Making Protein Quantitation-based LC-MS/MS Assays more Fluent, Automating High-throughput Sample Prep

TheHPVGardasilvaccineiscomprisedofL1 virus-like particles (VLPs) produced during fermentation in recombinant yeast cells. High-throughput and accurate quantitation of L1 protein during fermentation and downstream purification steps provides mechanistic insights for enhanced L1 productivity and product quality. To overcome throughput challenges with existing antibody-based L1 quantification methods, a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay with multiple reaction monitoring (MRM) for absolute quantification of L1 after protein digestion was developed. However, the sample preparation for this MRM assay is relatively time- and labor-intensive, requiring several simple yet time-consuming sample preparation steps totaling approximately 1.5 days. To alleviate this bottleneck and further improve throughput, the MRM sample preparation procedure was automated on a Tecan Fluent workstation equipped with a LiHa, RGA, and MCA. The sample preparation begins with dilution of samples, followed by reduction, alkylation, and enzymatic digestion of protein, and ends with a formic acid quench. The samples are prepared in 96-well plates for MRM analysis on a Sciex Triple Quad 6500 LC-MS/MS platform. An Excel workbook with VBA programming allows for flexibility in the number of samples as well as dilution levels for each sample via Fluent worklisting. The procedure utilizes on-deck incubation at 37°C on integrated Inheco CPAC heaters. We have demonstrated equivalent performance of manual vs. automated preparation by comparing linearity and accuracy of calibration curves using commercially available bacterially-derived VLP material and quantitation precision using Merck HPV L1 final aqueous product from multiple types. Over 400 samples can be tested per week with this optimized high-throughput workflow. The fully automated sample preparation can also be applied for other high-throughput LC-MS based assays in need of high throughput analysis in the future.