Pursuing Impactful Quantitative Proteomics Using QC-Channels in Every Spectrum and Trend-Design in Experiment

Abstract

False changes discovered by quantitative proteomics reduce the trust of biologists in proteomics and limit the applications of proteomics to unlock biological mechanisms, which suppresses the application of proteomics techniques in the pharmaceutical industry more than it does in academic research. To remove false changes that arise during LC-MS/MS data acquisition, we evaluated the contributions of peptide abundance and number of unique peptides on reproducibility. Lower abundance and only one unique peptide have a higher risk of generating a higher coefficient of variation (CV), resulting in less accurate quantification. However, the abundance of peptides in samples is not adjustable and discarding proteins quantified by only one unique peptide is not a choice either. Indeed, a large percentage of proteins are accurately quantified by only one unique peptide. Therefore, to improve the calculations of the CV, we leverage a new function in PEAKS called QC-channels which enables technical replicates of each spectrum to be evaluated prior to calculation of the CV. While the QC-channels function in PEAKS significantly reduced the false quantification, random false changes still exist due to known or unknown reasons. To address this challenge, we present the idea of Trend-design to track trend changes rather than changes from two points to remove false quantifications and reveal consequential changes responding to a treatment or condition. The idea was confirmed by molecules with different affinity and dose in the current study. The combination of QC-channels and Trend-design enables a more impactful quantitative proteomics to allow unlocking biological mechanisms using proteomics.