Single-cell measurement of higher-order 3D genome organization with scSPRITE

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.

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

10.1101/579573 ◽

2019 ◽

Cited By ~ 1

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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Probing multi-way chromatin interaction with hypergraph representation learning

10.1101/2020.01.22.916171 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ruochi Zhang ◽

Jian Ma

Keyword(s):

Genome Organization ◽

Representation Learning ◽

Higher Order ◽

Computational Method ◽

Chromatin Interaction ◽

Interaction Data ◽

3D Genome ◽

Chromatin Interactions ◽

Single Nucleus ◽

And Function

AbstractAdvances in high-throughput mapping of 3D genome organization have enabled genome-wide characterization of chromatin interactions. However, proximity ligation based mapping approaches for pairwise chromatin interaction such as Hi-C cannot capture multi-way interactions, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution. The very recent development of ligation-free chromatin interaction mapping methods such as SPRITE and ChIA-Drop has offered new opportunities to uncover simultaneous interactions involving multiple genomic loci within the same nuclei. Unfortunately, methods for analyzing multi-way chromatin interaction data are significantly underexplored. Here we develop a new computational method, called MATCHA, based on hypergraph representation learning where multi-way chromatin interactions are represented as hyperedges. Applications to SPRITE and ChIA-Drop data suggest that MATCHA is effective to denoise the data and make de novo predictions of multi-way chromatin interactions, reducing the potential false positives and false negatives from the original data. We also show that MATCHA is able to distinguish between multi-way interaction in a single nucleus and combination of pairwise interactions in a cell population. In addition, the embeddings from MATCHA reflect 3D genome spatial localization and function. MATCHA provides a promising framework to significantly improve the analysis of multi-way chromatin interaction data and has the potential to offer unique insights into higher-order chromosome organization and function.

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Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

Frontiers in Genetics ◽

10.3389/fgene.2021.788318 ◽

2022 ◽

Vol 12 ◽

Author(s):

Brittany Baur ◽

Da-Inn Lee ◽

Jill Haag ◽

Deborah Chasman ◽

Michael Gould ◽

...

Keyword(s):

Breast Cancer ◽

Genome Organization ◽

Mammary Epithelial Cells ◽

Cancer Susceptibility ◽

Three Dimensional ◽

Breast Cancer Susceptibility ◽

Higher Order ◽

Mammary Epithelial ◽

3D Genome

Cancer risk by environmental exposure is modulated by an individual’s genetics and age at exposure. This age-specific period of susceptibility is referred to as the “Window of Susceptibility” (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.

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Multiscale and integrative single-cell Hi-C analysis with Higashi

10.1101/2020.12.13.422537 ◽

2020 ◽

Author(s):

Ruochi Zhang ◽

Tianming Zhou ◽

Jian Ma

Keyword(s):

Single Cell ◽

Genome Organization ◽

Single Cells ◽

Representation Learning ◽

3D Genome ◽

Extensive Evaluation ◽

Genome Features ◽

And Function ◽

Cell Variation ◽

Cell To Cell Variability

The advent of single-cell Hi-C (scHi-C) technologies offers an unprecedented opportunity to unveil cell-to-cell variability of 3D genome organization. However, the development of computational methods that can effectively enhance scHi-C data quality and extract 3D genome features in single cells remains a major challenge. Here, we report Higashi, a new algorithm that enables robust analysis of scHi-C data based on hypergraph representation learning. Extensive evaluation and integrative analysis demonstrate that Higashi significantly outperforms existing methods for embedding and imputation of scHi-C data. Higashi is uniquely able to reveal multiscale 3D genome features (such as compartmentalization and TAD-like domain boundaries), allowing markedly refined analysis of structure and function connections at single-cell resolution. We show that Higashi can also be applied to the recently developed single-cell co-assays that jointly profile scHi-C and additional epigenomic features. Higashi enables much improved delineation of functionally important cell-to-cell variation of 3D genome organization using scHi-C data.

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A single-cell method to map higher-order 3D genome organization in thousands of individual cells reveals structural heterogeneity in mouse ES cells

10.1101/2020.08.11.242081 ◽

2020 ◽

Author(s):

Mary V. Arrastia ◽

Joanna W. Jachowicz ◽

Noah Ollikainen ◽

Matthew S. Curtis ◽

Charlotte Lai ◽

...

Keyword(s):

Single Cell ◽

Genome Organization ◽

Single Cells ◽

Genome Structure ◽

Higher Order ◽

Nuclear Bodies ◽

Nuclear Function ◽

Genome Wide ◽

A Genome ◽

Order Structures

ABSTRACTIn eukaryotes, the nucleus is organized into a three dimensional structure consisting of both local interactions such as those between enhancers and promoters, and long-range higher-order structures such as nuclear bodies. This organization is central to many aspects of nuclear function, including DNA replication, transcription, and cell cycle progression. Nuclear structure intrinsically occurs within single cells; however, measuring such a broad spectrum of 3D DNA interactions on a genome-wide scale and at the single cell level has been a great challenge. To address this, we developed single-cell split-pool recognition of interactions by tag extension (scSPRITE), a new method that enables measurements of genome-wide maps of 3D DNA structure in thousands of individual nuclei. scSPRITE maximizes the number of DNA contacts detected per cell enabling high-resolution genome structure maps within each cells and is easy-to-use and cost-effective. scSPRITE accurately detects chromosome territories, active and inactive compartments, topologically associating domains (TADs), and higher-order structures within single cells. In addition, scSPRITE measures cell-to-cell heterogeneity in genome structure at different levels of resolution and shows that TADs are dynamic units of genome organization that can vary between different cells within a population. scSPRITE will improve our understanding of nuclear architecture and its relationship to nuclear function within an individual nucleus from complex cell types and tissues containing a diverse population of cells.

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