MetaboLightsapplication/xmlftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440/m_MTBLS14440_LC-MS_negative_reverse-phase_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440/m_MTBLS14440_LC-MS_positive_reverse-phase_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440/a_MTBLS14440_LC-MS_negative_reverse-phase.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440/a_MTBLS14440_LC-MS_positive_reverse-phase.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440/s_MTBLS14440.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14440<p>Metabolite identification was performed by matching accurate mass values within 25 ppm and MS/MS spectra against an in-house database. KEGG annotation was performed by matching metabolite names to KEGG compound numbers. Metabolic pathways involving the identified metabolites were extracted from the KEGG database, and pathway mapping was performed using KEGG Mapper.</p>MetaboLightsPublicLiquid Chromatography MS - negative - reverse-phaseLiquid Chromatography MS - positive - reverse-phase<p>Chromatographic separation was performed using an Agilent 1290 Infinity LC ultra-high-performance liquid chromatography system equipped with a Waters ACQUITY UPLC BEH Amide column (1.7 μm, 2.1 mm × 100 mm). The column temperature was maintained at 25°C. The flow rate was 0.5 mL/min, and the injection volume was 2 μL. Mobile phase A consisted of water containing 25 mM ammonium acetate and 25 mM ammonium hydroxide, and mobile phase B was acetonitrile. The gradient elution program was as follows: 0–0.5 min, 95% B; 0.5–7 min, 95% to 65% B; 7–8 min, 65% to 40% B; 8–9 min, 40% B; 9–9.1 min, 40% to 95% B; and 9.1–12 min, 95% B. Samples were kept at 4°C in the autosampler during analysis. Samples were analyzed in random order, and QC samples were inserted into the sequence to monitor system stability.</p>Untargeted metabolomics dataset of fruits from three core Solanum germplasms.Guizhou universityhh jfruitmass spectrometry assay<p>Samples were slowly thawed at 4°C. For each sample, 100 μL was mixed with 400 μL of precooled methanol/acetonitrile solution (1:1, v/v), vortexed, incubated at -20°C for 30 min, and centrifuged at 14,000 g at 4°C for 20 min. The supernatant was collected and vacuum-dried. Before LC-MS/MS analysis, the dried extracts were reconstituted in 100 μL acetonitrile/water solution (1:1, v/v), vortexed, and centrifuged at 14,000 g at 4°C for 15 min. The supernatant was used for injection.</p>Solanum melongenaSolanum undatumSolanum aethiopicumhttps://www.ebi.ac.uk/metabolights/MTBLS14440HAOHAO jiang. Guizhou University. 2650953692@qq.com.<p>Raw LC-MS/MS data were converted to mzML format using ProteoWizard. Peak alignment, retention time correction, and peak area extraction were performed using XCMS. The extracted data were checked for completeness, and metabolites with more than 50% missing values within a group were removed. Outliers were deleted, and total peak area normalization was performed to improve comparability among samples and metabolites. The processed data were imported into SIMCA-P 16.1 for pattern recognition analysis. After Pareto-scaling preprocessing, multivariate statistical analyses, including PCA, PLS-DA, and OPLS-DA, were performed. Univariate statistical analyses included Student’s t-test and fold-change analysis, and volcano plots were generated using R software.</p>Genotype2650953692@qq.com<p>Samples were collected according to the study design and immediately stored at low temperature after collection. All samples were transported to the laboratory on dry ice or under frozen conditions and stored at -80°C until metabolomic analysis. Before extraction, samples were slowly thawed at 4°C.</p>MetabolomicsMetabolomicsSolanum melongenaAB SCIEX TripleTOF 6600+untargeted analysisfruitKEGG pathway enrichmentdata-dependent acquisitionAgilent 1290 Infinity LCMetabolic diversityEggplantSolanum undatumDifferential metabolitesexperimental blankSolanumSolanum aethiopicumUntargeted metabolomicsMetabolomicsSolanum melongenaAB SCIEX TripleTOF 6600+untargeted analysisfruitKEGG pathway enrichmentdata-dependent acquisitionAgilent 1290 Infinity LCMetabolic diversityEggplantSolanum undatumDifferential metabolitesexperimental blankSolanumSolanum aethiopicumUntargeted metabolomics<p>Mass spectrometric analysis was performed using a Triple TOF 6600+ mass spectrometer (AB SCIEX) equipped with an electrospray ionization (ESI) source. Data were acquired in both positive and negative ionization modes. The ESI source parameters were as follows: Gas1, 60; Gas2, 60; curtain gas, 30 psi; ion source temperature, 600°C; ion spray voltage floating, ±5500 V. The mass range was 60–1000 Da for MS1 and 25–1000 Da for MS/MS. The accumulation time was 0.20 s/spectrum for MS1 and 0.05 s/spectrum for MS/MS. MS/MS data were acquired using information-dependent acquisition (IDA) with high-intensity ion selection. The declustering potential was ±60 V, and the collision energy was 35 ± 15 eV. Dynamic exclusion of isotopic ions was set to 4 Da, and 10 fragment spectra were collected per 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metabolomics dataset of fruits from three core Solanum germplasmsSolanum is one of the most economically valuable genera in the Solanaceae family, covering cultivated vegetable crops, medicinal plants and wild germplasms, while systematic metabolomics reference datasets for diverse Solanum germplasms remain scarce. Here, we performed untargeted metabolomics profiling on fruit samples of three core Solanum germplasms: common cultivated eggplant S. melongena, S. undatum, S. aethiopicum, with 6 biological replicates for each germplasm grown under uniform field conditions. Standard sample pretreatment and downstream analyses (repeatability test, PCA, differential metabolite screening and KEGG pathway enrichment analysis) were conducted. KEGG enrichment analysis (VIP > 1, P < 0.05) revealed that biosynthesis of amino acids and cofactors was the core pathway driving metabolic differentiation under both ionization modes. Stress-related secondary metabolic pathways were exclusively enriched in positive ionization mode, while galactose and carbon metabolism were significantly enriched in negative ionization mode. Collectively, metabolic variations among the three germplasms mainly concentrated in amino acid and cofactor pathways, coupled with stress-related secondary metabolism in positive mode and sugar/carbon metabolism in negative mode.2026-05-062026-05-06MTBLS14440