Project Summary/Abstract The candidate, Wenpin Hou, PhD, is an applied mathematician and computational biologist serving as a postdoctoral fellow of Biostatistics at Johns Hopkins Bloomberg School of Public Health (JHSPH). Her long-term career goal is to improve clinical practice in therapy for diseases by developing computational and statistical methods to decipher spatial and temporal gene regulatory programs using multi-omics data and implementing these methods into developmental process and diseases. The research she proposes entitled Computational Methods for Inferring Single-cell DNA Methylation and its Spatial Landscape combines advanced spatial transcriptomics techniques with computational methods to infer spatial DNA methylation landscape, which enables the accurate evaluation of the epigenomic spatial variability and epigenomic targets in therapy for diseases. Dr. Hou completed her PhD in Mathematics at The University of Hong Kong where she focused on inferring and controlling gene regulatory networks (GRNs). Her first postdoctoral research (2017-2019) with Drs. Aravinda Chakravarti and Suchi Saria at Johns Hopkins University (JHU) represented the first shift in focus of GRNs from theoretical to computational genomics. Her second postdoctoral research (2019-present) with Drs. Hongkai Ji and Stephanie Hicks at JHSPH has further provided complementary training in reconstructing and predicting spatial transcriptomic and epigenomic landscape using single-cell data. Dr. Hou's mentoring team consists of Hongkai Ji (primary mentor), PhD, an expert in developing computational and statistical tools for analyzing single-cell genomic data, including reconstructing and predicting temporal and spatial transcriptomic and epigenomic landscape; Stephanie Hicks (co-mentor), PhD, an expert in developing statistical methods to address technical variability and spatial transcriptomics in single cells; and Andrew Feinberg (co-mentor), PhD, one of the founders of the field of cancer epigenetics who directs the first NIH funded Epigenome Center. This offers the opportunity to tackle significant challenges in the intersection of statistics, epigenomics and spatial transcriptomics with advanced experimental techniques. Her scientific advisors are Drs. Kasper Hansen, Gregory Hager and Xiaobin Wang who have leading expertise in computational epigenomics, deep learning, clinical translation and disease prevention, respectively. Leveraging the intellectual, experimental, and computing resources from all mentors and advisors as well as through JHSPH, JHU and Johns Hopkins Medicine, Dr. Hou will receive intensive training, mentoring and career development to achieve the goals proposed in this application and have productive outcomes during the award period. Aim 1 will develop methods to predict DNA methylation landscape using bulk gene expression. Aim 2 will develop methods to predict single-cell DNA methylation and differential DNA methylation. Aim 3 will reconstruct tissue-spatial DNA methylation landscape at the single-cell level, generate evaluation datasets in spatial context, and perform across-study assessments using data from The Encyclopedia of DNA Elements (ENCODE), Human Cell Atlas, and Recount2.

Project Narrative Genomic and epigenomic abnormalities collaborate in disease initiation and progression, as exemplified by frequent mutations in protein-coding genes which can vary spatially in separated sub-regions of tissues. This project will develop computational methods to infer tissue-spatial epigenomic landscape which can not be measured using current techniques, leveraging the emerging technologies of spatial transcriptomics, and develop novel statistical methods to identify differential epigenomic signals across patients or cell populations. The outcome of this project can lead to the development of new epigenetic targeted therapy and new epigenetic therapeutic drugs for various diseases that helps the broad community.