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High-Sensitive Spatial Proteomics for Pancreatic Cancer Progression Analysis.

Jongmin Woo1, Zhenyu Sun1, Yingwei Hu1

  • 1Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States.

Analytical Chemistry
|February 18, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed SP-Max, a spatial proteomics workflow, to analyze protein changes during pancreatic cancer progression. This method aids in identifying early detection markers for pancreatic ductal adenocarcinoma from precursor lesions.

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Area of Science:

  • Oncology
  • Proteomics
  • Biochemistry

Background:

  • Pancreatic cancer diagnosis and treatment are challenging due to late symptom onset and limited early detection methods.
  • Intraductal papillary mucinous neoplasms (IPMNs) are noninvasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC).
  • Understanding protein expression changes during progression from normal ductal (ND) cells to IPMN and PDAC is crucial for early detection.

Purpose of the Study:

  • To present an optimized spatial tissue proteomics workflow, SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample).
  • To maximize protein recovery and quantification from limited laser microdissected (LMD) samples of pancreatic tissues.
  • To identify molecular differences and potential markers associated with pancreatic cancer progression.

Main Methods:

  • Development and optimization of the SP-Max workflow for spatial proteomics.
  • Application of SP-Max to Formalin-Fixed Paraffin-Embedded (FFPE) pancreatic tissue samples.
  • Comparative proteomic analysis across normal ductal (ND), IPMN, and PDAC tissues.

Main Results:

  • SP-Max workflow enabled identification of over 6000 proteins and quantification of over 5200 protein groups.
  • Comparative analysis revealed critical molecular differences in protein pathways between ND, IPMN, and PDAC.
  • Identified potential protein markers associated with the progression of pancreatic lesions.

Conclusions:

  • SP-Max provides a systematic and reproducible approach for high-resolution proteomic analysis of pancreatic tissues.
  • The workflow enhances the study of precancerous lesions and cancer progression.
  • Findings contribute to understanding molecular changes in pancreatic cancer development and may inform early detection strategies.