Predicting gene regulatory networks from cell atlases

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Cell Type Specific ◽

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.

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 ◽

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Multiple Cell ◽

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.

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 ◽

Regulatory Networks ◽

Cell Types ◽

Cell Fate Determination ◽

Cell Type ◽

Fate Determination ◽

Cell Type Specific ◽

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.

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

Cell Reports ◽

10.1016/j.celrep.2020.108472 ◽

2020 ◽

Vol 33 (10) ◽

pp. 108472

Author(s):

Zhaoning Wang ◽

Miao Cui ◽

Akansha M. Shah ◽

Wei Tan ◽

Ning Liu ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Specific Gene ◽

Heart Regeneration ◽

Cell Type ◽

Neonatal Heart ◽

Cell Type Specific ◽

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 ◽

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

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

Cell Reports ◽

10.1016/j.celrep.2021.109211 ◽

2021 ◽

Vol 35 (8) ◽

pp. 109211

Author(s):

Zhaoning Wang ◽

Miao Cui ◽

Akansha M. Shah ◽

Wei Tan ◽

Ning Liu ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Specific Gene ◽

Heart Regeneration ◽

Cell Type ◽

Neonatal Heart ◽

Cell Type Specific ◽

Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

10.1101/2022.01.09.475548 ◽

2022 ◽

Author(s):

Chirag Gupta ◽

Jielin Xu ◽

Ting Jin ◽

Saniya Khullar ◽

Xiaoyu Liu ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Cell Types ◽

Network Biology ◽

Cell Type ◽

Cell Network ◽

Cell Type Specific ◽

Gene Regulatory ◽

Type Gene ◽

And Control

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell-type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular. Several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment). Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals mechanisms on cell-type gene dyregulation in AD.

Single cell and single nucleus RNA-Seq reveal cellular heterogeneity and homeostatic regulatory networks in adult mouse stria vascularis

10.1101/756635 ◽

2019 ◽

Author(s):

Soumya Korrapati ◽

Ian Taukulis ◽

Rafal Olszewski ◽

Madeline Pyle ◽

Shoujun Gu ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Stria Vascularis ◽

Cell Types ◽

Cellular Heterogeneity ◽

Genetic Mechanisms ◽

Single Nucleus ◽

AbstractThe stria vascularis (SV) generates the endocochlear potential (EP) in the inner ear and is necessary for proper hair cell mechanotransduction and hearing. While channels belonging to SV cell types are known to play crucial roles in EP generation, relatively little is known about gene regulatory networks that underlie the ability of the SV to generate and maintain the EP. Using single cell and single nucleus RNA-sequencing, we identify and validate known and rare cell populations in the SV. Furthermore, we establish a basis for understanding molecular mechanisms underlying SV function by identifying potential gene regulatory networks as well as druggable gene targets. Finally, we associate known deafness genes with adult SV cell types. This work establishes a basis for dissecting the genetic mechanisms underlying the role of the SV in hearing and will serve as a basis for designing therapeutic approaches to hearing loss related to SV dysfunction.

IReNA: integrated regulatory network analysis of single-cell transcriptomes

10.1101/2021.11.22.469628 ◽

2021 ◽

Author(s):

junyao jiang ◽

Seth Blackshaw ◽

Jiang Qian ◽

Jie Wang

Keyword(s):

Network Analysis ◽

Single Cell ◽

Regulatory Network ◽

Regulatory Networks ◽

R Package ◽

Integrated Network ◽

Binding Motifs ◽

Gene Regulatory ◽

Public Datasets

While single-cell RNA sequencing (scRNA-seq) is widely used to profile gene expression, few methods are available to infer gene regulatory networks using scRNA-seq data. Here, we developed and extended IReNA (Integrated Regulatory Network Analysis) to perform regulatory network analysis using scRNA-seq profiles. Four features are developed for IReNA. First, regulatory networks are divided into different modules which represent distinct biological functions. Second, transcription factors significantly regulating each gene module can be identified. Third, regulatory relationships among modules can be inferred. Fourth, IReNA can integrate ATAC-seq data into regulatory network analysis. If ATAC-seq data is available, both transcription factor footprints and binding motifs are used to refine regulatory relationships among co-expressed genes. Using public datasets, we showed that integrated network analysis of scRNA-seq data with ATAC-seq data identified a higher fraction of known regulators than scRNA-seq data alone. Moreover, IReNA provided a better performance of network analysis than currently available methods. Beyond the reconstruction of regulatory networks, IReNA can modularize regulatory networks, and identify key regulators and significant regulatory relationships for modules, facilitating the systems-level understanding of biological regulatory mechanisms. The R package IReNA is available at https://github.com/jiang-junyao/IReNA.

The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution

Nature Communications ◽

10.1038/s41467-021-23675-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Michael W. Dorrity ◽

Cristina M. Alexandre ◽

Morgan O. Hamm ◽

Anna-Lena Vigil ◽

Stanley Fields ◽

...

Keyword(s):

Arabidopsis Thaliana ◽

Single Cell ◽

Regulatory Networks ◽

Cell Types ◽

Analytical Framework ◽

Cell Type ◽

Tissue Heterogeneity ◽

Shared Information ◽

Cell Type Specific ◽

Regulatory Landscape

AbstractThe scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell’s regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

An Arabidopsis root phloem pole cell atlas reveals PINEAPPLE genes as transitioners to autotrophy

10.1101/2021.08.31.458411 ◽

2021 ◽

Author(s):

Sofia Otero ◽

Iris Sevilem ◽

Pawel Roszak ◽

Yipeng Lu ◽

Valerio Di Vittori ◽

...

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Developmental Trajectories ◽

Cell Types ◽

Holistic View ◽

Arabidopsis Root ◽

Single Cell Sequencing ◽

Gene Regulatory ◽

Dof Transcription Factors

AbstractSingle cell sequencing has recently allowed the generation of exhaustive root cell atlases. However, some cell types are elusive and remain underrepresented. Here, we use a second- generation single cell approach, where we zoom in on the root transcriptome sorting with specific markers to profile the phloem poles at an unprecedented resolution. Our data highlight the similarities among the developmental trajectories and gene regulatory networks communal to protophloem sieve element (PSE) adjacent lineages in relation to PSE enucleation, a key event in phloem biology.As a signature for early PSE-adjacent lineages, we have identified a set of DNA-binding with one finger (DOF) transcription factors, the PINEAPPLEs (PAPL), that act downstream of PHLOEM EARLY DOF (PEAR) genes, and are important to guarantee a proper root nutrition in the transition to autotrophy.Our data provide a holistic view of the phloem poles that act as a functional unit in root development.