Quality Control and Outlier Detection of Targeted Mass Spectrometry Data from Multiplex Protein Panels

Increased throughput as well as increased multiplexing of liquid chromatography coupled to selected reaction monitoring mass spectrometry (LC-SRM-MS) assays for protein quantification challenges routine data analysis. Despite the measurement of multiple transitions from multiple peptides, for clinical applications a single (quantifier) transition from one (quantifier) signature peptide is used to represent the protein quantity with most data used solely to validate the quantifier result. To support the generation of reliable protein results from multiplexed LC-SRM-MS assays with large sample numbers, we developed a data analysis process for quality control and outlier detection using data from an 11-protein multiplex LC-SRM-MS method for dried blood samples (195 492 chromatographic peaks from 1481 samples * 11 proteins * 2 peptides * 3 transitions * 2 isotopologues). The 2-tiered data analysis process detects outliers for ion transition ratio, peptide ratio, and % difference between duplicates, applying less stringent criteria to samples with a small % difference between duplicates (Tier 1) and more stringent criteria to samples with unassessed or a large % difference between duplicates (Tier 2). After manual peak review, 1127 samples (76%) were selected based on the sample quality. The data analysis process thereafter automatically selected quantifier transitions/peptides, removed quality control failures and outliers (8%), averaged duplicates, and generated a comprehensive report listing 6085 quality controlled protein-level results. The proposed data analysis process serves as a starting point toward standardized data analysis of multiplexed LC-SRM-MS protein assays.