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44 results for “Untargeted metabolomics”
Untargeted metabolomics analysis of RPE cells during six month in culture
<p>Primary RPE cell cultures were established with human fetal RPE cells acquired from ScienCell (Cat. No, 6540) seeded at passage 3 (P3) in 12-well Transwell<sup>®</sup> inserts (Corning Inc., Cat. No. CLSS3460) coated with 2% v/v Geltrex<sup>®</sup> matrix (Thermo Fisher Scientific; Cat. No. A1413202). Cells were cultured for a total time of 6 months (25 weeks), following the same protocol as previous works (16,59). Samples were acquired for protein immunolocalization, transcriptomic and metabolomics analysis from independent cell cultures along the total 6 months culture time (specifically at 4, 12, 17 and 25 weeks in culture), collecting 3 biological replicates per type of analysis and time point.<em> </em>Cell metabolites were quenched and extracted using MeOH:H<sub>2</sub>O (80:20, -20ºC) containing a spiked solution of isotopically enriched low molecular mass internal standard<strong>. </strong>Quality control (QC) samples were prepared by creating a pool of equal volumes from each biological replicate and were analyzed after a blank solution every fifth sample to monitor the performance, stability, and reproducibility of the LC-MS run. A liquid chromatography (LC) system 1290 Infinity II (Agilent Technologies) was coupled to an Agilent 6560B Ion Mobility quadrupole-time-of-flight mass spectrometer (IM-QTOF-MS) equipped with an Agilent G1607A dual jetstream ESI source and the MassHunter WorkSation 11.0. A reference solution containing purine and hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazene for mass correction was added using the second ESI source. Chromatographic, ion source and MS conditions were optimized using QCs and selected parameters are shown in Supporting Information. MS analysis was conducted with an untargeted approach, operating the instrument in both positive and negative ionization modes. All samples were analysed in a randomized order. Raw data was processed using the software Profinder B10.00 (Agilent) and refined data were exported as CEF files<strong>. </strong>Data were converted to mzml files using MS convert.</p>
Anti-inflammatory compounds in probiotic yeast revealed by untargeted metabolomics
<p><strong>Abstract</strong></p> <p>The saccharomyces strain <em>Saccharomyces cerevisiae </em>var.<em> boulardii</em> has exhibited efficacy in ameliorating symptoms of gastrointestinal disorders, including inflammatory diseases. The molecular origin of the anti-inflammatory activity has remained largely elusive to this day. Earlier studies suggest a small, secreted, yet undefined, molecule as the active principle and thus an untargeted metabolomics approach towards its identification was adopted. We used LCMS-analysis to interrogate the secreted metabolome of <em>S. cerevisiae </em>var.<em> boulardii</em> in comparison to a <em>S. cerevisiae </em>reference strain. Statistical analysis of the data revealed several compounds unique to <em>S. cerevisiae </em>var.<em> boulardii</em>, that were partially annotated and confirmed by comparison with authentic standards. Furthermore, anti-inflammatory properties were experimentally assigned to several small molecules, indicating that this property of the yeast variant is due to several factors. Our data suggest that the anti-inflammatory properties of <em>S. cerevisiae </em>var.<em> boulardii</em> can be linked to the activity of small molecules in its secreted metabolome.</p>
Untargeted leaf metabolomes of Noccaea praecox and N. caerulescens
<p>The dataset represents untargeted leaf metabolomes of Noccaea caerulescens from the un-polluted site Lokovec (Slovenia), N. praecox from the same site, and N. praecox from metal-enriched site Zerjav (Slovenia).</p>
Raw data and supporting files for "Modular comparison of untargeted metabolomics processing steps"
<p>Raw data and supporting files for the Paper titled "Modular comparison of untargeted metabolomics processing steps". The dataset encompasses 42 samples, with 3 solvent blanks, 7 QC samples, and 32 biological samples (4 biological replicates: Banane, Bergrose, Narbe, Ricky) spiked with 42 compounds in different concentrations (0 ngmL, 30 ngmL, 100 ngmL, 300 ngmL). The files were uploaded in the vendor format (.raw) and in the open format (.mzML). Also, further supporting data for the processing results was uploaded as well.</p>
Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"
<p>This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM<sup>12</sup>) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM<sup>12</sup> interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, <em>Enterococcus faecalis</em> KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM<sup>12</sup> interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.</p>
Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web
<p>This is the data and analysis scripts needed to recreate the analyses shown in "Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web"</p> <p>The abstract of the manuscript summarizes the goal of the paper:</p> <p> </p> <p>Stable isotope tracing is a crucial technique for understanding the metabolic wiring of biological systems, determining metabolic flux through pathways of interest, and detecting novel metabolites and pathways. Despite the potential insights provided by this technique, its application remains limited to a small number of targeted molecules and pathways. Because previous software tools usually require chemical formulas to find relevant features, and the data are highly complex, especially in untargeted metabolomics and when the reactions and metabolites downstream the labeled substrates are poorly characterized. We report here Khipu version 2 and its new user-friendly web application. New functions are added to enhance analyzing stable isotope tracing data including metrics that evaluate peak enrichment in labeled samples, scoring methods to facilitate robust detection of intensity patterns and integrated natural abundance correction. We demonstrate that this approach can be applied to untargeted metabolomics to systematically extract isotope-labeled compounds and annotate the unidentified metabolites.</p> <p> </p> <p>This repository stores the code and data needed to recreate all presented analyses. The code and instructions are in the AnalysisCode.zip. The DDA in the dda_mzML.zip, the MS1 in the dataset_mzml.zip, and the asari results in the AsariResults.zip. The readme is in the AnalysisCode.zip and has more detailed instructions. This directory also has the output data in tabular format for the figures as the figures were mostly made with Excel. </p>
Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking
<p>Comparative analysis of SIRIUS to evaluate our method, Biotransformation-based Annotation Method (BAM). This dataset includes all scripts, data, and results relevant to this analysis. BAM can be found on GitHub (https://github.com/HassounLab/BAM). </p> <p> </p> <p> </p>
High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds
<h3><em><strong>Content</strong></em></h3> <p>Dataset of the study: "High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds.</p>
Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation
<p><strong>The unprocessed and processed benchmarking data used in the paper of "Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation"</strong></p>
MALDI-MS dataset for use with open-source untargeted metabolomic workflow for complex biological samples
<p class="MsoNormal">Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free to-use data processing and analysis tools exist for various untargeted MS approaches, but choosing the 'correct' pipeline isn't straight-forward. This data set can be used in conjunction with a user-friendly online guide which presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. The workflow provides practical advice concerning experimental design, organisation of data and downstream analysis, and offers details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common allowing contributions and improvements to the workflow via the online repository. </p>
Untargeted metabolomic approach using UHPLC-HRMS to unravel the impact of fermentation on color and phenolic composition of Rosé wines
<p>Color is a major quality trait of rosé wines due to their packaging in clear glass bottles. This color is due to the presence of phenolic pigments extracted from grapes to wines and products of reactions taking place during the wine-making process. This study focuses on changes occurring during alcoholic fermentation of Syrah, Grenache and Cinsault musts, conducted at laboratory (250 mL) and pilot (100 L) scales. Color and phenolic composition of the musts and wines were analyzed using UV-visible spectrophotometry and metabolomics fingerprints were acquired by Ultra-High Performance Liquid Chromatography−High Resolution Mass Spectrometry. The acquisition dataset protocol is available in the affiliated publication on Molecules</p>
TBT exposure dataset of the manuscript "Assessment of endocrine disruptors effects on zebrafish (Danio rerio) embryos by untargeted LC-HRMS metabolomic analysis"
<p><strong>Raw LC-HRMS data of the TBT exposure of zebrafish embryos (for more details see https://doi.org/10.1016/j.scitotenv.2018.03.369)</strong></p> <p>The exposure protocol involved zebrafish embryos exposed in groups of 20 to various concentrations of chemical compounds, with five replicates per treatment. The concentrations ranged from the lowest observed effect concentrations (LOECs) to control levels. After exposure, embryos were collected, washed, frozen, and stored. Metabolites were extracted from individual embryo pools using methanol and methionine sulfone. The extraction process included vortexing, sonication, and centrifugation, followed by addition of water and chloroform. The aqueous fraction was dried and reconstituted using acetonitrile-water solution. Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) was used for analysis. Chromatographic separations were carried out on a hydrophilic interaction liquid chromatography (HILIC) column. Mass spectrometry was performed using an Orbitrap mass spectrometer with electrospray ionization in positive and negative modes. The mass spectra were acquired at high resolution, and fragmentation scans were used for metabolite identification. The overall process aimed to analyze the metabolomic profile of zebrafish embryos exposed to different chemical concentrations.</p> <p><strong>Data files</strong></p> <blockquote> <p>TBT ESI+ (tbt_pos.rar) - CDF files</p> <p>- QC (4 replicates)</p> <p>- Control (5 replicates)</p> <p>- TBT 3 nM (5 replicates)</p> <p>- TBT 10 nM (5 replicates)</p> <p>- TBT 30 nM (5 replicates)</p> <p>- TBT 100 nM (5 replicates)</p> </blockquote> <p> </p> <blockquote> <p>TBT ESI- (tbt_neg.rar) - CDF files</p> <p>- QC (6 replicates)</p> <p>- Control (5 replicates)</p> <p>- TBT 3 nM (5 replicates)</p> <p>- TBT 10 nM (5 replicates)</p> <p>- TBT 30 nM (5 replicates)</p> <p>- TBT 100 nM (5 replicates)</p> </blockquote>
Untargeted metabolomics data from Faecalibacterium prausnitzii FAAH experiment
Open the record for dataset details and reuse information.
MALDI-MS dataset for use with open-source untargeted metabolomic workflow for complex biological samples
Open the record for dataset details and reuse information.
Untargeted metabolomics molecular features data for plasma of 20 Peromyscus leucopus and 20 Mus musculus treated with LPS or controls
Open the record for dataset details and reuse information.
Pathway enrichments from untargeted metabolomics of plasma of Peromyscus leucopus and Mus musculus with or without LPS treatment
Open the record for dataset details and reuse information.
Untargeted LC–MS metabolomics reveals an adverse effect of high-fat diet on hepatic metabolism of Oreochromis niloticus
<p>Hepatic steatosis commonly occurs in intensively farmed tilapia. This disease is harmful to fish growth and health, but knowledge of the metabolic changes in tilapia with fatty liver is limited. In the present study, we compared genetically improved farmed tilapia (GIFT, <em>Oreochromis niloticus</em>) fed a high-fat diet (HFD) with those fed a normal-fat diet (NFD) for 8 weeks using LC-MS-based hepatic metabolomic assays and traditional nutritional assessments. Juvenile GIFT fed a HFD displayed higher fat disposition in the liver than did those fed a NFD. The metabolomic analyses revealed 61 differentially accumulated metabolites between the groups, and these metabolites were involved in 37 signaling pathways. Our results illustrate the development of metabolic disorders related to various hepatic biological processes, including protein metabolism, lipid metabolism, carbohydrate metabolism, and nucleotide metabolism in HFD-fed GIFT. These physiological changes may be related to the lower growth rate of GIFT. Overall, our study reveals the metabolic disorders in GIFT fed HFD, and enhance our knowledge of the mechanism of fatty liver formation in GIFT.</p>
Revealing Taxonomic and Diel Variation Through Untargeted Metabolomics
<p>A repository for the data and analysis used in B. Gordon's thesis, Chapter 6</p>
Data from: Untargeted metabolomic profiling of urine from healthy dogs and dogs with chronic hepatic disease
Chronic hepatic disease can present a diagnostic challenge with different etiologies being associated with similar clinical and laboratory findings. The histopathological assessment of a liver biopsy specimen is usually required in order to make a definitive diagnosis and the availability of non-invasive prognostic biomarkers is limited. The emerging science of metabolomics is used to detect changes in endogenous low molecular weight metabolites in biological samples and offers the possibility of identifying noninvasive markers of disease. The objective of this study was to investigate differences in the urine metabolome between healthy dogs, dogs with chronic hepatitis, dogs with hepatocellular carcinoma, and dogs with a congenital portosystemic shunt. Stored urine samples from 10 healthy dogs, 10 dogs with chronic hepatitis, 6 dogs with hepatocellular carcinoma, and 5 dogs with a congenital portosystemic shunt were analyzed. The urine metabolome was analyzed by gas chromatography – quadrupole time of flight mass spectrometry and 220 known metabolites were identified. Principal component analysis and heat dendrogram plots of the metabolomics data showed clustering between groups. Random forest analysis showed differences in the abundance of various metabolites including putrescine, gluconic acid, sorbitol, and valine. Based on univariate statistics, 37 metabolites were significantly different between groups. In, conclusion, the urine metabolome varies between healthy dogs, dogs with chronic hepatitis, dogs with hepatocellular carcinoma, and dogs with a congenital portosystemic shunt. Further targeted assessment of these metabolites is needed to assess their diagnostic utility.
V28_9 Untargeted Metabolomics Synechococcus elongatus WT vs. delta CutA
<p>V28_9 Untargeted metabolomics of Synechococcus elongatus sp. PCC 7942 WT or delta CutA in exponential or stationary phase. 5 or 10 uL injections on LC/MS column.</p><p> </p>
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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.