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bioRxiv · 10.1101/2025.08.16.670684

SpatialFinder: A Human-in-the-Loop Vision-Language Framework for Prioritizing High-Value Regions in Spatial Transcriptomics

Xu, J.·Jiang, M.·Koga, S.·Zhang, N.·Huang, Z.
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Abstract

Sequencing an entire spatial transcriptomics slide can cost thousands of dollars per assay, making routine use impractical. Focusing on smaller regions of interest (ROIs) based on adjacent routine H&E slides offers a practical alternative, but there is (i) no reliable way to identify the most informative areas from standard H&E images alone; and (ii) limited solutions for clinicians to prioritize the microenvironment of their own interests. Here we introduce SpatialFinder, a framework that combines a biomedical vision-language model (VLM) with a human-in-the-loop optimization pipeline to predict gene expression heterogeneity and rank high-value ROIs across routine H&E tissue slides. Evaluated across four Visium HD tissue types, SpatialFinder consistently outperforms baseline VLMs in selecting regions with high cellular diversity and tumor presence, achieving up to 89% correlation with ground truth rankings. These results demonstrate the potential of human-AI collaboration to make spatial transcriptomics more cost-effective and clinically actionable.

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Xu, J., Jiang, M., Koga, S., Zhang, N., Huang, Z.. 2025-08-21. SpatialFinder: A Human-in-the-Loop Vision-Language Framework for Prioritizing High-Value Regions in Spatial Transcriptomics. https://doi.org/10.1101/2025.08.16.670684

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