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

Hi-CO: 3D genome structure analysis with nucleosome resolution

2021 ◽  
Author(s):  
Masae Ohno ◽  
Tadashi Ando ◽  
David G. Priest ◽  
Yuichi Taniguchi
Keyword(s):  
Structure Analysis ◽  
Genome Structure ◽  
3D Genome ◽  
3D Genome Structure

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 Repeat elements organise 3D genome structure and mediate transcription in the filamentous fungus Epichloë festucae

PLoS Genetics ◽  
2018 ◽  
Vol 14 (10) ◽  
pp. e1007467 ◽  
Author(s):  
David J. Winter ◽  
Austen R. D. Ganley ◽  
Carolyn A. Young ◽  
Ivan Liachko ◽  
Christopher L. Schardl ◽  
...  
Keyword(s):  
Filamentous Fungus ◽  
Genome Structure ◽  
Repeat Elements ◽  
3D Genome ◽  
Epichloë Festucae ◽  
3D Genome Structure

scholarly journals Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Giancarlo Bonora ◽  
Vijay Ramani ◽  
Ritambhara Singh ◽  
He Fang ◽  
Dana L. Jackson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Nuclear Structure ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Chromatin Accessibility ◽  
X Inactivation ◽  
Rna Seq ◽  
X Chromosomes ◽  
Allele Specific

Abstract Background Mammalian development is associated with extensive changes in gene expression, chromatin accessibility, and nuclear structure. Here, we follow such changes associated with mouse embryonic stem cell differentiation and X inactivation by integrating, for the first time, allele-specific data from these three modalities obtained by high-throughput single-cell RNA-seq, ATAC-seq, and Hi-C. Results Allele-specific contact decay profiles obtained by single-cell Hi-C clearly show that the inactive X chromosome has a unique profile in differentiated cells that have undergone X inactivation. Loss of this inactive X-specific structure at mitosis is followed by its reappearance during the cell cycle, suggesting a “bookmark” mechanism. Differentiation of embryonic stem cells to follow the onset of X inactivation is associated with changes in contact decay profiles that occur in parallel on both the X chromosomes and autosomes. Single-cell RNA-seq and ATAC-seq show evidence of a delay in female versus male cells, due to the presence of two active X chromosomes at early stages of differentiation. The onset of the inactive X-specific structure in single cells occurs later than gene silencing, consistent with the idea that chromatin compaction is a late event of X inactivation. Single-cell Hi-C highlights evidence of discrete changes in nuclear structure characterized by the acquisition of very long-range contacts throughout the nucleus. Novel computational approaches allow for the effective alignment of single-cell gene expression, chromatin accessibility, and 3D chromosome structure. Conclusions Based on trajectory analyses, three distinct nuclear structure states are detected reflecting discrete and profound simultaneous changes not only to the structure of the X chromosomes, but also to that of autosomes during differentiation. Our study reveals that long-range structural changes to chromosomes appear as discrete events, unlike progressive changes in gene expression and chromatin accessibility.

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 Modification of Nuclear Compartments and the 3D Genome in the Course of a Viral Infection

Acta Naturae ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 34-46
Author(s):  
S. V. Razin ◽  
A. A. Gavrilov ◽  
O. V. Iarovaia
Keyword(s):  
Viral Infection ◽  
Cell Nucleus ◽  
Genome Structure ◽  
Antiviral Defense ◽  
3D Genome ◽  
Nuclear Compartments ◽  
Defense Systems ◽  
3D Genome Structure ◽  
Cellular Genome

The review addresses the question of how the structural and functional compartmentalization of the cell nucleus and the 3D organization of the cellular genome are modified during the infection of cells with various viruses. Particular attention is paid to the role of the introduced changes in the implementation of the viral strategy to evade the antiviral defense systems and provide conditions for viral replication. The discussion focuses on viruses replicating in the cell nucleus. Cytoplasmic viruses are mentioned in cases when a significant reorganization of the nuclear compartments or the 3D genome structure occurs during an infection with these viruses.

scholarly journals Single-cell transcriptomics predicts relapse in MLL-rearranged acute lymphoblastic leukemia in infants

2020 ◽  
Author(s):  
Tito Candelli ◽  
Pauline Schneider ◽  
Patricia Garrido Castro ◽  
Luke A. Jones ◽  
Rob Pieters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Risk Stratification ◽  
Single Cell ◽  
Rna Sequencing ◽  
Lymphoblastic Leukemia ◽  
Leukemic Cells ◽  
List Type ◽  
Relapse Risk ◽  
Single Cell Rna Sequencing

AbstractInfants with MLL-rearranged acute lymphoblastic leukemia (ALL) undergo intense therapy to counter a highly aggressive leukemia with survival rates of only 30-40%. The majority of patients initially show therapy response, but in two-thirds of cases the leukemia returns, typically during treatment. Accurate relapse prediction would enable treatment strategies that take relapse risk into account, with potential benefits for all patients. Through analysis of diagnostic bone marrow biopsies, we show that single-cell RNA sequencing can predict future relapse occurrence. By analysing gene modules derived from an independent study of the gene expression response to the key drug prednisone, individual leukemic cells are predicted to be either resistant or sensitive to treatment. Quantification of the proportion of cells classified by single-cell transcriptomics as resistant or sensitive, accurately predicts the occurrence of future relapse in individual patients. Strikingly, the single-cell based classification is even consistent with the order of relapse timing. These results lay the foundation for risk-based treatment of MLL-rearranged infant ALL, through single-cell classification. This work also sheds light on the subpopulation of cells from which leukemic relapse arises. Leukemic cells associated with high relapse risk are characterized by a smaller size and a quiescent gene expression program. These cells have significantly fewer transcripts, thereby also demonstrating why single-cell analyses may outperform bulk mRNA studies for risk stratification. This study indicates that single-cell RNA sequencing will be a valuable tool for risk stratification of MLL-rearranged infant ALL, and shows how clinically relevant information can be derived from single-cell genomics.Key PointsSingle-cell RNA sequencing accurately predicts relapse in MLL-rearranged infant ALLIdentification of cells from which MLL-rearranged infant ALL relapses occur

scholarly journals Exploring the Effects of Genetic Variation on Gene Regulation in Cancer in the Context of 3D Genome Structure

2021 ◽  
Author(s):  
Noha Osman ◽  
Abd-El-Monsif Shawky ◽  
Michal Brylinski
Keyword(s):  
High Risk ◽  
Genetic Variants ◽  
Large Scale ◽  
Target Genes ◽  
Genome Structure ◽  
Regulatory Elements ◽  
3D Genome ◽  
Risk Snps ◽  
Prostate Cancers ◽  
3D Genome Structure

Abstract Background: Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.Results: In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.Conclusions: Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

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