NucHMM: a method for quantitative modeling of nucleosome organization identifying functional nucleosome states distinctly associated with splicing potentiality

AbstractWe develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes.

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Accurate imputation of histone modifications using transcription

10.1101/2020.04.08.032730 ◽

2020 ◽

Cited By ~ 3

Author(s):

Zhong Wang ◽

Alexandra G. Chivu ◽

Lauren A. Choate ◽

Edward J. Rice ◽

Donald C. Miller ◽

...

Keyword(s):

Transcription Initiation ◽

Cell Types ◽

Chromatin Accessibility ◽

Cell Type ◽

Active Chromatin ◽

Histone Marks ◽

Repressive Mark ◽

Cell Type Specific ◽

The Relationship ◽

Machine Learning Tool

AbstractWe trained a sensitive machine learning tool to infer the distribution of histone marks using maps of nascent transcription. Transcription captured the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. The relationship between active histone marks and transcription was conserved in all cell types examined, allowing individual labs to annotate active functional elements in mammals with similar richness as major consortia. Using imputation as an interpretative tool uncovered cell-type specific differences in how the PRC2-dependent repressive mark, H3K27me3, corresponds to transcription, and revealed that transcription initiation requires both chromatin accessibility and an active chromatin environment demonstrating that initiation is less promiscuous than previously thought.

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Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types

Epigenetics & Chromatin ◽

10.1186/s13072-019-0308-3 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Lila Rieber ◽

Shaun Mahony

Keyword(s):

K562 Cells ◽

Cell Types ◽

Data Sets ◽

Cell Type ◽

Histone Marks ◽

Joint Inference ◽

3D Localization ◽

Topologically Associated Domains ◽

Cell Type Specific

Abstract Background Comparisons of Hi–C data sets between cell types and conditions have revealed differences in topologically associated domains (TADs) and A/B compartmentalization, which are correlated with differences in gene regulation. However, previous comparisons have focused on known forms of 3D organization while potentially neglecting other functionally relevant differences. We aimed to create a method to quantify all locus-specific differences between two Hi–C data sets. Results We developed MultiMDS to jointly infer and align 3D chromosomal structures from two Hi–C data sets, thereby enabling a new way to comprehensively quantify relocalization of genomic loci between cell types. We demonstrate this approach by comparing Hi–C data across a variety of cell types. We consistently find relocalization of loci with minimal difference in A/B compartment score. For example, we identify compartment-independent relocalizations between GM12878 and K562 cells that involve loci displaying enhancer-associated histone marks in one cell type and polycomb-associated histone marks in the other. Conclusions MultiMDS is the first tool to identify all loci that relocalize between two Hi–C data sets. Our method can identify 3D localization differences that are correlated with cell-type-specific regulatory activities and which cannot be identified using other methods.

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STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

10.1101/2021.09.08.459458 ◽

2021 ◽

Author(s):

Dongqing Sun ◽

Yihan Xiao ◽

Zhaoyang Liu ◽

Taiwen Li ◽

Qiu Wu ◽

...

Keyword(s):

Single Cell ◽

Three Dimensional ◽

Cell Types ◽

Computational Method ◽

Cellular Heterogeneity ◽

Integrated Analysis ◽

Cell Type ◽

Specificity And Sensitivity ◽

Cell Type Specific ◽

Spatial Locations

AbstractThe recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell-types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrate STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell-types to spatial locations but also improved the identification of spatial localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions, and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/DongqingSun96/STRIDE.

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Robust decomposition of cell type mixtures in spatial transcriptomics

10.1101/2020.05.07.082750 ◽

2020 ◽

Cited By ~ 5

Author(s):

Dylan M. Cable ◽

Evan Murray ◽

Luli S. Zou ◽

Aleksandrina Goeva ◽

Evan Z. Macosko ◽

...

Keyword(s):

Cell Types ◽

Substantial Improvement ◽

Computational Method ◽

Cell Type ◽

Sequencing Data ◽

Type Assignment ◽

Spatial Environment ◽

A Cell ◽

Cell Type Specific ◽

Definition Of

AbstractSpatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTD’s recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.

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High Resolution Repli-Seq defines the temporal choreography of initiation, elongation and termination of replication in mammalian cells

10.1101/755629 ◽

2019 ◽

Cited By ~ 1

Author(s):

Peiyao A. Zhao ◽

Takayo Sasaki ◽

David M. Gilbert

Keyword(s):

Dna Replication ◽

Mammalian Cells ◽

Embryonic Stem ◽

Replication Timing ◽

Cell Types ◽

S Phase ◽

List Type ◽

Cell Type ◽

Histone Marks ◽

Cell Type Specific

ABSTRACTDNA replication in mammalian cells occurs in a defined temporal order during S phase, known as the replication timing (RT) programme. RT is developmentally regulated and correlated with chromatin conformation and local transcriptional potential. Here we present RT profiles of unprecedented temporal resolution in two human embryonic stem cell lines, human colon carcinoma line HCT116 as well as F1 subspecies hybrid mouse embryonic stem cells and their neural progenitor derivatives. Strong enrichment of nascent DNA in fine temporal windows reveals a remarkable degree of cell to cell conservation in replication timing and patterns of replication genome-wide. We identify 5 patterns of replication in all cell types, consistent with varying degrees of initiation efficiency. Zones of replication initiation were found throughout S phase and resolved to ~50kb precision. Temporal transition regions were resolved into segments of uni-directional replication punctuated with small zones of inefficient initiation. Small and large valleys of convergent replication were consistent with either termination or broadly distributed initiation, respectively. RT correlated with chromatin compartment across all cell types but correlations of initiation time to chromatin domain boundaries and histone marks were cell type specific. Haplotype phasing revealed previously unappreciated regions of allele-specific and alleleindependent asynchronous replication. Allele-independent asynchrony was associated with large transcribed genes that resemble common fragile sites. Altogether, these data reveal a remarkably deterministic temporal choreography of DNA replication in mammalian cells.Highly homogeneous replication landscape between cells in a populationInitiation zones resolved within constant timing and timing transition regionsActive histone marks enriched within early initiation zones while enrichment of repressive marks is cell type specific.Transcribed long genes replicate asynchronously.

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Joint inference and alignment of genome structures enables characterization of compartment-independent reorganization across cell types

10.1101/545194 ◽

2019 ◽

Author(s):

Lila Rieber ◽

Shaun Mahony

Keyword(s):

Nuclear Periphery ◽

Cell Types ◽

Cell Type ◽

Specific Chromosome ◽

Chromosome Conformation ◽

Histone Marks ◽

Joint Inference ◽

Differential Gene Regulation ◽

Main Driver ◽

Cell Type Specific

AbstractCell-type-specific chromosome conformation is correlated with differential gene regulation. Broad compartmentalization into two compartments (A & B) is proposed to be the main driver of cell-specific chromosome organization. However it is unclear what fraction of chromosome conformation changes between cell types and conditions is independent of changes in compartmentalization and whether any such compartment-independent reorganization is functionally important. We developed MultiMDS to jointly infer and align 3D chromosomal structures, thereby enabling a quantitative comparison of locus-specific changes across Hi-C datasets. We compared Hi-C datasets from yeast, which lack compartmentalization, grown with and without galactose. These comparisons confirmed known relocalizations as well as identifying additional examples. We also compared mammalian datasets across a variety of cell lines. We found a consistent enrichment for changes along the A/B compartment (nuclear interior/nuclear periphery) axis, even when comparing the same cell type from different individuals. Despite the prevalence of compartment changes, we consistently find compartment-independent relocalizations of loci that are within the A compartment in both compared cell types. Some such intra-compartment relocalizations involve loci that display enhancer-associated histone marks in one cell type and polycomb-associated histone marks in the other. MultiMDS thus enables a new way to compare chromosome conformations across two Hi-C datasets.Availabilityhttps://github.com/seqcode/multimds

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