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High-Efficiency Cell-Type Proteomics Strategy Deciphers Cholangiocarcinoma Fibrosis-Associated Pathological

Zhiyang Su1,2,3, Honghua Zhang4, Hongke Hu5

  • 1Department of Chemistry, School of Science, Southern University of Science and Technology, Shenzhen 518055, China.

Analytical Chemistry
|March 4, 2025
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We developed an AI algorithm, π-CellSeg-CCA, to precisely analyze cholangiocarcinoma (CCA) fibrosis heterogeneity. This approach enhances proteomic analysis of small tissue samples, identifying MUC16 as a marker for worse prognosis in CCA.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Proteomics

Background:

  • Cholangiocarcinoma (CCA) exhibits significant heterogeneity, complicating diagnosis and prognosis.
  • Fibrosis is a key feature of CCA progression, but its heterogeneity is poorly understood.
  • Traditional bulk tissue proteomic analysis averages results, masking cell-specific information crucial for CCA research.

Purpose of the Study:

  • To develop an automated pathological image analysis algorithm for precise annotation of CCA regions.
  • To establish a novel strategy for deciphering fibrosis-associated heterogeneity in CCA using advanced proteomic techniques.
  • To identify novel protein markers associated with CCA progression and prognosis.

Main Methods:

  • Development of π-CellSeg-CCA, a pathological image analysis algorithm using Mask R-CNN and ResNet-18 for automated region annotation.
  • Integration of π-CellSeg-CCA with laser microdissection (LMD), Simple and Integrated Spintip-based Proteomics technology (SISPROT), and high-sensitivity mass spectrometry.
  • Proteomic profiling of small (1 mm²) CCA tissue samples to identify differentially expressed proteins.

Main Results:

  • π-CellSeg-CCA achieved approximately 90% accuracy in recognizing CCA and normal bile duct regions.
  • The integrated strategy identified over 8000 proteins, including specific markers for CCA.
  • The protein MUC16 was identified as upregulated in fibrosis CCA, correlating with a worse prognosis and contributing to CCA progression.

Conclusions:

  • The algorithm-assisted, cell-type-specific proteomics strategy is effective for studying tumor microenvironments with limited clinical samples.
  • This approach offers a promising method for dissecting pathological heterogeneity in cancers like CCA.
  • Identification of MUC16 provides a potential therapeutic target and prognostic biomarker for CCA.