Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards.
Bryson C GibbonsThomas L FillmoreYuqian GaoRonald J MooreTao LiuErnesto S NakayasuThomas O MetzSamuel H PaynePublished in: Journal of proteome research (2018)
Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.
Keyphrases
- mass spectrometry
- liquid chromatography
- high resolution
- quality control
- electronic health record
- high resolution mass spectrometry
- gas chromatography
- pet imaging
- cancer therapy
- high performance liquid chromatography
- capillary electrophoresis
- tandem mass spectrometry
- quality improvement
- amino acid
- mental health
- big data
- data analysis
- simultaneous determination
- ms ms
- computed tomography
- machine learning
- solid phase extraction
- deep learning
- positron emission tomography
- label free