PROJECT SUMMARY/ABSTRACT (DAC) Skin diseases (including cancer) affect 84.5 million patients in the US alone and cost $75M in medical care costs. The 2017 report on the National Burden of skin disease highlighted the need for “...prevention and early detection methods to reduce morbidity and mortality from preventable diseases such as skin cancer and occupational diseases such as dermatitis and improved diagnostic tools and treatment options for common and rare skin diseases”. The goal of our Data Analysis Core (DAC) is to address the needs for a multi-marker/multimodal reference atlas of skin which can ultimately faciliate the development of new prevention and treatment approaches. Spatial information for various biomolecules in tissue sections will be acquired using multiplexed fluorescent imaging (MxIF, Cell DIVE), MALDI-IMS for lipidomics and targeted spatial transcriptomics (NanoString GeoMx). Harmonizing the data derived from these parallel assays and mapping them into a common 3-dimensional (3D) space can be a major challenge. We have begun to address this challenge under our current response to intervention (RTI) funding (Award Number UH3CA246594) using C++ and Python-based solutions. We propose to scale this workflow to a much larger cohort (n=96 patients) using our automated segmentation and registration pipeline developed under the RTI. We will export our data analysis pipeline to the GE Research (GE) high-performance computing resource to run our automatic segmentation and 3D- reconstruction pipeline. Our Data Analysis Core (DAC) will translate the image files from our OSP into 3D cellular maps and co-register the resulting multi-modal data. Within these maps, we will identify key organ architecture, cell types and states, and generate density maps for cell types and estimation of cell populations in UV exposed/not, young vs. aged skin, and by Fitzpatrick scale. We will share these maps and the resulting data with the HuBMAP Integration, Visualization, and Engagement (HIVE) team using common, interoperable data formats to coordinate in the creation of an atlas of the skin in context of the human body as whole. In addition, we will make all to-date and future data and code available on HubMAP GitHub such that 3D-mapping of cells in other tissues/organs will systematically increase the coverage of the human reference atlas at large.