Introduction:
A major impediment in introducing mass spectrometry (MS)-based proteomics into a clinical setting is the lack of validation studies demonstrating reproducibility on a high-throughput scale. Standardisation through defined quality control measures and use of analytical standards are required for MS-based proteomics to be implemented in the clinic. Here we aim to develop a quality control pipeline (QCP) suitable for managing future clinical grade workflows.
Method:
We evaluated the effectiveness of quality control measures used in generating reproducible high quality MS data. Over a 4-year span >85,000 MS files (~43,000 samples, ~31,000 BSA standards and ~7,000 HEK293 MS standards) were acquired in a single laboratory across 6 instruments. The quality control measures encompassed sample preparation standards, both simple and complex MS standards, instrument-specific MS1 and MS2 thresholds, LC stability tolerances and an automated search result pipeline. All complex samples were acquired on six SCIEX 6600 Q-TOFs with 90 min runs in data independent acquisition (DIA) mode with microflow LC gradients.
Results:
The effectiveness of the QCP measures is shown by an average of 5,500 protein IDs per run obtained from ~10,000 cancer patient MS acquisitions over a 6-month period across six instruments. High reproducibility was demonstrated in a series of longitudinal replicates comprised of 46 cancer samples run in technical replicates (n=3 to 6) from 1 week up to 3 years apart. The resulting replicates maintained a per sample correlation of >0.85 and remained clustered with unsupervised hierarchical clustering.
Conclusion:
Our QC standards and QCP enables high-throughput collection of MS data to a standard that will facilitate translation of this data into a clinical setting. The data demonstrates the quality of the results achievable with the real-world implementation of such a QCP over a period of 4-years and across more than 85,000 MS acquisitions.