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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE255888 | GEO | 2024/09/02

REPOSITORIES: GEO

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Project description:DNA methylation is one of the key epigenetic modifications involved in gene regulation. While whole-genome bisulfite sequencing (WGBS) allows the genome-wide quantification of DNA methylation at the single-base pair resolution, the contribution of DNA methylation in disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to accurately detect differentially methylated regions (DMRs) from WGBS data, and employ ABBA to map DNA methylation changes in disease. ABBA enables the genome-wide modeling of single-base resolution DNA methylation data, automatically adapting to different correlation structures in CpG methylation across the genome without requiring user-defined parameters and accounts for covariates and non-genetic confounding factors. First, we demonstrate the approach by an extensive simulation study and benchmarked ABBA against recently proposed alternative methods. We show the better performance of ABBA across a range of relevant parameters, including sequencing depth, number of replicates and noise level originating from incomplete bisulfite conversion. Second, we generated WGBS data from primary macrophages derived from an experimental rat model of glomerulonephritis and control rats, and employed ABBA to identify DMRs (>1,000 at 5% FDR) genome-wide that were associated with disease susceptibility. Investigation of the DMRs revealed differential DNA methylation localized to the promoter of the Ifitm3 gene and comparisons with existing DMR detection methods shows the enhanced interpretability of the results provided by ABBA. Additional ChIP-seq and RNA-seq analyses in the same experimental system showed differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis) and consistent differential expression of Ifitm3. Taken together, these data suggest a role for DNA methylation alteration of the Ifitm3 gene in the pathogenesis of glomerulonephritis, and provide a proof of concept of using ABBA for differential methylation analysis of WGBS data.

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

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