Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags

Mass
spectrometry (MS) has become an accessible tool for whole
proteome quantitation with the ability to characterize protein expression
across thousands of proteins within a single experiment. A subset
of MS quantification methods (e.g., SILAC and label-free) monitor
the relative intensity of intact peptides, where thousands of measurements
can be made from a single mass spectrum. An alternative approach,
isobaric labeling, enables precise quantification of multiple samples
simultaneously through unique and sample specific mass reporter ions.
Consequently, in a single scan, the quantitative signal comes from
a limited number of spectral features (≤11). The signal observed
for these features is constrained by automatic gain control, forcing
codependence of concurrent signals. The study of constrained outcomes
primarily belongs to the field of compositional data analysis. We
show experimentally that isobaric tag proteomics data are inherently
compositional and highlight the implications for data analysis and
interpretation. We present a new statistical model and accompanying
software that improves estimation accuracy and the ability to detect
changes in protein abundance. Finally, we demonstrate a unique compositional
effect on proteins with infinite changes. We conclude that many infinite
changes will appear small and that the magnitude of these estimates
is highly dependent on experimental design.