3115 – PACESS: PRACTICAL AI-BASED CELL EXTRACTION AND SPATIAL STATISTICS FOR LARGE 3D BIOLOGICAL IMAGES

ABSTRACT Efficient methodologies to fully extract and analyse large datasets remain the Achilles heels of 3D tissue imaging. Here we present PACESS, a pipeline for large-scale data extraction and spatial statistical analysis from 3D biological images. First, using 3D object detection neural networks trained on annotated 2D data, we identify and classify the location of hundreds of thousands of cells contained in large biological images. Then, we introduce a series of statistical techniques tailored to work with spatial data, resulting in a 3D statistical map of the tissue from which multi-cellular interactions can be clearly understood. As illustration of the power of this new approach, we apply this analysis pipeline to an organ known to have a complex and still poorly understood cellular structure: the bone marrow. The analysis reveals coherent, useful biological information on multiple cell population interactions. This novel and powerful spatial analysis pipeline can be broadly used to unravel complex multi-cellular interaction towards unlocking tissue complexity.