Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions

2019 ◽  
pp. 211-238 ◽  
Author(s):  
Thalia E. Chan ◽  
Michael P. H. Stumpf ◽  
Ann C. Babtie
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Fate Decisions ◽  
Gene Regulatory ◽  
Cell Data

scholarly journals GATA-targeted compounds modulate cardiac subtype cell differentiation in dual reporter stem cell line

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mika J. Välimäki ◽  
Robert S. Leigh ◽  
Sini M. Kinnunen ◽  
Alexander R. March ◽  
Ana Hernández de Sande ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Cell Fate ◽  
Reporter Gene ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Fate Decisions ◽  
Dual Reporter ◽  
Gene Regulatory ◽  
Reporter Gene Assays

AbstractBackgroundPharmacological modulation of cell fate decisions and developmental gene regulatory networks holds promise for the treatment of heart failure. Compounds that target tissue-specific transcription factors could overcome non-specific effects of small molecules and lead to the regeneration of heart muscle following myocardial infarction. Due to cellular heterogeneity in the heart, the activation of gene programs representing specific atrial and ventricular cardiomyocyte subtypes would be highly desirable. Chemical compounds that modulate atrial and ventricular cell fate could be used to improve subtype-specific differentiation of endogenous or exogenously delivered progenitor cells in order to promote cardiac regeneration.MethodsTranscription factor GATA4-targeted compounds that have previously shown in vivo efficacy in cardiac injury models were tested for stage-specific activation of atrial and ventricular reporter genes in differentiating pluripotent stem cells using a dual reporter assay. Chemically induced gene expression changes were characterized by qRT-PCR, global run-on sequencing (GRO-seq) and immunoblotting, and the network of cooperative proteins of GATA4 and NKX2-5 were further explored by the examination of the GATA4 and NKX2-5 interactome by BioID. Reporter gene assays were conducted to examine combinatorial effects of GATA-targeted compounds and bromodomain and extraterminal domain (BET) inhibition on chamber-specific gene expression.ResultsGATA4-targeted compounds 3i-1000 and 3i-1103 were identified as differential modulators of atrial and ventricular gene expression. More detailed structure-function analysis revealed a distinct subclass of GATA4/NKX2-5 inhibitory compounds with an acetyl lysine-like domain that contributed to ventricular cells (%Myl2-eGFP+). Additionally, BioID analysis indicated broad interaction between GATA4 and BET family of proteins, such as BRD4. This indicated the involvement of epigenetic modulators in the regulation of GATA-dependent transcription. In this line, reporter gene assays with combinatorial treatment of 3i-1000 and the BET bromodomain inhibitor (+)-JQ1 demonstrated the cooperative role of GATA4 and BRD4 in the modulation of chamber-specific cardiac gene expression.ConclusionsCollectively, these results indicate the potential for therapeutic alteration of cell fate decisions and pathological gene regulatory networks by GATA4-targeted compounds modulating chamber-specific transcriptional programs in multipotent cardiac progenitor cells and cardiomyocytes. The compound scaffolds described within this study could be used to develop regenerative strategies for myocardial regeneration.

scholarly journals Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Sumin Jang ◽  
Sandeep Choubey ◽  
Leon Furchtgott ◽  
Ling-Nan Zou ◽  
Adele Doyle ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Cell Fate Decisions ◽  
Transcriptomics Data ◽  
Gene Regulatory

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development.

scholarly journals scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

Patterns ◽  
2020 ◽  
Vol 1 (9) ◽  
pp. 100139
Author(s):  
Daniel Osorio ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Jianhua Z. Huang ◽  
James J. Cai
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory ◽  
Cell Data

scholarly journals Inferring gene regulatory networks from single-cell data: a mechanistic approach

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Ulysse Herbach ◽  
Arnaud Bonnaffoux ◽  
Thibault Espinasse ◽  
Olivier Gandrillon
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Mechanistic Approach ◽  
Gene Regulatory ◽  
Cell Data

scholarly journals Uncovering the Gene Regulatory Networks Underlying Macrophage Polarization Through Comparative Analysis of Bulk and Single-Cell Data

2021 ◽  
Author(s):  
Klebea Carvalho ◽  
Elisabeth Rebboah ◽  
Camden Jansen ◽  
Katherine Williams ◽  
Andrew Dowey ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Macrophage Polarization ◽  
Cell Polarization ◽  
Rna Seq ◽  
Gene Regulatory ◽  
Cell Subpopulations ◽  
Cell Data

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

scholarly journals Towards inferring causal gene regulatory networks from single cell expression Measurements

2018 ◽  
Author(s):  
Xiaojie Qiu ◽  
Arman Rahimzamani ◽  
Li Wang ◽  
Qi Mao ◽  
Timothy Durham ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Network Reconstruction ◽  
Series Data ◽  
Causal Gene ◽  
Downstream Target ◽  
Gene Regulatory

AbstractSingle-cell transcriptome sequencing now routinely samples thousands of cells, potentially providing enough data to reconstruct causal gene regulatory networks from observational data. Here, we present Scribe, a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs Restricted Directed Information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime” ordered single-cell data compared to true time series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as “RNA velocity” restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses therefore highlight an important shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and point the way towards overcoming it.

scholarly journals Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Laura Serrano-Ron ◽  
Javier Cabrera ◽  
Pablo Perez-Garcia ◽  
Miguel A. Moreno-Risueno
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Root Development ◽  
Regulatory Networks ◽  
Primary Root ◽  
Cell Fate Specification ◽  
Rna Seq ◽  
Fate Specification ◽  
Gene Regulatory

Over the last decades, research on postembryonic root development has been facilitated by “omics” technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

scholarly journals Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Patterns ◽  
2021 ◽  
Vol 2 (9) ◽  
pp. 100332
Author(s):  
N. Alexia Raharinirina ◽  
Felix Peppert ◽  
Max von Kleist ◽  
Christof Schütte ◽  
Vikram Sunkara
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Higher Order ◽  
Rna Seq ◽  
Higher Order Moments ◽  
Gene Regulatory
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