Missing Value Monitoring to Address Missing Values in Quantitative Proteomics

Area of Science:

• Proteomics
• Biochemistry
• Molecular Biology

Background:

• Key functional proteins like transcription factors are often at very low concentrations in the proteome.
• Classical data-dependent acquisition (DDA) proteomics methods struggle to detect these low-abundance proteins.
• DDA methods in shotgun proteomics experiments exhibit significant missing values due to stochastic acquisition, hindering robust quantitative analysis.

Purpose of the Study:

• To overcome the limitations of DDA in quantifying low-abundance proteins.
• To develop a robust workflow for monitoring the dynamics of low-abundance proteins.
• To improve the reliability of quantitative proteomics for critical protein classes.

Main Methods:

• Development of a novel workflow named missing value monitoring (MvM).
• Implementation of strategies to address missing values inherent in DDA methods.
• Focus on systematic quantitative analysis of low-concentration proteins.

Main Results:

• The MvM workflow successfully overcomes obstacles in detecting low-abundance proteins.
• Improved robustness in quantitative analysis by addressing missing values.
• Enables the tracking of dynamics for proteins previously invisible to DDA.

Conclusions:

• The missing value monitoring (MvM) workflow provides a robust solution for studying low-abundance protein dynamics.
• This method enhances the capabilities of quantitative proteomics, particularly for essential functional proteins.
• MvM improves the reliability and scope of proteomic analyses.