Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry

Area of Science:

• Proteomics
• Biotechnology
• Analytical Chemistry

Background:

• Stable-isotope labeling is crucial for quantitative proteomics, enabling comparative analysis of biological samples.
• Mass spectrometry differentiates molecules based on stable-isotope composition, appearing as distinct isotopic clusters.
• Existing methods face challenges in accurately quantifying peptide abundance due to incomplete isotope incorporation.

Purpose of the Study:

• To develop advanced statistical models for accurate quantification in (16)O/(18)O stable-isotope labeling experiments.
• To address challenges related to incomplete heavy isotope incorporation in proteomic analyses.
• To enhance downstream statistical analyses, such as differential expression analysis.

Main Methods:

• Two multivariable linear regression models were developed for (16)O/(18)O stable-isotope labeled data.
• Models jointly analyze pairs of isotopic clusters from the same peptide.
• They quantify peptide abundance in two biological samples while accounting for peptide-specific isotope incorporation rates.

Main Results:

• The regression models accurately quantify peptide abundance in paired biological samples.
• Models correct for unlabeled peptide abundance arising from incomplete (18)O incorporation.
• Quantified abundance measures allow for robust downstream statistical analyses.

Conclusions:

• The proposed multivariable regression models offer a significant advancement for quantitative proteomics using (16)O/(18)O stable-isotope labeling.
• These models improve the accuracy of differential expression analysis by providing reliable peptide abundance estimates.
• The methodology is generalizable to other stable-isotope labeling techniques in mass spectrometry.