Multiparameter Optimization of Two Common Proteomics Quantification Methods for Quantifying Low-Abundance Proteins

Quantitative
proteomics has been extensively applied in the screening
of differentially regulated proteins in various research areas for
decades, but its sensitivity and accuracy have been a bottleneck for
many applications. Every step in the proteomics workflow can potentially
affect the quantification of low-abundance proteins, but a systematic
evaluation of their effects has not been done yet. In this work, to
improve the sensitivity and accuracy of label-free quantification
and tandem mass tags (TMT) labeling in quantifying low-abundance proteins,
multiparameter optimization was carried out using a complex 2-proteome
artificial sample mixture for a series of steps from sample preparation
to data analysis, including the desalting of peptides, peptide injection
amount for LC-MS/MS, MS1 resolution, the length of LC-MS/MS gradient,
AGC targets, ion accumulation time, MS2 resolution, precursor coisolation
threshold, data analysis software, statistical calculation methods,
and protein fold changes, and the best settings for each parameter
were defined. The suitable cutoffs for detecting low-abundance proteins
with at least 1.5-fold and 2-fold changes were identified for label-free
and TMT methods, respectively. The use of optimized parameters will
significantly improve the overall performance of quantitative proteomics
in quantifying low-abundance proteins and thus promote its application
in other research areas.