Dissecting continuous phenotypic transitions in cellular states with novel quantitative experimental and computational methods - Project Summary A central question in biology is how genetically identical cells generate diverse phenotypes. Cells often respond differently to the same perturbation, emphasizing the key role of internal cellular states in shaping these outcomes. Enhancing our ability to predict cellular behaviors could improve tissue regeneration, accelerate drug discovery, and better anticipate drug resistance. To address this, my lab integrates computational biology, systems biology, and cutting-edge genomic technologies to investigate how cellular diversity arises from a single genome, with applications across ovarian aging, cardiovascular disease, and hormone-dependent cancers. This proposal aims to develop a new framework for studying the causal mechanisms driving continuous phenotypic transitions in cellular states. By using PROTACs (proteolysis-targeting chimeras) as fast-acting chemical perturbations, we will induce rapid protein degradation, and apply single-cell proteogenomics to quantify how upstream biochemical changes lead to downstream gene regulatory changes. Compared to CRISPR-based genetic screens, PROTACs provide a temporally precise observation of gene expression changes before compensatory mechanisms emerge, providing clearer insights into causal relationships. This approach will enable us to build next-generation predictive models for cellular responses, advancing both fundamental biology and therapeutic development. Additionally, we are developing P3-seq, a novel single-cell technology that simultaneously measures intracellular proteins, protein-protein interactions (PPIs), and transcriptomes. PPIs are central to pathway integration and cellular signaling networks, yet no scalable single-cell method for quantifying them exists. P3- seq will provide deeper insights into how combinatorial protein interactions influence cellular outcomes—an area that remains underexplored. Over the next five years, our goal is to apply these discoveries to build predictive models that improve the ability to precisely control cellular behaviors, particularly in response to therapeutic interventions.
