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 ].本数据集包含针对39份人乳（Human Milk, HM）样本的分析所得的质谱（Mass Spectrometry, MS）与串联质谱（MS/MS）数据，分析过程采用基于参考数据库（人类代谢组数据库Human Metabolome Database, HMDB）的预注释代谢特征的靶向动态迭代式数据依赖性采集（Data-Dependent Acquisition, DDA），以及划分为多个质荷比区间的非靶向迭代式DDA。 15份HM样本标记为"LM"，为早产产妇的原乳样本；另有12份HM样本标记为"LMD"，取自人乳库，分别在Holder巴氏灭菌前（"Pre"）与灭菌后（"Post"）完成分析。 质谱数据集部分：在序列起始阶段注入2份空白样本与1组质控样本（Quality Controls, QCs；正离子模式电喷雾电离ESI(+)下为5份，负离子模式电喷雾电离ESI(-)下为4份），用于系统适配与二级质谱（MS2）数据采集。随后对包含39份乳样本、10份质控样本（每6份样本设置1份质控，样本序列首尾各设置2份质控）以及2份空白样本（置于样本批次末尾）的样本批次完成分析（B_Synergi_pos_and_neg*）。 此外，在样本批次分析完成后，采用非靶向迭代式DDA在[70-200]、[200-300]、[300-400]、[400-500]、[500-6000]、[600-700]、[700-800]、[800-900]、[900-1200]及[1200-1500]道尔顿（Dalton, Da）区间内对连续的质控重复样本采集MS2谱图（i-DDA）。其中，ESI(-)模式下[1200-1500]质荷比区间的采集失败，无有效数据。 峰表生成采用XCMS软件完成，峰检测使用centWave算法，参数设置如下：质量精度15 ppm；峰宽范围(5,20)；信噪比阈值snthresh=6；预过滤参数prefilter=(3,100)；噪声阈值0；重叠峰最小差值0.01 Da；各特征的强度加权质荷比通过wMean函数计算。积分所用峰限为经墨西哥帽滤波处理的数据。 保留时间（Retention Time, RT）校正采用"obiwarp"算法。峰分组采用"density"密度算法，参数设置为mzwid=0.015、bw=5、minFraction=0.5。缺失数据点通过fillPeaks算法对缺失峰区域的原始数据文件重新积分进行补全。 采用CAMERA软件包基于峰形分析、同位素信息及样本间强度相关性识别伪谱峰簇。原始MS/MS数据（.D格式）通过ProteoWizard转换为.ms2与.mgf格式。.ms2数据直接导入MATLAB（采用Synergi_ms2_data_pos_and_neg数据结构）。 *注：本数据集包含数据集对象，需使用PLS-Toolbox（Eigenvector Research Inc.公司开发）进行检视。 资助说明：本研究得到西班牙卡洛斯三世健康研究所（资助编号CD19/00176与CP16/00034）、西班牙科学与创新部（资助编号IJC2018-036209-I）、巴伦西亚自治区（项目编号GV/2021/186）以及欧盟"地平线2020"研究与创新框架计划下的Nutrishield项目（https://nutrishield-project.eu/，资助协议编号818110）资助。