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Related Experiment Video

Updated: Jul 28, 2025

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

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747

A High-Throughput Workflow for FFPE Tissue Proteomics.

Ganesh P Pujari1, Kiran K Mangalaparthi1, Benjamin J Madden2

  • 1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.

Journal of the American Society for Mass Spectrometry
|June 2, 2023
PubMed
Summary
This summary is machine-generated.

Adaptive Focused Acoustics (AFA) technology simplifies and accelerates sample processing for high-throughput proteomic analysis of laser capture microdissection (LCM) FFPE tissues. This automated workflow reduces handling time and improves protein and peptide identification for clinical and research applications.

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Area of Science:

  • Proteomics
  • Biotechnology
  • Analytical Chemistry

Background:

  • Laser capture microdissection (LCM) is crucial for targeted proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissues.
  • Low protein yields and laborious processing hinder proteomic analysis of LCM samples.
  • Automation is needed for efficient high-throughput FFPE-based proteomics.

Purpose of the Study:

  • To present a simplified, rapid workflow using adaptive focused acoustics (AFA) for FFPE tissue sample processing.
  • To evaluate AFA-assisted extraction and digestion for high-throughput proteomic analysis.
  • To assess the impact of AFA on protein and peptide recovery and quality.

Main Methods:

  • Evaluated three workflows: standard extraction/overnight digestion, AFA-assisted extraction/overnight digestion, and AFA-assisted extraction/simultaneous digestion.
  • Utilized AFA-based ultrasonication for automated sample processing in 96-well and 384-well formats.
  • Performed LC-MS/MS analysis to compare protein and peptide identification across workflows.

Main Results:

  • AFA-based workflows enabled automated, high-throughput processing of LCM-FFPE tissues.
  • Accelerated trypsin digestion with AFA significantly reduced overall processing times.
  • AFA accelerated workflows showed slightly higher protein and peptide identifications without compromising peptide quality.

Conclusions:

  • The AFA-based workflow offers rapid, high-throughput sample processing for LCM-FFPE tissues.
  • This method significantly reduces sample handling and is amenable to automation.
  • The described workflow enhances efficiency for clinical and research proteomic analyses.