MetaboLights

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differential metabolites were annotated for their metabolic pathways using the KEGG database (https://www.kegg.jp/kegg/pathway.html), and the pathways in which the differential metabolites were involved were obtained. The pathway enrichment analysis was performed using the Python software package scipy.stats, and the biological pathways most relevant to the experimental treatment were identified through Fisher's exact test.MetaboLightsPublicLiquid Chromatography MS - alternating - reverse phase Ultra-high performance liquid chromatography coupled with Fourier transform mass spectrometry (UHPLC-QExactiveHF-X), manufactured by Thermo Fisher Scientific, USA; HSS T3 chromatographic column (100mm × 2.1mm i.d., 1.8µm), from Waters Corporation, USA; JXDC-20 nitrogen purge instrument, from Shanghai Jingxin Industrial Development Co., Ltd.; LNG-T88 benchtop rapid centrifugal concentration and drying apparatus, from Huamei Biochemical Instrument Factory in Taicang City; Wonbio-96c high-throughput tissue homogenizer, from Shanghai Wanbo Biotechnology Co., Ltd.; SBL-10DT ultrasonic cleaning machine 300W-10L, from Ningbo Xinzhi Biotechnology Co., Ltd.; Centrifuge 5430R high-speed refrigerated centrifuge, from Germany Eppendorf Company; NewClassic MF MS105DU electronic balance, from Switzerland Mettler Company. The specific chromatographic conditions for untargeted metabolomics analysis were as follows: Mobile phase A consisted of 95% water + 5% acetonitrile (containing 0.1% formic acid), while mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid). The injection volume was 3 μL, and the column temperature was maintained at 40℃. Mass spectrometry conditions: Samples were ionized via electrospray ionization, and mass spectrometry signals were collected using both positive and negative ion scanning modes. The specific parameters were as follows: Scan type (m/z) range: 70-1050 (m/z); Sheath gas flow rate (arb): 50; Aux gas flow rate (arb): 13; Heater temperature (℃): 425; Capillary temperature (℃): 325; Spray voltage (positive mode) (V): 3500; Spray voltage (negative mode) (V): -3500; S-Lens RF Level: 50; Normalized collision energy (%): 20, 40, 60; Resolution (Full MS): 60000; Resolution (MS2): 7500. Quality control (QC) samples were prepared by mixing equal volumes of the extracts from all samples. Each QC sample had the same volume as the test samples, was processed and detected using the same method as the analyzed samples, and was inserted every 5-15 analyzed samples during instrument analysis to assess the stability of the entire detection process.  Multi-omics insights into the formation mechanism of aging quality in white tea (Bai Mudan) during storage.Jiangnan UniversityWei YashuCamellia sinensismass spectrometry Take 100mg of solid sample and add it to a 2 mL centrifuge tube, along with a 6mm diameter grinding bead. 800 μL of the extraction solution (methanol: water = 4:1 (v:v)) containing four internal standards (L-2-chloropropylalanine (0.02mg/mL) etc.) is used for metabolite extraction.The sample solution is ground for 6 minutes in a frozen tissue grinder (-10℃, 50Hz), followed by ultrasonic extraction at a low temperature for 30 minutes (5℃, 40kHz).The sample is left at -20℃ for 30 minutes, then centrifuged for 15 minutes at 4℃, 13000g. The supernatant is transferred to a sample injection vial with an internal insert and analyzed on the instrument. Take 100 μL of the liquid sample and add it to a 1.5 mL centrifuge tube. Then, add 400 μL of the extraction solution (acetonitrile: methanol = 1:1) containing four internal standards (L-2-chloropropylalanine (0.02 mg/mL) etc.). Vortex mix for 30 seconds, then perform low-temperature ultrasonic extraction for 30 minutes (5°C, 40 KHz). Let the sample stand at -20°C for 30 minutes. At 4°C, centrifuge at 13000g for 15 minutes, then transfer the supernatant. Dry it with nitrogen gas, and re-suspend it with 100 μL of the reconstitution solution (acetonitrile: water = 1:1). Perform ultrasonic extraction for 5 minutes at 5°C, 40 KHz. At 4°C, centrifuge again at 13000g for 10 minutes, and transfer the supernatant to a sample vial with an insert for injection into the instrument for analysis. Camellia sinensishttps://www.ebi.ac.uk/metabolights/MTBLS13336long Li. Jiangnan University. No. 1800, Lihu Avenue, Wuxi, 214122, P. R. China. 13133200695@163.com.Shu Wei. Jiangnan University. No. 1800, Lihu Avenue, Wuxi, 214122, P. R. China. 1748320288@qq.com.After the machine operation was completed, the original LC-MS data was imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. Eventually, a data matrix with retention time, mass-to-charge ratio, and peak intensity was obtained. At the same time, the MS and MSMS mass spectrometry information was matched with the public metabolomics databases HMDB (http://www.hmdb.ca/) and Metlin (https://metlin.scripps.edu/), as well as the self-built database of MG, to obtain the metabolite information.Storage years1748320288@qq.com2.5 Untargeted metabolomics analysisTake 50 mg of tea powder were placed into a 2 mL centrifuge tube, followed by the addition of 400 μL extraction solvent (methanol:water = 4:1, v:v). The mixture was subjected to cryogenic grinding for 6 min, then ultrasonic extraction was performed at 5℃ for 30 min under 40 kHz. After standing at -20℃ for 30 min, the sample was centrifuged at 13,000 × g for 15 min at 4℃. The supernatant was collected for instrumental analysis. Additionally, 20 μL of supernatant from each sample was transferred and pooled as a quality control (QC) sample. For untargeted metabolomics analysis, a UHPLC-Q Exactive HF-X system (Thermo Fisher Scientific) equipped with an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters, Milford, USA) was employed. Specific chromatographic, mass spectrometry parameters, and metabolomics data processing can be found in Supplementary Method 1.Supplementary Method 1: Specific parameters of non-targeted metabolomics testingThe specific chromatographic conditions for untargeted metabolomics analysis were as follows: Mobile phase A consisted of 95% water + 5% acetonitrile (containing 0.1% formic acid), while mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid). The injection volume was 3 μL, and the column temperature was maintained at 40℃. Mass spectrometry conditions: Samples were ionized via electrospray ionization, and mass spectrometry signals were collected using both positive and negative ion scanning modes. The specific parameters were as follows: Scan type (m/z) range: 70-1050 (m/z); Sheath gas flow rate (arb): 50; Aux gas flow rate (arb): 13; Heater temperature (℃): 425; Capillary temperature (℃): 325; Spray voltage (positive mode) (V): 3500; Spray voltage (negative mode) (V): -3500; S-Lens RF Level: 50; Normalized collision energy (%): 20, 40, 60; Resolution (Full MS): 60000; Resolution (MS2): 7500. Quality control (QC) samples were prepared by mixing equal volumes of the extracts from all samples. Each QC sample had the same volume as the test samples, was processed and detected using the same method as the analyzed samples, and was inserted every 5-15 analyzed samples during instrument analysis to assess the stability of the entire detection process. The raw data were imported into the metabolomics processing software Progenesis QI v3.0 (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, peak alignment, etc., ultimately yielding a data matrix containing information such as retention time, mass-to-charge ratio, and peak intensity. Subsequently, this software was used for feature peak library search and identification, matching the MS and MS/MS mass spectrometry information with metabolic databases. The MS mass error was set to less than 10 ppm, and metabolites were identified based on the secondary mass spectrometry matching score. The main databases used were the public database at http://www.hmdb.ca/ and a self-built database.MetabolomicsTeaMulti-omics studyuntargeted metabolitesTeaMulti-omics studyuntargeted metabolitesThe sample mass spectrometry signal acquisition adopts the positive and negative ion scanning mode. The mass scanning range is m/z: 70 - 1050. The ion spray voltage is 3500V for positive ions and -3000V for negative ions. The sheath gas is 50 arb, and the auxiliary heating gas is 13 arb. The heating temperature of the ion source is 450℃, and the cyclic collision energy is 20 - 40 - 60V.Unidentified metabolitefalseMulti-omics insights into the formation mechanism of aging quality in white tea (Bai Mudan) during storage: integrating sensory evaluation, volatilomics, metabolomics, and metagenomicsWhite tea (WT) develops unique aged quality during long-term storage, characterized by progressive changes in sensory properties and chemical composition. However, integrated insights into the sensory, metabolites, and microbial contribution underlying this process remain limited. This study systematically investigated the quality evolution of Bai Mudan tea (a typical WT) over 0 to 11 years of storage using an integrated multi-omics approach. Sensory analysis revealed a flavor transition from fresh (grassy, fruity) to aged (woody, stale) characteristics. Volatilomics identified 72 volatile compounds, with cedrol and isophorone as aged characteristic aroma compounds (rOAV ≥ 1, VIP ≥ 1, p ≤ 0.05). Metabolomics identified 1146 non-metabolites, showing accumulation of fatty acyls and flavonoids, and a decrease in amino acids. Metagenomic sequencing analysis revealed that bacterial communities (mainly Sphingomonas, Pedobacter, Methylobacterium, and Chryseobacterium) dominated during the first 7 years of storage, followed by a potential shift toward a bacterial - fungal synergy pattern in later stages as the abundances of Monascus and Aspergillus increased. KEGG annotation highlighted the roles of carbohydrate metabolism and amino acid metabolism, and flavonoid biosynthesis was activated during storage. Multi-omics correlation networks demonstrated that fungi positively regulated volatile flavors, while bacteria may be involved in taste balance. Monascus, Sphingomonas, Sphingobium, and Novosphingobium were identified as core functional taxa that may contribute to WT quality formation during storage through aged flavor synthesis, flavonoid accumulation, and lipid degradation. In conclusion, this study provides a multi-dimensional scientific basis for a comprehensive understanding of the formation mechanism of aged quality in white tea during storage.2025-11-182025-11-18MTBLS13336