Intercellular communication and spatial organization of cells are two critical aspects of a tissue’s function. Understanding these aspects requires integrating data from single-cell RNA-Seq (scRNA-seq) and spatial transcriptomics (ST), the two cutting edge technologies that offer complementary insights into tissue composition, architecture, and function. Integrating these data types is non-trivial since they differ widely in the number of profiled genes and often do not share marker genes for given cell-types. We developed STANN, a neural network model that overcomes these methodological challenges. Given ST and scRNA-seq data of a tissue, STANN models cell-types in the scRNA-seq dataset from the genes that are profiled by both ST and scRNA-seq. The trained STANN model then assigns cell-types to the ST dataset. We apply STANN to assign cell-types in a recent ST dataset (SeqFISH+) of mouse olfactory bulb (MOB). Our analysis of STANN’s assigned cell-types revealed principles of tissue architecture and intercellular communication at unprecedented detail. We find that cell-type compositions are disproportionate in the tissue, yet their relative proportions are spatially consistent within individual morphological layers. Surprisingly, within a morphological layer, there is a high spatial variation in cell-type colocalization patterns and intercellular communication mechanisms. Our analysis suggests that spatially localized gene regulatory networks may account for such variability in intercellular communication mechanisms.