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44 results for “untargeted metabolomics”
untargeted metabolomics_data
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untargeted metabolomics for IFN-γ treated BMECs, pos mode
<p>all the metabolites identified by untargeted metabolomics for IFN-γ treated BMECs in positive ion mode</p>
untargeted metabolomics for IFN-γ treated BMECs, neg
<p>all the metabolites identified by untargeted metabolomics for IFN-γ treated BMECs in negative ion mode</p>
Lipidomics and metabolomics datasets for "Adverse effects of arsenic uptake in rice metabolome and lipidome revealed by untargeted liquid chromatography coupled to mass spectrometry (LC-MS) and regions of interest multivariate curve resolution"
<p><strong>Files description</strong></p> <p>Raw files for lipidomics and metabolics studies on the impact of arsenic exposure on rice growth.</p> <p>File details on the worksheets lipids_files.xlsx and metabolomics_files.xlsx</p> <p>Files have been organized as follows:</p> <p><strong>Lipidomics</strong></p> <blockquote> <p>1) Control samples: lip_controls.rar<br> 2) Watering low As exposure: lip_water_1.rar<br> 3) Watering high As exposure: lip_water_1000.rar<br> 4) Soil low As exposure: lip_soil_5.rar<br> 5) Soil high As exposure: lip_soil_50.rar<br> 6) QC samples: lip_qcs.rar</p> </blockquote> <p><strong>Metabolomics (positive ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_pos_controls.rar<br> 2) Watering low As exposure: met_pos_water_1.rar<br> 3) Watering high As exposure: met_pos_water_1000.rar<br> 4) Soil low As exposure: met_pos_soil_5.rar<br> 5) Soil high As exposure: met_pos_soil_50.rar<br> 6) QC samples: met_pos_qcs.rar</p> </blockquote> <p><strong>Metabolomics (negative ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_neg_controls.rar<br> 2) Watering low As exposure: met_neg_water_1.rar<br> 3) Watering high As exposure: met_neg_water_1000.rar<br> 4) Soil low As exposure: met_neg_soil_5.rar<br> 5) Soil high As exposure: met_neg_soil_50.rar<br> 6) QC samples: met_neg_qcs.rar<br> </p> </blockquote> <p> </p> <p><strong>Experimental details</strong></p> <blockquote> <p><strong>Arsenic Exposure</strong></p> <p>Arsenic was supplied through two main routes: watering with contaminated water or soil containing arsenic. In addition, this new study includes metabolomic as well as lipidomic analysis, in order to have a more global overview of arsenic exposure.</p> <p>For the watering treatment, during the first 11 days, rice was irrigated with Milli-Q water. From that day until harvesting, plants were watered with 1 and 1000 μM of As (V) for the two concentration levels of exposure, and with Milli-Q water for control samples. The lowest concentration was established at 1 μM as it is the limit of the acceptable arsenic concentration in water by European legislation. The upper concentration was set at 1000 μM, a threshold established to ensure that the experiment was performed under sub-lethal arsenic concentration for the plant, based on previous studies.</p> <p>For the soil treatment, two containers were prepared with 1 kg of soil two days before planting. Soil from the container was exposed to two arsenic concentration levels (5 and 50 mg L<sup>-1</sup>). Once sowing, rice was irrigated the whole growth period with a solution containing 0.001 μM of As (V). The lowest arsenic limit in this treatment was set at 5 mg L<sup>-1</sup> as a maximum value of common arsenic leaches without toxic characteristics, although background soil content of arsenic varies between one and 40 ppm according to the US food and drug administration (FDA) report. The highest arsenic limit was established to 50 mg L<sup>-1</sup>, as a considerably high arsenic content in the soil, slightly above the maximum frequently encountered levels.</p> <p><strong>Lipidomic Analysis</strong></p> <p>The lipidomic analysis was performed using a Waters Acquity UPLC system (Waters Corporation, MA, USA), connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters), operated in both positive and negative electrospray (ESI) ionization modes. Full scan spectra were acquired from 50 to 1500 Da.</p> <p>The chromatographic column employed was a Kinetex C8 (100 x 2.1 mm, 1.7 μm) (Phenomenex) under the following conditions (already used in [47]): temperature at 30˚C, injection volume at 10 μL, and flow rate at 0.3 mL min<sup>-1</sup>. Mobile phases selected were (A) MeOH 1mM ammonium formate, and (B) H<sub>2</sub>O 2mM ammonium formate, both at 0.2% formic acid. The gradient started at 80% A, increased to 90% A in 3 min, from 3 to 6 min remained at 90% A, changed to 99 % A until minute 15, remained constant 1 min, and returned to initial conditions until minute 20.</p> <p><strong>Metabolomic analysis</strong></p> <p>The metabolomic analysis was performed using a Waters Acquity UPLC system connected to a Q-Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) equipped with a quadrupole-Orbitrap mass analyzer. Electrospray (ESI) was used as an ionization source in both positive and negative ion modes. Full scan mass range was set from <em>m/z</em> 90 to 1000, and all ion fragmentation (AIF) was performed with normalized collision energy (NCE) of 35 eV.</p> <p>The column employed was an HILIC TSK gel amide-80 column (250 x 2.0 mm i.d., 5 μm) provided by Tosoh Bioscience (Tokyo, Japan), under the following experimental conditions (already employed in [45]): flow rate at 0.15 mL min<sup>-1</sup>, at room temperature, and 5 μL injection volume. Mobile phases were (A) AcN, and (B) 5 mM ammonium acetate, adjusted at pH 5.5 with acetic acid. The gradient employed was: starting conditions at 25% B, then increased until 30% B in 8 min; a 60% B was reached at 10 min, held for 2 min more and then back to 25% B until minute 14 min; lastly, a re-equilibration step was added and from 14 to 20 min at 25% B.</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). </p> <p> </p>
A benchmarking dataset for peak detection methods in untargeted metabolomics using LC/HRMS
<p>Randomly selected 20,000 mz and rt pairs were manually evaluated for true positive and true negative signals in a single LC/HRMS data file (003.mzML) from the study MTBLS1684. The data file was processed using four peak detection methods - IDSL.IPA, XCMS, MZMINE and MSDIAL. </p>
Met4DX: A mass spectrum-oriented computational framework for ion mobility-resolved untargeted metabolomics
<p><strong>Raw LC-IM-MS data</strong> to run Met4DX and <strong>RT recalibration table</strong> for multi-dimensional match</p> <p>Each dataset was archieved into a zip, containing raw LC-IM-MS data (.d format) and corresponding RT recalibration table (.csv format).</p>
Proteomics data for "Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section"
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Cell-Type Resolved Protein Atlas of Brain Lysosomes Identifies SLC45A1-Associated Disease as a Lysosomal Disorder: Untargeted Metabolomics and Lipidomics Data Deposition
<p>Raw data files used for untargeted metabolomics and lipidomics in the manuscript "Cell-Type Resolved Protein Atlas of Brain Lysosomes Identifies SLC45A1-Associated Disease as a Lysosomal Disorder".</p> <p>The PDF document <strong>(Data_Deposition_Naming_Info.pdf)</strong> contains information on the file naming system.</p>
Untargeted metabolomic analysis of thyroid cancer and benign thyroid nodule
<p>Thyroid cancer (TC) is the most common endocrine malignancy with increasing incidence in recent years. Fine-needle aspiration biopsy (FNAB), as a quick and cost-effective method, serves as a gold standard for initial evaluation of thyroid nodules. However, this technique fails to cover all the cytopathologic conditions resulting in a high rate of indeterminate results. There is an urgent need for better classification of thyroid cancer from benign thyroid nodule (BTN). Here, we conducted untargeted metabolomics in 25 plasma samples from 10 patients with TC and 15 patients with BTN.</p>
Supporting Information: High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics
<p>F1: Physiological Constraints: Growth rate, glucose uptake, and intracellular 13C succinate concentration</p> <p>F2: PAVE input and adduct list</p> <p>F3: Credentialing results, HILIC data</p> <p>F4: Credentialing results, RP lipids data</p> <p>F5: HILIC Level 2A annotation with MS-DIAL and manual mass shift quality control</p> <p>F6: R script for the MetFrag/PCLite-based annotation</p> <p>F7: HILIC annotation with MetFrag/PCLite</p> <p>F8: RP-lipids annotation with MS-DIAL</p> <p>F9: Level 1 compounds identification</p> <p>F10: InChKey-based YMDB and HMDB recovery analysis </p> <p>F11: InChKey-based comparison with PAVE publication</p> <p>F12: Statistic Results</p> <p>F13: Supernatant measurements</p>
Fig. 8. Untargeted metabolomic analysis A in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France
Fig. 8. Untargeted metabolomic analysis A. Principle component analysis of the 63 chemotypes of hop studied. Each symbol represents a single plant from the different accessions. Commercial varieties (10 accessions), heirloom varieties (3 accessions), wild hops collected on different locations (50 accessions, Fig. 3). B. Principle component analysis of the chemical markers.
G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics
<p>Benchmark datasets, manual annotation results, evaluation methods and results of the paper "G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics".</p>
Using Untargeted Metabolomics to Identify Urinary Biomarkers of Onion Intake
ClinicalTrials.gov study NCT05133986. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Untargeted metabolomic profiling of urine from healthy dogs and dogs with chronic hepatic disease
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Untargeted LC–MS metabolomics reveals an adverse effect of high-fat diet on hepatic metabolism of Oreochromis niloticus
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V28_1 Untargeted Metabolomics S.elongatus WT vs delta CutA
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Data from: Untargeted metabolomics profiles delineate metabolic alterations in mouse plasma during lung carcinoma development using UPLC-QTOF/MS in MSE mode
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Toxplasma gondii wild-type vs. ASH4 knockout untargeted metabolomics raw data
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Untargeted LC-HRMS-based metabolomic profiling reveals distinct metabolomic profiles in patients with metabolic dysfunction-associated steatotic liver disease (MASLD)
<p>We analyzed liver tissues, liver secretome (conditioned medium), and blood plasma samples from 109 obese patients undergoing bariatric surgery spanning early stages of metabolic dysfunction-associated steatotic liver diseases (MASLD). We used a combination of data-dependent acquisition (DDA) and data-independent acquisition (DIA or SWATH-MS) on SCIEX TripleTOF 6600,</p> <p>Metabolites and lipids were extracted using modified chloroform-methanol biphasic extraction methods for both biological matrices. Organic and aqueous fractions were separated using RRHD Eclipse Plus C18 and SeQuant ZIC-cHILIC columns prior to DDA and DIA acquisitions, respectively.</p> <p>All mzML files are deposited with ZENODO.</p> <p> </p> <p>*** DDA files are deposited in this dataset repository ***</p> <p><strong>Child directories for SWATH data files</strong></p> <table> <tbody> <tr> <td>Samples</td> <td>Polarity</td> <td>Zenodo DOI</td> </tr> <tr> <td>Organic fraction - Reverse Phase</td> <td>Positive & Negative</td> <td> <p>10.5281/zenodo.14096635</p> </td> </tr> <tr> <td>Aqueous fraction - HILIC</td> <td>Positive</td> <td> <p>10.5281/zenodo.14096753</p> </td> </tr> <tr> <td>Aqueous fraction - HILIC</td> <td>Negative</td> <td> <p>10.5281/zenodo.14136832</p> </td> </tr> </tbody> </table> <p> </p>
Metabolic Characteristics of Type 2 Diabetes Mellitus Combined With Acute Myocardial Infarction: A Untargeted Metabolomics Study
ClinicalTrials.gov study NCT05601310. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.