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

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.

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Inferring relevant tissues and cell types for complex traits in genome-wide association studies

10.1101/2021.06.09.447805 ◽

2021 ◽

Author(s):

Rujin Wang ◽

Danyu Lin ◽

Yuchao Jiang

Keyword(s):

Single Cell ◽

Complex Traits ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cell Type ◽

Disease Etiology ◽

Genome Wide ◽

Cell Type Specific

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific omics measurements from single-cell sequencing. We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant tissues or cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We extend our framework to single-cell transcriptomic data and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and single-cell datasets and further validated using PubMed search and existing bulk case-control testing results.

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Multiomics analysis reveals extensive epigenome remodeling during cortical development

10.1101/2020.08.07.241828 ◽

2020 ◽

Author(s):

Florian Noack ◽

Silvia Vangelisti ◽

Madalena Carido ◽

Faye Chong ◽

Boyan Bonev

Keyword(s):

Single Cell ◽

Transcriptional Activation ◽

Molecular Mechanisms ◽

Chromatin Accessibility ◽

Cell Type ◽

Lineage Specification ◽

3D Genome ◽

Epigenetic Remodeling ◽

Cell Type Specific

AbstractDespite huge advances in stem-cell, single-cell and epigenetic technologies, the precise molecular mechanisms that determine lineage specification remain largely unknown. Applying an integrative multiomics approach, e.g. combining single-cell RNA-seq, single-cell ATAC-seq together with cell-type-specific DNA methylation and 3D genome measurements, we systematically map the regulatory landscape in the mouse neocortex in vivo. Our analysis identifies thousands of novel enhancer-gene pairs associated with dynamic changes in chromatin accessibility and gene expression along the differentiation trajectory. Crucially, we provide evidence that epigenetic remodeling generally precedes transcriptional activation, yet true priming appears limited to a subset of lineage-determining enhancers. Notably, we reveal considerable heterogeneity in both contact strength and dynamics of the generally cell-type-specific enhancer-promoter contacts. Finally, our work suggests a so far unrecognized function of several key transcription factors which act as putative “molecular bridges” and facilitate the dynamic reorganization of the chromatin landscape accompanying lineage specification in the brain.

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008173 ◽

2020 ◽

Vol 16 (9) ◽

pp. e1008173 ◽

Cited By ~ 1

Author(s):

Hyeon-Jin Kim ◽

Galip Gürkan Yardımcı ◽

Giancarlo Bonora ◽

Vijay Ramani ◽

Jie Liu ◽

...

Keyword(s):

Single Cell ◽

Topic Modeling ◽

Cell Type ◽

Cell Type Specific

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

10.1101/2021.12.08.471788 ◽

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/.

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Conditional out-of-distribution generation for unpaired data using transfer VAE

Bioinformatics ◽

10.1093/bioinformatics/btaa800 ◽

2020 ◽

Vol 36 (Supplement_2) ◽

pp. i610-i617

Author(s):

Mohammad Lotfollahi ◽

Mohsen Naghipourfar ◽

Fabian J Theis ◽

F Alexander Wolf

Keyword(s):

Single Cell ◽

Generative Models ◽

Response To Treatment ◽

High Dimensional ◽

Compact Representation ◽

Hair Color ◽

Great Success ◽

Cell Type ◽

Style Transfer ◽

Cell Type Specific

Abstract Motivation While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. Results We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. Availability and implementation The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

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.

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Single-cell RNA sequencing of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

PLoS ONE ◽

10.1371/journal.pone.0205883 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0205883 ◽

Cited By ~ 9

Author(s):

Joseph C. Mays ◽

Michael C. Kelly ◽

Steven L. Coon ◽

Lynne Holtzclaw ◽

Martin F. Rath ◽

...

Keyword(s):

Gene Expression ◽

Pineal Gland ◽

Single Cell ◽

Rna Sequencing ◽

Cell Type ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Mammalian Pineal Gland ◽

Daily Patterns

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In vivo single-cell profiling of lncRNAs during Ebola virus infection

10.1101/2022.01.12.476002 ◽

2022 ◽

Author(s):

Luisa Santus ◽

Raquel García-Pérez ◽

Maria Sopena-Rios ◽

Aaron E Lin ◽

Gordon C Adams ◽

...

Keyword(s):

Viral Infection ◽

Single Cell ◽

Ebola Virus ◽

Cell Type ◽

Protein Coding ◽

Expression Variation ◽

Lncrna Expression ◽

Ebov Infection ◽

Cell Type Specific

Long non-coding RNAs (lncRNAs) are pivotal mediators of systemic immune response to viral infection, yet most studies concerning their expression and functions upon immune stimulation are limited to in vitro bulk cell populations. This strongly constrains our understanding of how lncRNA expression varies at single-cell resolution, and how their cell-type specific immune regulatory roles may differ compared to protein-coding genes. Here, we perform the first in-depth characterization of lncRNA expression variation at single-cell resolution during Ebola virus (EBOV) infection in vivo. Using bulk RNA-sequencing from 119 samples and 12 tissue types, we significantly expand the current macaque lncRNA annotation. We then profile lncRNA expression variation in immune circulating single-cells during EBOV infection and find that lncRNAs' expression in fewer cells is a major differentiating factor from their protein-coding gene counterparts. Upon EBOV infection, lncRNAs present dynamic and mostly cell-type specific changes in their expression profiles especially in monocytes, the main cell type targeted by EBOV. Such changes are associated with gene regulatory modules related to important innate immune responses such as interferon response and purine metabolism. Within infected cells, several lncRNAs have positively and negatively correlated expression with viral load, suggesting that expression of some of these lncRNAs might be directly hijacked by EBOV to attack host cells. This study provides novel insights into the roles that lncRNAs play in the host response to acute viral infection and paves the way for future lncRNA studies at single-cell resolution.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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Functional annotation of human long noncoding RNAs using chromatin conformation data

10.1101/2021.01.13.426305 ◽

2021 ◽

Author(s):

Saumya Agrawal ◽

Tanvir Alam ◽

Masaru Koido ◽

Ivan V. Kulakovskiy ◽

Jessica Severin ◽

...

Keyword(s):

Functional Annotation ◽

Rna Binding ◽

Functional Characterization ◽

Cell Types ◽

Chromatin Interaction ◽

Spatial Proximity ◽

Chromatin Conformation ◽

Cell Type ◽

Cell Type Specific ◽

Rna Domains

AbstractTranscription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

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