Capturing heterogeneity: single-cell structures of the 3D genome

2017 ◽  
Vol 24 (5) ◽  
pp. 437-438 ◽  
Author(s):  
Elzo de Wit
Keyword(s):  
Single Cell ◽  
3D Genome ◽  
Cell Structures

The 3D Genome Structure of Single Cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Spatial Organization ◽  
Method Development ◽  
Single Cells ◽  
Genome Structure ◽  
Computational Method ◽  
Publication Date ◽  
3D Genome ◽  
Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data

2019 ◽  
Author(s):  
Hyeon-Jin Kim ◽  
Galip Gürkan Yardımcı ◽  
Giancarlo Bonora ◽  
Vijay Ramani ◽  
Jie Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Topic Modeling ◽  
Biological Information ◽  
Chromatin Interaction ◽  
Cell Type ◽  
3D Genome ◽  
Genome Wide ◽  
Significant Barrier ◽  
Chromatin Structural ◽  
Cell Type Specific

AbstractSingle-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate twelve different single-cell combinatorial indexed Hi-C (sciHi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 25,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sciHi-C data in the form of “chromatin topics.” We further show enrichment of particular compartment structures associated with locus pairs in these topics.

scholarly journals Combining fluorescence imaging with Hi-C to study 3D genome architecture of the same single cell

2018 ◽  
Vol 13 (5) ◽  
pp. 1034-1061 ◽  
Author(s):  
David Lando ◽  
Srinjan Basu ◽  
Tim J Stevens ◽  
Andy Riddell ◽  
Kai J Wohlfahrt ◽  
...  
Keyword(s):  
Single Cell ◽  
Fluorescence Imaging ◽  
Genome Architecture ◽  
3D Genome

scholarly journals HiCluster: A Robust Single-Cell Hi-C Clustering Method Based on Convolution and Random Walk

2018 ◽  
Author(s):  
Jingtian Zhou ◽  
Jianzhu Ma ◽  
Yusi Chen ◽  
Chuankai Cheng ◽  
Bokan Bao ◽  
...  
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Genome Structure ◽  
Real Data ◽  
Cell Types ◽  
3D Genome ◽  
Cell Clustering

3D genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe HiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real data as benchmarks, HiCluster significantly improves clustering accuracy when applied to low coverage Hi-C datasets compared to existing methods. After imputation by HiCluster, structures similar to topologically associating domains (TADs) could be identified within single cells, and their consensus boundaries among cells were enriched at the TAD boundaries observed in bulk samples. In summary, HiCluster facilitates visualization and comparison of single-cell 3D genomes.

scholarly journals Single-cell-resolved dynamics of chromatin architecture delineate cell and regulatory states in wildtype and cloche/npas4l mutant zebrafish embryos

2020 ◽  
Author(s):  
Alison C. McGarvey ◽  
Wolfgang Kopp ◽  
Dubravka Vučićević ◽  
Rieke Kempfer ◽  
Kenny Mattonet ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptional Activity ◽  
Regulatory Elements ◽  
Zebrafish Embryos ◽  
Computational Tool ◽  
Post Translational Modifications ◽  
Future Studies ◽  
3D Genome ◽  
Dna Accessibility ◽  
Intricate Mechanism

DNA accessibility of cis regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many of the genes that regulate embryonic development have been described, the underlying CRE dynamics controlling their expression remain largely unknown. To address this, we applied single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq) to whole 24 hours post fertilization (hpf) stage zebrafish embryos and developed a new computational tool, ScregSeg, that selects informative genome segments and classifies complex accessibility dynamics. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization, expanding knowledge of regulatory principles between chromatin modalities. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos revealed novel cellular roles for this hemato-vascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work constitutes a valuable resource for future studies in developmental, molecular, and computational biology.

scholarly journals Experience-independent transformation of single-cell 3D genome structure and transcriptome during postnatal development of the mammalian brain

2020 ◽  
Author(s):  
Longzhi Tan ◽  
Wenping Ma ◽  
Honggui Wu ◽  
Yinghui Zheng ◽  
Dong Xing ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Genome Structure ◽  
Sensory Experience ◽  
Mammalian Genome ◽  
Brain Cells ◽  
List Type ◽  
3D Genome ◽  
Allele Specific ◽  
3D Genome Structure

SUMMARYBoth transcription and 3D organization of the mammalian genome play critical roles in neurodevelopment and its disorders. However, 3D genome structures of single brain cells have not been solved; little is known about the dynamics of single-cell transcriptome and 3D genome after birth. Here we generate a transcriptome atlas of 3,517 cells and a 3D genome atlas of 3,646 cells from the developing mouse cortex and hippocampus, using our high-resolution MALBAC-DT and Dip-C methods. In adults, 3D genome “structure types” delineate all major cell types, with high correlation between A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first postnatal month. In neurons, 3D genome is rewired across multiple scales, correlated with gene expression modules and independent of sensory experience. Finally, we examine allele-specific structure of imprinted genes, revealing local and chromosome-wide differences. These findings uncover a previously unknown dimension of neurodevelopment.HIGHLIGHTSTranscriptomes and 3D genome structures of single brain cells (both neurons and glia) in the developing mouse forebrainCell type identity encoded in the 3D wiring of the mammalian genome (“structure types”)Major transformation of both transcriptome and 3D genome during the first month of life, independent of sensory experienceAllele-specific 3D structure at 7 imprinted gene loci, including one that spans a whole chromosome

scholarly journals RF Breakdown in Normal Conducting Single-Cell Structures

2006 ◽  
Author(s):  
V.A. Dolgashev ◽  
S.G. Tantawi ◽  
C.D. Nantista ◽  
Y. Higashi ◽  
T. Higo
Keyword(s):  
Single Cell ◽  
Cell Structures ◽  
Rf Breakdown

scholarly journals Single-cell multi-omic profiling of chromatin conformation and DNA methylation

2019 ◽  
Author(s):  
Dong-Sung Lee ◽  
Chongyuan Luo ◽  
Jingtian Zhou ◽  
Sahaana Chandran ◽  
Angeline Rivkin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Level ◽  
3D Genome ◽  
Heterogeneous Samples ◽  
Single Data

Abstract The ability to profile epigenomic features in single cells is facilitating the study of the variation in transcription regulation at the single cell level. Single cell methods have also facilitated the generation of cell-type resolved transcriptomic and epigenetic profiles of lineages derived from complex heterogeneous samples. However, integrating different epigenetic features remain challenging, as many current methods profile a single data type at at time. Furthermore, some epigenetic features, such as 3D genome organization, are intrinsically variable between single cells of the same lineage, so it remains unclear how well these methods may resolve cell-types from complex mixtures. Here we describe a method for profiling 3D genome organization and DNA methylation in single cells. This protocol accompanies Lee et al. (Nature Methods 2019) after peer review to aid potential users in applying the method to their own samples.

scholarly journals Chrysosphaera sieminskae species nova, a new Chrysosphaera species from Poland

2014 ◽  
Vol 63 (1) ◽  
pp. 97-99
Author(s):  
Jan Matuła
Keyword(s):  
New Species ◽  
Single Cell ◽  
Water Bodies ◽  
Animal Host ◽  
Poor Fen ◽  
Cell Structures ◽  
A New Species ◽  
Shape And Size

A new species of <i>Chrysosphaera</i> - <i>Ch. sieminskae</i> has been found in Poland. It is an alga forming compact colonies consisting of single cell structures settled down at <i>Testacea's</i> transluscent carapace (e.g. <i>Euglypha</i>, <i>Centropyxis</i>, <i>Difflugia</i>). The new species, which grows on specific animal host and is characterized by a peculiar organization of colonies, differs in shape and size of its cells from all other taxons belonging to the same genus. This new species has been found in the conditions of poor fen habitats, in water bodies at pH from 3.9 to 5.0.

scholarly journals Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

2019 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Ruolan Qiu ◽  
Choli Lee ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Three Dimensional ◽  
Population Based ◽  
Order Structure ◽  
Chromosome Conformation ◽  
3D Genome ◽  
A Cell ◽  
Cell To Cell Variability

AbstractThe highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

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