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

Background & 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 & Results: Spatial multiomics, including imaging mass cytometry (IMC, n=155 in-house), spatial proteomics (n=155 in-house), spatial transcriptomics (n=4 in-house), multiplex immunofluorescence (mIF, 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 iCCA patients. Specifically, CD163hi M2-like resident-tissue macrophages suppress anti-tumor immunity by directly interacting with CD8+ T cells, resulting in poorer patient survival. Additionally, five 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 iCCA patients 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.