Evaluating the Performance Factors of a Targeted Label-Free Protein Quantitation Approach on an Ultra-High Resolution API-Qq-TOF.

RP-121 Targeted label-free protein quantitation approaches became a significant complement to established quantitation methods like 2D-GEL, label or MRM based methods for biomarker studies. However, identifying and quantifying trace compounds based on accurate mass LC/MS in complex matrices such as serum, plasma or complete cell lysates requires very high resolution. The scope of this work was to evaluate the performance factors of a targeted label-free quantitation approach on a further improved ultra-high resolution API-Qq-TOF. The experiments have been performed with a nano-HPLC connected to an ultra-high resolution API-Qq-TOF that has been further improved regarding mass resolution, mass accuracy and dynamic range. While quantification is purely obtained from MS data, identification is done as subsequent step involving a targeted approach to obtain sequence information of relevant peptides and proteins from MS/MS data. Using a newly developed bioinformatics platform we analyzed the performance factors of our label-free quantitation approach. As suitable sample we have chosen digested E. Coli cell lysate spiked with protein standards in a dilution series. For every concentration level we have performed 6 technical replicates. We have evaluated the following performance factors: precision of quantitation, limit of quantitation, limit of identification, linearity, and dynamic range as well as the mass accuracy in MS and MS/MS. As expected, most performance factors could be improved significantly compared to a previous API-Qq-TOF platform with lower resolution. The study covers the current status, the limitations and chances of MS based label-free quantification and show that the used instrument and bioinformatics platform will become an excellent tool for this application. The goals of the ongoing work are identification and validation of potential biomarkers from clinical studies.