Research
Overview
How do cells transform combinations of finite signals into coherent gene expression programs? We use hormone signaling as a model system to answer this question. Hormones play a critical role in many aspects of human health, including aging, cardiovascular disease, and cancer. Intriguingly, hormones have evolved to regulate a wide range of processes across cell types and over the life stages of an organism, underscoring the complexity of hormone signal processing.
We aim to address this complexity through our three-pronged approach: technology, perturbations, and computation. Our approach is to pioneer genomic technologies and computational methods to study how the precise coordination of hormone signaling across time and space impacts tissue function.
Technology
Perturbations
Computation
Current Research Topics
Uncovering the spatiotemporal dynamics of ovarian aging
The ovary is one of the first organs to age. The composition of ovarian cell types, their cellular programs, and interactions within the ovary change dynamically across the estrus/menstrual cycle and aging to control both the reproductive and endocrine functions of the ovary. This recurrent process requires the precise coordination of intra- and inter-cellular signaling. For example, stromal and granulosa cells regulate follicle activation and the delivery of key signaling factors from the vasculature, all of which affect ovulation. Upon ejecting its oocyte, follicles take on a second life by transforming into the corpus luteum, an endocrine structure.
Remarkably, cycling ceases with aging. How these complex cellular interactions change with age is unknown. Because cells in the ovary are structurally organized to form spatially cohesive functional units such as the follicle, cellular functions must be profiled in native tissue contexts. We are applying cutting-edge spatial transcriptomics technologies to understand how diverse cell types contribute to ovarian function and its decline, coupled with machine learning algorithms to model how cells in space coordinate changes in their cellular states over time.
Examining the heterogeneity in cellular response to targeted protein degradation therapeutics for hormone-dependent cancers
A central goal in modern medicine is to transform diseased cell states into healthy ones. Degraders are small molecules that harness the ubiquitin system to “delete” target proteins, such as regulatory proteins, thereby altering the genome, epigenome, and transcriptome of a cell. To better understand the molecular mechanisms underlying the varied effects of degraders, including clinical toxicities and cell-type-specific responses, we use single-cell proteogenomic tools like inCITE-seq to simultaneously measure the heterogeneity in drug impact on protein degradation and transcriptomic changes in individual cells.
Novel single-cell and spatial proteogenomics tools
Transforming signals into gene expression is a fundamental cellular computation that drives many biological processes. Building on inCITE-seq (Chung et al. Nature Methods 2021), we are developing advanced single-cell and spatial proteogenomic tools to enable high-dimensional multi-modal measurements that elucidate how signaling patterns drive transcriptional response.
Our lab is part of the Yale Cardiovascular Research Center and the Yale Center for Research on Aging.
We are grateful to the GCRLE for funding.
