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Related Concept Videos

Optimizing Chromatographic Separations01:15

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Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
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Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
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MALDI-TOF Mass Spectrometry01:19

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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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High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
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Mass Spectrometry: Overview01:19

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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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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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GOAT--a simple LC-MS/MS gradient optimization tool.

David C Trudgian1, Roman Fischer, Xiaofeng Guo

  • 1Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Proteomics
|April 12, 2014
PubMed
Summary
This summary is machine-generated.

Optimized non-linear gradients improve peptide and protein identification in proteomics. A new tool, GOAT, helps create these gradients for better data coverage in nano-liquid chromatography-mass spectrometry (LC-MS/MS) analyses.

Keywords:
BioinformaticsGradientLC-MS/MSLiquid chromatographyOptimizationSeparation

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Mass Spectrometry

Background:

  • Nano-high-performance liquid chromatography (HPLC) enables precise solvent gradient control for peptide separation.
  • Linear gradients are common in nano-LC-MS/MS, but non-linear gradients show potential for increased peptide and protein identifications.

Purpose of the Study:

  • To investigate the efficacy of per-fraction optimized non-linear gradients for analyzing fractionated peptide samples.
  • To determine if optimized gradients enhance peptide coverage and identification rates in mass spectrometry-based proteomics.

Main Methods:

  • Development and application of a gradient optimization method for nano-LC-MS/MS.
  • Testing optimized non-linear gradients on peptide-level fractionated samples.
  • Evaluation of peptide distribution and identification gains across different mass spectrometry (MS) instrumentation.

Main Results:

  • Optimized gradients significantly improved peptide distribution throughout the analysis.
  • Substantial gains in peptide and protein identifications were observed with previous-generation MS instrumentation.
  • The improvement in identifications was less pronounced with current, faster MS platforms.

Conclusions:

  • Per-fraction optimized non-linear gradients enhance peptide coverage and identification in proteomics.
  • The developed gradient optimization tool (GOAT) is user-friendly and MS-vendor independent.
  • The method offers a valuable approach to maximize data acquisition in LC-MS/MS experiments.