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Updated: Nov 2, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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A Universal and High-Throughput Proteomics Sample Preparation Platform.

Andrew P Burns1, Ya-Qin Zhang1, Tuan Xu1

  • 1National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.

Analytical Chemistry
|June 10, 2021
PubMed
Summary
This summary is machine-generated.

A new automated platform significantly enhances proteomics sample preparation throughput by 20-40 fold. This high-throughput method enables comprehensive proteome discovery and quantitation from small cell numbers.

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Area of Science:

  • Proteomics
  • Biotechnology
  • Analytical Chemistry

Background:

  • Advances in mass spectrometry have improved proteome analysis.
  • High-throughput sample preparation remains a bottleneck in proteomics.

Purpose of the Study:

  • To develop a highly automated, reproducible, and adaptable 384-well plate platform for high-throughput proteomics sample preparation.
  • To demonstrate the platform's universality across various human cell types.

Main Methods:

  • Development of an automated 384-well plate sample preparation system for protein extraction and digestion.
  • Utilized high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) for proteome-wide analysis.
  • Employed HPLC-multiple reaction monitoring (MRM) for targeted protein quantitation.

Main Results:

  • Achieved 20- to 40-fold improvement in sample preparation throughput (approx. 300 min from cells to peptides).
  • Identified and quantified approximately 4,000 proteins per sample from 100-10K cells across six human cell types.
  • Demonstrated excellent linearity for targeted protein quantitation using HPLC-MRM with internal standards.

Conclusions:

  • The developed automated platform offers a robust and efficient solution for high-throughput proteomics sample preparation.
  • The platform's universality and efficiency support its application in clinical research for protein quantitation.