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Related Concept Videos

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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...
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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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Related Experiment Video

Updated: Jul 7, 2026

Histone Modification Screening using Liquid Chromatography, Trapped Ion Mobility Spectrometry, and Time-Of-Flight Mass Spectrometry
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Mixed-effects statistical model for comparative LC-MS proteomics studies.

D S Daly1, K K Anderson, E A Panisko

  • 1Pacific Northwest National Laboratory, 900 Battelle Boulevard, P.O. Box 999, Richland, Washington 99352, USA. ds.daly@pnl.gov

Journal of Proteome Research
|February 7, 2008
PubMed
Summary
This summary is machine-generated.

A new mixed-effects model improves protein concentration comparisons in proteomics by accounting for peptide measurement variability in liquid chromatography-mass spectrometry (LC-MS). This method offers more accurate and reliable quantitative results across different treatments.

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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biostatistics

Background:

  • Comparative proteomics studies often rely on liquid chromatography-mass spectrometry (LC-MS) for protein quantification.
  • Unequal peptide digestion, separation, and ionization in LC-MS introduce significant variability, hindering accurate protein concentration comparisons.
  • Existing methods struggle to fully address this peptide measurability issue, impacting the reliability of results.

Purpose of the Study:

  • To introduce a novel mixed-effects statistical model for comparative LC-MS proteomics.
  • To address and quantify the unequal measurability of peptides in LC-MS data.
  • To provide a robust statistical framework for accurate protein concentration comparisons across treatments.

Main Methods:

  • Developed a linear mixed-effects model to describe peptide abundance, incorporating terms for unequal LC-MS measurability.
  • Employed REstricted Maximum Likelihood (REML) estimation for fitting the model to potentially incomplete LC-MS data.
  • Validated the model using a dilution series experiment with a known protein mixture from *Trichoderma reesei*.

Main Results:

  • The mixed-effects model successfully described LC-MS measurements for a significant portion (781/1546) of *T. reesei* proteins.
  • The model's measurability terms effectively accounted for major sources of uncertainty in peptide quantification.
  • Relative concentration estimates showed improved accuracy, with 90% within 0.5-fold of true values, and reduced bias compared to the common ratio method, especially with more peptides per protein.

Conclusions:

  • Mixed-effects statistical modeling provides a flexible and powerful approach for comparative proteomics.
  • The proposed model effectively handles the inherent variability in peptide LC-MS measurability.
  • This methodology enables more informative and reliable quantitative comparisons of protein concentrations across experimental treatments with objective uncertainty measures.