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Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
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Elements have a set number of protons that determines their atomic number (Z). For example, all atoms with eight protons are oxygen; however, the number of neutrons can vary for atoms of the same element. The sum of the number of protons and the number of neutrons is the mass number (A). Atoms with the same atomic number but different mass numbers are called isotopes. Elements can have multiple isotopes, for example, carbon-12, carbon-13, and carbon-14.
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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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Author Spotlight: Quantification of Complex Lipidomic Samples Using Stable Isotope Labeling
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Automated generic analysis tools for protein quantitation using stable isotope labeling.

Wen-Lian Hsu1, Ting-Yi Sung

  • 1Institute of Information Science, Academia Sinica, Taipei, Taiwan. hsu@iis.sinica.edu.tw

Methods in Molecular Biology (Clifton, N.J.)
|December 17, 2009
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Summary

Automated tools Multi-Q and MaXIC-Q enhance quantitative proteomics by analyzing mass spectrometry data. These platforms improve protein quantitation accuracy from complex proteomic experiments.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Isotope labeling coupled with LC-MS/MS is a powerful technique for quantitative proteomics.
  • Protein quantitation relies on MS/MS scans (e.g., iTRAQ) or MS scans (e.g., SILAC, ICAT, (18)O labeling).
  • Large-scale proteomic studies generate vast amounts of spectral data, complicated by noise, requiring robust analysis tools.

Purpose of the Study:

  • To present two automated software tools, Multi-Q and MaXIC-Q, for quantitative proteomics data analysis.
  • To provide generic platforms capable of processing data from various search engines and mass spectrometers.
  • To enhance the accuracy and reliability of protein quantitation in complex LC-MS proteomic experiments.

Main Methods:

  • Development of Multi-Q for MS/MS-based quantitation analysis.
  • Development of MaXIC-Q for MS-based quantitation analysis.
  • Tools accommodate SEQUEST/Mascot search results and mzXML files; incorporate instrument detection limits and projected ion mass spectra validation.

Main Results:

  • Multi-Q and MaXIC-Q offer automated solutions for analyzing large-scale quantitative proteomic data.
  • The tools are designed for broad compatibility with different data sources and mass spectrometry platforms.
  • Implementation of detection limit filtering and spectral validation enhances quantitation accuracy.

Conclusions:

  • Multi-Q and MaXIC-Q provide robust and accurate automated quantitation for LC-MS/MS proteomic experiments.
  • These tools address challenges posed by data noise and complexity in large-scale proteomic studies.
  • The developed platforms facilitate more reliable protein quantitation, advancing proteomic research.