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Updated: May 2, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial

Jonathan Xu¹, Michelle Jiang², Shunsuke Koga^3,4

¹The Wharton School, University of Pennsylvania, Philadelphia, PA, United States.

Frontiers in Bioinformatics

|May 1, 2026

Summary

This summary is machine-generated.

SpatialFinder enhances spatial transcriptomics by using AI to pinpoint informative regions on H&E slides. This cost-effective approach aids clinicians in prioritizing microenvironments for deeper analysis.

Keywords:

clinical decision making digital pathology human-in-the-loop spatial transcriptomics vision-language models (VLMs)

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Area of Science:

Computational Biology
Pathology
Genomics

Background:

Spatial transcriptomics is costly, limiting its routine application.
Identifying informative regions of interest (ROIs) from H&E slides for spatial transcriptomics is challenging.
Clinician-driven prioritization of microenvironments for analysis is currently limited.

Purpose of the Study:

To develop a framework, SpatialFinder, for cost-effective and clinically actionable spatial transcriptomics.
To predict gene expression heterogeneity and rank high-value ROIs from H&E slides.
To enable human-AI collaboration for improved ROI selection.

Main Methods:

SpatialFinder integrates a biomedical vision-language model (VLM) with a human-in-the-loop optimization pipeline.
The framework analyzes standard H&E tissue slides to predict gene expression patterns.
ROIs are ranked based on predicted gene expression heterogeneity and clinical relevance.

Main Results:

SpatialFinder significantly outperforms VLM-only baselines in ranking informative ROIs.
Achieved high performance metrics, including Spearman's rho up to 0.89 and Overlap@10% up to 78.8%.
Demonstrated an absolute 24.9 percentage-point gain over the strongest VLM baseline.

Conclusions:

SpatialFinder offers a practical and cost-effective solution for spatial transcriptomics.
Human-AI collaboration via SpatialFinder enhances the clinical actionability of spatial transcriptomics data.
The framework has the potential to make spatial transcriptomics more accessible for research and clinical applications.