Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis

Zhenzhen Xun, Xinyu Ding, Yao Zhang, Benyan Zhang, Shujing Lai, Duowu Zou, Junke Zheng, Guoqiang Chen, Bing Su, Leng Han () and Youqiong Ye ()
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Zhenzhen Xun: Shanghai Jiao Tong University School of Medicine
Xinyu Ding: Shanghai Jiao Tong University School of Medicine
Yao Zhang: Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Benyan Zhang: Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Shujing Lai: Shanghai Jiao Tong University School of Medicine
Duowu Zou: Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Junke Zheng: Shanghai Jiao Tong University School of Medicine
Guoqiang Chen: Shanghai Jiao Tong University School of Medicine (SJTU-SM)
Bing Su: Shanghai Jiao Tong University School of Medicine
Leng Han: Texas A&M University
Youqiong Ye: Shanghai Jiao Tong University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets.

Date: 2023
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DOI: 10.1038/s41467-023-36560-7

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