After LC-MS detection, the raw data files were imported into the Progenesis QI analysis system. Preprocessing steps including filtering, peak identification, integration, retention time correction, and peak alignment were performed to generate a data matrix containing retention time, mass-to-charge ratio (m/z), and peak intensity. Metabolites were identified by matching MS/MS spectra against the plant metabolite database MJDBPM. After merging and deduplicating the chromatographic datasets, they were uploaded to the Majorbio cloud platform for subsequent analysis [5]. Preprocessing workflow: First, exclude variables with >20% missing values across samples. Next, normalize peak intensities. Then, remove data points with RSD >30% from QC samples. Finally, apply log10 transformation to obtain the data matrix.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - alternating"],"chromatography_protocol":["LC-MS analysis was performed using Thermo Fisher Scientific's UHPLC-Q Exactive system. Chromatographic separation employed a BEH C18 column, with a sample injection volume set at 3 μL. The mobile phase system comprised mobile phase A and mobile phase B: Mobile phase A was an aqueous solution containing 2% acetonitrile (ACN) with 0.1% formic acid (FA) added to optimize separation efficiency; mobile phase B was an acetonitrile solution containing 0.1% formic acid. Throughout the chromatographic analysis, the flow rate was maintained at 0.40 mL/min. Column temperature was precisely controlled at 40°C via the column oven to ensure separation efficiency and result stability.
"],"publication":["Metabolomics Data of Root and Stem Tissues in Five-Year-Old Sophora flavescens."],"submitter_affiliation":["Xi'an Medical University"],"submitter_name":["ning zhang"],"organism_part":["mixture","root","stem"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["Take 100 mg of tissue sample and place it into a 2 mL centrifuge tube containing grinding beads. Eight hundred μL of extraction reagent (methanol:water = 4:1, volume ratio) was added to the tube. The solution contains four internal standards (L-2-chlorophenylalanine at a concentration of 0.02 mg/mL). Homogenize the sample solution in a cryogenic tissue grinder for 6 minutes (parameters: -10°C, 50 Hz), then sonicate in a cryogenic environment for 30 minutes (parameters: 5°C, 40 kHz). The sample was then allowed to stand at -20°C for 30 min and centrifuged for 15 min (4°C, 13000 g). The supernatant was transferred to an injection vial with an insert for further analysis.
"],"organism":["Sophora flavescens"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS13994"],"author":["Zhang Ning. Xi’an Medical University. No. 1, Xinwang Road, Weiyang District, Xi 'an, 710021,China. zhangning@xiyi.edu.cn."],"data_transformation_protocol":["The raw data files were imported into the Progenesis QI analysis system. Preprocessing steps including filtering, peak identification, integration, retention time correction, and peak alignment were performed to generate a data matrix containing retention time, mass-to-charge ratio (m/z), and peak intensity. Metabolites were identified by matching MS/MS spectra against the plant metabolite database MJDBPM. After merging and deduplicating the chromatographic datasets, they were uploaded to the Majorbio cloud platform for subsequent analysis [5]. Preprocessing workflow: First, exclude variables with >20% missing values across samples. Next, normalize peak intensities. Then, remove data points with RSD >30% from QC samples. Finally, apply log10 transformation to obtain the data matrix.
"],"study_factor":["TissueType"],"submitter_email":["zhangytyning@nwafu.edu.cn"],"sample_collection_protocol":["To study the differences in flavonoids and alkaloids in the roots and stems of the plants, the root and stem parts of S. flavescens grown under uniform growth conditions were sampled in July 2025. Three biological replicates were collected for each tissue. In Qin Chakou Village, Luonan County, Shaanxi Province, China, S. flavescens Plants Grow immediately after planting. The cells were rinsed with PBS, quickly immersed in liquefied nitrogen for fixation, and stored at -80 degree centigrade for metabolome detection.
"],"omics_type":["Metabolomics"],"study_design":["Metabolomics","LC-MS","Plant metabolomics","Natural products","Sophora flavescens"],"curator_keywords":["Metabolomics","LC-MS","Plant metabolomics","Natural products","Sophora flavescens"],"mass_spectrometry_protocol":["LC-MS analysis was performed using Thermo Fisher Scientific's UHPLC-Q Exactive system. Chromatographic separation employed a BEH C18 column, with a sample injection volume set at 3 μL . The mobile phase system comprised mobile phase A and mobile phase B: Mobile phase A was an aqueous solution containing 2% acetonitrile (ACN) with 0.1% formic acid (FA) added to optimize separation efficiency; mobile phase B was an acetonitrile solution containing 0.1% formic acid. Throughout the chromatographic analysis, the flow rate was maintained at 0.40 mL/min. Column temperature was precisely controlled at 40°C via the column oven to ensure separation efficiency and result stability.
"],"metabolite_name":["Matrine","Oxymatrine"],"additional_accession":[]},"is_claimable":false,"name":"Metabolomic profiling of Sophora flavescens under different tissue types using LC–MS-based untargeted metabolomics","description":"Sophora flavescens (Kushen) is a traditional medicinal plant widely used in East Asia for its anti-inflammatory, antimicrobial, and antitumor properties. Its pharmacological activities are mainly attributed to diverse secondary metabolites, including alkaloids, flavonoids, and phenolic compounds. However, the metabolic differences between distinct tissues remain incompletely characterized.
In this study, an untargeted metabolomics approach based on liquid chromatography–mass spectrometry (LC–MS) was employed to comprehensively profile the metabolic composition of Sophora flavescens. Root and stem tissues were collected and analyzed to investigate tissue-specific metabolic variations. Metabolite extraction was performed using organic solvent-based protocols, followed by high-resolution LC–MS analysis in both positive and negative ionization modes.
Multivariate statistical analyses, including principal component analysis (PCA) and differential abundance analysis, were applied to evaluate metabolic differences between tissues. Differentially accumulated metabolites were further annotated and mapped to KEGG pathways to elucidate the biological processes and metabolic pathways underlying tissue-specific metabolite distribution.
This dataset provides a valuable resource for understanding the metabolic basis of bioactive compound accumulation in Sophora flavescens and supports further studies on medicinal plant metabolomics, functional metabolism, and natural product discovery.
","dates":{"publication":"2026-03-24","submission":"2026-03-06"},"accession":"MTBLS13994","cross_references":{"HMDB":["HMDB0000001","HMDB0000002"]}}