MuSpAn: A toolbox for Multiscale Spatial Analysis

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• Joshua Moore (University of Oxford)
• Thursday 18 September 2025, 10:30-10:35
• Seminar Room 1, Newton Institute.

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OOEW07 - Mathematical Foundations of Oncological Digital Twins

Digital twins in oncology promise to revolutionize personalised medicine by simulating tumour progression and treatment response at multiple biological scales. A key challenge in realising this vision lies in accurately modelling the spatial organisation of cells within the tumour microenvironment, a complex and dynamic system that profoundly influences disease trajectory. Spatial biology provides essential insights into these cellular ecosystems, yet the multiscale complexity of mammalian tissues, and the lack of standardised analytical frameworks, continues to limit the interpretability and reproducibility of spatial data. To address these challenges, we introduce MuSpAn, a Python-based toolbox for Multiscale Spatial Analysis of spatial data. Designed to support the development of computational models such as digital twins, MuSpAn offers an imaging technology-agnostic platform for analysing spatial features from the subcellular to the tissue scale. By integrating mathematical methods from spatial statistics, network theory, topological data analysis, and morphology, MuSpAn enables quantitative, scalable, and customisable analysis of spatial patterns in healthy and diseased tissues. Unlike rigid, predefined pipelines, MuSpAn supports both the design of high-throughput automated workflows and flexible exploratory analysis, empowering users to tailor their approach to specific research questions. It also facilitates querying and integration of diverse spatial elements, such as point-like cellular coordinates and polygonal tissue regions, lowering computational barriers for interdisciplinary teams. Extensive documentation, tutorials, and exemplar analyses from published studies (available at www.muspan.co.uk) further support adoption across the research community. By equipping researchers with robust mathematical tools for spatial data analysis, MuSpAn contributes to the foundational infrastructure necessary for building interpretable, data-driven oncological digital twins, enhancing our capacity to model disease progression and improve clinical outcomes.

This talk is part of the Isaac Newton Institute Seminar Series series.

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