CosMxScope: Scalable Reconstruction and Digital Pathology Integration of Imaging-Based Spatial Transcriptomics Data

Abstract

Spatial transcriptomics technologies have transformed the capacity to quantify gene expression from human tissues, simultaneously capturing both the cell functional state and spatial organization at cellular and subcellular resolution. The CosMx Spatial Molecular Imager (SMI) is one of the leading platforms at single-cell spatial multi-modal omics profiling, capable of measuring thousands of RNA or protein targets per cell across whole slide sections. Data exported includes field-of-view (FOV) image tiles, subcellular transcript coordinates, and cell segmentation polygons, outputs that are rich in spatial information but not directly compatible with widely used digital pathology tools. Here we present CosMxScope, a lightweight open-source Python framework that bridges CosMx spatial outputs with histopathology visualization environments. CosMxScope provides functions for stitching FOV image tiles into reconstructed whole-slide images, converting cell segmentation polygons and transcript coordinates into GeoJSON objects which enabled further assessments within QuPath, and generating spatial visualization plots of cell types, transcript locations, and gene expression patterns. The framework is designed for practical use in translational research settings, enabling interactive exploration of spatial transcriptomic data alongside cell morphology. CosMxScope has been applied in multiple ongoing research projects involving CosMx profiling of human and mouse tissues, supporting pathology-based spatial analysis workflows. This open-source software is available at https://github.com/AivaraX-AI/CosMxScope.