Computed Cell-Cell Interactions Correlate with Physical Location of Cells in C. elegans

Behaviors, functions and phenotypes of multicellular organisms are shaped by the coordination of cellular activities and properties. Those cellular activities and properties depend on activation of cell‐cell communication pathways and proper intercellular interactions amongst cells. The most important factors that influence how cells communicate and interact are localization of cells in the organism, expression levels of membrane proteins and secreted proteins. We sought to understand how intercellular interactions may be shaped by cell proximity, by analyzing protein‐protein interactions that could lead cell‐cell communication and interaction. We evaluated cell‐cell communication in C. elegans by integrating single‐cell RNA‐seq data and a list of ligand‐receptor pairs. To assess the potential of interaction, we computed a cell‐cell interaction (CCI) score that captures the number of ligand‐receptor pairs that are active between two cells versus the active ligands or receptors in each of those cells. We used this approach for every cell pair in the transcriptomics data. To associate the CCI score to the physical location of cells we used a published 3D‐map of cells to create a distance matrix among cell pairs. Afterwards, we calculated a correlation score between CCI score and physical distance of cell pairs. The score was used as an objective function to be maximized as ligand‐receptor pairs were randomly dropped from the list by a genetic algorithm (GA). Subsequently, a permutation analysis was run to evaluate the extent of significance of the ligand‐receptor pairs that were selected to maximize the correlation. To understand the role of those protein‐protein interactions, we performed a functional analysis on the list obtained by the GA. From these analyses, we obtained a negative spearman correlation between CCI scores and physical distances, showing that the protein‐protein interactions obtained by the GA capture properties of 3D‐location of cells and their potential of interaction given their proximity to certain cells. In addition, functional analysis of enriched ligand‐receptor pairs gave us more insights about the role that they could have given the cell locations. Our approach of computing CCI scores will facilitate data‐driven discoveries such as detecting key ligand‐receptor pairs for given properties or phenotypes of cells, and in the case of C. elegans , demonstrates that physical location of cells may be predicted by molecular signatures associated with the proteins mediating the interactions. Support or Funding Information This work was supported by the Keck Foundation, and scholarship awards from Becas Chile (CONICYT) and the Fulbright Commission.