Protein identification and quantification by data-independent acquisition and multi-parallel collision-induced dissociation mass spectrometry (MSE) in the chloroplast stroma proteome

Abstract

We report here a systematic evaluation of a multiplex mass spectrometry method coupled with ion mobility separation (HD-MS(E)) for the identification and quantification of proteins in the chloroplast stroma. We show that this method allows the robust quantification of reference proteins in mixtures, and it detects concentration differences with high sensitivity when three replicas are performed. Applied to the analysis of the chloroplast stroma proteome, HD-MS(E) identified and quantified many chloroplast proteins that were not previously identified in large-scale proteome analyses, suggesting HD-MS(E) as a suitable complementary tool for discovery proteomics. We find that HD-MS(E) tends to underestimate protein abundances at concentrations above 25fmol, which is likely due to ion transmission loss and detector saturation. This limitation can be circumvented by omitting the ion mobility separation step in the HD-MS(E) workflow. The robustness of protein quantification is influenced by the selection of peptides and their intensity distribution, therefore critical scrutiny of quantification results is required. Based on the HD-MS(E) quantification of chloroplast stroma proteins we performed a meta-analysis and compared published quantitative data with our results, using a parts per million normalization scheme. Important pathways in the chloroplast stroma show quantitative stability against different experimental conditions and quantification strategies. Our analysis establishes MS(E)-based Hi3 quantification as a tool for the absolute quantification of proteins in the chloroplast stroma. The meta-analysis performed with a parts per million normalization scheme shows that quantitative proteomics data acquired in different labs and with different quantification strategies yield comparable results for some metabolic pathways, while others show a higher variability. Our data therefore indicate that such meta-analyses allow distinguishing robust from fine-controlled metabolic pathways.