Kilgallon, A. | Search bioRxiv

bioRxiv2025

SM3DD with Segmented PCA: A Comprehensive Method for Interpreting 3D Spatial Transcriptomics

We developed Standardised Minimum 3D Distance (SM3DD), an entirely cell segmentation/annotation-free approach to the analysis of spatial RNA datasets, using it to compare lung tissue from 16 clinically normal individuals to those of 18 SARS-CoV-2 patients who died from acute respiratory distress syndrome. RNA spatial coordinates were determined using the CosMx Spatial Molecular Imager (Bruker Spatial Biology, US). For each individual transcript location, we calculated the three-dimensional distances to the nearest transcript of each transcript type, standardising the distances to each transcript type. Mean SM3DDs were compared between normal and SARS-CoV-2 patients. Notably, hierarchical clustering of the directional log10(P) values organized genes by functionality, making it easier to interpret biological contexts and for FKBP11, where a decrease in distance to MZT2A was the most significant difference, suggesting a role in interferon signaling. Using a segmented principal components analysis of the entire SM3DD dataset, we identified multiple pathways, including SARS-CoV-2 infection, even though the assay did not include any SARS-CoV-2 transcripts.

bioinformatics↗

bioRxiv2024

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.

bioinformatics↗