Project Summary A comprehensive 3D molecular map of the human body would provide valuable information that is critical for studying human-related processes and biological systems such as development, aging, and disease. Towards this goal of constructing such a map, multidisciplinary consortia such as the Human Cell Atlas (HCA) and the Human BioMolecular Atlas Program (HuBMAP) have developed technologies for profiling the transcriptome and proteome in single cells. Out of these technologies, methods for single-cell spatial proteomics have only very recently been developed; for example, recent advances in multiplexed imaging have enabled the profiling of tens to hundreds of proteins per cell. While the generation of single-cell spatial proteomics data promise to revolutionize our ability to study cell-cell interactions, it also raises several computational and modeling challenges. Cell segmentation remains a long-standing problem that usually requires tailored solutions for each bioimaging experiment. Even after cells are segmented, using expression values to infer cell type and organization is challenging. There are currently no standardized methods developed that jointly incorporate spatial and molecular information to analyze the complex biological interactions from rich spatial proteomics datasets. This project proposes to develop computational methods to provide a comprehensive solution for the use of spatial proteomics data for building 3D molecular maps of the human body. We hypothesize that jointly profiling spatial and molecular relationships from spatial proteomics datasets captures biological patterns that would otherwise be missed. In Aim 1, a method will be developed for RAnking Markers for CEll Segmentation (RAMCES) in order to choose the optimal protein markers to use for cell segmentation. In Aim 2, a unified learning framework that incorporates both protein expression and cell neighborhood information will be constructed in order to assign cells to phenotypes and reveal spatial patterns. In Aim 3, methods will be developed to infer cell-cell and protein-protein interactions in spatial proteomics data. The methods developed in this project will be integrated into the HuBMAP processing pipeline to analyze spatial proteomics datasets. We will also apply and validate these methods using data from pancreatic lymph nodes that profile individuals with and without Type 1 diabetes to analyze changes associated with the disease at an unprecedented scale. Together, completing the proposed aims will enable the HuBMAP project to uncover new biological interactions in cells and tissues and expand our understanding of molecular interactions at a single-cell level. This proposal outlines a training plan that comprises of mentored research training, coursework, and professional development. The knowledge and skillset developed during the training period will be necessary for the applicant's long-term goal of becoming a successful independent sci...