Quantifying compartment-associated variations of protein abundance in proteomics data

Area of Science:

• Proteomics and Bioinformatics
• Cell Biology and Aging Research

Background:

• Quantitative mass spectrometry is widely used to analyze protein abundance across various biological conditions.
• Current analysis methods often assume most proteins remain unchanged, overlooking significant morphological variations between cell states (e.g., organelle size/number).

Purpose of the Study:

• To investigate how morphological differences impact proteomics data analysis.
• To develop and validate a novel method for detecting and normalizing morphological effects in quantitative proteomics.
• To improve the accuracy of differential expression analysis and understand protein stoichiometry.

Main Methods:

• Analysis of multiple published quantitative mass spectrometry datasets.
• Development of a computational method to identify and correct for coordinated changes in protein abundance linked to cellular morphology.
• Application of the method to diverse datasets, including sub-cellular proteomes from *Caenorhabditis elegans* aging studies.

Main Results:

• Observed that proteins within specific cellular compartments often show coordinated abundance changes between conditions, reflecting morphological differences.
• Demonstrated that these morphological effects can significantly bias traditional differential expression analyses.
• Successfully applied the proposed normalization method to various datasets, yielding more accurate insights.

Conclusions:

• Morphological variations are a critical confounder in proteomics data analysis that must be addressed.
• The developed method effectively detects and normalizes morphological effects, offering a complementary approach to standard differential expression analysis.
• This normalization allows for better discrimination between true stoichiometric variations and abundance changes driven by cellular morphology, particularly relevant in aging research.