2.4 LC-MS data processing
We followed the LC-MS data processing protocol described in Ristoket al. (2019) with minor changes. We converted the LC-qToF-MS raw data to the mzXML format by using the CompassXport utility of the DataAnalysis vendor software. We then trimmed each data file by excluding the same non-informative regions at the beginning and end of each run using the msconvert function of ProteoWizard v3.0.10095 (Chambers et al., 2012). We performed peak picking, feature alignment, and feature group collapse in R v3.3.3 (R Core Team, 2020) using the Bioconductor (Huber et al., 2015) packages ‘xcms’ (Benton et al., 2010; Smith et al., 2006; Tautenhahn et al., 2008) and ‘CAMERA’ (Kuhl et al., 2012). We used the following ‘xcms’ parameters: peak picking method “centWave” (snthr = 10; ppm = 5; peakwidth = 4, 10); peak grouping method “density” (minfrac = 0.75; bw = 6, 3; mzwid = 0.01); retention time correction method “symmetric”. We used ‘CAMERA’ to annotate adducts, fragments, and isotope peaks with the following parameters: extended rule set (https://gitlab.com/R_packages/chemhelper/-/tree/master/inst/extdata); perfwhm = 0.6; calcIso = TRUE; calcCaS = TRUE, graphMethod = lpc. Finally, we collapsed each annotated feature group, hereafter referred to as ‘metabolite’ which is described by mass-to-charge ratio (m/z) and retention time (rt), using a maximum heuristic approach (Ristok et al., 2019). The intensity of each metabolite was subsequently normalized to the amount of dried ground plant tissue extracted. We processed all data separately for each experiment, species, and tissue.