Proteomic analysis of plant material
Extraction of leaf proteins and analysis of protein content using gel-free LC-MS/MS was carried out as described by Miller et al.(2017). Briefly, frozen ground leaf samples (4 replicates per treatment) were extracted in a buffer containing Rapigest. After reduction, alkylation and trypsin digestion, samples were analysed by LC-MS/MS using an UltiMate® 3000 Rapid Separation LC (RSLC, Dionex Corporation, Sunnyvale, CA) coupled to an Orbitrap Elite mass spectrometer (Thermo Fisher Scientific, MA, USA). Raw data were imported into Progenesis QI (build 2.0.5556.29015; Nonlinear Dynamics, Newcastle, United Kingdom) and runs were aligned according to the default settings. Only ions with a charge state of up to +4 were considered. MS/MS data were searched against the A. thaliana TAIR 10 database and assigned to peptides using Mascot version 2.4.0 (Matrix Science, London, United Kingdom). A maximum of one missed cleavage (Trypsin) was permitted, with a peptide mass tolerance of 10 ppm and an MS/MS tolerance of 0.5 Da. Data were then re-imported into Progenesis to allow for assignment of proteins from peptide data. Raw protein intensities were then exported from Progenesis and normalised to the sample with the median total protein content for that treatment, as described previously (Miller et al. , 2017). Total protein for each sample was calculated by summing the intensities of all the quantified proteins.
A principal component analysis (PCA) was performed in the R software package using log2 scaled protein intensities using the pcaMethods R package. The ”svd” function was used, and 10 principal components were included in the calculation. Proteins were considered to have significantly changed in abundance when a p value of <0.05 was reached, with a fold change of 1.2 or greater. For hierarchical clustering analysis, log2 scaled protein values were used. Hierarchical clustering was performed using Euclidean distance and the complete linkages method. For heatmap/cluster analysis, fold change data were calculated relative to the wild-type Col-0 at LL and log2 scaled. A heatmap was then generated using the heatmap.2 package in R software, using the Euclidean distance algorithm for dendrogram creation. The dendrogram was cut into 6 clusters.