The effect of Holder pasteurization on the lipid and metabolite composition of human milk

The data set includes MS and MSMS data collected from the analysis of 39 human milk (HM) samples using targeted dynamic iterated DDA of (pre)annotated metabolic features using a reference database (HMDB) and untargeted iterated DDA split into several m/z intervals. 15 HM samples (labelled as "LM") were raw milk samples from mothers of preterm infants and 12 HM samples (labelled as "LMD") were from the Human Milk Bank and were analyzed before ("Pre") and after ("Post") Holder pasteurization. MS data sets: 2 blanks and a set of QCs (5 and 4 in ESI(+) and ESI(-), respectively) were injected at the beginning of the sequence for system conditioning and MS2 data acquisition. Then, the sample batch including 39 milk samples, 10 QCs (1 QC every 6 samples and 2 at the beginning and at the end of the samples) and 2 blanks (end of sample batch) were analyzed (B_Synergi_pos_and_neg*). In addition, after the sample batch, MS2 spectra were also acquired in consecutive QC replicates using untargeted iterated DDA in the [70-200], [200-300], [300-400], [400-500], [500-6000], [600-700], [700-800], [800-900], [900-1200] and [1200-1500] Da ranges (i-DDA). For the ESI(-), the acquisition in the m/z interval [1200-1500] failed and hence data was not available. Peak table generation was carried out using XCMS software. The centWave method was used for peak detection with the following parameters: mass accuracy, 15 ppm; peak width, (5,20); snthresh, 6; prefilter, (3,100); noise, 0; minimum difference for overlapping peaks: 0.01 Da; intensity weighted m/z values of each feature were calculated using the wMean function; Peak limits used for integration: Mexican hat filtered data. RT correction was carried out using the “obiwarp” method. Peak grouping was carried out using the “density” method using mzwid = 0.015, bw = 5 and minFraction = 0.5. Missing data points were filled by reintegrating the raw data files in the regions of the missing peaks using the fillPeaks method. The CAMERA package was used for the identification of pseudospectra based on peak shape analysis, isotopic information and intensity correlation across samples. Raw MSMS data (.D) was converted into .ms2 and .mgf format using ProteoWizard. ms2 data was directly imported into MATLAB (Synergi_ms2_data_pos_and_neg data structures). *Contains dataset objects and PLS-Toolbox (Eigenvector Research Inc.) for their inspection is required. Funding Sources This work was supported by the Instituto de Salud Carlos III, Spain [grant numbers CD19/00176 and CP16/00034 ]; the Ministry of Science and Innovation, Spain [grant number IJC2018-036209-I ], Generalitat Valenciana [project number GV/2021/186 ], and the European Union's Horizon 2020 Research and Innovation Programme through the Nutrishield project (https://nutrishield-project.eu/) [Grant Agreement No 818110 ].