Raw data supplementary to: Differential Metabolic Changes in Zebrafish Embryos AreInduced by Discontinued Citalopram Exposure

LC-MS/MS Metabolite Analysis Samples were subjected to liquid chromatography–mass spectrometry (LC-MS) analysis using an Acquity I-Class UPLC system coupled via an electrospray ionization source to a Waters Synapt XS mass spectrometer (Waters, Milford, MA, USA). Metabolite separation was achieved with a Waters BEH-HILIC column (2.1 × 100 mm) operated at a flow rate of 400 μL/min. The mobile phase consisted of solvent A (95% water, 5% acetonitrile, 0.1% formic acid) and solvent B (95% acetonitrile, 5% water, 0.1% formic acid). The 19 min gradient progressed from 95% to 25% solvent B over 12 min, followed by a 5 min wash at 25% solvent B, with each run beginning with a 2 min equilibration period. Quality control blanks were introduced after every 12 injections to monitor instrument stability and detect potential spectral drift. All samples were analyzed using standard MS1 acquisition, while pooled samples were additionally subjected to LC-MS/MS with a fixed collision energy ramp ranging from 20 to 50 V. Data from each run were manually reviewed to ensure consistent instrument performance and identify any anomalies. Global Metabolomic Profiling LC-MS datasets, including mass-to-charge (m/z) values, ion intensities, and retention times, were processed using Progenesis QI (Nonlinear Dynamics, Newcastle, UK) in conjunction with MetaboAnalyst. To address deviations from normality, the dataset was subjected to quantile normalization, log transformation, and autoscaling. MetaboAnalyst was used for statistical evaluation and visualization of metabolomic data. Volcano plot analysis was applied to evaluate both the magnitude and statistical significance of observed changes (Figure S1). Principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA) were performed to examine similarities and differences between control and citalopram-exposed metabolomic profiles. Predictive performance of the PLS-DA models was assessed using Q2 cross-validation metrics (Figure S2). Hierarchical clustering analysis (HCA) was conducted to group metabolite features with similar regulation patterns. Together, these approaches enabled the identification of metabolite groups exhibiting differential regulation across treatments. Functional pathway enrichment analysis within MetaboAnalyst was used to infer biologically relevant metabolic networks associated with the detected features. Figures and graphical outputs were generated using GraphPad Prism (version 10.6.1), R (version 4.5.1), and Adobe Illustrator (version 30.0). Metabolite identification based on LC-MS/MS data was carried out in Progenesis QI. Both MS1 and MS2 centroid data were imported for alignment and peak detection. Compound annotations were determined by comparing experimental fragmentation patterns with reference spectra from the Human Metabolome Database (HMDB) and an in-house standards library (Mass Spectrometry Library of Standards, IROA Technologies, Ann Arbor, MI, USA). Candidate metabolites were accepted only if they achieved a Progenesis confidence score above 30, taking into account mass accuracy, isotope distribution, and fragmentation agreement. Features with mass errors exceeding 20 parts per million (ppm) were excluded from further consideration.Grouping Key: C = control UL = 0.03 ng/mL citalopram L = 0.9 ng/mL citalopram M = 50 ng/mL citalopram H = 250 ng/mL citalopram