Ezinfo 3

Unprocessed UPLC-MS data were uploaded into Progenesis QI software (version 2.0; Waters) for noise elimination, maximum acquisition, alignment, selection, and standardization to obtain the ion retention time-m/z ratio-peak comparative intensity matrix. Normalized results were then transferred into EZinfo 3.0 (Waters) for the analysis of multiple variables. Principal component analysis (PCA) was used to identify general variations between groups, whereas orthogonal partial least squares discriminant analysis (OPLS-DA) was employed to discern the characteristic compounds that varied within groups and to validate the BDS model. On the basis of fold change (FC) > 1.5, t-tests of intergroup changes (p < 0.05), and variable projection importance (VIP) > 1, potential BDS biomarkers in urine and serum were identified.
To determine the Rt-m/z of potential biomarkers, relative standards and Internet databases such as HMDB (https://hmdb.ca) were employed. The MS/MS data were then entered via the MassLynx-nested MassFragment™ application manager (Waters) for structural verification and to align fragment masses. To further elucidate the metabolic pathways involved, BDS-related differential metabolites were screened using MetaboAnalyst 5.0 (http://www.metaboanalyst.ca/).

Raw data was imported to Progenesis QI for small molecules software (Non-Linear Dynamics, Waters, Milford, MA, USA) for automatic alignment, normalization, deconvolution, and compound pre-identification over all samples separating the aqueous and organic phases. The RT range was limited from 0.5 to 35 min for pre-identification method. Pre-identification was performed using Chemspider Databases (PlantCyc, Plant Metabolic Network, KEGG, HMDB and ChEBI) and with an in-house database with a minimum match of 90% for precursor ions, MS/MS data and isotope distribution was included for increasing match score values. Statistics and graphics were performed using EZinfo 3.0 (Waters, Milford, MA, USA) and R (3.3.3v, Vienna, Austria) [65 ] software. Compounds were grouped according to their compound classes. The resulting data was first mean centered and scaled to Pareto and then submitted to a principal component analysis (PCA) using the first three components. Results were analyzed using one-way ANOVA and q-values were established using the false discovery rate (FDR < 0.01) to correct multiple comparisons by the Benjamini–Hochberg procedure [66 ].

Data of UPLC-MS were processed through Progenesis QI 2.0 software and Ezinfo 3.0 (Waters), which performed automatic baseline correction, alignment, and peak peaking. The peaks of missing values were removed by the 80% rule (25 (link)). The elected peak index with accurate m/z and fragment information were submitted to online library search, including Metlin (http://metlin.scripps.edu), MoNA (https://mona.fiehnlab.ucdavis.edu//), and HMDB. Multivariate analysis, Student’s t-test, and correlation analysis between IVF clinical data and differentially changed metabolites were performed using STATA software (version 15.0, Stata Corporation, College Station, TX, USA). The heat map and ANOVA result were constructed by R (version 3.3.1). The data were presented as mean ± SEM. A significant difference was defined as p < 0.01.

Analysis was undertaken using UPLC/Q-TOF MS (SYNAPT G2-Si, Waters, USA) with an ACQUITY UPLC^® HSS T3 chromatographic column (1.8 μm, 2.1 mm × 100 mm, Waters, USA) kept at 40 °C. The method was based on the methods described previously [40 (link)]. Briefly, samples (3 μL) were separated using 0.1% (v/v) formic acid (mobile phase A) and 100% acetonitrile (mobile phase B): 0 min at 30% B, 6 min at 45% B, 18 min at 60% B, 23 min at 90% B, at 0.5 mL min⁻¹ flow rate. Mass detection was conducted by electrospray ionization (ESI) in the positive ion mode. The QTOF-MS conditions were as follows: sample cone, 40 V; source temperature, 120 °C; desolvation temperature, 450 °C; cone gas flow, 50 L h⁻¹; and desolvation gas flow, 800 L/h; capillary voltage, 1000 V. The ramp collision energy was set as 20–65 eV for the high-energy scans. Data analysis was performed using the MassLynx V4.1, Progenesis QI 3.0.3 and EZinfo 3.0 software (Waters). Online database included KEGG, MassBank, Nature Communications, Nature Chemistry, Nature Chemical Biology, and ChemSpider. Variable importance parameter (VIP) value > 1.

UPLC-MS data were processed by Progenesis QI 2.0 software and Ezinfo 3.0 (Waters), which performed automatic baseline correction, alignment, and peak peaking. The peaks of missing values were removed by the 80% rule [14 (link)]. Selected peak indices with accurate m/z and segmentation information were submitted to online library searches, including Human Metabolome Database (HMDB), Kyoto Encyclopedia of Genes and Genomes (KEGG), ChemSpider, and LipidMAPS. Statistical analysis was performed by SPSS 21 software (SPSS Inc., Chicago, IL), including the Kolmogorov–Smirnov test, Student’s t-test or Mann–Whitney U test, and correlation analysis.

The instrument was controlled by the Masslynx 4.1 software (Waters Corp., Milford, MA, United States). All the MS^E continuum data were processed using the apex peak detection and alignment algorithms in UNIFI 1.8 (Waters Corp., Milford, United States). This processing procedure enables related ions (quasimolecular ion peaks, salt adduct ions, and dehydration fragment ions) to be analyzed as a single entity. Both the TCM library and an inhouse library were employed to characterize the metabolites. All the MS^E centroid data were processed using Progenesis QI V2.0 (Waters Corp., Milford, United States). Multiple adduct ions, including [M−H]⁻, [M+HCOO]⁻, [M+Cl]⁻, [2M−H]⁻, [2M+HCOO]⁻, [M−2H]²⁻, [M−H+HCOO]²⁻, and [M−3H]³⁻, were selected or self-edited to remove redundant adduct ion species. The data of apex peak detection and alignment algorithms were processed in Progenesis QI. The intensity of each ion was normalized by the total ion count to generate a marker consisting of m/z value, normalized peak area, and the retention time. The normalized peak areas and peak ID (RT and m/z pair) were exported to the EZinfo 3.0 software (Waters Corporation, Milford, MA, United States) for multivariate analysis. Analysis methods of PCA and OPLS-DA were used to analyze the main difference components between MOR and PMOR.

Multivariate data analysis was performed by Waters Progenesis QI 3.0.3 (Waters Corporation) and Ezinfo 3.0.3 (Waters Corporation). Firstly, the collected raw data were inputted into the QI software for peak alignment processing. Secondly, the samples were divided into smoking and control groups for peak extraction. The compound information obtained by peak picking was then imported into Ezinfo 3.0.3. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was carried out for the purpose of identifying the factors that were most responsible for discrimination, with the following selection criteria: variable influence on projection (VIP) > 1, p < 0.05, fold change (FC) > 2. Finally, the differentiated lipids between the two groups were imported into MetaboAnalyst 4.0 (https://dev.metaboanalyst.ca/, accessed on 17 January 2023), and the metabolite set enrichment analysis (MSEA) algorithm from Lipid Maps in MetaboAnalyst was used to explore the lipid metabolism pathways enriched by the screened differential lipids.
Statistical significance was calculated with the Student’s t-test using IBM SPSS Statistics 21.0 (IBM, Armonk, NY, USA); a statistical probability of p < 0.05 was considered significant. For all analyses, *** p < 0.001; ** p < 0.01; * p < 0.05.