Deep learning-enabled integration of histology and transcriptomics for tissue spatial profile analysis Journal Article


Authors: Ge, Y.; Leng, J.; Tang, Z.; Wang, K.; U, K.; Zhang, S. M.; Han, S.; Zhang, Y.; Xiang, J.; Yang, S.; Liu, X.; Song, Y.; Wang, X.; Li, Y.; Zhao, J.
Article Title: Deep learning-enabled integration of histology and transcriptomics for tissue spatial profile analysis
Abstract: patially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics. © 2025 American Association for the Advancement of Science. All rights reserved.
Keywords: gene expression; lung cancer; transcriptomics; tissue; microenvironments; sequence data; profile analysis; subcellular resolution; genes expression; measurements of; comprehensive measurement; spatial context; spatial profiles
Journal Title: Research
Volume: 8
ISSN: 2096-5168
Publisher: Amer Assoc Advancement Science  
Date Published: 2025-01-01
Start Page: 0568
Language: English
DOI: 10.34133/research.0568
PROVIDER: scopus
PMCID: PMC11739434
PUBMED: 39830364
DOI/URL:
Notes: Source: Scopus
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  1. Kai Cheng U
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