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Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell–cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.
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This content will become publicly available on March 13, 2026
Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues
Objective: We developed 3-dimensional spatially resolved gene neighborhood network embedding (3D-spaGNN-E) to find subcellular gene proximity relationships and identify key subcellular motifs in cell–cell communication (CCC). Impact Statement: The pipeline combines 3D imaging-based spatial transcriptomics and graph-based deep learning to identify subcellular motifs. Introduction: Advancements in imaging and experimental technology allow the study of 3D spatially resolved transcriptomics and capture better spatial context than approximating the samples as 2D. However, the third spatial dimension increases the data complexity and requires new analyses. Methods: 3D-spaGNN-E detects single transcripts in 3D cell culture samples and identifies subcellular gene proximity relationships. Then, a graph autoencoder projects the gene proximity relationships into a latent space. We then applied explainability analysis to identify subcellular CCC motifs. Results: We first applied the pipeline to mesenchymal stem cells (MSCs) cultured in hydrogel. After clustering the cells based on the RNA count, we identified cells belonging to the same cluster as homotypic and those belonging to different clusters as heterotypic. We identified changes in local gene proximity near the border between homotypic and heterotypic cells. When applying the pipeline to the MSC–peripheral blood mononuclear cell (PBMC) coculture system, we identified CD4+ and CD8+ T cells. Local gene proximity and autoencoder embedding changes can distinguish strong and weak suppression of different immune cells. Lastly, we compared astrocyte–neuron CCC in mouse hypothalamus and cortex by analyzing 3D multiplexed-error-robust fluorescence insitu hybridization (MERFISH) data and identified regional gene proximity differences. Conclusion: 3D-spaGNN-E distinguished distinct CCCs in cell culture and tissue by examining subcellular motifs
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- Award ID(s):
- 2338935
- PAR ID:
- 10589272
- Publisher / Repository:
- Science Partner Journal
- Date Published:
- Journal Name:
- BME Frontiers
- Volume:
- 6
- ISSN:
- 2765-8031
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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