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  1. Home
  3. Cosmxscope: Scalable Reconstruction And Digital Pathology Integration Of Imaging-based Spatial Transcriptomics Data.
  1. Home
  3. Cosmxscope: Scalable Reconstruction And Digital Pathology Integration Of Imaging-based Spatial Transcriptomics Data.

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

Jie Chen1,2, Brian Isett1,2, Qiangqiang Gu3

  • 1UPMC Hillman Cancer Center, Pittsburgh, PA, USA.

Biorxiv : the Preprint Server for Biology
|April 10, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

CosMxScope is a new open-source Python tool that integrates spatial transcriptomics data from CosMx Spatial Molecular Imager (SMI) with digital pathology platforms. It enables seamless visualization and analysis of gene expression and cell morphology in human tissues.

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Area of Science:

  • Spatial biology and transcriptomics
  • Bioinformatics and computational biology
  • Digital pathology and histopathology

Background:

  • Spatial transcriptomics provides high-resolution gene expression data within tissue context.
  • CosMx Spatial Molecular Imager (SMI) generates rich spatial omics data, including transcript locations and cell segmentation.
  • Existing digital pathology tools lack direct compatibility with CosMx data outputs.

Purpose of the Study:

  • To develop an open-source framework, CosMxScope, for integrating CosMx SMI data with histopathology visualization environments.
  • To enable stitching of field-of-view (FOV) image tiles and conversion of spatial data into compatible formats.
  • To facilitate interactive exploration and analysis of spatial transcriptomic data alongside cell morphology.

Main Methods:

  • Developed CosMxScope, a Python framework for data integration and visualization.
  • Implemented functions for stitching FOV image tiles into whole-slide images.
  • Created tools for converting cell segmentation polygons and transcript coordinates into GeoJSON objects for QuPath compatibility.
  • Generated spatial visualization plots for cell types, transcript locations, and gene expression patterns.

Main Results:

  • CosMxScope successfully bridges CosMx spatial outputs with histopathology visualization tools.
  • The framework enables reconstruction of whole-slide images and conversion of spatial data into analysis-ready formats.
  • Spatial visualization plots effectively display cell types, transcript locations, and gene expression patterns.
  • CosMxScope has been applied in ongoing research projects for pathology-based spatial analysis.

Conclusions:

  • CosMxScope is a valuable, lightweight, open-source tool for translational research.
  • It enhances the utility of CosMx SMI data by enabling integration with digital pathology workflows.
  • The framework supports interactive exploration of spatial transcriptomic data, advancing tissue-based research.