PRISM: A Python Package for Interactive and Integrated Analysis of Multiplexed Tissue Microarrays

Tissue microarrays (TMAs) enable researchers to analyse hundreds of tissue samples simultaneously by embedding multiple samples into single arrays, enabling conservation of valuable tissue samples and experimental reagents. Moreover, profiling TMAs allows efficient screening of tissue samples for translational and clinical applications. Multiplexed imaging technologies allow for spatial profiling of proteins at single cell resolution, providing insights into tumour microenvironments (TMEs) and disease mechanisms. High-plex spatial single cell protein profiling is a powerful tool for biomarker discovery and translational cancer research, however, there remain limited options for end-to-end computational analysis of this type of data. Here, we introduce PRISM, a Python package for interactive, end-to-end analyses of TMAs with a focus on translational and clinical research using multiplexed proteomic data from the CODEX, Phenocycler Fusion (Akoya Biosciences), Comet (Lunaphore), MACSima (Miltenyi Biotec), CosMx and Cellscape (Bruker Spatial Biology) platforms. PRISM leverages the SpatialData framework to standardise data storage and ensure interoperability with single cell and spatial analysis tools. It consists of two main components: TMA Image Analysis for marker-based tissue masking, TMA dearraying, cell segmentation, and single cell feature extraction; and AnnData Analysis for quality control, clustering, iterative cell-type annotation, and spatial analysis. Integrated as a plugin within napari, PRISM provides an intuitive and purely interactive graphical interface for real-time and human-in-the-loop analyses. PRISM supports efficient multi-resolution image processing and accelerates bioinformatics workflows using efficient scalable data structures, parallelisation and GPU acceleration. By combining modular flexibility, computational efficiency, and a completely interactive interface, PRISM simplifies the translation of raw multiplexed images to actionable clinical insights, empowering researchers to explore and interact effectively with spatial omics data.