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DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

ABSTRACT: DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

PROVIDER: PRJNA1076917 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR27983700.fastq.gz	Fastqsanger.gz
	SRR27983701.fastq.gz	Fastqsanger.gz
	SRR27983702.fastq.gz	Fastqsanger.gz
	SRR27983695_1.fastq.gz	Fastqsanger.gz
	SRR27983695_2.fastq.gz	Fastqsanger.gz

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DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

Project description:Characterizing cell-cell communication and tracking its variability over time is essential for understanding the coordination of biological processes mediating normal development, progression of disease, or responses to perturbations such as therapies. Existing tools lack the ability to capture time-dependent intercellular interactions, such as those influenced by therapy, and primarily rely on existing databases compiled from limited contexts. We present DIISCO, a Bayesian framework for characterizing the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method uses structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their co-evolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from CAR-T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.

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A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data.

Project description:Characterizing cell-cell communication and tracking its variability over time are crucial for understanding the coordination of biological processes mediating normal development, disease progression, and responses to perturbations such as therapies. Existing tools fail to capture time-dependent intercellular interactions and primarily rely on databases compiled from limited contexts. We introduce DIISCO, a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method utilizes structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their coevolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from T cells cocultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell cross talk.

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Project description:Intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are severely lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq using graphical causal modeling and conditional independence. We used human cortical organoid samples collected at D18 and D35 to demonstrate the functions of FlowSig.

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