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

in Genome research by Cameron Y Park, Shouvik Mani, Nicolas Beltran-Velez, Katie Maurer, Teddy Huang, Shuqiang Li, Satyen Gohil, Kenneth J Livak, David A Knowles, Catherine J Wu, Elham Azizi

TLDR

  • DIISCO is a tool that helps researchers understand how cells communicate with each other over time. It uses single-cell RNA sequencing data to uncover dynamic cell-cell crosstalk and its variability over time. The study shows that DIISCO is able to do this better than existing tools. This information is important for understanding how cells work together and how they respond to different conditions or treatments.

Abstract

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 existing 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 co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.

Overview

  • DIISCO is a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA sequencing data from multiple time points. The study aims to uncover dynamic cell-cell crosstalk and its variability over time. The 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. The primary objective of the study is to demonstrate the interpretability of DIISCO in simulated data and new data collected from T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.

Comparative Analysis & Findings

  • The study compares the outcomes observed under different experimental conditions or interventions detailed in the study. The results show that DIISCO is able to uncover dynamic cell-cell crosstalk and its variability over time. The study identifies significant differences or similarities in the results between these conditions. The key findings of the study demonstrate the potential of DIISCO to uncover dynamic cell-cell crosstalk and its variability over time, which is not captured by existing tools.

Implications and Future Directions

  • The study's findings have significant implications for the field of research and clinical practice. The study identifies limitations of existing tools that fail to capture time-dependent intercellular interactions. The study suggests possible future research directions that could build on the results of the study, explore unresolved questions, or utilize novel approaches. The study highlights the importance of characterizing cell-cell communication and tracking its variability over time for understanding the coordination of biological processes mediating normal development, disease progression, and responses to perturbations such as therapies.
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