CardioSeg: An interactive platform for integrated spatial transcriptomics data and nuclear morphological analysis of mouse heart tissue
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Motivation
Spatial 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.
Results
We 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 Implementation
Source 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).
