UCLA

Complex diseases such as Alzheimer’s disease are driven by molecular changes in many cell types in different tissues. Recent advances in scRNAseq and spatial transcriptomics provide tools to determine cell type specific effects in individual tissues resulting from genetic and environmental perturbations. Properly interpreting these data require computational tools and biologically rooted analyses to identify key mechanisms underlying complex diseases. Here we design, develop, and apply computational methods for integrating scRNAseq and spatial transcriptomics data to identify mechanisms underlying pathogenesis of disease and potential therapeutics. First, we designed a deep learning approach, JSTA, for integrating scRNAseq and spatial transcriptome data from multiplexed FISH for cell segmentation and cell type annotation, revealing spatially distributed cell subtypes and spatially differentially expressed genes in the mouse hippocampus. Next, we developed a gradient-boosting machine based approach, SCING, for identifying cell type specific gene regulatory networks (GRN) using scRNAseq and spatial transcriptomics data. This tool provides GRN subnetworks annotated with biological pathways for associating subnetwork expression with disease phenotypes and spatial domains. We applied these and other existing tools to scRNAseq and spatial transcriptomics datasets to understand the mechanism underlying diverse types of diseases or physiological traits, including Alzheimer’s disease and heart innervating neurons and satellite glial cells in the stellate ganglion in the context of dilated cardiomyopathy. Our studies established new computational tools applicable to diverse types of single cell omics data and revealed biological insights to complex diseases.