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STHELAR, a multi-tissue dataset linking spatial transcriptomics and histology for cell type annotation.

Félicie Giraud-Sauveur^1,2, Quentin Blampey^3,4, Hakim Benkirane^3,4

¹Paris-Saclay University, CentraleSupelec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France. felicie.giraud-sauveur@centralesupelec.fr.

Scientific Data

|March 13, 2026

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This study introduces STHELAR, a large dataset integrating spatial transcriptomics and histology images for cancer research. It enables predicting cell types from standard tissue images, advancing tumor microenvironment analysis.

Area of Science:

Oncology
Computational Biology
Bioinformatics

Background:

Tumor microenvironment analysis is crucial for cancer research.
Spatial transcriptomics offers insights into tissue architecture and cellular heterogeneity.
High cost and complexity of spatial transcriptomics limit its widespread adoption.

Purpose of the Study:

To develop STHELAR, a large-scale dataset integrating spatial transcriptomics and Hematoxylin and Eosin (H&E) whole-slide images.
To facilitate cell type annotation within the tumor microenvironment.
To enable the development of models for predicting cell types directly from histological images.

Main Methods:

Integrated spatial transcriptomics data with H&E whole-slide images for 31 human Xenium FFPE sections across 16 tissue types.

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Utilized Tangram-based alignment to single-cell reference atlases for cell type annotation.

Performed slide-specific clustering, differential expression analysis, and extracted over 500,000 image patches with segmentation masks.

Main Results:

Created STHELAR, a dataset with over 11 million cells assigned to ten curated cell-type categories for a pan-cancer setting.
Successfully co-registered H&E images with spatial transcriptomics data.
Implemented quality control steps to ensure annotation integrity and segmentation accuracy.

Conclusions:

STHELAR serves as a valuable reference resource for computational pathology and cancer research.
The dataset supports the development of machine learning models for predicting cell types from H&E images.
This approach aims to overcome the limitations of spatial transcriptomics by leveraging readily available histological data.