especially a strengthening of heterochromatin compartments which establish strong contacts spanning tens of megabases, especially in brain cells. These compartments contain olfactory and taste receptor genes, which are de-repressed in a subpopulation of PGNs with molecular signatures of long-term potentiation (LTP). We also show extensive reorganisation of topological domains where activation of neuronal or oligodendrocyte genes coincides with formation of new TAD borders. Finally, we discover loss of TAD organisation, or ‘TAD melting’, at long (>1Mb) neuronal genes when they are most highly expressed. Our work shows that the 3D organisation of the genome is highly cell-type specific in terminally differentiated cells of the brain, and essential to better understand brain-specific mechanisms of gene regulation.

Chromosome conformation capture methods have identified subchromosomal structures of higher-order chromatin interactions called topologically associated domains (TADs) that are separated from each other by boundary regions1, 2. By subdividing the genome into discrete regulatory units, TADs restrict the contacts that enhancers establish with their target genes3, 4, 5. However, the mechanisms that underlie partitioning of the genome into TADs remain poorly understood. Here we show by chromosome conformation capture (capture Hi-C and 4C-seq methods) that genomic duplications in patient cells and genetically modified mice can result in the formation of new chromatin domains (neo-TADs) and that this process determines their molecular pathology. Duplications of non-coding DNA within the mouse Sox9 TAD (intra-TAD) that cause female to male sex reversal in humans6, showed increased contact of the duplicated regions within the TAD, but no change in the overall TAD structure. In contrast, overlapping duplications that extended over the next boundary into the neighbouring TAD (inter-TAD), resulted in the formation of a new chromatin domain (neo-TAD) that was isolated from the rest of the genome. As a consequence of this insulation, inter-TAD duplications had no phenotypic effect. However, incorporation of the next flanking gene, Kcnj2, in the neo-TAD resulted in ectopic contacts of Kcnj2 with the duplicated part of the Sox9 regulatory region, consecutive misexpression of Kcnj2, and a limb malformation phenotype. Our findings provide evidence that TADs are genomic regulatory units with a high degree of internal stability that can be sculptured by structural genomic variations. This process is important for the interpretation of copy number variations, as these variations are routinely detected in diagnostic tests for genetic disease and cancer. This finding also has relevance in an evolutionary setting because copy-number differences are thought to have a crucial role in the evolution of genome complexity.

Powerful technologies have been developed to probe chromatin 3D physical interactions genome-wide, such as Hi-C, GAM and SPRITE. Due to their intrinsic differences and without a benchmarking reference, it is currently difficult to assess how well each method represents the genome 3D structure and their relative performance. Here, we develop a computational approach to implement Hi-C, GAM and SPRITE in-silico to compare the three methods in a simplified, yet controlled framework against known polymer 3D structures. We test our approach on models of three 6-Mb genomic regions, around the Sox9 and the HoxD genes in mouse ES cells, and around the Epha4 gene in mouse CHLX-12 cells. The model-derived contact matrices consistently match Hi-C, GAM and SPRITE experiments. We show that in-silico Hi-C, GAM and SPRITE average data are overall faithful to the 3D structures of the polymer models. We find that the inherent variability of model single-molecule 3D conformations and experimental efficiency differently affect the contact data of the different methods. Similarly, the noise-to-signal levels vary with genomic distance differently in in-silico Hi-C, SPRITE and GAM. We benchmark the performance of each technology in bulk and in single-cell experiments, and identify the minimal number of cells required for replicates to return statistically consistent chromatin contact measures. Under the same experimental conditions, SPRITE requires the lowest number of cells, Hi-C is close to SPRITE, while GAM is the most reproducible method to capture interactions at large genomic distances.