MIA-Sig: multiplex chromatin interaction analysis by signal processing and statistical algorithms.

The single-molecule multiplex chromatin interaction data are generated by emerging 3D genome mapping technologies such as GAM, SPRITE, and ChIA-Drop. These datasets provide insights into high-dimensional chromatin organization, yet introduce new computational challenges. Thus, we developed MIA-Sig, an algorithmic solution based on signal processing and information theory. We demonstrate its ability to de-noise the multiplex data, assess the statistical significance of chromatin complexes, and identify topological domains and frequent inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains. Together, MIA-Sig represents a novel algorithmic framework for multiplex chromatin interaction analysis

Chromatin topology reorganization and transcription repression by PML-RARα in acute promyeloid leukemia.

BACKGROUND: Acute promyeloid leukemia (APL) is characterized by the oncogenic fusion protein PML-RARα, a major etiological agent in APL. However, the molecular mechanisms underlying the role of PML-RARα in leukemogenesis remain largely unknown. RESULTS: Using an inducible system, we comprehensively analyze the 3D genome organization in myeloid cells and its reorganization after PML-RARα induction and perform additional analyses in patient-derived APL cells with native PML-RARα. We discover that PML-RARα mediates extensive chromatin interactions genome-wide. Globally, it redefines the chromatin topology of the myeloid genome toward a more condensed configuration in APL cells; locally, it intrudes RNAPII-associated interaction domains, interrupts myeloid-specific transcription factors binding at enhancers and super-enhancers, and leads to transcriptional repression of genes critical for myeloid differentiation and maturation. CONCLUSIONS: Our results not only provide novel topological insights for the roles of PML-RARα in transforming myeloid cells into leukemia cells, but further uncover a topological framework of a molecular mechanism for oncogenic fusion proteins in cancers

Nuclear pore protein NUP210 depletion suppresses metastasis through heterochromatin-mediated disruption of tumor cell mechanical response.

Mechanical signals from the extracellular microenvironment have been implicated in tumor and metastatic progression. Here, we identify nucleoporin NUP210 as a metastasis susceptibility gene for human estrogen receptor positive (ER+) breast cancer and a cellular mechanosensor. Nup210 depletion suppresses lung metastasis in mouse models of breast cancer. Mechanistically, NUP210 interacts with LINC complex protein SUN2 which connects the nucleus to the cytoskeleton. In addition, the NUP210/SUN2 complex interacts with chromatin via the short isoform of BRD4 and histone H3.1/H3.2 at the nuclear periphery. In Nup210 knockout cells, mechanosensitive genes accumulate H3K27me3 heterochromatin modification, mediated by the polycomb repressive complex 2 and differentially reposition within the nucleus. Transcriptional repression in Nup210 knockout cells results in defective mechanotransduction and focal adhesion necessary for their metastatic capacity. Our study provides an important role of nuclear pore protein in cellular mechanosensation and metastasis

MCI-frcnn: a deep learning method for topological micro-domain boundary detection

Chromatin structural domains, or topologically associated domains (TADs), are a general organizing principle in chromatin biology. RNA polymerase II (RNAPII) mediates multiple chromatin interactive loops, tethering together as RNAPII-associated chromatin interaction domains (RAIDs) to offer a framework for gene regulation. RAID and TAD alterations have been found to be associated with diseases. They can be further dissected as micro-domains (micro-TADs and micro-RAIDs) by clustering single-molecule chromatin-interactive complexes from next-generation three-dimensional (3D) genome techniques, such as ChIA-Drop. Currently, there are few tools available for micro-domain boundary identification. In this work, we developed the MCI-frcnn deep learning method to train a Faster Region-based Convolutional Neural Network (Faster R-CNN) for micro-domain boundary detection. At the training phase in MCI-frcnn, 50 images of RAIDs from Drosophila RNAPII ChIA-Drop data, containing 261 micro-RAIDs with ground truth boundaries, were trained for 7 days. Using this well-trained MCI-frcnn, we detected micro-RAID boundaries for the input new images, with a fast speed (5.26 fps), high recognition accuracy (AUROC = 0.85, mAP = 0.69), and high boundary region quantification (genomic IoU = 76%). We further applied MCI-frcnn to detect human micro-TADs boundaries using human GM12878 SPRITE data and obtained a high region quantification score (mean gIoU = 85%). In all, the MCI-frcnn deep learning method which we developed in this work is a general tool for micro-domain boundary detection.Ministry of Education (MOE)National Research Foundation (NRF)Published versionThis work was supported by grants from the National Natural Science Foundation of China (32170644), the National Key R&D Program of China (20222YFC3400400), the Shenzhen Fundamental Research Programme (JCYJ20220530115211026), and the Shenzhen Innovation Committee of Science and Technology (ZDSYS20200811144002008). MF is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative and by a Ministry of Education Tier II grant awarded to MF (T2EP30120- 0020)

Long-read ChIA-PET for base-pair-resolution mapping of haplotype-specific chromatin interactions.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a robust method for capturing genome-wide chromatin interactions. Unlike other 3C-based methods, it includes a chromatin immunoprecipitation (ChIP) step that enriches for interactions mediated by specific target proteins. This unique feature allows ChIA-PET to provide the functional specificity and higher resolution needed to detect chromatin interactions, which chromosome conformation capture (3C)/Hi-C approaches have not achieved. The original ChIA-PET protocol generates short paired-end tags (2 × 20 base pairs (bp)) to detect two genomic loci that are far apart on linear chromosomes but are in spatial proximity in the folded genome. We have improved the original approach by developing long-read ChIA-PET, in which the length of the paired-end tags is increased (up to 2 × 250 bp). The longer PET reads not only improve the tag-mapping efficiency but also increase the probability of covering phased single-nucleotide polymorphisms (SNPs), which allows haplotype-specific chromatin interactions to be identified. Here, we provide the detailed protocol for long-read ChIA-PET that includes cell fixation and lysis, chromatin fragmentation by sonication, ChIP, proximity ligation with a bridge linker, Tn5 tagmentation, PCR amplification and high-throughput sequencing. For a well-trained molecular biologist, it typically takes 6 d from cell harvesting to the completion of library construction, up to a further 36 h for DNA sequencing andreads. Nat Protoc 2017 May; 12(5):899-915

DataSheet1_MCI-frcnn: A deep learning method for topological micro-domain boundary detection.docx

Chromatin structural domains, or topologically associated domains (TADs), are a general organizing principle in chromatin biology. RNA polymerase II (RNAPII) mediates multiple chromatin interactive loops, tethering together as RNAPII-associated chromatin interaction domains (RAIDs) to offer a framework for gene regulation. RAID and TAD alterations have been found to be associated with diseases. They can be further dissected as micro-domains (micro-TADs and micro-RAIDs) by clustering single-molecule chromatin-interactive complexes from next-generation three-dimensional (3D) genome techniques, such as ChIA-Drop. Currently, there are few tools available for micro-domain boundary identification. In this work, we developed the MCI-frcnn deep learning method to train a Faster Region-based Convolutional Neural Network (Faster R-CNN) for micro-domain boundary detection. At the training phase in MCI-frcnn, 50 images of RAIDs from Drosophila RNAPII ChIA-Drop data, containing 261 micro-RAIDs with ground truth boundaries, were trained for 7 days. Using this well-trained MCI-frcnn, we detected micro-RAID boundaries for the input new images, with a fast speed (5.26 fps), high recognition accuracy (AUROC = 0.85, mAP = 0.69), and high boundary region quantification (genomic IoU = 76%). We further applied MCI-frcnn to detect human micro-TADs boundaries using human GM12878 SPRITE data and obtained a high region quantification score (mean gIoU = 85%). In all, the MCI-frcnn deep learning method which we developed in this work is a general tool for micro-domain boundary detection.</p

MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification

The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure.Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionNational Natural Science Foundation of China (32170644); Shenzhen Innovation Committee of Science and Technology (ZDSYS20200811144002008). M.J.F. is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative and by a Ministry of Education Tier II grant awarded to M.J.F. (T2EP30120-0020). D.P. is co-supported by Warsaw University of Technology within the Excellence Initiative-Research University (IDUB) programme, co-supported by Polish National Science Centre (2019/35/O/ST6/02484 and 2020/37/B/NZ2/03757) and EUfunded the Marie Sklodowska-Curie action (MSCA) Innovative Training Network

Multiplex chromatin interactions with single-molecule precision.

The genomes of multicellular organisms are extensively folded into 3D chromosome territories within the nucleus1. Advanced 3D genome-mapping methods that combine proximity ligation and high-throughput sequencing (such as chromosome conformation capture, Hi-C)2, and chromatin immunoprecipitation techniques (such as chromatin interaction analysis by paired-end tag sequencing, ChIA-PET)3, have revealed topologically associating domains4 with frequent chromatin contacts, and have identified chromatin loops mediated by specific protein factors for insulation and regulation of transcription5-7. However, these methods rely on pairwise proximity ligation and reflect population-level views, and thus cannot reveal the detailed nature of chromatin interactions. Although single-cell Hi-C8 potentially overcomes this issue, this method may be limited by the sparsity of data that is inherent to current single-cell assays. Recent advances in microfluidics have opened opportunities for droplet-based genomic analysis9 but this approach has not yet been adapted for chromatin interaction analysis. Here we describe a strategy for multiplex chromatin-interaction analysis via droplet-based and barcode-linked sequencing, which we name ChIA-Drop. We demonstrate the robustness of ChIA-Drop in capturing complex chromatin interactions with single-molecule precision, which has not been possible using methods based on population-level pairwise contacts. By applying ChIA-Drop to Drosophila cells, we show that chromatin topological structures predominantly consist of multiplex chromatin interactions with high heterogeneity; ChIA-Drop also reveals promoter-centred multivalent interactions, which provide topological insights into transcription

CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription.

Spatial genome organization and its effect on transcription remains a fundamental question. We applied an advanced chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) strategy to comprehensively map higher-order chromosome folding and specific chromatin interactions mediated by CCCTC-binding factor (CTCF) and RNA polymerase II (RNAPII) with haplotype specificity and nucleotide resolution in different human cell lineages. We find that CTCF/cohesin-mediated interaction anchors serve as structural foci for spatial organization of constitutive genes concordant with CTCF-motif orientation, whereas RNAPII interacts within these structures by selectively drawing cell-type-specific genes toward CTCF foci for coordinated transcription. Furthermore, we show that haplotype variants and allelic interactions have differential effects on chromosome configuration, influencing gene expression, and may provide mechanistic insights into functions associated with disease susceptibility. 3D genome simulation suggests a model of chromatin folding around chromosomal axes, where CTCF is involved in defining the interface between condensed and open compartments for structural regulation. Our 3D genome strategy thus provides unique insights in the topological mechanism of human variations and diseases. Cell 2015 Dec 17; 163(7):1611-27