DCF and CBZ transformation product standards were analyzed using Agilent Profinder B.06.00 (Agilent Technologies). They were detected in the P. australis extracts, with masses estimated at ±10 ppm and RT ±0.3 min of the exact mass and RT of the standards, respectively. The metabolites were identified and suspected (when analytical standards were not available) after RPLC-HILIC-ESI-TOF-MS separation in the suspect analysis. (Suspects screening typically is performed with accurate and high-resolution mass spectrometers to observe the empirical formula of each molecule present and/or with tandem-mass spectrometry to observe specific fragment spectra). A local database was built using MassHunter PCDL Manager B.04.00 (Agilent Technologies, Waldbronn, Germany). Further, the logD (pH7) was the third parameter used to certify the identity of metabolites. The highly polar to polar compounds eluted from the HILIC column at RT < 15 min, with logD values below zero. The nonpolar compounds were eluted from the RP column at RT > 15 min, with logD values above zero. Metabolites within the criteria of mass, RT and logD (pH7) in the suspect analysis were considered.
Agilent profinder b 06
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Agilent Profinder B.06.00 is a software application designed for the processing and analysis of mass spectrometry data. It provides tools for compound identification, quantification, and data exploration.
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6 protocols using agilent profinder b 06
1
Identification of Metabolites in P. australis Extracts
2
Quantification of Plasma CMPF Levels
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CMPF was identified using a synthetically produced CMPF standard (Toronto Research Chemicals, Toronto, Canada). The identity of CMPF was confirmed by matching the plasma measurements of the m/z ratio, retention time, and fragmentation spectra (Figure S1 ) with data acquired from the synthetic standard. A correct match corresponds to the highest level of annotation confidence according to the Metabolite Standards Initiative (Sumner et al., 2007 (link)). CMPF peak areas were integrated using the Agilent Profinder B.06.00 (Agilent Technologies) and were normalized to measurements in the quality control samples (Supporting Material S1 ). CMPF values were ln‐transformed to better represent a normal distribution and were subsequently mean‐centred and univariance‐scaled to get interpretable coefficients from the regression models. The normalization was performed in R software (version 3.6.1; R Foundation for Statistical Computing, Vienna, Austria). The variability of measured CMPF in the quality control samples is presented in Figure S2 .
3
Metabolite Quantification Using Agilent Profinder
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Metabolite peak areas were integrated using Agilent Profinder B.06.00 (Agilent Technologies, Santa Clara, CA, USA). The allowed ion adducts included (M+H)+ and (M+NH4)+. Quality control samples were injected every eight analytical samples, in order to ensure high analytical repeatability. A total of 33 metabolites were normalized to the heavy-isotope labeled internal standards and the remaining metabolites were normalized using measurements in the quality control samples using low-order nonlinear locally estimated smoothing functions (supplementary material 2) [31 (link)]. Information about which normalization method that was used for each measured metabolite is presented in Supplementary Materials Table S1 .
4
Plasma Metabolite Profiling Using LC-MS
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Profiling of plasma metabolites was performed using LC-MS using a UPLC-QTOF-MS System (Agilent Technologies 1290 LC, 6550 MS, Santa Clara, CA, USA) and has been described elsewhere [27 (link)]. Briefly, over-night fasted plasma samples were extracted and subsequently separated on an Acquity UPLC BEH Amide column (1.7 μm, 2.1 × 100 mm; Waters Corporation, Milford, MA, USA).
We identified metabolites by matching the measured mass-over charge ratio (m/z) and chromatographic retention times with an in-house metabolite library consisting of 111 metabolites that were measurable on all three cohorts (Additional file1 : Table S1). Out of 111 metabolites, 25 of them, mostly consisting of acylcarnitines had putative identities based on their fragmentation spectra and the rest had confirmed identities (Additional file 1 : Table S1). Metabolite peak areas were integrated using Agilent Profinder B.06.00 (Agilent Technologies, Santa Clara, CA, USA). The normalisation process of metabolite levels is described in the supplementary method (Additional file 1 : Supplementary method) [28 (link)].
We identified metabolites by matching the measured mass-over charge ratio (m/z) and chromatographic retention times with an in-house metabolite library consisting of 111 metabolites that were measurable on all three cohorts (Additional file
5
Quantitative Bovine Milk Oligosaccharide Profiling
6
Normalization of Metabolite Levels
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ADMA, ADMA‐d6, and DMGV peak areas were integrated using Agilent Profinder B.06.00 (Agilent Technologies). Quality‐control samples were injected every 6 analytical samples, in order to ensure high analytical repeatability. ADMA was normalized to the internal standard, ADMA‐d6, and DMGV was normalized using DMGV measurements in the quality‐control samples. First, a low‐order nonlinear locally estimated smoothing function was fitted to the DMGV signals in the quality‐control samples as a function of the injection order. The α‐parameter, reflecting the proportion of samples to be used when constructing the correction curve, was set to 2/3. Using this function, a correction curve for the analytical samples was interpolated, to which the DMGV measurements in the analytical samples were normalized.17 The normalization was performed in R software (version 3.4.3; R Foundation for Statistical Computing, Vienna, Austria). The variability of measured DMGV in the quality‐control samples is presented in Figure S4 ).
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