Recurrence plot reconstruction reveals chromosomal reorganization before territory formation.

Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.

A deletion of 15q11-q13 and uniparental disomy 15 lead to Prader-Labhart-Willi syndrome (PWS) or Angelman syndrome (AS) because this region contains genes expressed exclusively from the paternal (PWS) or maternal (AS) chromosome, respectively. DNA methylation plays a role in the control of imprinted gene expression, but so far only a few 5'-CG-3' dinucleotides within the recognition sites of the methylation sensitive enzymes have been studied. As part of a study on DNA methylation patterns in the human genome, we have applied the bisulfite protocol of genomic sequencing to study all 5'-CG-3' dinucleotides around exon 1 of SNRPN and at the D15S63 locus, which contains a start site for alternative SNRPN transcripts possibly involved in imprint switching during gametogenesis. At least 17 PCR products derived from single chromosomes of normal individuals as well as PWS and AS patients have been sequenced. We have found that cytosine residues outside 5'-CG-3' dinucleotides are always unmethylated. However, > 96% of all of the 23 5'-CG-3' dinucleotides around SNRPN exon 1 are methylated on the maternal chromosome and completely devoid of methylation on the paternal chromosome. This finding is in contrast to the D15S63 locus, where only the two Cfol/Hhal sites are methylated on the maternal chromosome at the same frequency as seen for the SNRPN segment. At the other five 5'-CG-3' dinucleotides, differential methylation is less pronounced, i.e. 45-70% on the maternal chromosome and 5-14% on the paternal chromosome. The differences between SNRPN and D15S63 methylation may reflect different biological functions of the alternative SNRPN transcripts. The systematic investigation of 5'-CG-3' methylation patterns as reported here will provide the basis for a PCR-based methylation test to diagnose PWS and AS.

MotivationHi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide averaging (piling-up) of peaks, or other features, annotated in high-resolution datasets, to measure their prominence in less deeply sequenced data. However, current tools do not provide a computationally efficient and versatile implementation of this approach.ResultsHere, we describe coolpup.py—a versatile tool to perform pile-up analysis on Hi-C data. We demonstrate its utility by replicating previously published findings regarding the role of cohesin and CTCF in 3D genome organization, as well as discovering novel details of Polycomb-driven interactions. We also present a novel variation of the pile-up approach that can aid the statistical analysis of looping interactions. We anticipate that coolpup.py will aid in Hi-C data analysis by allowing easy to use, versatile and efficient generation of pile-ups.Availability and implementation Coolpup.py is cross-platform, open-source and free (MIT licensed) software. Source code is available from https://github.com/Phlya/coolpuppy and it can be installed from the Python Packaging Index.

Hi-C is a powerful method to investigate genome-wide, higher-order chromatin and chromosome conformations averaged from a population of cells. To expand the potential of Hi-C for single-cell analysis, we developed single-cell Hi-C. Similar to the existing "ensemble" Hi-C method, single-cell Hi-C detects proximity-dependent ligation events between cross-linked and restriction-digested chromatin fragments in cells. A major difference between the single-cell Hi-C and ensemble Hi-C protocol is that the proximity-dependent ligation is carried out in the nucleus. This allows the isolation of individual cells in which nearly the entire Hi-C procedure has been carried out, enabling the production of a Hi-C library and data from individual cells. With this new method, we studied genome conformations and found evidence for conserved topological domain organization from cell to cell, but highly variable interdomain contacts and chromosome folding genome wide. In addition, we found that the single-cell Hi-C protocol provided cleaner results with less technical noise suggesting it could be used to improve the ensemble Hi-C technique.

Single-cell Hi-C technology is utilized to obtain chromosome interaction information at the single-cell level and further study the differences in genome structures between different cell types. However, there are few accurate and efficient clustering methods for single-cell Hi-C data, with the following manifestations: The clustering efficacy on the dataset with a large number of cells is not very satisfactory, and it is difficult to cluster these cells with small number in the whole dataset. In this study, we propose a high-performance single-cell Hi-C clustering framework, called scHiCSC. A new smoothing method based on contact number weight is first proposed to generate cell embedding with more accurate cell features. In addition, a novel feature fusion method is proposed to further supplement the feature information of cells by fusing the chromosome structure information within cells and the distance information between cells. The experimental results show that scHiCSC has a strong generalization ability on different sizes of datasets and outperforms the existing single-cell Hi-C clustering frameworks. Moreover, scHiCSC achieves an optimal and stable clustering efficacy in the datasets with large-scale cell numbers and can cluster the cells with small number in the whole dataset. The source code of scHiCSC is freely available at https://github.com/HaoWuLab-Bioinformatics/scHiCSC.

TADs and Their Borders: Free Movement or Building a Wall?

Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to reference-free methods (e.g., TOAST, and NMF) and is more robust than reference-dependent methods (e.g. MuSiC). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.

Single-cell high-throughput chromatin conformation capture technologies (scHi-C) has been used to map chromatin spatial organization in complex tissues. However, computational tools to detect differential chromatin contacts (DCCs) from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify DCCs between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 Kb resolution in mouse hippocampal and human prefrontal cortical tissues, demonstrating that DCCs detected in the hippocampal and cortical cell types are generally associated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.

Are the kinase feedback loops that regulate activation and centromeric targeting of the chromosomal passenger complex (CPC), functional during mitosis in human embryos? Investigation of the regulatory kinase pathways involved in centromeric CPC targeting revealed normal phosphorylation dynamics of histone H2A at T120 (H2ApT120) by Bub1 kinase and subsequent recruitment of Shugoshin, but phosphorylation of histone H3 at threonine 3 (H3pT3) by Haspin failed to show the expected centromeric enrichment on metaphase chromosomes in the zygote. Human cleavage stage embryos show high levels of chromosomal instability. What causes this high error rate is unknown, as mechanisms used to ensure proper chromosome segregation in mammalian embryos are poorly described. In this study, we investigated the pathways regulating CPC targeting to the inner centromere in human embryos. We characterized the distribution of the CPC in relation to activity of its two main centromeric targeting pathways: the Bub1-H2ApT120-Sgo-CPC and Haspin-H3pT3-CPC pathways. The study was conducted between May 2012 and March 2014 on human surplus embryos resulting from in vitro fertilization treatment and donated for research. In zygotes, nuclear envelope breakdown was monitored by time-lapse imaging to allow timed incubations with specific inhibitors to arrest at prometaphase and metaphase, and to interfere with Haspin and Aurora B/C kinase activity. Functionality of the targeting pathways was assessed through characterization of histone phosphorylation dynamics by immunofluorescent analysis, combined with gene expression by RT-qPCR and immunofluorescent localization of key pathway proteins. Immunofluorescent analysis of the CPC subunit Inner Centromere Protein revealed the pool of stably bound CPC proteins was not strictly confined to the inner centromere of prometaphase chromosomes in human zygotes, as observed in later stages of preimplantation development and somatic cells. Investigation of the regulatory kinase pathways involved in centromeric CPC targeting revealed normal phosphorylation dynamics of histone H2A at T120 (H2ApT120) by Bub1 kinase and subsequent recruitment of Shugoshin. However, phosphorylation of histone H3 at threonine 3 (H3pT3) by Haspin kinase failed to show the expected centromeric enrichment on metaphase chromosomes in the zygote, but not at later stages. Inhibition of Haspin revealed this activity to be essential for proper mitotic checkpoint complex activation in human zygotes, thus demonstrating an active mitotic checkpoint under normal conditions. Abolishment of H3pT3 during zygotic prometaphase further shows that centromeric H2ApT120 alone is not sufficient for proper shugoshin and CPC localization. As the removal of H3pT3 from the chromosome arms during prometaphase normally contributes to further centromeric enrichment of the CPC in somatic cells, CPC targeting may be less accurate in human zygotes. Owing to ethical limitations, tripronuclear zygotes were used in functional experiments. Although these represent the best available models, it is unknown if they are completely representative for dipronuclear zygotes. In addition, further research is needed to determine to what extent the differences we observed in H3T3 phosphorylation dynamics and CPC localization affect chromosome attachment. In the zygote, paternal and maternal chromosomes coming from two separate pronuclei, and with contrasting epigenetic signatures, need to be aligned on a single metaphase plate. Our results suggest that adaptations in mechanisms regulating CPC targeting exist in the human zygote, to ensure symmetric recruitment despite the epigenetic asymmetry between maternal and paternal chromosomes. This adaptation may come at a price regarding chromosome segregation fidelity. This study was funded by the Portuguese Fundação para a Ciência e Tecnologia and the Netherlands Organization for Scientific Research. The authors have no conflicts of interest to declare.

Hi-C technique is an important tool for the study of 3D genome organization. In the past few years, we have seen an explosion of Hi-C data in a variety of cell/tissue types. While these publicly available data presents an unprecedented opportunity to interrogate chromosomal architecture, how to quantitatively compare Hi-C data from different tissues and identify tissue-specific chromatin interactions remains challenging. Here, we present HiCComp, a comprehensive framework for comparing Hi-C data. HiCComp utilizes convolutional neural networks to extract key features in Hi-C interaction matrices in a fully automatic way. The core component of HiCComp is a triplet network, which contains three identical convolutional neural networks with shared parameters. The inputs to our network are three Hi-C matrices: two of them are biological replicates from the same cell type and the third one is from another cell type. The HiCComp network takes advantages of the two biological replicates to estimate the natural variation in the experiments and further use it to identify significant variations between Hi-C matrices from different cell types. Furthermore, we incorporate systematic occluding method into our framework so that we can identify the dynamic interaction regions from Hi-C maps. Finally, we show that the dynamic regions between two cell types are enriched for transcription factor binding sites and histone modifications that are associated with cis-regulatory functions, suggesting these variations in 3D genome structure are potentially gene regulatory events.

Maintenance of CTCF- and Transcription Factor-Mediated Interactions from the Gametes to the Early Mouse Embryo.

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e.,peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the "true" interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

Three-dimensional chromatin ensemble reconstruction via stochastic embedding reveals a missing link from COVID-19 infection to anosmia.

Construction of chromosomes 3D models based on single cell Hi-C data constitute an important challenge. We present a reconstruction approach, DPDchrom, that incorporates basic knowledge whether the reconstructed conformation should be coil-like or globular and spring relaxation at contact sites. In contrast to previously published protocols, DPDchrom can naturally form globular conformation due to the presence of explicit solvent. Benchmarking of this and several other methods on artificial polymer models reveals similar reconstruction accuracy at high contact density and DPDchrom advantage at low contact density. To compare 3D structures insensitively to spatial orientation and scale, we propose the Modified Jaccard Index. We analyzed two sources of the contact dropout: contact radius change and random contact sampling. We found that the reconstruction accuracy exponentially depends on the number of contacts per genomic bin allowing to estimate the reconstruction accuracy in advance. We applied DPDchrom to model chromosome configurations based on single-cell Hi-C data of mouse oocytes and found that these configurations differ significantly from a random one, that is consistent with other studies.