Evaluating cPILOT Data toward Quality Control Implementation

Multiplexing enablesthe monitoring of hundreds to thousands ofproteins in quantitative proteomics analyses and increases samplethroughput. In most mass-spectrometry-based proteomics workflows,multiplexing is achieved by labeling biological samples with heavyisotopes via precursor isotopic labeling or isobaric tagging. Enhancedmultiplexing strategies, such as combined precursor isotopic labelingand isobaric tagging (cPILOT), combine multiple technologies to affordan even higher sample throughput. Critical to enhanced multiplexinganalyses is ensuring that analytical performance is optimal and thatmissingness of sample channels is minimized. Automation of samplepreparation steps and use of quality control (QC) metrics can be incorporatedinto multiplexing analyses and reduce the likelihood of missing information,thus maximizing the amount of usable quantitative data. Here, we implementedQC metrics previously developed in our laboratory to evaluate a 36-plexcPILOT experiment that encompassed 144 mouse samples of various tissuetypes, time points, genotypes, and biological replicates. The evaluationfocuses on the use of a sample pool generated from all samples inthe experiment to monitor the daily instrument performance and toprovide a means for data normalization across sample batches. Ourresults show that tracking QC metrics enabled the quantification of & SIM;7000 proteins in each sample batch, of which & SIM;70% hadminimal missing values across up to 36 sample channels. Implementationof QC metrics for future cPILOT studies as well as other enhancedmultiplexing strategies will help yield high-quality data sets.