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Related Experiment Video

Updated: May 12, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Data processing methods and quality control strategies for label-free LC-MS protein quantification.

Marianne Sandin1, Johan Teleman, Johan Malmström

  • 1Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden.

Biochimica Et Biophysica Acta
|April 10, 2013
PubMed
Summary
This summary is machine-generated.

Selecting the right liquid chromatography-mass spectrometry (LC-MS) workflow for protein quantification is complex. This review guides users through software solutions and quality control for accurate label-free quantification (LFQ) in proteomics.

Keywords:
LC–MSLabel freeProtein quantificationQuality controlQuantitative proteomicsSelected reaction monitoring

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Last Updated: May 12, 2026

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Quantitative Analysis of Chromatin Proteomes in Disease
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Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) is a standard technique for protein quantification.
  • Choosing appropriate experimental setups and data processing software for LC-MS is challenging.
  • Label-free quantification (LFQ) is a key method in quantitative proteomics.

Purpose of the Study:

  • To review software issues and approaches for quantitative LC-MS experiments.
  • To provide guidance on selecting optimal workflows for specific biological questions.
  • To elaborate on quality control for accurate protein quantification.

Main Methods:

  • Focus on label-free quantification (LFQ) strategies.
  • Discussion of data processing for both LC-MS/MS and LC-SRM.
  • Review of current quality control methodologies.

Main Results:

  • Identified key challenges in quantitative LC-MS software.
  • Presented various software approaches for LC-MS data processing.
  • Highlighted the importance of quality control in protein quantification.

Conclusions:

  • Navigating quantitative LC-MS workflows requires careful consideration of software and methodology.
  • Effective quality control is crucial for reliable protein quantification results.
  • This review aids researchers in selecting appropriate LC-MS strategies for their studies.