[SS21] Transformation and Integration of Data and Modeling in Medicine

[SS21-08] Deciphering inter-organ interactions from multi-organ single cell RNA-seq data using optimal transport

Yunosuke Kataoka², Yuichiro Yada¹, *Honda Naoki^1,2 (1. Nagoya University (Japan), 2. Hiroshima University (Japan))

Keywords:

single cell RNA-seq data,Optimal transport,Wasserstein distance,organ-organ interaction

Understanding inter-organ interactions in both healthy and pathological states is essential for comprehending systemic biological functions and disease mechanisms. While recent studies have utilized single-cell RNA sequencing (scRNA-seq) to analyze individual organs and their disease-related changes, comprehensive investigations into inter-organ interactions remain limited. To address this gap, we developed a novel computational framework that leverages scRNA-seq data to infer dynamic inter-organ interactions.Our computational framework models organs as distributions of heterogeneous cell populations and estimates their interactions across multiple timepoints. Specifically, we calculate the gene expression distribution of a given cell type within an organ and evaluate its influence on other organs using the Wasserstein distance, a metric from optimal transport theory. This allows us to quantitatively assess how cell populations from one organ impact those in another over time. By integrating data from multiple organs and timepoints, our computational framework provides a more holistic and dynamic perspective on inter-organ networks.