Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

Carstens, Simeon; Nilges, Michaël; Habeck, Michael

doi:10.1371/journal.pcbi.1005292

Cited by 55 publications

(73 citation statements)

References 59 publications

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“…However, this relaxation is unsatisfying, as it yields non-integer counts modeled as random Poisson variables. More recently, two separate research groups have developed methods for modeling diploid genomes from single-cell data [6,32]. However, these methods cannot be directly applied to bulk Hi-C data, which is much more widely available.…”

Section: Introductionmentioning

confidence: 99%

Inferring diploid 3D chromatin structures from Hi-C data

Cauer

Yardimci

Vert

et al. 2019

Preprint

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The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuarcy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not.

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Section: Introductionmentioning

confidence: 99%

Inferring diploid 3D chromatin structures from Hi-C data

Cauer

Yardimci

Vert

et al. 2019

Preprint

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“…Recently, the development of single cell HiC assays enabled the modelling of genomes of individual cells [21,74,75]. In Stevens et al 10 individual genome structures of haploid mouse embryonic stem cells were modelled from single cell HiC data at 100kb resolution and validated by fluorescence imaging [75].…”

Section: Approaches To Integrative Modellingmentioning

confidence: 99%

“…From left to right: transcription repressive complex CLOCK:BMAL1 (brain and muscle Arnt-like protein 1) bound to CRY1 (cryptochrome-1) (adapted from [78]), atomic model built based on data from SAXS, NMR, Size Exclusion Chromatography and X-ray crystallography; structure of the fungal toxin Pleurotolysin (adapted from [38]), model built based on 11 Å resolution cryo EM map and X-ray crystallography; model of 40S-eIF1-eIF3 complex built using data from, XL-MS and X-ray crystallography (~25 Å negative stain EM map used for validation) (adapted from [9]); model of flagellin-induced NAIP5/NLRC4 inflammasome built based on data from 4nm resolution subtomogram average and X-ray crystallography (adapted from [79]); Ensemble of mouse X chromosome conformations from single cell HiC at 500kb resolution (adapted from [74]); example of a genome model for haploid mouse embryonic stem cells from HiC experiments using chain particles representing 100kb DNA (adapted from [75]).…”

Section: Figurementioning

confidence: 99%

Integrative modelling of cellular assemblies

Joseph

Polles

Alber

et al. 2017

Current Opinion in Structural Biology

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A wide variety of experimental techniques can be used for understanding the precise molecular mechanisms underlying the activities of cellular assemblies. The inherent limitations of a single experimental technique often requires integration of data from complementary approaches, to gain sufficient insights into the assembly structure and function. Here, we review popular computational approaches for integrative modelling of cellular assemblies, including protein complexes and genomic assemblies. We provide recent examples of integrative models generated for such assemblies by different experimental techniques, especially including 3D electron microscopy (3D-EM) and HiC data, respectively. We highlight general concepts in integrative modelling and discuss the need for careful formulation and merging of different types of information.

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“…In this communication, we extend the scope of our previous work on Bayesian chromosome structure inference from single-cell Hi-C data (23) to population-averaged contact data, combining the explicit modeling of chromatin structure populations with a rigorous probabilistic interpretation of the data. We leverage the full power of Bayesian inference to compute multi-state models of chromosome structures, which together reproduce the population contact data.…”

mentioning

confidence: 95%

“…We model the physical interactions within a single structure x k by a coarse-grained beads-on-a-string model similar to our model for single-cell data (23). Further details on our statistical model for population-averaged contact data and our choice of prior distributions are explained in Supplementary Notes.…”

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confidence: 99%

Bayesian inference of chromatin structure ensembles from population Hi-C data

Carstens

Nilges

Habeck

2018

Preprint

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High-throughput chromosome conformation capture (Hi-C) experiments are typically performed on a large population of cells and therefore only yield average numbers of genomic contacts. Nevertheless population Hi-C data are often interpreted in terms of a single genomic structure, which ignores all cell-to-cell variability. We propose a probabilistic, statistically rigorous method to infer chromatin structure ensembles from population Hi-C data that takes the ensemble nature of the data explicitly into account and allows us to infer the number of structures required to explain the data.In recent years, chromosome conformation capture (3C) methods have emerged as a powerful tool to investigate the three-dimensional organization of genomes on previously inaccessible length scales.Thanks to experimental advances and decreasing sequencing costs, genome-wide 3C methods such as Hi-C (1) and Hi-C variants (2; 3) have yielded contact maps with resolutions of up to 1 kb. Highresolution contact maps have provided fascinating insights into the organization of chromatin domains and compartments of various sizes and their role in gene regulation (1; 4; 5; 6; 3).Although Hi-C can be performed in single cells (7; 8; 9; 10; 11), much richer data are usually obtained by analyzing populations of cells at the price of a more difficult interpretation of the data:Contact maps obtained in population Hi-C experiments show an average over millions of cells. Given that single-cell Hi-C experiments revealed significant structural variability among cell nuclei (7), it remains difficult to assess the information content and degree of structural heterogeneity reflected by population Hi-C data.1 Much effort has gone into the development of chromatin structure determination approaches, in which a single consensus structure is calculated (12). In light of recent results on cell-to-cell variability and considering experimental limitations and biases, consensus structure approaches are fundamentally limited, if not flawed (13). Several approaches taking into account the heterogeneity of cell populations have been explored. Matrix deconvolution has been used to unmix Hi-C matrices without explicitly modeling 3D structures (14; 15). While some structural information is already apparent in contact matrices, it is useful to find an approximate 3D representation giving rise to the data. For example, a 3D chromosome model allows the detection of three-way interactions not accessible in 3C data (16). Furthermore, data from other sources (e.g., imaging) can be included in the modeling process. A second approach is thus to calculate an ensemble of structures that reflect the structural heterogeneity of chromosomes more realistically than consensus structure approaches and reproduce the contact map only on average. This can be achieved in two different ways: First, one can optimize a set of structures generated from a polymer model so as to reproduce, on average, the experimental data (2; 17; 18; 19). A second possibility is to adjust the parameters of a pol...

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Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

Cited by 55 publications

References 59 publications

Inferring diploid 3D chromatin structures from Hi-C data

Inferring diploid 3D chromatin structures from Hi-C data

Integrative modelling of cellular assemblies

Bayesian inference of chromatin structure ensembles from population Hi-C data

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