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Related Concept Videos

Proteomics01:33

Proteomics

7.3K
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...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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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...
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Updated: Jun 28, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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An XIC-Centric Strategy for Improved Identification and Quantification in Proteomic Data Analyses.

Guanghui Wang1, Zheng Zhang1, Yi Liu1

  • 1Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.

Journal of Proteome Research
|April 10, 2024
PubMed
Summary

This study introduces a bidirectional, XIC-centric approach for proteomic data analysis. This method enhances identification and quantification accuracy, improving overall reproducibility in mass spectrometry-based proteomics.

Keywords:
XICXIC-centricglycopeptideidentificationmonoisotopequantificationreproducibilityvalidation

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Biochemistry

Background:

  • Reproducibility in proteomics remains a significant challenge.
  • Current data analysis workflows often use a unidirectional approach for identification and quantification.
  • Extracted ion chromatograms (XICs) are crucial but can be subject to errors.

Purpose of the Study:

  • To propose and validate an XIC-centric, bidirectional data analysis approach for proteomics.
  • To enhance the accuracy of glycopeptide identification and quantification.
  • To improve the overall reproducibility of proteomic data analysis.

Main Methods:

  • Developed an XIC-centric strategy with bidirectional data flow.
  • Employed XIC-based monoisotope repicking for data validation.
  • Utilized liquid chromatography-mass spectrometry data from glycoprotein and human hair samples.

Main Results:

  • The XIC-centric approach significantly improved identification and quantification accuracy.
  • Monoisotope repicking uncovered misidentifications and rescued non-top-ranked glycopeptide hits.
  • Reduced instances of one XIC peak being associated with multiple unique identifications, a key source of irreproducibility.

Conclusions:

  • The proposed XIC-centric strategy enhances proteomic data analysis accuracy.
  • This method addresses common challenges in glycopeptide identification.
  • The approach leads to improved identification and quantification accuracy, ultimately boosting reproducibility in proteomics.