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. 2025 Feb 25.
doi: 10.1097/HEP.0000000000001283. Online ahead of print.

Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma

Libing Hong  1 , Jie Mei  2   3 , Xuqi Sun  1 , Yifan Wu  4   5 , Zhen Dong  6 , Yuzhi Jin  1 , Liaoliao Gao  1 , Jinlin Cheng  7 , Weihong Tian  8 , Chuan Liu  1 , Bin Li  1 , Pingping Hu  6 , Lulu Liu  1 , Shan Xin  9 , Xiaomeng Dai  1   10 , Peng Zhao  1   10 , Rongping Guo  2   3 , Minshan Chen  2   3 , Jingping Yun  3   11 , Bo Lin  4   5 , Wei Wei  2   3 , Weijia Fang  1   10 , Xuanwen Bao  1   10
Affiliations

Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma

Libing Hong et al. Hepatology. .

Abstract

Background and aims: The prognoses and therapeutic responses of patients with intrahepatic cholangiocarcinoma (iCCA) depend on spatial interactions among tumor microenvironment (TME) components. However, the spatial TME characteristics of iCCA remain poorly understood. The aim of this study was to generate a comprehensive spatial atlas of iCCA using artificial intelligence-assisted spatial multiomics patterns and to identify spatial features associated with prognosis and immunotherapy.

Approach and results: Spatial multiomics, including imaging mass cytometry (n=155 in-house), spatial proteomics (n=155 in-house), spatial transcriptomics (n=4 in-house), multiplex immunofluorescence (n=20 in-house), single-cell RNA sequencing (scRNA-seq, n=9 in-house and n=34 public), bulk RNA-seq (n=244 public), and bulk proteomics (n=110 in-house and n=214 public), were employed to elucidate the spatial TME of iCCA. More than 1.06 million cells were resolved, and the findings revealed that spatial topology, including cellular deposition patterns, cellular communities, and intercellular communications, profoundly correlates with the prognosis of patients with iCCA. Specifically, CD163 hi M2-like resident-tissue macrophages suppress antitumor immunity by directly interacting with CD8 + T cells, resulting in poorer patient survival. Additionally, 5 spatial subtypes with distinct prognoses were identified, and potential therapeutic options were generated for these subtypes. Furthermore, a spatial TME deep learning system was developed to predict the prognosis of patients with iCCA with high accuracy from a single 1-mm 2 tumor sample.

Conclusions: This study offers preliminary insights into the spatial TME ecosystem of iCCA, providing valuable foundations for precise patient classification and the development of personalized treatment strategies.

Keywords: deep learning; imaging mass cytometry; macrophage; spatial feature; spatially resolved proteomics.

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