scholarly journals Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain

2021 ◽  
Vol 12 ◽  
Author(s):  
Neetesh Pandey ◽  
Omkar Chandra ◽  
Shreya Mishra ◽  
Vibhor Kumar
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Long Range ◽  
Target Genes ◽  
Interaction Analysis ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Cell Type ◽  
Range Interaction ◽  
Long Range Interactions

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles. Our method, termed as single-cell epigenome based chromatin-interaction analysis (scEChIA) exploits signal imputation and refined L1 regularization. For a few single-cell open-chromatin profiles, scEChIA outperformed other tools even in terms of accuracy of prediction. Using scEChIA, we predicted almost 0.7 million interactions among genomic sites across seven cell types in the human brain. Further analysis revealed cell type for connection between genes and expression quantitative trait locus (eQTL) in the human brain and making insight about target genes of human-accelerated-elements and disease-associated mutations. Our analysis enabled by scEChIA also hints about the possible action of a few transcription factors (TFs), especially through long-range interaction in brain endothelial cells.

scholarly journals Improving chromatin-interaction prediction using single-cell open-chromatin profile and making insight about the cis-regulatory landscape of the human brain

2020 ◽  
Author(s):  
Neetesh Pandey ◽  
Omkar Chandra ◽  
Shreya Mishra ◽  
Vibhor Kumar
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Long Range ◽  
Target Genes ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Range Interaction ◽  
Chromatin Interactions ◽  
A Cell

AbstractSingle-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell-type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin-interactions despite the fact that long-range interaction among genomic sites plays a significant role in gene regulation. Here, we propose a method that predicts both local and long-range interactions among genomic sites using single-cell open chromatin profiles. Using our method’s better sensitivity, we could predict almost 0.7 million interactions among genomic sites across 7 cell-types in the human brain. The chromatin-interactions estimated in the human brain revealed surprising but useful insight about target genes of human-accelerated-elements and disease-associated mutations.

scholarly journals Efficient pre-processing of Single-cell ATAC-seq data

2021 ◽  
Author(s):  
Fan Gao ◽  
Lior Pachter
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Healthy Adult ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Cell Type ◽  
Cell Groups ◽  
Cell Type Specific ◽  
Interaction Traces ◽  
Regulatory Landscape

The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 18 times faster than Cell Ranger on human samples, and that uses 33% less RAM when 8 CPU threads are used. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signals and interaction traces for cell groups. We demonstrate the utility of scATAK in an exploration of the chromatin regulatory landscape of a healthy adult human brain and show that it can reveal cell-type-specific features. scATAK is available at https://pachterlab.github.io/scATAK/.

scholarly journals Revealing the transcription factor regulatory context of human specific cortical development using single-cell multi-omics

2021 ◽  
Author(s):  
Yan Wu ◽  
Blue Lake ◽  
Brandon Sos ◽  
Song Chen ◽  
Thu E. Duong ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Human Brain ◽  
Single Cell ◽  
Target Genes ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Specific Sequence ◽  
Enhancer Activity ◽  
Genomic Regions ◽  
Human Specific

AbstractHuman behaviors are at least partially driven by genomic regions that influence human-specific neurodevelopment. This includes genomic regions undergoing human specific sequence acceleration (Human Accelerated Regions or HARs) and regions showing human-specific enhancer activity (Human Gained Enhancers or HGEs) not present in other primates. However, prior studies on HAR/HGE activities involved mixtures of brain cell types and focused only on putative downstream target genes. Here, we directly measured cell type specific HAR/HGE activity in the developing fetal human brain using two independent single-cell chromatin accessibility datasets with matching single-cell gene expression data. Transcription factor (TF) motif analyses identified upstream TFs binding to HARs/HGEs and identified LHX2, a key regulator of forebrain development, as an active HGE regulator in neuronal progenitors. We integrated our TF motif analyses with published chromatin interaction maps to build detailed regulatory networks where TFs are linked to downstream genes via HARs/HGEs. Through these networks, we identified a potential regulatory role for NFIC in human neuronal progenitor networks via modulating the Notch signaling and cell adhesion pathways. Therefore, by using a single cell multi-omics approach, we were able to capture both the upstream and downstream regulatory context of HARs/HGEs, which may provide a more comprehensive picture of the roles HARs/HGEs play amongst diverse fetal cell types of the developing human brain.

scholarly journals Comprehensive identification of fetal cis-regulatory elements in the human genome by single-cell multi-omics analysis

2021 ◽  
Author(s):  
Hao Yu ◽  
Na Ai ◽  
Ping Peng ◽  
Yu wen Ke ◽  
Xue peng Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcription Initiation ◽  
Complex Disease ◽  
Target Genes ◽  
First Trimester ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Cell Type ◽  
Tissue Samples

The regulatory programs driving early organogenesis in human is complex and still poorly understood. We performed parallel profiling of gene expression and chromatin accessibility to 28 human fetal tissue samples representing 14 organs in the first trimester. Collectively, we have generated 415,793 single-cell profiles. By integration analysis of transcriptome and chromatin accessibility, we detected 225 distinct cell types and 848,475 candidate accessible cis-regulatory elements (aCREs). By linking regulatory elements to their putative target genes, we identified not only 108,699 enhancers, but also 23,392 silencers elements. We uncovered thousands of genes regulated by both enhancers and silencers in an organ or cell-type-specific manner. Furthermore, our unique approach revealed a substantial proportion of distal DNA elements are transcribed CREs (tCREs), which show both open chromatin signal and transcription initiation activity of non-coding transcript. The landscape of fetal cis-regulatory elements facilitates the interpretation of the genetic variant of complex disease and infer the cell type of origin for cancer. Overall, our data provide a comprehensive map of the fetal cis-regulatory elements at single-cell resolution and a valuable resource for future study of human development and disease.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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.

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
Author(s):  
William Renthal
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Cell Types ◽  
Susceptibility Genes ◽  
Brain Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

Statistical theory of adsorption with long-range interaction

1938 ◽  
Vol 34 (2) ◽  
pp. 238-252 ◽  
Author(s):  
J. S. Wang
Keyword(s):  
Statistical Theory ◽  
Long Range ◽  
Unit Area ◽  
Continuous Distribution ◽  
Lattice Points ◽  
Circular Region ◽  
Range Interaction ◽  
Dipole Interactions ◽  
Long Range Interactions ◽  
Long Range Interaction

The statistical theory of long-range interactions between adsorbed particles on a plane lattice is worked out approximately, by treating in detail the distribution of adsorbed particles among a few sites inside and on the boundary of a circular region, and regarding the distribution outside the circle as uniform and continuous with a density Kθ per unit area, where K is the number of lattice points per unit area and θ is the fraction of surface covered by adsorbed particles. The continuous distribution begins at a distance ρ from the centre of the circle, ρ being determined by the condition that the probability of occupation of a first shell site is equal to the probability θ of occupation of the central site. Using this method, general formulae for the adsorption isotherm and the heat of adsorption are obtained. Numerical applications for dipole interactions and for quadratic and hexagonal lattices are worked out in detail and the case in which the dipole moment varies with θ is discussed.

LONG-RANGE INTERACTIONS IN THE HELICOIDAL DNA DYNAMICS

2013 ◽  
Vol 27 (24) ◽  
pp. 1350143 ◽  
Author(s):  
MIRABEAU SAHA ◽  
TIMOLEON C. KOFANÉ
Keyword(s):  
Rna Polymerase ◽  
Long Range ◽  
Power Law ◽  
Dna Dynamics ◽  
Extended Version ◽  
Range Interaction ◽  
Dna Molecule ◽  
Long Range Interactions ◽  
Long Range Interaction

In this paper, the comparison between power-law long-range interaction and Kac–Baker long-range interaction in the DNA molecule is investigated. This is done by employing an extended version of spin-like model of the DNA molecule with long-range interaction between intra-strand nucleotides and helicoidal coupling between inter-strand nucleotides when an RNA-polymerase binds to the DNA at biological temperature. Results show that LRIs have an undeniable effect on the DNA dynamics and that one is free to use either PLLRI or KBLRI to study DNA behaviors.

scholarly journals SnapHiC: a computational pipeline to map chromatin contacts from single cell Hi-C data

2020 ◽  
Author(s):  
Miao Yu ◽  
Armen Abnousi ◽  
Yanxiao Zhang ◽  
Guoqiang Li ◽  
Lindsay Lee ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Target Genes ◽  
Embryonic Stem ◽  
Cell Type ◽  
Cortical Cells ◽  
Chromatin Loops ◽  
Chromatin Architecture ◽  
Prefrontal Cortical ◽  
Cell Type Specific

Single cell Hi-C (scHi-C) analysis has been increasingly used to map the chromatin architecture in diverse tissue contexts, but computational tools to define chromatin contacts at high resolution from scHi-C data are still lacking. Here, we describe SnapHiC, a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. We benchmark SnapHiC against HiCCUPS, a common tool for mapping chromatin contacts in bulk Hi-C data, using scHi-C data from 742 mouse embryonic stem cells. We further demonstrate its utility by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells. We uncover cell-type-specific chromatin loops and predict putative target genes for non-coding sequence variants associated with neuropsychiatric disorders. Our results suggest that SnapHiC could facilitate the analysis of cell-type-specific chromatin architecture and gene regulatory programs in complex tissues.

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