Introduction
Transitioning putative biomarker candidates from untargeted discovery experiments to targeted verification requires LC-MSn workflow capabilities delivering expand sample coverage, extended quantitative sensitivity and greater sample throughput facilitating increased hypothesis testing. To do this, we have expanded PRM acquisition concepts to perform multiple target monitoring (MTM) which intelligently determines scan events that parallelize precursor sampling. MTM provides two benefits, increasing targeted scale almost 60% or enabling 20-50% greater throughput relative to traditional PRM acquisition. In addition, new software tools have been developed to streamline method creation and data processing to ensure productivity for the entire workflow.
Method
We assessed MTM performance for highly multiplexed peptide quantitation using high throughput UHPLC methods consisting of 5- and 8-minute active gradients (125 and 100 samples per day (SPD), respectively. We utilize a 3-proteome mix consisting of yeast and E coli digests spiked into human HeLa with known spiking levels to evaluate analytical merit for discovery of unknown biomarkers and quantitative performance. We utilized fully-automated applications, PRM Conductor and Expert Review, to generate assay and refine peak picking, respectively.
Results
We developed a PRM assay targeting ~800 unique peptides at 100 SPD. Using MTM, we developed an assay of the same size with similar analytical performance at 125 SPD, representing a 25% increase in throughput. We also demonstrate that for the same throughput (100 SPD), MTM enables a 50% increase in assay size, enabling evaluation of more candidate biomarkers. We compared performance to traditional DIA, and found that despite nominally covering fewer peptides, MTM enables discovery of more low abundance and low fold-change biomarkers. Additionally, we will demonstrate the improvements in post-acquisition peak picking routines to minimize missing data, reducing incorrect automated peak picking from 18% to less than 0.5% for over 10,000 instances.