The following abstract is drawn from a recently published paper in the Nature Communications. We invite you to read the full paper and join the conversation, become a member of the Pathology News community to share your thoughts, ask questions, and engage with others around this work.
Authors: Xiao Tan1,2, Onkar Mulay1,2, Jacky Xie3, Samual MacDonald4,5, Taehyun Kim6, Chenhao Zhou7, Zherui Xiong1,2, Samuel X. Tan7, Nan Ye3, Amy McCart Reed8, Kiarash Khosrotehrani7, Fred Roosta3,4, Maciej Trzaskowski1,5,9, Peter T. Simpson8,10, Quan Nguyen1,2
Abstract
Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using an ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology.
Read the full article: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data | Nature Communications
1Genomics and Machine Learning Lab, Institute for Molecular Bioscience, St Lucia, QLD, Australia.
2Queensland Institute of Medical Research Berghofer; QIMRB National Centre for Spatial Tissue and AI Research (NCSTAR), Herston, QLD, Australia.
3School of Mathematics and Physics, The University of Queensland, St Lucia, QLD, Australia.
4ARC Training Centre for Information Resilience, The University of Queensland, St Lucia, QLD, Australia.
5Max Kelsen, Spring Hill, QLD, Australia.
6Pathology Queensland, Royal Brisbane & Women’s Hospital, Herston, QLD, Australia.
7FrazerInstitute, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Woolloongabba, QLD, Australia.
8UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Herston, QLD, Australia.
9IntelMagik, WestEnd, QLD, Australia.
10School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, St Lucia, QLD, Australia.
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