3D reconstruction of genomic regions from sparse interaction data

Chromosome Conformation Capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterise short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organisation from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalisation, detection of significant interactions, and reconstruction of the full 3D organisation of the genomic region despite of the data sparseness. Specifically, it produces reliable reconstructions, in line with the ones obtained from dense interaction matrices, with as low as the 2-3% of the data from the matrix. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organisational features such as the formation of different networks of active gene communities.

[1]  M. L. Le Gros,et al.  Population-based 3D genome structure analysis reveals driving forces in spatial genome organization , 2016, Proceedings of the National Academy of Sciences.

[2]  J. Lawrence,et al.  The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules , 2011, Nature Structural &Molecular Biology.

[3]  Veronica J. Buckle,et al.  Coregulated human globin genes are frequently in spatial proximity when active , 2006, The Journal of cell biology.

[4]  A. Tanay,et al.  Single cell Hi-C reveals cell-to-cell variability in chromosome structure , 2013, Nature.

[5]  Philippe Collas,et al.  Manifold Based Optimization for Single-Cell 3D Genome Reconstruction , 2015, PLoS Comput. Biol..

[6]  Wouter de Laat,et al.  The β-globin nuclear compartment in development and erythroid differentiation , 2003, Nature Genetics.

[7]  Ben M. Webb,et al.  Putting the Pieces Together: Integrative Modeling Platform Software for Structure Determination of Macromolecular Assemblies , 2012, PLoS biology.

[8]  Charles H. Li,et al.  Mediator and RNA polymerase II clusters associate in transcription-dependent condensates , 2018, Science.

[9]  B. Tabak,et al.  Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus , 2018, Cell.

[10]  S. Q. Xie,et al.  Complex multi-enhancer contacts captured by Genome Architecture Mapping (GAM) , 2017, Nature.

[11]  J. Sedat,et al.  Spatial partitioning of the regulatory landscape of the X-inactivation centre , 2012, Nature.

[12]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[13]  P. Fraser,et al.  Nuclear organization of the genome and the potential for gene regulation , 2007, Nature.

[14]  W. Beyer CRC Standard Probability And Statistics Tables and Formulae , 1990 .

[15]  Jesse R. Dixon,et al.  Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions , 2012, Nature.

[16]  Nan Hua,et al.  Producing genome structure populations with the dynamic and automated PGS software , 2018, Nature Protocols.

[17]  Diego di Bernardo,et al.  Colocalization of Coregulated Genes: A Steered Molecular Dynamics Study of Human Chromosome 19 , 2013, PLoS Comput. Biol..

[18]  Numérisation de documents anciens mathématiques,et al.  Mathematical modelling and numerical analysis : Modélisation mathématique et analyse numérique. , 1985 .

[19]  A. Pombo,et al.  Methods for mapping 3D chromosome architecture , 2019, Nature Reviews Genetics.

[20]  Philip A. Ewels,et al.  Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C , 2015, Nature Genetics.

[21]  Michael Q. Zhang,et al.  In Situ Capture of Chromatin Interactions by Biotinylated dCas9 , 2017, Cell.

[22]  Wei Xie,et al.  The role of 3D genome organization in development and cell differentiation , 2019, Nature Reviews Molecular Cell Biology.

[23]  Bing Ren,et al.  A Compendium of Promoter-Centered Long-Range Chromatin Interactions in the Human Genome , 2019, Nature Genetics.

[24]  L. Mirny,et al.  Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data , 2013, Nature Reviews Genetics.

[25]  Cameron S. Osborne,et al.  Myc Dynamically and Preferentially Relocates to a Transcription Factory Occupied by Igh , 2007, PLoS biology.

[26]  Neva C. Durand,et al.  A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping , 2014, Cell.

[27]  W. Rocchia,et al.  Chromatin Compaction Multiscale Modeling: A Complex Synergy Between Theory, Simulation, and Experiment , 2020, Frontiers in Molecular Biosciences.

[28]  Jonathan M. Cairns,et al.  Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data , 2019, bioRxiv.

[29]  Jianlin Cheng,et al.  An Overview of Methods for Reconstructing 3-D Chromosome and Genome Structures from Hi-C Data , 2019, Biological Procedures Online.

[30]  Cameron S. Osborne,et al.  The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements , 2015, Genome research.

[31]  David Torrents,et al.  Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes , 2019, Nature Genetics.

[32]  Jonathan M. Cairns,et al.  Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters , 2016, Cell.

[33]  Vera Pancaldi,et al.  ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C , 2018 .

[34]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[35]  Natalie de Souza Genomics: Micro-C maps of genome structure , 2015, Nature Methods.

[36]  Britta A. M. Bouwman,et al.  Enhancer hubs and loop collisions identified from single-allele topologies , 2018, Nature Genetics.

[37]  D. Jackson,et al.  Visualization of focal sites of transcription within human nuclei. , 1993, The EMBO journal.

[38]  J. Bungert,et al.  Phase Separation and Transcription Regulation: Are Super‐Enhancers and Locus Control Regions Primary Sites of Transcription Complex Assembly? , 2018, BioEssays : news and reviews in molecular, cellular and developmental biology.

[39]  Yannick G. Spill,et al.  Restraint‐based three‐dimensional modeling of genomes and genomic domains , 2015, FEBS letters.

[40]  Wendy A. Bickmore,et al.  Transcription factories: gene expression in unions? , 2009, Nature Reviews Genetics.

[41]  Javier Quilez,et al.  OneD: increasing reproducibility of Hi-C samples with abnormal karyotypes , 2017, bioRxiv.

[42]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[43]  F. Grosveld,et al.  The beta-globin nuclear compartment in development and erythroid differentiation. , 2003, Nature genetics.

[44]  Ivan Junier,et al.  Assessing the limits of restraint-based 3D modeling of genomes and genomic domains , 2015, Nucleic acids research.

[45]  R. Hardison,et al.  Comparative analysis of three-dimensional chromosomal architecture identifies a novel fetal hemoglobin regulatory element , 2017, Genes & development.

[46]  M. Groudine,et al.  Nuclear localization and histone acetylation: a pathway for chromatin opening and transcriptional activation of the human beta-globin locus. , 2000, Genes & development.

[47]  Jonathan M. Cairns,et al.  CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data , 2015, Genome Biology.

[48]  E. Polak,et al.  Note sur la convergence de méthodes de directions conjuguées , 1969 .

[49]  N. Hannett,et al.  Transcription Factors Activate Genes through the Phase-Separation Capacity of Their Activation Domains , 2018, Cell.

[50]  Damien Devos,et al.  4Cin: A computational pipeline for 3D genome modeling and virtual Hi-C analyses from 4C data , 2018, PLoS Comput. Biol..

[51]  D. Jackson,et al.  Active RNA polymerases are localized within discrete transcription "factories' in human nuclei. , 1996, Journal of cell science.

[52]  G. Grest,et al.  Dynamics of entangled linear polymer melts: A molecular‐dynamics simulation , 1990 .

[53]  Howard Y. Chang,et al.  HiChIP: efficient and sensitive analysis of protein-directed genome architecture , 2016, Nature Methods.

[54]  Borbala Mifsud,et al.  GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data , 2017, PloS one.

[55]  Cameron S. Osborne,et al.  Active genes dynamically colocalize to shared sites of ongoing transcription , 2004, Nature Genetics.

[56]  A. Tanay,et al.  Three-Dimensional Folding and Functional Organization Principles of the Drosophila Genome , 2012, Cell.

[57]  L. Mirny,et al.  Iterative Correction of Hi-C Data Reveals Hallmarks of Chromosome Organization , 2012, Nature Methods.

[58]  William Stafford Noble,et al.  HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient , 2017, bioRxiv.

[59]  P. Levings,et al.  The human beta-globin locus control region. , 2002, European journal of biochemistry.

[60]  F. Grosveld,et al.  Each hypersensitive site of the human beta-globin locus control region confers a different developmental pattern of expression on the globin genes. , 1993, Genes & development.

[61]  I. Amit,et al.  Comprehensive mapping of long range interactions reveals folding principles of the human genome , 2011 .

[62]  Ralf Everaers,et al.  Structure and Dynamics of Interphase Chromosomes , 2008, PLoS Comput. Biol..

[63]  M. Martí-Renom,et al.  Transcriptional activation during cell reprogramming correlates with the formation of 3D open chromatin hubs , 2020, Nature Communications.

[64]  Elzo de Wit,et al.  4C technology: protocols and data analysis. , 2012, Methods in enzymology.

[65]  Ilya M. Flyamer,et al.  Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition , 2017, Nature.

[66]  A. Tanay,et al.  Multiscale 3D Genome Rewiring during Mouse Neural Development , 2017, Cell.

[67]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[68]  Anders S. Hansen,et al.  Resolving the 3D landscape of transcription-linked mammalian chromatin folding , 2019, bioRxiv.

[69]  Marc A. Martí-Renom,et al.  Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors , 2017, PLoS Comput. Biol..

[70]  M. Martí-Renom,et al.  Chromatin globules: a common motif of higher order chromosome structure? , 2011, Current opinion in cell biology.

[71]  Pietro Liò,et al.  The BioMart community portal: an innovative alternative to large, centralized data repositories , 2015, Nucleic Acids Res..

[72]  Andre J. Faure,et al.  3D structure of individual mammalian genomes studied by single cell Hi-C , 2017, Nature.

[73]  P. Levings,et al.  The human β‐globin locus control region , 2002 .

[74]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[75]  William Stafford Noble,et al.  Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells , 2019, bioRxiv.

[76]  Anandashankar Anil,et al.  HiCapTools: a software suite for probe design and proximity detection for targeted chromosome conformation capture applications , 2017, Bioinform..