Thyra: Bridging Mass Spectrometry Imaging and SpatialData for Unified Multi-Modal Analysis

Abstract Mass Spectrometry Imaging (MSI) is a powerful technique for mapping molecular distributions, and its integration with other imaging modalities is crucial for comprehensive understanding of molecular systems. Fragmented data formats and the limitations of existing standards like imzML, challenge spatial biology centric multi-modal data analysis and adherence to FAIR data principles. This paper introduces Thyra, a modern Python library designed to convert MSI data into the SpatialData framework, a unified and extensible multi-platform file format that crucially integrates MSI into the broader spatial omics ecosystem. Thyra’s modular architecture, intelligent mass axis resampling, and sparse matrix backend address performance bottlenecks and facilitate seamless integration with tools for advanced spatial statistics and visualization. By adopting SpatialData, Thyra not only improves data interoperability, accessibility, and reusability, but also unlocks new avenues for multi-modal research, empowering scientists to integrate rich chemical information into diverse biological workflows.