Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA349134 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions
| PRJNA349131 | ENA
Project description:Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions [I]
| PRJNA349135 | ENA
Project description:Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using a Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.
2018-02-06 | GSE88927 | GEO
Project description:Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using a Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.
2018-02-06 | GSE88926 | GEO
Project description:We used the paradigmatic 'GATA-PU.1 axis’ to explore, at systems-level, dynamic relationships between transcription factor (TF) binding and global gene expression programs as multipotent cells differentiate. We combined global ChIPSeq of GATA1, GATA2 and PU.1 with expression profiling during differentiation to erythroid and neutrophil lineages. Our analysis reveals (i) differential complexity of sequence motifs bound by GATA1, GATA2 and PU.1; (ii) the scope and interplay of the GATA1 and GATA2 programs within, and during transitions between, different cell compartments, and the extent of their hard-wiring by DNA motifs; (iii) the potential to predict gene expression trajectories based on global associations between TF-binding data and target gene expression and (iv) how dynamic modeling of DNA-binding and gene expression data can be used to infer regulatory logic of TF circuitry. This 'rubric' exemplifies the utility of this cross-platform resource for deconvoluting the complexity of transcriptional programs controlling stem/progenitor cell fate in hematopoiesis. We examine binding of three factors Gata1, Gata2 and Pu1 at 3 stages of differentiation (multipotential, 5 days of erytroid and neutrophil) , while looking at 2 intermeditate stages of erythroid differentiation for factors Gata2 and Gata1. We also look at Gata2 and Gata1 binding after Gata1 induction using an ER fusion protein.
2013-10-17 | E-GEOD-50406 | biostudies-arrayexpress
Project description:Primary hematopoietic cells from mouse bone marrow were sorted and hybridised expression microarrays as part of a study investigating the differentiation of a multipotential cell-line to erthroid and myeloid fates. We used the paradigmatic 'GATA-PU.1 axis’ to explore, at systems-level, dynamic relationships between transcription factor (TF) binding and global gene expression programs as multipotent cells differentiate. We combined global ChIPSeq of GATA1, GATA2 and PU.1 with expression profiling during differentiation to erythroid and neutrophil lineages. Our analysis reveals (i) differential complexity of sequence motifs bound by GATA1, GATA2 and PU.1; (ii) the scope and interplay of the GATA1 and GATA2 programs within, and during transitions between, different cell compartments, and the extent of their hard-wiring by DNA motifs; (iii) the potential to predict gene expression trajectories based on global associations between TF-binding data and target gene expression and (iv) how dynamic modeling of DNA-binding and gene expression data can be used to infer regulatory logic of TF circuitry. This 'rubric' exemplifies the utility of this cross-platform resource for deconvoluting the complexity of transcriptional programs controlling stem/progenitor cell fate in hematopoiesis. Primary hematopoietic cells from mouse bone marrow sorted using the dissection as described by Pronk et al (2007,Cell Stem Cell) where lin was defined as B220, CD3, CD4, CD8, CD41, Mac1, Gr1, Ter119.
2013-10-17 | E-GEOD-49988 | biostudies-arrayexpress
Project description:An expression time course experiment to investigate gene expression changes during differentiation of multipotential cells over two lineages using the model cell line FDCPmix differentiating towards erytroid and myeloid fates. We used the paradigmatic 'GATA-PU.1 axis’ to explore, at systems-level, dynamic relationships between transcription factor (TF) binding and global gene expression programs as multipotent cells differentiate. We combined global ChIPSeq of GATA1, GATA2 and PU.1 with expression profiling during differentiation to erythroid and neutrophil lineages. Our analysis reveals (i) differential complexity of sequence motifs bound by GATA1, GATA2 and PU.1; (ii) the scope and interplay of the GATA1 and GATA2 programs within, and during transitions between, different cell compartments, and the extent of their hard-wiring by DNA motifs; (iii) the potential to predict gene expression trajectories based on global associations between TF-binding data and target gene expression and (iv) how dynamic modeling of DNA-binding and gene expression data can be used to infer regulatory logic of TF circuitry. This 'rubric' exemplifies the utility of this cross-platform resource for deconvoluting the complexity of transcriptional programs controlling stem/progenitor cell fate in hematopoiesis. Multipotential FDCPmix cells were placed in two separate cytokine conditions and differentiated to erythroid and myeloid cells. Samples were taken at the time intervals indicated and hybridised to Agilent expression arrays. Each time point was done in triplicate.
2013-10-17 | E-GEOD-49987 | biostudies-arrayexpress
Project description:FDCPmix cells were infected with Gata, Pu1 shRNAs and microarrayed 5 days after infecton as part of a study investigating the differentiation of a multipotential cell-line to erthroid and myeloid fates. We used the paradigmatic 'GATA-PU.1 axis’ to explore, at systems-level, dynamic relationships between transcription factor (TF) binding and global gene expression programs as multipotent cells differentiate. We combined global ChIPSeq of GATA1, GATA2 and PU.1 with expression profiling during differentiation to erythroid and neutrophil lineages. Our analysis reveals (i) differential complexity of sequence motifs bound by GATA1, GATA2 and PU.1; (ii) the scope and interplay of the GATA1 and GATA2 programs within, and during transitions between, different cell compartments, and the extent of their hard-wiring by DNA motifs; (iii) the potential to predict gene expression trajectories based on global associations between TF-binding data and target gene expression and (iv) how dynamic modeling of DNA-binding and gene expression data can be used to infer regulatory logic of TF circuitry. This 'rubric' exemplifies the utility of this cross-platform resource for deconvoluting the complexity of transcriptional programs controlling stem/progenitor cell fate in hematopoiesis. FDCPmix cells were infected with lentivirus containing either Pu1 or Gata2 shRNAs. After 5 days cells were washed, GFP sorted and microarrayed. Controlled against empty vector.
2013-10-17 | E-GEOD-49989 | biostudies-arrayexpress
Project description:We used the paradigmatic 'GATA-PU.1 axis’ to explore, at systems-level, dynamic relationships between transcription factor (TF) binding and global gene expression programs as multipotent cells differentiate. We combined global ChIPSeq of GATA1, GATA2 and PU.1 with expression profiling during differentiation to erythroid and neutrophil lineages. Our analysis reveals (i) differential complexity of sequence motifs bound by GATA1, GATA2 and PU.1; (ii) the scope and interplay of the GATA1 and GATA2 programs within, and during transitions between, different cell compartments, and the extent of their hard-wiring by DNA motifs; (iii) the potential to predict gene expression trajectories based on global associations between TF-binding data and target gene expression and (iv) how dynamic modeling of DNA-binding and gene expression data can be used to infer regulatory logic of TF circuitry. This 'rubric' exemplifies the utility of this cross-platform resource for deconvoluting the complexity of transcriptional programs controlling stem/progenitor cell fate in hematopoiesis.
2013-10-17 | GSE50406 | GEO
Project description:A systems biology approach in which qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify 15 pluripotency regulating cytokines or cytokine related genes. IL-11 was validated as a novel factor capable of maintaing the undifferentiated state of human embryonic stem cells in the absence of exogenously added bFGF to the culture acting via a different mechanims than bFGF. Transcriptomic microarray data of eight overexpression and knock-down experiments were used for qualitative modeling based on boolean networks to predict novel factors - mainly cytokines - that maintain pluripotent human embryonic stem cells in the absence of bFGF in culture. The culture was maintained for at least 9 passages using and stained positive for alcaline phosphatase staining, OCT3/4, SOX2, NANOG, TRA1-60. Microarray based gene expression profiling showed that the IL-11 treatment occupies an intermediate state, between bFGF treatment and the negative control which is no cytokine treatment as judged by hierarchical clustering. Moreover, KEGG pathway analysis indicates common and additional distinct mechanisms of bFGF and IL-11 dependant pluripotency dependant mechanisms.
2013-09-26 | E-GEOD-51159 | biostudies-arrayexpress