RAAMS

RAAMS applies regression analysis to mass spectrometry data to interpret differential proteomics from 16O/18O labeling and quantify protein expression for biomarker discovery.

Key Features:

• Automatic Interpretation: Interprets mass spectrometry spectra derived from 18O-labeled peptides without requiring chemical composition information from product ion spectra.
• Handling Complex Isotope Patterns: Models and resolves contributions from unlabeled 18O(0), singly labeled 18O(1), doubly labeled 18O(2) species and naturally occurring isotopes such as 13C and 15N.
• Variable Label Incorporation: Measures effective 18O incorporation rates by accounting for enzyme substrate specificity during isotope exchange reactions.
• Correction for Residual Abundance: Corrects for residual 18O(0) abundance in labeled samples, including effects arising from a two-step digestion/labeling procedure.
• Distinguishing Peptide Species: Differentiates between pure 18O(0) and pure 18O(2) peptides using impure H2 18O.
• High-Speed Processing: Operates on centroided peak lists and processes large datasets (nine chromatograms with an average of 1,168 spectra and 6,761 isotopic clusters) at approximately 45 seconds per chromatogram (average 38 ms/spectrum), enabling information-dependent MS/MS analysis on a chromatographic time scale for species exceeding ratio thresholds.

Scientific Applications:

• Quantitative Differential Proteomics: Provides precise quantification of protein expression changes in experiments using 16O/18O labeling.
• Biomarker Discovery for Diagnostics: Supports identification of candidate diagnostic biomarkers by quantifying differential protein levels.
• Biomarker Discovery for Prognostics: Supports identification of candidate prognostic biomarkers through comparative proteomic quantification.
• Information-Dependent MS/MS Selection: Enables selection and triggering of MS/MS acquisition on chromatographic time scales for species that meet predetermined ratio thresholds.

Methodology:

Performs regression analysis on centroided peak lists to model contributions of 18O(0), 18O(1), 18O(2) and natural isotopes (13C, 15N); estimates effective 18O incorporation rates accounting for enzyme substrate specificity; corrects residual 18O(0) from two-step digestion/labeling; distinguishes 18O(0) versus 18O(2) using impure H2 18O; and enables information-dependent MS/MS triggering based on ratio thresholds.