In this work, a method for the separation of triacylglycerols (TAGs) present in human milk and other mammalian species by reversed-phase high performance liquid chromatography using a core-shell particle packed column with UV and evaporative lightscattering detectors is described. Under optimal conditions, a mobile phase containing acetonitrile/n-pentanol at 10 ºC gave an excellent resolution between more than 50 TAG peaks. A small-scale method for fat extraction in these milks (particularly of interest for human milk samples) using minimal amounts of sample and reagents was also developed.The proposed extraction protocol and the traditional method were compared, giving similar results, with respect to the total fat and relative TAG contents. Finally, a statistical study based on linear discriminant analysis on the TAG composition of different types of milks (human, cow, sheep and goat) was carried out to differentiate the samples according to its mammalian origin.

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC–MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC–MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.

Background & aims: Human milk is the gold standard for infant nutrition. Preterm infants whose mothers are unable to provide sufficient own mother's milk (OMM), receive pasteurized donor human milk (DHM). We studied metabolic signatures of OMM and DHM and their effect on the interplay of the developing microbiota and infant's metabolism. Methods: Metabolic fingerprinting of OMM and DHM as well as infant's urine was performed using liquid chromatographyemass spectrometry and the infant's stool microbiota was analyzed by 16S rRNA sequencing. Results: Significant differences in the galactose and starch and sucrose metabolism pathways when comparing OMM and DHM, and alterations of the steroid hormone synthesis and pyrimidine metabolism pathways in urine were observed depending on the type of feeding. Differences in the gut-microbiota composition were also identified. Conclusion:The composition of DHM differs from OMM and feeding of DHM has a significant impact on the metabolic phenotype and microbiota of preterm infants. Our data help to understand the origin of the observed changes generating new hypothesis: i) steroid hormones present in HM have a significant influence in the activity of the steroid hormone biosynthesis pathway in preterm infants; ii) the pyrimidine metabolism is modulated in preterm infants by the activity of gut-microbiota. Short-and longterm implications of the observed changes for preterm infants need to be assessed in further studies.

Human milk (HM) is considered the gold standard for infant nutrition. HM contains macro- and micronutrients, as well as a range of bioactive compounds (hormones, growth factors, cell debris, etc.). The analysis of the complex and dynamic composition of HM has been a permanent challenge for researchers. The use of novel, cutting-edge techniques involving different metabolomics platforms has permitted to expand knowledge on the variable composition of HM. This review aims to present the state-of-the-art in untargeted metabolomic studies of HM, with emphasis on sampling, extraction and analysis steps. Workflows available from the literature have been critically revised and compared, including a comprehensive assessment of the achievable metabolome coverage. Based on the scientific evidence available, recommendations for future untargeted HM metabolomics studies are included.

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