Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

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scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

10.1101/2020.12.30.424887 ◽

2020 ◽

Author(s):

Larisa M. Soto ◽

Juan P. Bernal-Tamayo ◽

Robert Lehmann ◽

Subash Balsamy ◽

Xabier Martinez-de-Morentin ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Data Driven ◽

Energy Landscapes ◽

Cell Type ◽

Cell Clustering ◽

Cell Type Specific ◽

Gene Regulatory ◽

Python Package

AbstractRecent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application.AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

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DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

10.1101/2021.02.03.429484 ◽

2021 ◽

Author(s):

Jiaxing Chen ◽

Chinwang Cheong ◽

Liang Lan ◽

Xin Zhou ◽

Jiming Liu ◽

...

Keyword(s):

Neural Network ◽

Single Cell ◽

Regulatory Networks ◽

Deep Neural Network ◽

Neighborhood Context ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Rna Seq ◽

Cell Type Specific ◽

Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

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scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks

10.1101/2021.03.22.436484 ◽

2021 ◽

Author(s):

Daniel Osorio ◽

Yan Zhong ◽

Guanxun Li ◽

Qian Xu ◽

Andrew E. Hillhouse ◽

...

Keyword(s):

Machine Learning ◽

Single Cell ◽

Gene Function ◽

Specific Gene ◽

Cell Type ◽

Cell Technology ◽

Cell Type Specific ◽

Gene Regulatory ◽

The Impact ◽

Cell Gene

Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

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LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

Nucleic Acids Research ◽

10.1093/nar/gkaa1017 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D125-D133

Author(s):

Peng Wang ◽

Qiuyan Guo ◽

Yangyang Hao ◽

Qian Liu ◽

Yue Gao ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Tumour Microenvironment ◽

Fine Tuning ◽

Specific Gene ◽

Related Data ◽

Tumour Metastasis ◽

Gene Regulatory ◽

The One

Abstract Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the ‘One Cell, One World’ theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.

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ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

10.1101/2020.06.05.135798 ◽

2020 ◽

Author(s):

Quan Xu ◽

Georgios Georgiou ◽

Gert Jan C. Veenstra ◽

Huiqing Zhou ◽

Simon J. van Heeringen

Keyword(s):

Transcription Factors ◽

Cell Fate ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Cell Fate Determination ◽

Cell Type ◽

Fate Determination ◽

Cell Type Specific ◽

Gene Regulatory

AbstractProper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate conversions using differential gene networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

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Random Forest Factorization Reveals Latent Structure in Single Cell RNA Sequencing Data

10.1101/2021.09.13.460168 ◽

2021 ◽

Author(s):

Boris M. Brenerman ◽

Benjamin D. Shapiro ◽

Michael C. Schatz ◽

Alexis Battle

Keyword(s):

Random Forest ◽

Single Cell ◽

Rna Sequencing ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Sequencing Data ◽

Random Forest Regression ◽

Single Cell Rna Sequencing ◽

Cell Type Specific ◽

Gene Regulatory

AbstractSingle-cell RNA sequencing data contain patterns of correlation that are poorly captured by techniques that rely on linear estimation or assumptions of Gaussian behavior. We apply random forest regression to scRNAseq data from mouse brains, which identifies the co-regulation of genes within specific cellular contexts. By analyzing the estimators of the random forest, we identify several novel candidate gene regulatory networks and compare these networks in aged and young mice. We demonstrate that cell populations have cell-type specific phenotypes of aging that are not detected by other methods, including the collapse of differentiating oligodendrocytes but not precursors or mature oligodendrocytes.

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Predicting gene regulatory networks from cell atlases

Life Science Alliance ◽

10.26508/lsa.202000658 ◽

2020 ◽

Vol 3 (11) ◽

pp. e202000658 ◽

Cited By ~ 1

Author(s):

Andreas Fønss Møller ◽

Kedar Nath Natarajan

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Multiple Cell ◽

Gene Regulatory

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

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The three-dimensional chromatin structure of the major human pancreatic cell types reveals lineage-specific regulatory architecture of T2D risk

10.1101/2021.11.30.470653 ◽

2021 ◽

Author(s):

Su Chun ◽

Long Gao ◽

Catherine L May ◽

James A Pippin ◽

Keith Boehm ◽

...

Keyword(s):

Single Cell ◽

Target Genes ◽

Genetic Diseases ◽

Three Dimensional ◽

Specific Gene ◽

Cell Type ◽

Pancreatic Cell ◽

Regulatory Architecture ◽

Cell Type Specific ◽

Gene Regulatory

Three-dimensional (3D) chromatin organization maps help to dissect cell type-specific gene regulatory programs. Furthermore, 3D chromatin maps have contributed to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and control of cell type-specific gene regulatory programs. We identified a total of 1,094 putative causal-variant-target-gene pairs at 129 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis

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