Project Summary/Abstract Understanding the spatial landscape of gene expression in tissues is a fundamental question for human health. Applications range from identifying the spatial organization of cell types to dysregulation of spatial-dependent gene expression associated with disease. Advances in technologies, such as multiplex imaging (MI), provide a wealth of data to investigate these questions. Images from these platforms are all preprocessed to produce a tabular dataset for each tissue sample where each row is an individual and each column contains characteristics of that cell including spatial coordinates and protein / cell signaling expression. Despite these similarities, these data often are not used to their full potential by new users and software developers due to existing challenges, including a lack of a standardized data infrastructure across MI platforms to enable robust and modular downstream analyses. To address this, we propose to develop standardized data infrastructure for single-cell and spatial multiplex imaging data in Bioconductor scalable for the analysis of large atlas-scale data. This data infrastructure will allow users and developers to quickly and efficiently extract deeper insights from HuBMAP multiplex imaging data.

Project Narrative Single-cell and spatial multiplex imaging technologies are poised to become ubiquitous and transformative in precision medicine, including the diagnosis, prognosis, and therapeutic response in disease. However, researchers who aim to appropriately interpret these data face new challenges with increasingly complex data, such how to store, access, and analyzes these single-cell and spatial multiplex images. Our goal is to address this challenge by developing standardized data infrastructure for single-cell and spatial multiplex imaging data in Bioconductor scalable for the analysis of large atlas-scale.