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0.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS15442/FILES/RAW_FILES/pos-PD/pos-1009.wiffftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS15442/FILES/RAW_FILES/neg-PD/neg-1139.wiffftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS15442/FILES/RAW_FILES/neg-HC/neg-HC0014.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS15442/FILES/RAW_FILES/neg-PD/neg-1156.wiffftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS15442/FILES/RAW_FILES/pos-PD/pos-1026.wiffprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS15442<p>Metabolite identification was performed using a multi-step workflow integrating MS-DIAL (version 4.70) with several curated spectral libraries. Raw LC–MS data were processed in MS-DIAL for peak detection, deconvolution, and retention-time alignment. Putative metabolite annotations were first assigned by matching experimental MS/MS spectra against the Public MS/MS (version 15) spectral library in MS-DIAL. To enhance annotation confidence, the resulting spectral matches were further cross-validated using external reference databases, including the MassBank of North America (MoNA) and HMDB.</p>MetaboLightsPublicLiquid Chromatography MS - positive - hilicLiquid Chromatography MS - negative - hilic<p>Instrument: SHIMADZU CBM30A Lite LC system</p><p>Column: Waters Acquity amide based HILIC column (2.1 × 100 mm, 1.7 μm)</p><p>Mobile Phase:</p><p>Phase A: 5 mM ammonium acetate with 0.1% formic acid (aqueous)</p><p>Phase B: Acetonitrile (organic)</p><p>Flow Rate: 0.3 mL/min</p><p>Gradient Program:</p><p>0–0.5 min: 98% B</p><p>0.5–13 min: linear decrease to 40% B</p><p>13–13.1 min: rapid return to 98% B</p><p>13.1–18 min: hold at 98% B</p><p>Injection Volume: 2 μL</p>Integrative gut microbiome and metabolomic analysis identifies fatty acid dysregulation as a modifiable target in Parkinson’s disease.The First Affiliated Hospital of Wenzhou Medical UniversityHAIJUN Heblood plasmamass spectrometry assay<p>For metabolomic extraction, 150 μL plasma was mixed with 400 μL pre-chilled methanol:water:dichloromethane (1:1:2, v/v/v) via vortexing for 60 seconds. After centrifugation at 15,000 rpm for 15 minutes at 4℃, the solvent-extracted plasma separated into three phases: upper polar compounds, lower lipophilic fractions, and intermediate protein/tissue precipitate. Supernatants from the upper and lower layers were collected into separate Eppendorf tubes, dried under nitrogen flow. The upper polar fraction was reconstituted in 100 μL ACN:water (1:1, v/v) and recentrifuged at 15,000 rpm for 15 minutes at 4℃, with the resulting supernatant subjected to UHPLC-MS/MS analysis. Quality control (QC) samples, prepared by pooling equal volumes of plasma from all participants, were interspersed randomly at 10-sample intervals.</p>Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS15442HAIJUN He. The First Affiliated Hospital of Wenzhou Medical University. hehaijun@wmu.edu.cn.<p>Post-acquisition, raw data were collected using MassHunter workstation software, converted to mzXML format, and preprocessed with XC-MS Plus (California, USA) for non-linear retention time alignment, peak detection, filtering, alignment, matching, and annotation. Key parameters were set as: peak width = 50, Bw = 0.1, Mzwid = 0.1, Mbundiac = 80%. Three-dimensional raw datasets (m/z, retention time [RT], peak intensity) were then exported to Microsoft Excel 2020 (Microsoft, Redmond, WA). Prior to univariate and multivariate analyses, LC-MS raw data were normalized via log10 transformation and Pareto scaling.</p>Grouphehaijun12345@163.com<p>Plasma specimens were collected from enrolled participants via peripheral venous puncture, with blood aliquots dispensed into sodium heparin-containing tubes. To mitigate degradation, samples unprocessed immediately were stored at 4℃ and processed within 2 hours. Plasma was separated by centrifugation at 3000 ×g for 10 minutes, and supernatants were stored at -80℃ until analysis.&nbsp;</p>MetabolomicsMetabolomicsAB SCIEX QTRAP 6500+blood plasmaShimadzu Nexera UHPLC system (CBM-30A controller)untargeted analysisShimadzu LC-30AD pump and CBM-20A controllerParkinson diseaseHomo sapiensShimadzu (China)MS-DIALexperimental sampleMetabolomicsAB SCIEX QTRAP 6500+blood plasmaShimadzu Nexera UHPLC system (CBM-30A controller)untargeted analysisShimadzu LC-30AD pump and CBM-20A controllerParkinson diseaseHomo sapiensShimadzu (China)MS-DIALexperimental sample<p>Mass Spectrometry Protocol</p><p>Instrument: API 6600 Q TRAP mass spectrometer (AB SCIEX, Foster City, CA, USA)</p><p>Ion Source: Electrospray ionization (ESI)</p><p>Polarity: Positive (ESI+) and Negative (ESI−) modes</p><p>Scan Range: 80–1200 m/z</p><p>Collision Energy: 40 V</p>falsePlasma metabolomics data in the Wenzhou Parkinson's disease cohortTo discriminate plasma metabolomic profiles between Parkinson’s disease (PD) and healthy controls (HC) in Wenzhou, China, we performed liquid chromatography–mass spectrometry (LC–MS) analysis in a cohort comprising 140 PD patients and 72 HC participants. Plasma samples were subjected to LC–MS metabolite profiling, and metabolite identification was conducted using a multi-step workflow integrating MS-DIAL (version 4.70) with curated spectral libraries. Raw LC–MS data were processed in MS-DIAL for peak detection, deconvolution, and retention-time alignment. Putative metabolite annotations were initially assigned by matching experimental MS/MS spectra against the Public MS/MS (version 15) spectral library in MS-DIAL. To enhance annotation confidence, spectral matches were further cross-validated using external reference databases, including the MassBank of North America (MoNA) and the Human Metabolome Database (HMDB).2026-08-242026-08-24MTBLS15442