CardioSeg: An interactive platform for integrated spatial transcriptomics data and nuclear morphological analysis of mouse heart tissue

Abstract

MotivationSpatial transcriptomics enables gene expression profiling within its spatial context in intact tissue sections. Existing workflows for segmentation, spatial annotation, and morphological analysis are often code-heavy and poorly integrated. This limits the joint analysis of spatial gene expression at a single-nucleus resolution, and corresponding nuclear morphology. ResultsWe present CardioSeg, an integrated computational platform for nuclei-resolved spatial transcriptomic analysis combining multi-threshold segmentation, transcriptomic aggregation, cell-type annotation, and interactive morphometric querying within a unified graphical interface. CardioSeg achieved robust segmentation performance across heterogeneous imaging conditions, with union-based inference outperforming the individual parameter configurations. CardioSeg achieved 0.88 in accuracy and 0.85 in balanced accuracy against reference labels, while also resolving spatial heterogeneity not captured by spot-based approaches. Analyses of pressure-overloaded cardiac tissue indicated altered cell composition, nuclear morphology and gene expression in specific segments, indicating the potential of CardioSeg to couple disease-specific nuclear morphology with the associated transcriptomics. Availability and ImplementationSource code is available at GitHub under the CC BY 4.0 license (https://github.com/SrijanKancherla/CardioSeg). A versioned release was archived in Zenodo (DOI: 10.5281/zenodo.20177171).