NIH New Innovators Award Abstract By melding imaging and genomics it is now possible to obtain spatially resolved transcriptomic datasets; however, computational methods for analyzing such datasets have lagged behind experimental developments. To realize the full potential of spatial transcriptomic (ST) data, we cannot rely on the methods that have been developed for analyzing single cell data that divorce cells from their microenvironment. As with experimental developments that saw ST breakthroughs by melding imaging and sequencing, we argue that the same will hold true in the computational domain, and, therefore, propose a framework for the analysis of this data that integrates imaging and sequencing with causality to infer regulatory mechanisms underlying spatially driven processes. We propose to achieve this through an innovative unification of two vibrant areas in machine learning (ML); representation learning and causal inference. This is a momentous task since representation learning, although successful in predictive tasks like recommender systems, does not generally elucidate causal relationships. To overcome this, we will use representation learning to identify correlations that are present in all data modalities available in ST, and thereby discern spurious correlations from causal ones using the principle of invariance. In addition, we will build on three fundamental concepts in ML: - Image inpainting: to identify motifs in tissue architecture as well as anomalous tissue patches - Optimal transport: to infer tissue lineages from snapshots in time - Causal structure discovery: to identify regulatory modules & predict the effect of perturbations This unification will result in an ML framework that integrates space, time, and expression to identify biological mechanisms underlying spatial processes. Although this framework will be broadly applicable, it is centered on three disease contexts, which will serve as the foreground to test and refine our methods and for which ST data have already been obtained: - Inflammation/fibrosis in the gut; to study cell recruitment, matrix deposition, and clearance; - Alzheimer's disease; to study questions of secretion and protein aggregation; and - Classic Hodgkin lymphoma; to study tumor-immune cell interactions & immunological invasion. Understanding the regulatory mechanisms of cell-cell communication in these disease contexts has the potential to give rise to new therapeutic targets that could be validated in partnership with our experimental collaborators and benefit patients' lives.

NIH New Innovators Award Project Narrative With the foreseeable explosion of spatial transcriptomic datasets in the coming years, it is of paramount importance to develop a computational framework that can complement the experimental revolution that made such datasets possible. In this proposal, we will develop a causal representation learning framework to obtain representations of the data that elucidate spatial processes underlying tissue homeostasis and disease. While broadly applicable, we will demonstrate our framework in three functional contexts, namely inflammation/fibrosis in the gut, Alzheimer's disease, and classic Hodgkin lymphoma, where understanding how cells regulate each other to modulate the microenvironment could provide novel therapeutic avenues.