Abstract: 

Spatially resolved transcriptomic technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) and sequential FISH generate high-dimensional datasets that reflect the organization of molecules within cells. Further advancements also allow the imaging of cells grown in 3D environments, such as hydrogel and Matrigel. Therefore, we developed the spatially-resolved gene neighborhood network (spaGNN) pipeline to identify subcellular spatially-resolved features that achieve cell-type identification and cell-cell communication (CCC) prediction. We first developed the spaGNN, suitable for 2D subcellular spatial transcriptomics data. This algorithm quantifies the physical proximity of RNA molecules. Spatially resolved gene neighborhoods are used to generate networks that are distinct at different parts of a single cell. We then examined the cell-type identification using the gene proximity features. We then extended the spaGNN pipeline into 3D environments and applied the pipeline to mesenchymal stem cells (MSCs) cultured in hydrogels. After clustering the cells based on the RNA count, we identified homotypic and heterotypic MSCs based on gene expression. 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 MERFISH data and identified regional gene proximity differences.