Abstract
Multiple Reaction Monitoring (MRM) conducted on a triple quadrupole mass spectrometer allows researchers to quantify the expression levels of a set of target proteins. Each protein is often characterized by several unique peptides that can be detected by monitoring predetermined fragment ions, called transitions, for each peptide. Concatenating large numbers of MRM transitions into a single assay enables simultaneous quantification of hundreds of peptides and proteins. In recognition of the important role that MRM can play in hypothesis-driven research and its increasing impact on clinical proteomics, targeted proteomics such as MRM was recently selected as the Nature Method of the Year. However, there are many challenges in MRM applications, especially data pre‑processing where many steps still rely on manual inspection of each observation in practice. In this paper, we discuss an analysis pipeline to automate MRM data pre‑processing. This pipeline includes data quality assessment across replicated samples, outlier detection, identification of inaccurate transitions, and data normalization. We demonstrate the utility of our pipeline through its applications to several real MRM data sets.
Highlights
Quantitative proteomics technologies estimate protein expression levels in a biological sample by measuring peptide abundance produced from each protein after trypsin digestion
We considered the assessments of transition and sample quality and data normalization
We suggest to examine the consistency of retention time and peak area across all the samples in the experiment
Summary
Quantitative proteomics technologies estimate protein expression levels in a biological sample by measuring peptide abundance produced from each protein after trypsin digestion. Multiple reaction monitoring (MRM) is a targeted proteomics approach that quantifies protein abundance through a pre-defined set of precursor/fragment ion pairs from proteins of interest. Skyline performs peak detection and quantification and provides quality measure [12] It evaluates background noise level for each quantified MS/MS peak and calculates a dot-product between quantified peak areas of the same precursor and their spectral library intensities. AuDIT is an automated quality assessment tool for isotope labeled MRM experiments aiming to detect any inconsistent transitions within each peptide [13]. It assumes that the area ratio between a pair of endogenous transitions should be consistent with the ratio between a pair of corresponding SIS peptides.
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