Gene Regulatory Network Reconfiguration in Direct Lineage Reprogramming
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE217675 | GEO | 2022/12/20
REPOSITORIES: GEO
Similar Datasets
Project description:Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that dynamically reconfigure to drive diverse cellular states. Single-cell transcriptomic technologies, such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine the transcriptional state at individual cell resolution, allowing the study of cellular heterogeneity and cell-type specific gene regulation in dynamic processes such as cell fate specification. However, current approaches to infer cell type-specific gene regulatory networks from these datasets are limited in their ability to integrate scRNA-seq and scATACseq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the gene regulatory network for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated, published, and newly collected single cell omic datasets, we show that scMTNI is able to accurately infer gene regulatory networks and captures meaningful network dynamics that identify GRN components associated with cell type transitions. Application of our method to mouse cellular reprogramming identified key regulators associated with cell populations that reprogram versus those that are stalled. Taken together, scMTNI is a powerful framework to infer cell type-specific gene regulatory networks and their dynamics from scRNA-seq and scATAC-seq datasets.
2023-04-14 | GSE208620 | GEO
Project description:CellOracle: Dissecting cell identity via network inference and in silico gene perturbation
| PRJNA606682 | ENA
Project description:Chickarmane2008 - Stem cell lineage determination
In this work, a dynamical model of lineage
determination based upon a minimal circuit, as discussed in PMID: 17215298
, which contains the Oct4/Sox2/Nanog core as well its interaction
with a few other key genes is discussed.
This model is described in the article:
A computational model for understanding stem cell, trophectoderm and endoderm lineage determination.
Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478
Abstract:
BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/
PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.
CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.
This model is hosted on BioModels Database
and identified
by: MODEL8390025091
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2008-12-05 | BIOMD0000000209 | BioModels
Project description:Chickarmane2008 - Stem cell lineage - NANOG GATA-6 switch
In this work, a dynamical model of lineage
determination based upon a minimal circuit, as discussed in PMID: 17215298
, which contains the Oct4/Sox2/Nanog core as well its interaction
with a few other key genes is discussed.
This model is described in the article:
A computational model for understanding stem cell, trophectoderm and endoderm lineage determination.
Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478
Abstract:
BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/
PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.
CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.
This model is hosted on BioModels Database
and identified
by: MODEL8389825246
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource
for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to the public
domain worldwide. Please refer to CC0 Public Domain
Dedication
for more information.
2008-12-05 | BIOMD0000000210 | BioModels
Project description:Cells require coordinated control over gene expression changes when responding to environmental stimuli. Recent advancements in single-cell technologies have enabled studies of regulatory dynamics associated with cellular perturbation response in immune cells. However, associating these changes to underlying differences in epigenetic regulation through integration of multi-omic data has not been achieved at single-cell resolution. Here, we apply single-cell ATAC-seq (scATAC-seq) and scRNA-seq to assay chromatin accessibility and gene expression in resting and stimulated human blood cells. Collectively, we generate ~91,000 high-coverage single-cell profiles, allowing us to probe the cis-regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multiomic data, we connect distal cis-regulatory elements to genes, identifying key regulatory hubs associated with immune signaling and stress response. Subsequently, we design FigR - a method to infer gene regulatory networks (GRNs) to identify candidate TF regulators across single cells. Importantly, construction of a GRN using stimulus response data identifies TF regulatory activity associated with genetic variants in inflammatory diseases. Lastly, we assess the time-dependent regulatory dynamics of immune stimulation, where we find that cells alter chromatin accessibility prior to productive gene expression, a process occurring at time scales of minutes. Extending this concept of chromatin-mediated priming, and incorporating regulatory relationships defined by FigR, we detect a rare subpopulation of monocytes marked by a primed chromatin state predictive of enhanced immunological response. Overall, we build upon computational tools for GRN construction and demonstrate the utility of GRNs for analysis of multi-omic single cell data.
2022-06-20 | GSE178430 | GEO
Project description:Cells require coordinated control over gene expression changes when responding to environmental stimuli. Recent advancements in single-cell technologies have enabled studies of regulatory dynamics associated with cellular perturbation response in immune cells. However, associating these changes to underlying differences in epigenetic regulation through integration of multi-omic data has not been achieved at single-cell resolution. Here, we apply single-cell ATAC-seq (scATAC-seq) and scRNA-seq to assay chromatin accessibility and gene expression in resting and stimulated human blood cells. Collectively, we generate ~91,000 high-coverage single-cell profiles, allowing us to probe the cis-regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multiomic data, we connect distal cis-regulatory elements to genes, identifying key regulatory hubs associated with immune signaling and stress response. Subsequently, we design FigR - a method to infer gene regulatory networks (GRNs) to identify candidate TF regulators across single cells. Importantly, construction of a GRN using stimulus response data identifies TF regulatory activity associated with genetic variants in inflammatory diseases. Lastly, we assess the time-dependent regulatory dynamics of immune stimulation, where we find that cells alter chromatin accessibility prior to productive gene expression, a process occurring at time scales of minutes. Extending this concept of chromatin-mediated priming, and incorporating regulatory relationships defined by FigR, we detect a rare subpopulation of monocytes marked by a primed chromatin state predictive of enhanced immunological response. Overall, we build upon computational tools for GRN construction and demonstrate the utility of GRNs for analysis of multi-omic single cell data.
2022-06-20 | GSE178429 | GEO
Project description:Cells require coordinated control over gene expression changes when responding to environmental stimuli. Recent advancements in single-cell technologies have enabled studies of regulatory dynamics associated with cellular perturbation response in immune cells. However, associating these changes to underlying differences in epigenetic regulation through integration of multi-omic data has not been achieved at single-cell resolution. Here, we apply single-cell ATAC-seq (scATAC-seq) and scRNA-seq to assay chromatin accessibility and gene expression in resting and stimulated human blood cells. Collectively, we generate ~91,000 high-coverage single-cell profiles, allowing us to probe the cis-regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multiomic data, we connect distal cis-regulatory elements to genes, identifying key regulatory hubs associated with immune signaling and stress response. Subsequently, we design FigR - a method to infer gene regulatory networks (GRNs) to identify candidate TF regulators across single cells. Importantly, construction of a GRN using stimulus response data identifies TF regulatory activity associated with genetic variants in inflammatory diseases. Lastly, we assess the time-dependent regulatory dynamics of immune stimulation, where we find that cells alter chromatin accessibility prior to productive gene expression, a process occurring at time scales of minutes. Extending this concept of chromatin-mediated priming, and incorporating regulatory relationships defined by FigR, we detect a rare subpopulation of monocytes marked by a primed chromatin state predictive of enhanced immunological response. Overall, we build upon computational tools for GRN construction and demonstrate the utility of GRNs for analysis of multi-omic single cell data.
2022-06-20 | GSE178428 | GEO
Project description:A gene expression profile of the conventional dendritic cell lineage 1 from murine mesenteric lymph nodes (MLN) were constructed based on RNA-seq from nine biological biopsies. The gene expression profile was afterwards used to infer (in silico) a cDC1-specific protein-protein interactome.
2020-07-01 | GSE150549 | GEO
Project description:Cell fate transitions are accompanied by global transcriptional, epigenetic and topological changes driven by transcription factors (TFs), as is strikingly exemplified by reprogramming somatic cells to pluripotent stem cells (PSCs) via expression of OCT4, KLF4, SOX2 and cMYC. How TFs orchestrate the complex molecular changes around their target gene loci in a temporal manner remains incompletely understood. Here, using KLF4 as a paradigm, we provide the first TF-centric view of chromatin reorganization and its association to 3D enhancer rewiring and transcriptional changes of linked genes during reprogramming of mouse embryonic fibroblasts (MEFs) to PSCs. Inducible depletion of KLF factors in PSCs caused a genome-wide decrease in the connectivity of enhancers, while disruption of individual KLF4 binding sites from PSC-specific enhancers was sufficient to impair enhancer-promoter contacts and reduce expression of associated genes. Our study provides an integrative view of the complex activities of a lineage-specifying TF during a controlled cell fate transition and offers novel insights into the order and nature of molecular events that follow TF binding.
2019-09-03 | PXD014631 | Pride
Project description:Eukaryotic transcription factors (TFs) form complexes with various partner proteins to recognize their genomic target sites. Yet, how the DNA sequence determines which TF complex forms at any given site is poorly understood. Here, we demonstrate that high-throughput in vitro DNA binding assays coupled with unbiased computational analysis provide unprecedented insight into how different DNA sequences select distinct compositions and configurations of homeodomain TF complexes. Using inferred knowledge about minor groove width readout, we design targeted protein mutations that destabilize homeodomain binding both in vitro and in vivo in a complex-specific manner. By performing parallel systematic evolution of ligands by exponential enrichment sequencing (SELEX-seq), chromatin immunoprecipitation sequencing (ChIP-seq), RNA sequencing (RNA-seq), and Hi-C assays, we not only classify the majority of in vivo binding events in terms of complex composition but also infer complex-specific functions by perturbing the gene regulatory network controlled by a single complex.
2020-01-27 | GSE125604 | GEO