Johns Hopkins School of Medicine

Single cell gene expression profiling has demonstrated that cell classification requires more than a simple collection of marker genes. Current approaches do not account for the dynamic nature of cell states and inherent variation in cell types. This is especially true in the central nervous system (CNS) where morphological, location, and activity- based definitions of cell types fail to correlate well with single cell transcriptional profiles. Given how cell these cell extrinsic features are essential for the collective behavior and functionality of individual cells, it is critical to resolve how cell intrinsic molecular programs interact with and affect cell extrinsic physiology. My lab pairs computation and experimental methods to link cell intrinsic programs from single cell molecular profiling to cell extrinsic spatiotemporal context and downstream phenotypic consequences. 

The goal of this research is to leverage high throughput data to determine how inherent human variation manifests differences in neurophysiology at single cell resolution. A primary challenge is the need to deconvolve a single measurement of a molecular species, i.e. a single gene’s activity, in a cell into the multiple biological processes in which it is being used.Thus, my lab develops 1) dimension reduction techniques to learn signatures of biological processes from single cell data; 2) transfer learning algorithms to assess the activity of those biological processes in other cells, molecular modalities, and species; and 3) mathematical models to predict the effects of specific perturbations of biological processes on a cell's behavior.

Additionally, we study human diseases as a source of variation in phenotypes and genotypes. For example, we are currently funded to study variable penetrance in Amyotrophic Lateral Sclerosis (ALS) and Frontal Temporal Dementia (FTD) using patient-derived human induced pluripotent stem cells (ihPSCs). Reproducible and meaningful research is a core value of the lab. Thus, we work with collaborators in human genetics, aging, and cancer to understand how inherent human variation manifests disease more broadly and to test the robustness of the methods that we develop.

The Stein-O’Brien lab is a hybrid environment working on 1) computational tools for semi-supervised learning, multimodal data integration and cross-species techniques, and mathematical modeling and 2) experimental methods to further our biological understanding and test the predictions of these computational methods. We support curiosity driven research and negative results that advance knowledge.