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.