Inferring a spatial code of cell-cell interactions across a whole animal body

Erick Armingol, Abbas Ghaddar, Chintan J Joshi, Hratch Baghdassarian, Isaac Shamie, Jason Chan, Hsuan-Lin Her, Samuel Berhanu, Anushka Dar, Fabiola Rodriguez-Armstrong, Olivia Yang, Eyleen J O’Rourke and Nathan E Lewis

PLOS Computational Biology, 2022, vol. 18, issue 11, 1-28

Abstract: Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans’ body. Leveraging a 3D atlas of C. elegans’ cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.Author summary: Neighboring cells coordinate gene expression through cell-cell interactions, enabling proper functioning in multicellular organisms. Hence, intercellular interactions can be inferred from gene expression. We use this strategy to define a molecular code bearing spatial information of cell-cell interactions across a whole animal body. We develop a computational framework to infer the first cell-cell interaction network in Caenorhabditis elegans from its single-cell transcriptome, and show a negative correlation between interactions and intercellular distances, which is driven by a combination of ligand-receptor pairs following spatial patterns across the C. elegans’ body, i.e., the spatial code. Thus, our framework uncovers molecular features crucial to defining spatial cell-cell interactions across a whole body; a strategy that can be readily applied in higher organisms.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010715

DOI: 10.1371/journal.pcbi.1010715

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