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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...

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Related Experiment Video

Updated: May 11, 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

Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations.

Richard E Higgs1, Jon P Butler, Bomie Han

  • 1Global Discovery and Development Statistics, Lilly Research Laboratories, Indianapolis, IN 46285, USA ; Lilly Corporate Center, DC 0720, Indianapolis, IN 46285, USA.

International Journal of Proteomics
|May 28, 2013
PubMed
Summary
This summary is machine-generated.

Label-free quantification using primary mass spectrum (MS1) data offers sensitive peptide detection for proteomics. Post-processing methods effectively support protein inference, rivaling targeted approaches.

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Last Updated: May 11, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Quantitative Proteomics Workflow using Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
09:04

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Advancements in mass spectrometry enable label-free quantification (LFQ) using MS1 data.
  • LFQ offers advantages over immunoassays and multiple reaction monitoring (MRM) for hypothesis generation.

Purpose of the Study:

  • Evaluate post-processing methods for peptide-level quantification to infer protein abundance.
  • Compare MS1-based LFQ with targeted MRM approaches.

Main Methods:

  • Assessed quantification methods by recovering a known protein dilution in complex matrices.
  • Compared MS1 quantification results with standard MRM on identical samples and instrument conditions.

Main Results:

  • Identified multiple peptides with MS1 quantification sensitivity comparable to MRM.
  • Demonstrated the effectiveness of MS1 data for protein-level inference across different background complexities.

Conclusions:

  • Post-processing of MS1 data is a viable strategy for sensitive protein quantification.
  • Recommendations provided for leveraging multiple peptide measurements for robust protein inference.