AccScience Publishing / TD / Volume 3 / Issue 1 / DOI: 10.36922/td.2049
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Artificial intelligence enabled spatially resolved transcriptomics reveal spatial tissue organization of multiple tumors

Teng Liu1,2† Jinxin Ye3† Chunnan Hu1† Zongbo Zhang1 Zhuomiao Ye2 Jiangnan Liao2 Mingzhu Yin1,2*
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1 Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, 404000, Chongqing, China
2 Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, 404000, Chongqing, China
3 Physics and Technique Department of Radiation Oncology, Cancer treatment institute, Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, 404000, Chongqing, China
Tumor Discovery 2024, 3(1), 2049
Submitted: 16 October 2023 | Accepted: 15 December 2023 | Published: 6 March 2024
© 2024 by the Author (s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Spatially resolved transcriptomics was honored as the Method of the Year 2020 by Nature Methods. This approach allows biologists to precisely discern mRNA expression at the cellular level within structurally preserved tissues. Leveraging artificial intelligence in spatial transcriptomic analysis enhances the understanding of cellular-level biological interactions and offers novel insights into intricate tissues, such as tumor microenvironments. Nevertheless, numerous existing clustering algorithms employing deep learning exhibit the potential for enhancement. In this paper, we focus on graph deep learning-based spatial domain identification for spatial transcriptomics (ST) data from multiple tumors. This identification enables the recognition of cell subpopulations in distinct spatial coordinates, aiding further studies on tumor progression, such as cell-cell communication, pseudo-time trajectory inference, and single-cell deconvolution. Initially, the gene expression profiles and spatial location information were transformed into a gene feature matrix and a cell adjacency matrix. A variational graph autoencoder was then applied to extract features and reduce the dimensions of these two matrices. Following training in the constructed graph neural networks, the latent embeddings of ST data were generated and could be leveraged for spatial domain identification. Through a comparison with established methods, our approach demonstrated superior clustering accuracy. The utilization of accurately segmented spatial regions enables downstream analyses of multiple tumors, encompassing the trajectory of tumor evolution, and facilitating differential gene expression analysis across various cell types.

Spatial transcriptomics
Artificial intelligence
Graph neural network
Spatial domain identification
Tumor progression
The National Key R&D Programs (NKPs) of China
The Science and Technology Research Program of Chongqing Municipal Education Commission
Joint project of Chongqing Health Commission and Science and Technology Bureau
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Conflict of interest
The authors declare that they have no competing interests.
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Tumor Discovery, Electronic ISSN: 2810-9775 Published by AccScience Publishing