Single-cell CRISPR screens in vivo map T cell fate regulomes in cancer [Rbpj_ATAC-seq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE216797 | GEO | 2023/11/15
REPOSITORIES: GEO
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Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
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Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
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Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
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Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
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Project description:The gene regulatory network (GRN) underlying CTL differentiation and whether Tex responses can be functionally reinvigorated are incompletely understood. Here, we systematically mapped causal GRNs via single-cell CRISPR (scCRISPR) screens in vivo and discovered checkpoints for CTL differentiation.
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Project description:Transcriptional control of any biological process occurs through the action of one to many gene regulatory networks (GRNs), but studies analyzing GRN interaction are lacking. To address this, we tested for the first time in vivo the hypothesis that two independent GRNs, which are active during formation of two discrete skeletal cell types (immature chondrocytes and osteoblasts), interact during differentiation of a third skeletal cell type (mature chondrocytes). These three cell types were isolated from the mouse embryo specifically using laser capture microdissection, and then RNA was extracted. Multiple analyses of corresponding RNA-seq data supported the hypothesis. Gene co-expression network analyses of differentially expressed genes suggested that one GRN containing the chondrogenic transcription factor Sox9 characterizes immature chondrocytes, one GRN containing the osteogenic transcription factor Runx2 characterizes osteoblasts, and both GRNs operate in mature chondrocytes. Indeed, mature chondrocytes differentially expressed fewer genes than the other cell types, consistent with the idea that overlapping actions of the immature chondrocyte and osteoblast GRNs regulate mature chondrocytes. Clustering analyses provided molecular insights into potential GRN interactions. Several genes in mature chondrocytes had expression levels that represented an averaging between levels in the immature chondrocyte and osteoblast. Interestingly, expression levels of one gene cluster, containing the hallmark mature chondrocyte genes Collagen type 10a1 and Indian hedgehog, suggested a synergistic interaction between immature chondrocyte and osteoblast GRNs. In addition to identifying novel genes expressed in mature chondrocytes, these results outline a novel in vivo experimental system through which to understand GRN organization and interaction.
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Project description:This a model from the article:
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network.
Kühn C, Wierling C, Kühn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ.
BMC Syst Biol.2009 Aug 23;3:83.
19698179,
Abstract:
BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.
The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5
This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.
2024-09-02 | BIOMD0000000235 | BioModels
Project description:Genetic screens using CRISPR technology are widely exploited to explore novel targets and discover resistance mechanisms of cancer treatments, including cancer immunotherapy. As it is a time- and resource-intensive manner to individually validate potential candidates and clarify their working mechanisms, single-cell CRISPR screen platforms are designed and developed to parallelly characterize these factors and explore underlying mechanisms in a high-throughput manner. By leveraging the resources generated in our previous studies, Perturb-seq featured with capture sequences integrated into gRNA-expressing vectors and CROP-seq featured with poly(A)-tailed gRNA transcripts under the control of RNAPII promoters were selected among several independent single-cell CRISPR screen platforms to directly evaluate the performance of different scCRISPR screen platforms in determining underlying mechanisms of tumor intrinsic immune regulation and optimize experimental conditions and data analysis in immune-related scCRISPR screens. In the study, the influence of each perturbation on the gene expression profile and the response to in vitro T cell killing and in vivo anti-PD-1 treatment were comprehensively appraised at the single-cell level. It is clearly demonstrated that scCRISPR screen platforms could efficiently characterize gene expression profiles and perturbation effects present in individual cells during both in vitro and in vivo immune-related screen processes. Particularly, scCRISPR screen platforms presented more detail to determine heterogeneous change of gene expression in distinct knockout tumor cells in the presence of immune attack which cannot be easily assessed by canonical bulk RNA-seq. Collectively, scCRISPR screen technologies provide scalable and reliable platforms to validate identified tumor intrinsic immune factors identified from genome-wide CRISPR screens and elucidate their potential working mechanisms.
2022-11-13 | GSE206107 | GEO