Data independent acquisition has become the go to method for deep and quantitative proteomic analysis, given the ability to sample large m/z windows in a reproducible and non-stochastic manner. Using a method termed dia-PASEF on a TIMS enabled Q-TOF lends additional advantages in both duty cycle and selectivity using the ion mobility space. Dia-PASEF allows for deep proteomes in short gradient times (<20 min.) and therefore 100’s of LCMS runs can be generated in short times. Data analysis in a streamlined automated fashion expedites the time from experiments to discovery. DIA-NN is a novel software package that uses neural networks providing best-in-class DIA output. Here we integrate DIA-NN onto the PaSER platform for a streamlined workflow for the analysis of many samples in a short analysis time with no file transfer or data migration.
Methods: A timsTOF Pro using variable CE’s and mobility windows in gradients ranging from 5 min. to 90 min. were used. DIA-NN was modified to become CCS-enabled and process data in the most expedient fashion. The PaSER GUI was designed such that first-pass analysis is predefined automatically triggering quantitative analysis. Retrospectively, match-between-runs (MBR) analysis can be triggered on the whole project or subset of user defined experiments.
Preliminary Data: Human, Yeast and E. coli (HYE) digested mixtures at different but known ratios with injection loads from 50ng to 600ng were run at different gradient lengths in replicate resulting in >2500 proteins at short gradients to >9000 proteins identified and quantified at longer gradients. Quantitative accuracy was shown to be <20% CV. Using the DIA-NN as integrated into PaSER creates a seamless approach to dia-PASEF analysis.
Novel Aspect: Automated workflows for dia-PASEF using DIA-NN on PaSER streamlines experiments to results