Odon: An ultra-fast viewer for spatial proteomics

Abstract Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine that substantially outperforms existing viewers. In benchmarking, Odon loaded a 32 GB, 36-plex whole-slide OME-Zarr image in under 1 second, compared with 10.14 seconds for QuPath and 35 seconds for Napari. Its GPU-based compositing pipeline also enables smooth rendering and interaction with more than 1,000,000 segmented cells, exceeding the practical limits of many existing tools. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data.