Data-independent acquisition strategies for quantitative proteomics

Abstract

In shotgun proteomics, data-dependent precursor acquisition (DDA) is widely used to profile protein components in complex samples. Although very popular, there are some inherent limitations to the DDA approach, such as irreproducible precursor ion selection, under-sampling and long instrument cycle times. Unbiased ‘data-independent acquisition’ (DIA) strategies try to overcome those limitations. In MSE, which is supported by Waters Q-TOF instrument platforms, such as the Synapt G2-S, a wide band pass filter is used for precursor selection. During acquisition, alternating MS scans are collected at low and high collision energy (CE), providing precursor and fragment ion information, respectively. Introduction of ion mobility separation (IMS), which provides an additional dimension of separation, leads to an increase of identified peptides and proteins in MSE workflows. For label-free quantification of ion mobility based MSE data, we developed a bioinformatics pipeline, ISOQuant, allowing retention time alignment, clustering, normalization, isoform/homology filtering, absolute quantification and report generation. Thus, we are able to reproducibly quantify up to 2,500 proteins in a single LC-MS run. The workflow can be adapted to different kinds of proteomic samples providing a robust platform for DIA label-free proteomics.