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Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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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.
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electron ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave behind a...
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Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms.

Jacob D Jaffe1, Caitlin M Feeney2,3, Jinal Patel2

  • 1The Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA. jjaffe@broadinstitute.org.

Journal of the American Society for Mass Spectrometry
|August 27, 2016
PubMed
Summary

Genetic algorithms enhance hybrid targeted/comprehensive mass spectrometry (MS) assays for proteomics. This approach improves quantitative agreement between targeted and comprehensive methods, enabling thousands of analyte detections.

Keywords:
AlgorithmComputational proteomicsDIAGenetic algorithmLinear modelsMass SpectrometryModelingModelsPRMProteomicsRSWATHTargeted proteomics

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Computational Biology

Background:

  • Targeted proteomic assays offer robust quantification but are limited to hundreds of analytes.
  • Comprehensive techniques like SWATH and DIA can analyze thousands of analytes but face data analysis challenges for quantitative rigor.
  • Integrating targeted and comprehensive approaches is desirable for high-throughput, quantitative proteomics.

Purpose of the Study:

  • To develop a method using genetic algorithms to configure hybrid targeted/comprehensive mass spectrometry (MS) assays.
  • To improve the quantitative agreement between traditional targeted assays and comprehensive MS techniques.
  • To enable the detection and quantification of thousands of analytes with high quantitative rigor.

Main Methods:

  • Utilized genetic algorithms to select optimal precursor-to-fragment transitions for hybrid assays.
  • Configured hybrid targeted/comprehensive MS assays by optimizing transition selection.
  • Evaluated quantitative agreement using linear models fitted to data from targeted and hybrid assays.

Main Results:

  • Genetic algorithms significantly improved quantitative agreement between targeted and hybrid comprehensive/targeted assays.
  • The developed algorithm demonstrated performance comparable to an experienced mass spectrometrist.
  • The approach successfully configured hybrid assays for enhanced quantitative proteomics.

Conclusions:

  • Genetic algorithms provide a powerful tool for developing quantitative hybrid targeted/comprehensive MS assays.
  • This method overcomes data analysis challenges in combining targeted and comprehensive proteomic strategies.
  • The approach facilitates the analysis of thousands of analytes with high quantitative accuracy in proteomics.