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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.
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The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
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Updated: Jul 25, 2025

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TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments.

Ross McBride1, Joe Wandy2, Stefan Weidt2

  • 1School of Computing Science, University of Glasgow, Glasgow G12 8RZ, United Kingdom.

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|June 26, 2023
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Summary
This summary is machine-generated.

TopNEXt enhances multi-sample Liquid Chromatography Tandem Mass Spectrometry by improving data acquisition. This framework increases the quality and quantity of fragmentation spectra for better metabolite identification.

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) is crucial for metabolite annotation.
  • Current Data-Dependent Acquisition (DDA) methods in LC-MS/MS often yield insufficient spectral quality and quantity.
  • Existing DDA approaches struggle with multi-sample analysis, lacking information sharing and requiring manual intervention.

Purpose of the Study:

  • To introduce TopNEXt, a novel real-time scan prioritization framework for enhanced data acquisition in multi-sample LC-MS/MS metabolomics.
  • To improve the quality and quantity of fragmentation spectra collected during metabolomics experiments.
  • To facilitate more accurate and comprehensive metabolite identification across diverse experimental contexts.

Main Methods:

  • TopNEXt employs a Region of Interest and intensity-based scoring system to extend Data-Dependent Acquisition exclusion methods.
  • The framework operates in real-time to prioritize scans during data acquisition.
  • TopNEXt is integrated into the existing ViMMS framework.

Main Results:

  • TopNEXt successfully increased fragmentation spectra acquisition for an additional 10% of target peaks.
  • The framework achieved an additional 20% increase in acquisition intensity.
  • Simulated and lab experiments validated the efficacy of TopNEXt in improving spectral data.

Conclusions:

  • TopNEXt significantly enhances data acquisition in multi-sample LC-MS/MS metabolomics.
  • The framework's improvements in spectral quality and quantity directly contribute to better metabolite identification.
  • TopNEXt offers a valuable tool with broad applicability across various experimental settings in metabolomics.