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Updated: Sep 15, 2025

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

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, MD 21231.

Biorxiv : the Preprint Server for Biology

|July 14, 2025

Summary

This summary is machine-generated.

A new spatial proteomics workflow, SP-Max, identifies thousands of proteins in pancreatic tissues. This method aids in understanding molecular changes during pancreatic cancer progression from normal ductal cells to invasive disease.

Keywords:

FFPE Tissue High-sensitivity Mass Spectrometry Intraductal Papillary Mucinous Neoplasm Pancreatic Ductal Adenocarcinoma Spatial Proteomics

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Published on: November 5, 2020

Area of Science:

Proteomics
Cancer Biology
Biochemistry

Background:

Pancreatic cancer is difficult to diagnose and treat due to late symptoms.
Intraductal papillary mucinous neoplasms (IPMNs) are precursors to 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.
To maximize protein recovery and quantification from limited laser microdissected (LMD) samples.
To identify molecular differences and potential markers of progression in pancreatic tissues.

Main Methods:

Developed and optimized SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample) workflow.
Applied workflow to Formalin-Fixed Paraffin-Embedded (FFPE) pancreatic tissue samples.
Performed comparative proteomic analysis across normal ductal (ND), IPMN, and PDAC tissues.

Main Results:

Identified over 6,000 proteins and quantified over 5,200 protein groups.
Revealed critical molecular differences in protein pathways between ND, IPMN, and PDAC.
Discovered potential protein markers associated with pancreatic cancer progression.

Conclusions:

SP-Max provides a systematic and reproducible method for high-resolution spatial proteomics in pancreatic tissues.
The workflow enhances the study of precancerous lesions and cancer progression.
Findings offer avenues for improved early detection of pancreatic cancer.