Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA349131 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR4431960_1.fastq.gz | Fastqsanger.gz | |||
| SRR4431960_2.fastq.gz | Fastqsanger.gz | |||
| SRR4431970_1.fastq.gz | Fastqsanger.gz | |||
| SRR4431970_2.fastq.gz | Fastqsanger.gz | |||
| SRR4431961_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 24 |
Similar Datasets
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:Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The mechanisms that maintain pluripotency, or that cause its destabilization to initiate development, are complex and incompletely understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to input signals. Our strategy used random asynchronous Boolean simulations (R-ABS) to simulate single-cell fate transitions and strongly connected components (SCCs) strategy to represent population heterogeneity. This framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) and used to simulate cellular responses to combinations of five signaling pathways. Our simulations predicted experimentally verified cell population compositions and input signal combinations controlling specific cell fate transitions. Extending the model to PSC differentiation, we predicted a combination of signaling activators and inhibitors that efficiently and robustly generated a Cdx2+Oct4- cells from naïve mESCs. Overall, this platform provides new strategies to simulate cell fate transitions and the heterogeneity that typically occurs during development and differentiation.
| S-EPMC5787708 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-02-05 | GSE88928 | GEO
Project description:While CAR T cells have altered the treatment landscape for B cell malignancies, the risk of on-target, off-tumor toxicity has hampered their development for solid tumors because most target antigens are shared with normal cells. Researchers have attempted to apply Boolean logic gating to CAR T cells to prevent on-target, off-tumor toxicity; however, a truly safe and effective logic-gated CAR has remained elusive. Here, we describe a novel approach to CAR engineering in which we replace traditional ITAM-containing CD3ζ domains with intracellular proximal T cell signaling molecules. We demonstrate that certain proximal signaling CARs, such as a ZAP-70 CAR, can activate T cells and eradicate tumors in vivo while bypassing upstream signaling proteins such as CD3ζ. The primary role of ZAP-70 is to phosphorylate LAT and SLP-76, which form a scaffold for the propagation of T cell signaling. We leveraged the cooperative role of LAT and SLP-76 to engineer Logic-gated Intracellular NetworK (LINK) CAR, a rapid and reversible Boolean-logic AND-gated CAR T cell platform that outperforms other systems in both efficacy and the prevention of on-target, off-tumor toxicity. LINK CAR will dramatically expand the number and types of molecules that can be targeted with CAR T cells, enabling the deployment of these powerful therapeutics for solid tumors and diverse diseases such as autoimmunity9 and fibrosis10. In addition, this work demonstrates that the internal signaling machinery of cells can be repurposed into surface receptors, a finding that could have broad implications for new avenues of cellular engineering.
2022-12-13 | GSE216286 | GEO
Project description:Coopting T cell proximal signaling molecules enables Boolean logic-gated CAR T cell control
| PRJNA892932 | ENA
Project description:Numerous gene expression datasets from diverse human tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available human datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119087 | GEO
Project description:Numerous gene expression datasets from diverse mouse tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available mouse datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119085 | GEO
Project description:Numerous gene expression datasets from diverse fly tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available fly datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119084 | GEO
Project description:Numerous gene expression datasets from diverse plant tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available plant datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119083 | GEO