MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508/m_MTBLS14508_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf-truncated.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508/m_MTBLS14508_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf-truncated.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508/s_MTBLS14508.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508/a_MTBLS14508_LC-MS_alternating_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508/a_MTBLS14508_LC-MS_positive_reverse-phase_metabolite_profiling.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14508Note: The Metabolite Annotation Files (MAF files) in this study contain a subset of the Features analyzed. For the complete lists of analyzed features, please refer to the DERIVED_FILES folder. Corresponding annotation files are provided in the SUPPLEMENTARY_FILES/4_Annotation_files directory on MetaboLights. The adducts and isotopes of the dataset were annotated using the CAMERA package 1 (version 1.66.0) on the pre-processed MS1 dataset.  A first annotation of the features was performed using the feature-based molecular networking 2 (FBMN) workflow on the web-based platform GNPS 3 (workflow version 2025.08.18; server version 2025.12.10).The precursor ion tolerance was set at 0.02 Da, the fragment tolerance at 0.02 Da, the cosine score at ≥ 0.7, the topK at 10, and the max component size at 100. Window filtering was applied, and the minimum number of matching peaks was set to 3 for both networks and the library search. Finally, the analog search was deactivated. For a more detailed description of the annotation workflow, please refer to the article A metabolomics dataset of plankton exometabolomes and dissolved organic compounds in the South Atlantic Ocean. <ol><li>Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets. Anal. Chem. 84, 283–289 (2012).</li><li>Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).</li><li>Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).</li></ol>MetaboLightsPublicLiquid Chromatography MS - alternating - reverse phaseLiquid Chromatography MS - positive - reverse phaseAnalyses were performed on a Vanquish UHPLC system (Thermo Scientific, Germany) coupled to an Exploris 480 mass spectrometer (Thermo Scientific, Germany). The analyses were conducted in 15 analytical batches, each lasting less than 48 hours, from December 14, 2024, to September 3, 2025, with solvent replenishment and instrument maintenance scheduled between batches. The analytical platform was conditioned before each batch by injecting eight pooled samples to coat the chromatographic column's active sites and stabilize the detector response, as prescribed by Broadhurst et al.1. The injection volume was set to 1 µL for all samples, blanks, and controls. Samples were randomized and analyzed in triplicate across multiple batches, with a pooled QC sample injected every 10 samples, followed by pure methanol to monitor potential carryover. A system suitability sample, consisting of a mix of all samples, was injected at the beginning and end of each analytical sequence to assess system performance, while blanks were analyzed at the start of each batch, before the conditioning bloc. Chromatographic separation was performed on an Accucore C18 column (2.6 µm, 80 Å, 100 × 2.1 mm, Thermo Scientific), coupled to an Accucore C18 defender guard column (2.6 µm, 10 × 2.1 mm, Thermo Scientific) with a flow rate of 0.4 mL/min and following the method described by Zerfass et al. 2 with minor modifications: 0–0.3 min, 100% A; 0.3–7.7 min, linear gradient to 100% B; 7.7–11 min, 100% B; 11–13 min, 100% A. Eluent A consisted of UHPLC-grade water containing 2% acetonitrile and 0.1% formic acid, and Eluent B of 100% acetonitrile (all solvents UHPLC-grade). Analytical sequences are available in SUPPLEMENTARY_FILES/6_Supplementary_files/2_Sequences on MetaboLights. Acquisition methods can be found in SUPPLEMENTARY_FILES/6_Supplementary_files/1_Acquisition_methods. <ol><li>Broadhurst, D. et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14, 72 (2018).</li><li>Zerfaß, C. et al. Groundwater metabolome responds to recharge in fractured sedimentary strata. Water Res. 223, 118998 (2022).</li></ol>A metabolomics dataset of plankton exometabolomes and dissolved organic compounds in the South Atlantic Ocean.Friedrich Schiller University JenaFriedrich Schiller UniversitÃÂ¤t JenaMaia Henrysolventexometabolomemass spectrometry assayThe samples were processed between September 2022 and September 2025 by three operators: one in charge of the elution and two responsible for the downstream processing steps. These latter two operators were assigned the codes A or B, which are reported in the metadata (column Code_inlab_operator). HLB cartridges were eluted with 4 mL of UHPLC-grade methanol. The eluate was then split into 2 glass vials and evaporated to dryness under vacuum for roughly 12 h. One aliquot was stored at -70 °C for long-term archiving, while the second was reconstituted in 70 µL of UHPLC-grade methanol for subsequent analyses. Following reconstitution, samples were transferred to 1.5 mL microcentrifuge tubes and centrifuged at 11,000 rcf for 20 min at 4 °C. The clarified supernatant was then transferred into glass vials equipped with inserts for LC-MS acquisition. “In-lab” technical blanks were generated by applying the same extraction procedure to unused 500 mg HLB cartridges. Solvent blanks consisted of direct injections of UHPLC-grade methanol. A global pooled quality-control sample was prepared by combining 5 µL of each individual sample (excluding blanks). Additional pooled samples, comprising fewer samples, were created to monitor analytical drift; their exact composition is provided in Factor Value[Pool composition] column of the Samples Metadata on MetaboLights. A small volume of methanol (10–40 µL) was added to a subset of samples prior to the first MS1 acquisition, as some of them had been previously analyzed for another project and had partially evaporated. Similarly, some methanol was added to several samples before DDA acquisition batches to ensure sufficient volume for measurement. Information on the methanol additions at both steps is provided in the metadata, in the columns MeOH_added_µL (first addition before the full MS acquisition) and MeOH_added_µL_DDA (second addition before DDA acquisition). Corresponding quality-flag information is also provided in the columns Evaporation_status (describing the degree of evaporation prior to MS acquisition, from minimal to high) and Modified_DDA_sample (indicating TRUE or FALSE depending on whether methanol was added prior to DDA analysis). Complete Metadata are available in SUPPLEMENTARY_FILES/1_Metadata on MetaboLights.sea waterblank samplehttps://www.ebi.ac.uk/metabolights/MTBLS14508Emilie Larsen. emgahola@gmail.com.Stéphane Pesant. European Bioinformatics Institute. EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.. pesant@ebi.ac.uk.Christian Zerfaß. University of Stuttgart. Bandtäle 2, 70569 Stuttgart, Germany.. christian.zerfass@iswa.uni-stuttgart.de.Georg Pohnert. Friedrich Schiller University Jena. Lessingstraße 8, 07743 Jena, Germany.. georg.pohnert@uni-jena.de.Mission Microbiomes AtlantECO. atlanteco-coordteam@googlegroups.com.Udita Chandola. Friedrich Schiller University Jena. udita.chandola@uni-jena.de.Marine Vallet. Friedrich Schiller University Jena. Lessingstraße 8, 07743 Jena, Germany.. mvallet@ice.mpg.de.Hannes Richter. Friedrich Schiller University Jena. Lessingstraße 8, 07743 Jena, Germany.. hannes.richter@uni-jena.de.Maïa Henry. Friedrich Schiller University Jena. Lessingstraße 8, 07743 Jena, Germany.. maia.henry@uni-jena.de.Julius Bartels. Friedrich Schiller University Jena. Lessingstraße 8, 07743 Jena, Germany.. julius.bartels@uni-jena.de.Nico Ueberschaar. Friedrich Schiller University Jena. Humboldtstr. 8, 07743 Jena, Germany. Nico.Ueberschaar@uni-jena.de.The data were pre-processed in two separate workflows corresponding to full-scan (MS1) and data-dependent acquisition (MS2) datasets. Raw files were centroided using the vendor-supplied algorithm implemented in ProteoWizard 1 (version 3.0.2501). Full-scan data were processed locally using the XCMS 2-5 (version 4.8.0) package in R 6 (version 4.5.2), whereas MS2 (DDA) data were processed on the Draco high-performance computing cluster at Friedrich Schiller University Jena using XCMS (version 4.2.3) in R (version 4.4.1). This separation allowed optimization of retention time alignment and peak grouping for each dataset. To support parameter validation at each step, we generated diagnostic plots for fifty-five features (See SUPPLEMENTARY_FILES/6_Supplementary_files/3_Quality_files on MetaboLigths). XCMS outputs are provided in SUPPLEMENTARY_FILES/2_Preprocessed_files on MetaboLights. The MS1 and MS2 datasets were mapped using the metabCombiner package 7-8 (version 1.20.0). This approach enables matching features across datasets acquired under different analytical conditions by selecting anchor feature pairs and modeling retention-time relationships between datasets using a Generalized Additive Model (GAM). metabCombiner outputs are provided in SUPPLEMENTARY_FILES/3_Correspondence_fullMS_DDA on MetaboLights. For a more detailed description of the data pre-processing workflow, please refer to the article A metabolomics dataset of plankton exometabolomes and dissolved organic compounds in the South Atlantic Ocean. <ol><li>Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).</li><li>Benton, H. P., Want, E. J. & Ebbels, T. M. D. Correction of mass calibration gaps in liquid chromatography–mass spectrometry metabolomics data. Bioinformatics 26, 2488–2489 (2010).</li><li>Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008).</li><li>Louail, P. et al. xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem. Anal. Chem. 97, 27639–27645 (2025).</li><li>Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 78, 779–787 (2006).</li><li>R Core Team. R: A language and environment for statistical computing. R Foundation for  Statistical Computing (2020).</li><li>Habra, H. et al. metabCombiner : Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets. Anal. Chem. 93, 5028–5036 (2021).</li><li>Habra, H. et al. metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics. Metabolites 14, 125 (2024).</li></ol>Total operator codeDay/nightLatitudeInjection timeWind speedDDA meoh volume addedCTD temperature maximumAir temperatureCTD depth minimumPool compositionTechnical replicateSample roleBiological replicateCTD salinity maximumCTD fluorescence medianModified DDA sampleCTD depth medianStation labelLongitudeCampaignAnalysisCTD salinity medianBioSamples URLSea surface salinitySample volumeNominal depthCTD oxygen medianSampling datetimeCTD salinity minimumSample barcodeBatchEvaporation statusCTD fluorescence minimumCTD temperature minimumInjection orderCTD temperature medianTopical studyAir pressureOnboard operator codeInjection dateCTD oxygen maximumMeOH volume addedSea surface temperatureIn-lab operator codeCTD fluorescence maximumCTD depth maximumMeteorological sea surface temperatureCTD oxygen minimumCTD source data flagCTD source accuracy flagCTD source granularity flaghenry.maia.mn@gmail.commaia.henry@uni-jena.deThis dataset comprises 367 samples collected across 107 sampling sites (defined as unique ESSENTIAL_station_label identifiers in the metadata), distributed over a large range of ecological niches and biogeochemical regimes in the South Atlantic and Southern Oceans. Sampling was conducted between August 24, 2021 and September 15, 2022. Samples were collected across different diel periods, with 343 samples acquired during daytime and 24 during nighttime, as defined by the metadata field ESSENTIAL_event_feature_day_night (see SUPPLEMENTARY_FILES/1_Metadata for the complete metadata file, in complement to the Sample file from MetaboLights).The dataset covers a broad spectrum of marine environments, including (i) river-influenced systems such as the Amazon River plume and African river plumes, (ii) coastal and upwelling regions including the Benguela Current system, (iii) open-ocean dynamic regions characterized by mesoscale and submesoscale activity, and (iv) high-latitude environments such as the Weddell Sea.Depths sampled ranged from the surface to 990 m. Samples were categorized into three depth layers: surface (0–5 m, n = 222), epipelagic (>5–200 m, n = 108), and mesopelagic (>200 m, n = 37). Although sampling depths varied among stations, most samples were collected at 3 m (n = 100) or within the 1–5 m interval (n = 73). Technical replicates were predominantly acquired at these surface depths, with up to four replicates per site, whereas deeper layers generally included fewer replicates. Exometabolomic samples were obtained through a series of sequential filtration steps designed to isolate the dissolved fraction. This workflow simultaneously produced endometabolomic samples representing different planktonic size classes, which were not considered in the present dataset. Sample collection was carried out by 12 different operators, each of whom was assigned an alphabetical code reported in the metadata (column Code_onboard_operator).Seawater was collected using two complementary approaches: the surface water (< 5 m) was sampled with an A20 pump, whereas deeper samples (> 5 m) were collected using Niskin bottles. Collected seawater was then pre-filtered through a 20 µm nylon mesh to remove large particulate material and the largest planktonic organisms, before being distributed into 2 L polycarbonate carboys.The carboy contents were subsequently filtered using peristaltic pumps through a series of pore-size–selective filters prior to solid-phase extraction (SPE). Water was first filtered through a 3 µm polycarbonate filter (i.e., MB320 protocol targeting the nanoplanktonic fraction of the microbiomes; 47 mm diameter filters, Merck, Germany) before filtration through a 0.2 µm polycarbonate filter (i.e., MB023 protocol targeting the picoplanktonic fraction of the microbiomes; 47 mm diameter filters, Merck, Germany). The resulting filtrate was then extracted using 500 mg Oasis cartridges that had been conditioned with 5 mL UHPLC-grade methanol (VWR International GmbH, Germany) and equilibrated with 5 mL Milli-Q water. Cartridges were subsequently rinsed with a full volume of Milli-Q water, dried, and transferred into 50 mL Falcon™ tubes (Corning Inc., USA). The exact volume of seawater used for each cartridge can be assessed in the Volume_L column of the metadata.Procedural blanks were processed onboard following the same protocol, replacing the 2 L seawater volume with 2 L Milli-Q water. All samples were immediately stored at −20 °C before their transfer to our laboratory, where they were stored at −70 °C for long-term preservation. For a more detailed description of the sampling design and of the extraction workflow, please refer to the article A metabolomics dataset of plankton exometabolomes and dissolved organic compounds in the South Atlantic Ocean.Complete Metadata are available in SUPPLEMENTARY_FILES/1_Metadata on MetaboLights. --------------------------------------------Acknowledgements -We wish to thank the Tara Ocean Foundation, the SV Tara crew and all those who participate in Mission Microbiomes AtlantECO and adopt its Data Sharing & Publication Best Practices (https://zenodo.org/communities/mission-microbiomes-atlanteco/).-This publication has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923 (project AtlantECO). This output reflects only the author’s view and the European Union cannot be held responsible for any use that may be made of the information contained therein.-We are keen to thank the commitment of the following institutions for their financial and scientific support that made Mission Microbiomes AtlantECO possible: Stazione Zoologica Anton Dohrn, European Bioinformatics Institute (EMBL-EBI), Centre national de la recherche scientifique (CNRS), Centre National de Sequencage (CNS, Genoscope), agnes b., BIC, Capgemini Engineering, Fondation Groupe EDF, Compagnie Nationale du Rhone, L'Oreal, Biotherm, Region Bretagne, Lorient Agglomeration, Billerudkorsnas, Havas Paris, Fondation Rothschild, Office Francais de la Biodiversite, AmerisourceBergen, Philgood Foundation, UNESCO-IOC, Etienne Bourgois. For the complete acknowledgements, please refer to SUPPLEMENTARY_FILES/Acknowledgements.pdf.Metabolomicsultra-performance liquid chromatography-mass spectrometryMetabolomicspooled quality control sampleMarine metabolomicsuntargeted analysissolvent blankLarge riversSecondary metabolitesblank sampleUpwelling systemsiceberguntargeted metabolitesEstuarine-oceanic gradientEddiessea watercentroid spectrumThermo Scientific Orbitrap Exploris 480microbiomesample preparation blankdata-independent acquisitionexperimental sampleThermo Scientific Vanquish UHPLC SystemNatural productsAntarcticaExometabolomesolventDissolved Organic Matterultra-performance liquid chromatography-mass spectrometrypooled quality control sampleMetabolomicsMarine metabolomicsuntargeted analysissolvent blankLarge riversSecondary metabolitesblank sampleUpwelling systemsiceberguntargeted metabolitesEstuarine-oceanic gradientEddiessea watercentroid spectrumThermo Scientific Orbitrap Exploris 480microbiomesample preparation blankexperimental sampledata-independent acquisitionThermo Scientific Vanquish UHPLC SystemNatural productsAntarcticaExometabolomesolventDissolved Organic MatterSamples were analyzed on an Exploris 480 mass spectrometer (Thermo Scientific, Germany), using heated electrospray ionization (HESI) and alternating between positive and negative ionization. Full MS scan acquisition was performed for each sample. MS/MS data-dependent acquisition was performed using DDA acquisition on 1 injection replicate of each sample. The total duration of the full-MS method was 13 min, with an MS acquisition window from 1.0 to 11.5 min and the application mode set to “Small molecules”. The source voltage was set to 3500 V in positive mode and 2500 V in negative mode. The resolution was set to 180,000, the scan range was from m/z 100 to 1500, and the AGC target was set to “Standard”. Each data-dependent MS/MS acquisition cycle began with a full MS¹ scan acquired at a resolution of 60,000 over an m/z range of 80–1200. Precursor ions were excluded after a single occurrence for 3 s within a mass tolerance of 5 ppm. Isotopic peaks were excluded, and no explicit intensity threshold was applied. The number of dependent scans was set to 5. The following ddMS² parameters were used: isolation window of 0.4 m/z, normalized collision energies (HCD) of 30, 60, and 90%, resolution of 15,000, scan range mode set to auto, standard AGC target, automatic maximum injection time, one microscan, and profile data acquisition. Analytical sequences are available in SUPPLEMENTARY_FILES/6_Supplementary_files/2_Sequences on MetaboLights.Acquisition methods can be found in SUPPLEMENTARY_FILES/6_Supplementary_files/1_Acquisition_methods.falseA metabolomics dataset of plankton exometabolomes and dissolved organic compounds in the South Atlantic OceanThe marine microbiome contributes to nearly half of our planet’s net primary production. It plays a central role in the ocean’s carbon cycle through both passive and active release of metabolites into seawater, forming a pool of dissolved compounds known as the marine exometabolome. Here, we introduce an untargeted metabolomics dataset of marine dissolved organic compounds collected aboard the scientific research vessel Tara during the AtlantECO Mission Microbiomes expedition (2021-2022). A total of 367 samples were collected from multiple depths across 107 sites in the South Atlantic Ocean, covering environments influenced by major river plumes (e.g., Amazon, Senegal), coastal upwelling systems, and the Weddell Sea. Metabolites were analyzed in positive ionization mode using ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) and tandem MS. Available data includes both raw (i.e, chromatograms and mass spectra) and pre-processed formats (i.e, intensity tables and annotation files). This dataset provides a resource for the investigation of marine metabolic diversity and dynamics and supports comparative studies across marine environments.2026-05-192026-05-17MTBLS14508