Contact Matrix Research Articles

TMIC-65. USING BIOINFORMATICS TO INVESTIGATE THE TUMOR MICROENVIRONMENT OF DIFFUSE MIDLINE GLIOMA

Abstract Diffuse midline gliomas, H3K27-altered (DMG) are highly malignant brain tumors with a 2-year survival rate of less than 10 percent. These tumors are primarily localized to the pons and thalamus regions of the brain. The cellular compartment of the tumor microenvironment (TME) which surrounds a tumor is composed of tumor and non-tumor cells, such as immune cells and oligodendrocytes. Communication between tumor cells and non-tumor cells in the TME can promote tumor growth and targeting communication networks could improve patient outcomes. Using a bioinformatics approach and publicly available pediatric DMG single-cell RNA-sequence (scRNA-seq) data, several methods of communication (secreted signaling, cell-cell contact, and extracellular matrix) within the DMG TME were identified. Based on the high communication probability, outgoing and incoming networks from non-tumor cells and tumor cells were inferred, such as the Complement, Galectin, and CD96 pathways. Specific ligand-receptor interactions between non-tumor and tumor cells were also found, including LGALS9-HAVCR2, PDGFB/PDGFRA, and COL4A5/ITGAV. A subsequent literature review was performed to validate these identified pathways in other tumors, including gliomas, and inform future in vitro experiments with primary DMG cell lines.

EXPRESSO: a multi-omics database to explore multi-layered 3D genomic organization.

The three-dimensional (3D) organization of the human genome plays a crucial role in gene regulation. EXPloration of Regulatory Epigenome with Spatial and Sequence Observations (EXPRESSO) is a novel multi-omics database for exploration and visualization of multi-layered 3D genomic features across 46 different human tissues. Integrating 1360 3D genomic datasets (Hi-C, HiChIP, ChIA-PET) and 842 1D genomic and transcriptomic datasets (ChIP-seq, ATAC-seq, RNA-seq) from the same biosample, EXPRESSO provides a comprehensive resource for studying the interplay between 3D genome architecture and transcription regulation. This database offers diverse 3D genomic feature types (compartments, contact matrix, contact domains, stripes as diagonal lines extending from a genomic locus in contact matrix, chromatin loops, etc.) and user-friendly interface for both data exploration and download. Other key features include REpresentational State Transfer application programming interfaces for programmatic access, advanced visualization tools for 3D genomic features and web-based applications that correlate 3D genomic features with gene expression and epigenomic modifications. By providing extensive datasets and tools, EXPRESSO aims to deepen our understanding of 3D genomic architecture and its implications for human health and disease, serving as a vital resource for the research community. EXPRESSO is freely available at https://expresso.sustech.edu.cn.

Overcoming artificial structures in resolution-enhanced Hi-C data by signal decomposition and multi-scale attention.

Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures in the enhanced data due to the need to divide the large contact matrix into small patches. In addition, previous deep learning methods largely focus on local patterns, which cannot fully capture the complexity of Hi-C data. Here we propose Smooth, High-resolution, and Accurate Reconstruction of Patterns (SHARP) for enhancing Hi-C data. It uses the novel approach of decomposing the data into three types of signals, due to one-dimensional proximity, contiguous domains, and other fine structures, and applies deep learning only to the third type of signals, such that enhancement of the first two is unaffected by the patches. For the deep learning part, SHARP uses both local and global attention mechanisms to capture multi-scale contextual information. We compare SHARP with state-of-the-art methods extensively, including application to data from new samples and another species, and show that SHARP has superior performance in terms of resolution enhancement accuracy, avoiding creation of artificial structures, identifying significant interactions, and enrichment in chromatin states.

Clusters of African countries based on the social contacts and associated socioeconomic indicators relevant to the spread of the epidemic

IntroductionIt is well known that social contact patterns differ from country to country. This variation coincides with significant socioeconomic heterogeneity that complicates the design of effective nonpharmaceutical interventions. This study examined how socioeconomic heterogeneity in selected African countries might be factored in to explain better patterns of social contact mix between countries.MethodsWe used a standardized contact matrix for 32 African countries, estimated in (Prem et al. in PLoS Comput Biol 17(7):e1009098, 2021). We scaled the matrices using an epidemic model from (Röst et al. in Viruses 12(7):708, 2020). We also analyzed aggregated data from the World Bank country website. The data includes 28 variables; social, economic, environmental, institutional, governance, health and well-being, education, gender inequality, and other development-related indicators that describe countries. Principal component analysis was used to visualize socioeconomic similarities between countries and identify the indicators for maximum variation. The (2D)2PCA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(2D)^{2} PCA$\\end{document} approach was used to reduce the dimension of synthetic contact matrices for each country to avoid the dimensionality curse. Then, hierarchical aggregate clustering was used to identify groups of countries with similar social patterns, taking into account the country’s socioeconomic performance.ResultsOur model yielded four meaningful clusters, each with a few distinguishing features. Social contacts varied between groups, but were generally similar within each set. The socioeconomic performance of the country influenced the clusters.ConclusionsOur results suggest that integrating socioeconomic factors into social contacts can better explain infectious disease transmission dynamics and that similar interventions can be implemented in countries within the cluster

Quantifying age-specific household contacts in Aotearoa New Zealand for infectious disease modelling.

Accounting for population age structure and age-specific contact patterns is crucial for accurate modelling of human infectious disease dynamics and impact. A common approach is to use contact matrices, which estimate the number of contacts between individuals of different ages. These contact matrices are frequently based on data collected from populations with very different demographic and socio-economic characteristics from the population of interest. Here we use a comprehensive household composition dataset based on Aotearoa New Zealand census and administrative data to construct a household contact matrix and a synthetic population that can be used for modelling. We investigate the behaviour of a compartment-based and an agent-based epidemic model parametrized using these data, compared with a commonly used contact matrix that was constructed by projecting international data onto New Zealand's population. We find that using the New Zealand household data, either in a compartment-based model or in an agent-based model, leads to lower attack rates in older age groups compared with using the projected contact matrix. This difference becomes larger when household transmission is more dominant relative to non-household transmission. We provide electronic versions of the synthetic population and household contact matrix for other researchers to use in infectious disease models.

HiCMC: High-Efficiency Contact Matrix Compressor

BackgroundChromosome organization plays an important role in biological processes such as replication, regulation, and transcription. One way to study the relationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformation. Such studies generate vast amounts of data. The problem is exacerbated by the fact that chromosome organization is dynamic, requiring snapshots at different points in time, further increasing the amount of data to be stored. We present a novel approach called the High-Efficiency Contact Matrix Compressor (HiCMC) for efficient compression of Hi-C data.ResultsBy modeling the underlying structures found in the contact matrix, such as compartments and domains, HiCMC outperforms the state-of-the-art method CMC by approximately 8% and the other state-of-the-art methods cooler, LZMA, and bzip2 by over 50% across multiple cell lines and contact matrix resolutions. In addition, HiCMC integrates domain-specific information into the compressed bitstreams that it generates, and this information can be used to speed up downstream analyses.ConclusionHiCMC is a novel compression approach that utilizes intrinsic properties of contact matrix, such as compartments and domains. It allows for a better compression in comparison to the state-of-the-art methods. HiCMC is available at https://github.com/sXperfect/hicmc.

Data‐driven dynamical analysis of an age‐structured model: A graph‐theoretic approach

The dynamics of the propagation and outspread of infectious diseases are eminently intricate, mainly due to the heterogeneity of the host individuals. In this paper, an age‐stratified SEIR (susceptible‐exposed‐infected‐recovered) epidemiological model incorporating saturated treatment function and heterogeneous contact rates is developed to study infectious disease transmission dynamics among various age groups. The expression for the basic reproduction number and conditions for the global stability of the system have been derived by a recently developed graph‐theoretic (GT) approach. Digraph reduction creates a GT version of the Gauss elimination method for computing the . The global dynamics results have been formed by constructing the Lyapunov function using a GT approach. The endemic equilibrium exists uniquely if , whereas the disease‐free equilibrium is observed to be globally stable if . The numerical simulations are demonstrated by extracting the daily reported COVID‐19 cases for the second wave in Italy. The age‐dependent contact matrix for the Republic of Italy (data sourced from the POLYMOD study) is computed via paper–diary methodology (PDM) grounded on a population‐prospective survey in European countries. Our numerical findings imply that (i) for the age group (20–49) years and (50–69) years, the number of infected persons is quite double as compared with the exposed cases; (ii) approximately 50% of positive cases lies in (20–69) years age group; (iii) for the (00–19) years age group, only half of the exposed individuals got infected; and (iv) a consistent graph is detected for the age group of (70–99) years in both cases; it shows that almost all the exposed got infected.

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices.

Hi-C and micro-C sequencing have shed light on the profound importance of 3D genome organization in cellular function by probing 3D contact frequencies across the linear genome. The resulting contact matrices are extremely sparse and susceptible to technical- and sequence-based biases, making their comparison challenging. The development of reliable, robust and efficient methods for quantifying similarity between contact matrices is crucial for investigating variations in the 3D genome organization in different cell types or under different conditions, as well as evaluating experimental reproducibility. We present a novel method, ENT3C, which measures the change in pattern complexity in the vicinity of contact matrix diagonals to quantify their similarity. ENT3C provides a robust, user-friendly Hi-C or micro-C contact matrix similarity metric and a characteristic entropy signal that can be used to gain detailed biological insights into 3D genome organization.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts. We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix. Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.

An identification method of chromatin topological associated domains based on spatial density clustering

The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.

Cost-Effectiveness of Adjuvanted Quadrivalent Influenza Vaccine for Adults over 65 in France.

In France, influenza accounts for an average of over one million consultations with GPs, 20,000 hospitalizations, and 9000 deaths per year, particularly among the over-65s. This study evaluates the cost-effectiveness of the adjuvanted quadrivalent influenza vaccine (aQIV) compared to standard (SD-QIV) and high-dose (HD-QIV) quadrivalent influenza vaccines for individuals aged 65 and older in France. The age-structured SEIR transmission model, calibrated to simulate a mean influenza season, incorporates a contact matrix to estimate intergroup contact rates. Epidemiological, economic, and utility outcomes are evaluated. Vaccine effectiveness and costs are derived from literature and national insurance data. Quality of life adjustments for influenza attack rates and hospitalizations are applied. Deterministic and probabilistic analyses are also conducted. Compared to SD-QIV, aQIV demonstrates substantial reductions in healthcare utilization and mortality, avoiding 89,485 GP consultations, 2144 hospitalizations, and preventing 1611 deaths. Despite an investment of EUR 110 million, aQIV yields a net saving of EUR 14 million in healthcare spending. Compared to HD-QIV, aQIV saves 62 million euros on vaccination costs. Cost-effectiveness analysis reveals an incremental cost-effectiveness ratio of EUR 7062 per QALY. This study highlights the cost-effectiveness of aQIV versus SD-QIV and HD-QIV, preventing influenza cases, hospitalizations, and deaths.

Investigating natural plant products as potential inhibitors to disrupt NS1 β-roll domain polymerisation in DENV2: a detailed computational chemistry approach

ABSTRACT The aim of this investigation is to design a novel drug, screened through various computational methods, that harnesses the historical evidence of plant products inhibiting viral infections. The beta-roll domain of Flavivirus NS1, crucial for its stability, membrane interaction and viral RNA replication, underscores its significance in disease pathogenesis, being the sole non-structural protein detected in the sera of DENV-infected patients. Our goal is to develop optimised compounds that disrupt NS1 β-roll domain polymerisation, mitigating hydrophobic core formation, to effectively inhibit DENV2 cytopathic infection by leveraging natural interference with viral replication. We created 10 plant-based ligand libraries and applied scaffold hopping and bioisosteric replacement to improve them. Computational methodologies, including virtual screening, molecular dynamics simulation, contact matrix and dynamic cross correlation matrix, were employed for analysing the dynamics of the protein with the association of ligand. ADMET analysis of ligand 126 revealed a favourable ADME profile with no observed toxicity properties. Calculations of pKd, pKi and pIC50 values indicated a heightened affinity of the ligand for the receptor protein, suggesting enhanced inhibitory action and a reduced dissociation rate. These encouraging findings suggest a greater potency of the drug, warranting further in vitro testing for validation.

SincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

Social contacts in Switzerland during the COVID-19 pandemic: Insights from the CoMix study

To mitigate the spread of SARS-CoV-2, the Swiss government enacted restrictions on social contacts from 2020 to 2022. In addition, individuals changed their social contact behavior to limit the risk of COVID-19. In this study, we aimed to investigate the changes in social contact patterns of the Swiss population. As part of the CoMix study, we conducted a survey consisting of 24 survey waves from January 2021 to May 2022. We collected data on social contacts and constructed contact matrices for the age groups 0–4, 5–14, 15–29, 30–64, and 65 years and older. We estimated the change in contact numbers during the COVID-19 pandemic to a synthetic pre-pandemic contact matrix. We also investigated the association of the largest eigenvalue of the social contact and transmission matrices with the stringency of pandemic measures, the effective reproduction number (Re), and vaccination uptake. During the pandemic period, 7084 responders reported an average number of 4.5 contacts (95% confidence interval, CI: 4.5–4.6) per day overall, which varied by age and survey wave. Children aged 5–14 years had the highest number of contacts with 8.5 (95% CI: 8.1–8.9) contacts on average per day and participants that were 65 years and older reported the fewest (3.4, 95% CI: 3.2–3.5) per day. Compared with the pre-pandemic baseline, we found that the 15–29 and 30–64 year olds had the largest reduction in contacts. We did not find statistically significant associations between the largest eigenvalue of the social contact and transmission matrices and the stringency of measures, Re, or vaccination uptake. The number of social contacts in Switzerland fell during the COVID-19 pandemic and remained below pre-pandemic levels after contact restrictions were lifted. The collected social contact data will be critical in informing modeling studies on the transmission of respiratory infections in Switzerland and to guide pandemic preparedness efforts.

Contact patterns between index patients and their close contacts and assessing risk for COVID-19 transmission during different exposure time windows: a large retrospective observational study of 450 770 close contacts in Shanghai

IntroductionTo characterise age-mixing patterns among index cases and contacts of COVID-19, and explore when patients are most infectious during the disease process.MethodsThis study examined all initial 90 885 confirmed index...

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision-making in the context of epidemic mitigation is multi-dimensional hence complex, reinforcement learning in combination with complex epidemic models provides a methodology to design refined prevention strategies. Current research focuses on optimizing policies with respect to a single objective, such as the pathogen’s attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting, criteria (i.a., mortality, morbidity, economic cost, well-being), a multi-objective decision approach is warranted to obtain balanced policies. To enhance future decision-making, we propose a deep multi-objective reinforcement learning approach by building upon a state-of-the-art algorithm called Pareto Conditioned Networks (PCN) to obtain a set of solutions for distinct outcomes of the decision problem. We consider different deconfinement strategies after the first Belgian lockdown within the COVID-19 pandemic and aim to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden induced by the mitigation measures. As such, we connected a multi-objective Markov decision process with a stochastic compartment model designed to approximate the Belgian COVID-19 waves and explore reactive strategies. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution set that PCN returns, and observe that it explored the whole range of possible social restrictions, leading to high-quality trade-offs, as it captured the problem dynamics. In this work, we demonstrate that multi-objective reinforcement learning adds value to epidemiological modeling and provides essential insights to balance mitigation policies.

Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection.

Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions. Unfortunately, it remains a challenge to identify TAD-like domains on single-cell Hi-C data due to its ultra-sparsity. We propose scKTLD, an in silico tool for the identification of TAD-like domains on single-cell Hi-C data. It takes Hi-C contact matrix as the adjacency matrix for a graph, embeds the graph structures into a low-dimensional space with the help of sparse matrix factorization followed by spectral propagation, and the TAD-like domains can be identified using a kernel-based changepoint detection in the embedding space. The results tell that our scKTLD is superior to the other methods on the sparse contact matrices, including downsampled bulk Hi-C data as well as simulated and experimental single-cell Hi-C data. Besides, we demonstrated the conservation of TAD-like domain boundaries at single-cell level apart from heterogeneity within and across cell types, and found that the boundaries with higher frequency across single cells are more enriched for architectural proteins and chromatin marks, and they preferentially occur at TAD boundaries in bulk cells, especially at those with higher hierarchical levels. scKTLD is freely available at https://github.com/lhqxinghun/scKTLD.

Quantitative characterization of tumor cell traction force on extracellular matrix by hydrogel microsphere stress sensor.

Cell traction force (CTF) is a kind of active force that is a cell senses external environment and actively applies to the contact matrix which is currently a representative stress in cell-extracellular matrix (ECM) interaction. Studying the distribution and variation of CTF during cell-ECM interaction help to explain the impact of physical factors on cell behaviors from the perspective of mechanobiology. However, most of the strategies of characterizing CTF are still limited by the measurement needs in three-dimensional (3D), quantitative characteristics and in vivo condition. Microsphere stress sensor (MSS) as a new type of technology is capable of realizing the quantitative characterization of CTF in 3D and in vivo. Herein, we employed microfluidic platform to design and fabricate MSS which possesses adjustable fluorescent performances, physical properties, and size ranges for better applicable to different cells (3T3, A549). Focusing on the common tumor cells behaviors (adhesion, spreading, and migration) in the process of metastasis, we chose SH-SY5Y as the representative research object in this work. We calculated CTF with the profile and distribution to demonstrate that the normal and shear stress can determined different cell behaviors. Additionally, CTF can also regulate cell adhesion, spreading, and migration in different cell states. Based on this method, the quantitative characterization of CFT of health and disease cells can be achieved, which further help to study and explore the potential mechanism of cell-ECM interaction.

Algorithm for calculating the problem of unilateral frictional contact with an increscent external load parameter

The subject of the study is the contact interaction of deformable elements of linear complementarity problem (LCP). To solve the linear complementarity problem, the Lemke method with the introduction of an increasing parameter of external loading is used. The proposed approach solves the degenerated matrix in a finite number of steps, while the dimensionality of the problem is limited to the area of contact. To solve the problem, the initial table of the Lemke method is generated using the contact matrix of stiffness and the contact load vector. The unknowns in the problem are mutual displacements and interaction forces of contacting pairs of points of deformable solids. The proposed approach makes it possible to evaluate the change in working schemes as the parameter of external load increases. The features of the proposed formulation of the problem are shown, the criteria for stopping the stepwise process of solving such problems are considered. Model examples for the proposed algorithm are given. The algorithm has shown its efficiency in application, including for complex model problems. Recommendations on the use of the proposed approach are given.

The 3D Chromatin Landscape of B-Cell Precursor Childhood Acute Lymphoblastic Leukemia

Whilst the molecular pathogenesis of childhood B-cell precursor (BCP) acute lymphoblastic leukemia (ALL) has been studied extensively, its 3D chromatin landscape remains poorly explored. Genome-wide chromosome conformation capture methods have provided the tools to investigate the different units of chromatin organization, such as transcriptionally active (A) and inactive (B) compartments, topologically associating domains (TADs), and fine-scale chromatin loops and enhancer-promoter interactions. The aim of this study is to elucidate the chromatin architecture and topological gene regulation in childhood BCP ALL. To date, 29 primary patient samples were included, comprising the high hyperdiploid (HeH) (n=11), ETV6:: RUNX1-positive (n=8), BCR:: ABL1-positive (n=2), TCF3:: PBX1-positive (n=2), DUX4-rearranged (n=2), intrachromosomal amplification of chromosome 21 (iAMP21) (n=1), KMT2A-rearranged (n=1), near-haploid (n=1) and near-triploid (n=1) genetic subtypes. Leukemic blast cells obtained at diagnosis were analyzed using Micro-C, a high-resolution variation of Hi-C (average number of total reads = 1.4 billion, highest resolution = 5 kb) combined with pair-end sequencing. Chromatin contact heatmaps were generated for each case using Juicer and Cooler. A/B compartments were identified using FanC at 500 kb resolution and visualized in the software IGV, while TAD calling was performed by Juicer, Domaincaller and Insulation Score. Differential chromatin interaction loop calling was made using Pareidolia and Mustache, and structural variant (SV) calling was carried out by EagleC. Preliminary principal component analysis of the 29 cases, based on the first two eigenvectors of the contact matrix (500 kb resolution), showed that HeH, ETV6:: RUNX1-positive, TCF3:: PBX1-positive and DUX4-rearranged cases each clustered based on their chromatin 3D organization. Furthermore, the A/B compartments of 11 HeH and 8 ETV6:: RUNX1-positive cases were analyzed at 500 kb resolution and compartment shifts among the two subtypes were annotated. A total of 390 shifts were detected, where activating shifts (from B to A compartment) happened more often in HeH (263 shifts) than in ETV6:: RUNX1-positive cases (127 shifts). Analysis of TAD boundary strength at 25 kb resolution revealed that HeH cases displayed significantly weaker boundaries compared to ETV6:: RUNX1-positive cases. TAD boundary strength showed no bias towards the frequently gained or non-gained chromosomes in HeH ALL. By merging individual heatmaps of all HeH and ETV6:: RUNX1-positive cases using Cooler, we created subtype-specific profiles and compared the intensity of chromatin interactions between the two genetic subtypes. Chromatin interaction intensity analysis was then combined with previously published RNA-sequencing data to identify transcriptional dysregulation events that could be associated with chromatin interaction changes. Preliminary results show that there was a chromatin loop missing close to the well-known leukemia-related gene IKZF1 in HeH compared to ETV6:: RUNX1-positive cases; this gene also showed lower expression in the RNA-sequencing data. FLT3 was associated with weakened chromatin interactions and down-regulated in ETV6:: RUNX1-positive cases compared to HeH, in agreement with its known high expression in HeH. Finally, we performed screening of SVs using EagleC and Micro-C heatmaps in HeH and ETV6:: RUNX1-positive samples. Out of the 19 included cases, previous whole-genome sequencing (WGS) data were available for 16. We detected 75 SVs, of which 50 were intrachromosomal rearrangements and 25 were translocations. Micro-C heatmaps allowed visual detection of SVs smaller than 1 Mb and permitted identification of the type of SVs. WGS detected 61% of the SVs found in the HeH samples with Micro-C and 51% of those in the ETV6:: RUNX1-positive cases. In summary, we present the first high-resolution genome-wide map of chromatin 3D organization in pediatric ALL. Our results indicate that different subtypes of childhood BCP ALL have distinct 3D chromatin landscapes and that abnormal chromatin architectures affect the regulation of leukemia-related genes.