Recent technology advances in spatial transcriptomics (ST) have enabled gene expression profiling while preserving spatial location information in tissues. ST has been applied to study diverse tissues, and these applications have transformed our views of transcriptome complexity. A popular ST technology is based on spatial barcoding followed by next-generation sequencing in which transcriptome-wide gene expression is measured in spatially barcoded spots. Data from ST technologies are typically complemented by high-resolution hematoxylin and eosin (H&E) stained histology images of the same tissue section, which are invaluable for examining cellular morphology and how it changes over embryonic development or disease progression. This project proposes to develop computational tools to combine gene expression and histological features within spatial context to inform the origin, developmental trajectory, and progression of complex diseases.

In collaboration with: Mingyao Li, Kyle Coleman, Michael Epstein