A Framework for Automated Quality Control in Mass Spectrometry-Based Proteomics
Advancements in liquid chromatography mass spectrometry (LC-MS/MS) have enabled the acquisition of high-resolution data in an increasingly high-throughput manner. Robust quality control (QC) at every stage of an LC-MS/MS based proteomics pipeline is critical to ensure accuracy and reproducibility of acquired data. This project provides a framework for automated QC monitoring and documentation of deviations across data acquisition and post-processing. Following the generation of spectral data by the mass spectrometer, CCTMS’s proteomics pipeline stores qualitative and quantitative results and project metadata in a relational database. We developed a software that queries this database using the R language and reports statistical measures of run quality. We used the tool to analyze previously acquired LC-MS/MS data for two sets of internal standards and determined threshold metrics that we then applied to experimental datasets to assess post-hoc quality of analysis. Validated thresholds will be further refined and integrated into CCTMS’ QC pipeline, enabling quasi-real-time evaluation of LC-MS data quality and facilitating swift remediation of discrepancies in the generated data.