Simultaneous profiling of 3D genome structure and DNA methylation in single human cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE130711 | GEO | 2019/09/09
REPOSITORIES: GEO
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Project description:Recent advances in the development of single cell epigenomic assays have facilitated the analysis of the gene regulatory landscapes in complex biological systems. Single-cell variations of methods such as DNA methylation-sequencing and ATAC-seq hold tremendous promise for delineating distinct cell types and identifying their critical cis-regulatory sequences. Emerging evidence in recent years has shown that in addition to cis-regulatory sequences, dynamic regulation of 3D chromatin conformation is a critical mechanism for the modulation of gene expressions during development and disease. While assays for the investigation of single-cell 3D chromatin structure have been developed, cell-type specific chromatin conformation in primary human tissues has not been extensively explored. It remains unclear whether single-cell Chromatin Conformation Capture (3C) or Hi-C profiles are suitable for cell type identification and allow the reconstruction of cell-type specific chromatin conformation maps. To address these challenges, we have developed a multi-omic method single-nucleus methyl-3C sequencing (sn-m3C) to profile chromatin conformation and DNA methylation from the same cell. We have shown that bulk m3C and sn-m3C accurately capture chromatin organization information and robustly separate mouse cell types. We have developed a fluorescent-activated nuclei sorting strategy based on DNA content that eliminates nuclei multiplets caused by crosslinking. The sn-m3C-seq method allows high-resolution cell-type classification using two orthogonal types of epigenomic information and the reconstruction of cell-type specific chromatin conformation maps.
2018-12-26 | GSE124391 | GEO
Project description:The identification of cell-type-specific 3D chromatin interactions between regulatory elements can help to decipher gene regulation and to interpret the function of disease-associated non-coding variants. However, current chromosome conformation capture (3C) technologies are unable to resolve interactions at this resolution when only small numbers of cells are available as input. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps and regulatory interactions from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility profiles across metacells, and predicted CTCF motif tracks as input features and employs a lightweight architecture to enable training on standard GPUs. Once trained on paired scATAC-seq and Hi-C data in human cell lines and tissues, ChromaFold can accurately predict both the 3D contact map and peak-level interactions across diverse human and mouse test cell types. In benchmarking against a recent deep learning method that uses bulk ATAC-seq, DNA sequence, and CTCF ChIP-seq to make cell-type-specific predictions, ChromaFold yields superior prediction performance when including CTCF ChIP-seq data as an input and comparable performance without. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations. ChromaFold thus achieves state-of-the-art prediction of 3D contact maps and regulatory interactions using scATAC-seq alone as input data, enabling accurate inference of celltype- specific interactions in settings where 3C-based assays are infeasible.
2024-08-31 | GSE246859 | GEO
Project description:NG Capture-C provides high resolution chromatin conformation capture (3C) interaction maps. NG Capture-C was carried out in erythroid and hESC cells at the Hba, Hbb, Myc, and Slc25a37 locus to generate sequence libraries for analytical software testing.
2020-11-02 | GSE129378 | GEO
Project description:Recent advances in chromatin architecture profiling technologies, such as single-cell Hi-C (scHi-C), allow us to dissect the heterogeneity of chromosome higher-order structures across diverse cell states and different individuals. However, scHi-C experiments are still expensive and not immediately available for population-scale profiling. Here, we present scENCORE, a computational method, to reconstruct personalized and cell-type-specific higher-order chromatin structures, such as A/B compartments, directly from more cost-effective and widely available single-cell epigenetic data (e.g., scATAC-seq). We apply scENCORE on scATAC-seq data from post-mortem prefrontal cortex brains and demonstrate its utility to 1) project Mega-base scale chromatin regions into lower dimensional space by leveraging graph embedding technologies based on cell-type-specific co-variability patterns, 2) define A/B compartments via unsupervised clustering, 3) perform an alignment algorithm for multi-graph embedding to derive comparable chromatin representations and highlight dynamic chromatin compartments across cell states and individuals. Validated by Hi-C experiments using FACS-sorted cells, scENCORE can faithfully reconstruct cell-type-specific chromatin compartments. Furthermore, scENCORE uniformly constructs chromosome conformation across population-scale scATAC-seq data and discovers key 3D structural switching events associated with psychiatric disorders. In summary, scENCORE allows cost-effective cell-type-specific and personalized reconstruction that delineate higher-order chromatin structures.
2023-09-27 | GSE216270 | GEO
Project description:To reconstruct the 3D genomes of single diploid human and mouse cells, we performed single-cell chromatin conformation capture by a novel method, Dip-C, on human cells, and re-analyzed published data on mouse cells by the Dip-C algorithm.
2018-08-15 | GSE117109 | GEO
Project description:Background: DNA in the nucleus of a living cell carries out its functions in the context of a complex, three-dimensional chromatin architecture. Several recently developed methods, each an extension of the chromatin conformation capture (3C) assay, have enabled the genome-wide profiling of chromatin contacts between pairs of loci in yeast, fruit fly, human and mouse. Especially in complex eukaryotes, data generated by these methods, coupled with other genome-wide datasets, demonstrated that non-random chromatin folding correlates strongly with cellular processes such as gene expression and DNA replication. Here we describe a novel assay to map genome-wide chromatin contacts, tethered multiple 3C (TM3C), that involves a simple protocol of restriction enzyme digestion and religation of fragments upon agarose gel beads followed by deep DNA paired-end sequencing. In addition to identifying contacts between pairs of loci, TM3C enables identification of contacts among more than two loci simultaneously. Results: We use TM3C to assay the genome architectures of two human cell lines: KBM7, a near-haploid chronic leukemia cell line, and NHEK, a normal diploid human epidermal keratinocyte cell line. We confirm that the contact frequency maps produced by TM3C exhibit features characteristic of existing genome architecture datasets, including the expected scaling of contact probabilities with genomic distance, as well as a low noise-to-signal ratio between inter- and intrachromosomal contacts. We also confirm that TM3C captures several known cell type-specific contacts, ploidy shifts and translocations, such as Ph+ formation in KBM7. Furthermore, we develop a two-phase mapping strategy that separately maps chimeric subsequences within a single read, allowing us to identify contacts involving three or four loci simultaneously, potentially corresponding to combinatorial regulation events. This mapping strategy also greatly increases the number of distinct binary contacts identified and, therefore, the coverage obtained for a fixed number of mapped reads. We confirm a subset of the triplet contacts involving the IGF2-H19 imprinting control region (ICR) using PCR analysis for KBM7 cells. Assaying the genome architecture of a near-haploid cell line allows us to create 3D models of a human cell line without averaging signal from two homologous copies of a chromosome. Our 3D models of KBM7 show clustering of small chromosomes with each other and large chromosomes with each other, consistent with previous studies of the genome architectures of other human cell lines. Conclusion: TM3C is a simple protocol for ascertaining genome architecture and can be used to identify simultaneous contacts among three or four loci. Application of TM3C to a near-haploid human cell line revealed large-scale features of chromosomal organization and complex chromatin loops that may play a role in regulating reciprocal expression of the IGF2 and H19 genes. Analysis of the spatial organization of two human cell lines (KBM7, a near-haploid chronic leukemia cell line, and NHEK, a normal diploid human epidermal keratinocyte cell line) using tethered multiple 3C (TM3C), a novel and simple protocol for ascertaining genome architecture which can be used to identify simultaneous contacts among three or four loci in addition to binary contacts that can be identified using traditional chromosome conformation capture coupled with next generation sequencing (Hi-C).
2015-04-29 | E-GEOD-60623 | biostudies-arrayexpress
Project description:Structural analysis of chromosome folding in vivo has been revolutionized by Chromosome Conformation Capture (3C) and related methods, which use proximity ligation to identify chromosomal loci in physical contact. We recently described a variant 3C technique, Micro-C, in which chromatin is fragmented to mononucleosomes using micrococcal nuclease, enabling nucleosome-resolution folding maps of the genome. Here, we describe an improved Micro-C protocol using long crosslinkers, termed Micro-C XL, which exhibits greatly increased signal to noise, and provides further insight into the folding of the yeast genome. We also find that signal to noise is much improved in Micro-C XL libraries generated from relatively insoluble chromatin as opposed to soluble material, providing a simple method to physically enrich for bona-fide long-range interactions. Micro-C XL maps of the budding and fission yeast genomes reveal both short-range chromosome fiber features such as chromosomally-interacting domains (CIDs), as well as higher-order features such as clustering of centromeres and telomeres, thereby addressing the primary discrepancy between prior Micro-C data and reported 3C and Hi-C analyses. Interestingly, comparison of chromosome folding maps of S. cerevisiae and S. pombe revealed widespread qualitative similarities, yet quantitative differences, between these distantly-related species. Micro-C XL thus provides a single assay suitable for interrogation of chromosome folding at length scales from the nucleosome to the full genome.
2016-09-13 | GSE85220 | GEO
Project description:Current catalogs of regulatory sequences in the human genome are still incomplete and lack cell type resolution. To profile the activity of human gene regulatory elements in diverse cell types and tissues in the human body, we applied single cell chromatin accessibility assays to 25 distinct human tissue types from multiple donors. The resulting chromatin maps comprising ~500,000 nuclei revealed the status of open chromatin for over 750,000 candidate cis-regulatory elements (cCREs) in 54 distinct cell types. We further delineated cell type-specific and tissue-context dependent gene regulatory programs, and developmental stage specificity by comparing with a recent human fetal chromatin accessibility atlas. We finally used these chromatin maps to interpret the noncoding variants associated with complex human traits and diseases. This rich resource provides a foundation for the analysis of gene regulatory programs in human cell types across tissues and organ systems.
2021-02-16 | GSE165659 | GEO
Project description:The advent of the chromosome conformation capture (3C) and related technologies has profoundly renewed our understaning of three-dimensional chromatin folding in mammalian nuclei. Alongside these experimental approaches, numerous computational tools for handling, normalizing, visualizing, and ultimately detecting interactions in 3C-type datasets are being developed. Here, we present Bloom, a comprehensive method for the analysis of 3C-type data matrices on the basis of Dirichlet process mixture models that addresses two important open issues. First, it retrieves occult interaction patterns from sparse data, like those derived from single-cell or “native” Hi-C experiments; thus, bloomed sparse data can now be used to study interaction landscapes at high-resolution. Second, it detects enhancer-promoter interactions with high sensitivity and inherently assigns an interaction frequency score (IFS) to each contact. Using enhancer perturbation data of different throughput, we show that IFS accurately quantifies the regulatory influence of each enhancer on its target promoter. As a result, Bloom allows decoding of complex regulatory landscapes by generating functionally-relevant enhancer atlases solely on the basis of 3C-type data.
2021-12-01 | GSE161636 | GEO
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2021-06-23 | GSE178733 | GEO