GC/MS Cattle urine analysis for peakpicking
Ontology highlight
ABSTRACT:
INSTRUMENT(S): 5973 series GC/MS (Agilent instrument model)
ORGANISM(S): Bos Taurus (ncbitaxon:9913)
SUBMITTER: 
Guitton Yann
PROVIDER: MSV000084146 | MassIVE | Mon Jul 29 04:07:00 BST 2019
REPOSITORIES: MassIVE
Similar Datasets
Project description:GC-MS is a commonly used metabolomic platform for the analysis of urine. A key step in the preparation of samples for GC-MS is derivatisation, in particular, methoximation and trimethylsilylation. This paper presents an assessment of automated derivatisation protocols for GC-MS-based untargeted metabolomic analysis of rat urine. Automated batch and in-time (a sample ready for injection every 70 minutes) derivatisation protocols were tested using BSTFA and MSTFA. Principal component analysis determined differences based upon protocol tested (PC-1; 19%) and silylation reagent (PC-2; 17%) used. Of 249 compounds, 40 compounds were significantly different (P<0.05) based upon reagent and 154 compounds were significantly different (P<0.05) based upon protocol. A key outcome of this study was the demonstrated effects of derivatisation including reagent and protocol (i.e. reaction duration, temperature and mixing speed) on individual urinary metabolites. It is hoped that the current work will provide a reference on which to base future GC-MS-based untargeted and targeted metabolomic analyses of urine.
2015-09-28 | MTBLS171 | MetaboLights
Project description:Urine passes through the entire kidney and urinary tract system starting from the glomerulus and ending to the urethra. Cells in the kidney and urinary tract could be exfoliated from the epithelium into the urine, while leukocyte could infiltrate from the local tissue into the urine, which makes the urine a useful subject for clinical evaluation of relevant diseases. We performed scRNA-seq on voided urine samples. 50–100 mL middle stream urine samples were collected from 12 Chinese healthy adults and combined for droplet-based single-cell RNA sequencing after flow cytometric sorting of live cells. We presented the first single-cell atlas of adult human urine and identified multiple previously unrecognized cell types. Based on our scRNA-seq analysis data, a SOX9+ cell population was identified in adult human urine which we speculated to have progenitor potential.
2021-01-25 | GSE165396 | GEO
Project description:Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic and debilitating pain disorder of the bladder and urinary tract with poorly understood etiology. A definitive diagnosis of IC/BPS can be challenging because many symptoms of IC/BPS are shared with other urological disorders. An analysis of urine presents an attractive and non-invasive resource for monitoring and diagnosing IC/BPS. Here, a non-targeted LC-MS and LC-MS/MS-based peptidomics analysis of urine samples collected from IC/BPS patients were compared to urine samples from asymptomatic controls.
2022-06-06 | PXD031843 | Pride
Project description:Cigarette smoking significantly increases the risk of cancer and cardiovascular diseases as well as premature death. Aromatic amines (AAs) such as o-toluidine, 2-aminonaphthalene and 4-aminobiphenyl are found in cigarette smoke and are well-established human bladder carcinogens presumably acting via the formation of DNA adducts. These amines may be metabolized in the liver to acetylated or glucuronidated forms or oxidized to a hydroxylamine which may react with protein and DNA to form adducts. Free, acetylated and glucuronidated AAs are excreted in urine and can be measured as exposure biomarkers. Using isotope dilution GC-MS/MS, our laboratory quantifies six urinary AAs that are known or suspected carcinogens-o-toluidine, 2,6-dimethylaniline, o-anisidine, 1-aminonaphthalene, 2-aminonaphthalene and 4-aminobiphenyl-for large population studies such as the National Health and Nutrition Examination Survey (NHANES). We also monitor two additional corresponding structural isomers-2-aminobiphenyl and 3-aminobiphenyl-to verify isomer separation. A new and improved automated sample preparation method was developed to quantify these AAs, in which, sample cleanup was done via Supported Liquid Extraction (SLE+ ISOLUTE®) on a Hamilton STAR™ workstation. This automated method increased sample throughput by reducing sample cleanup time from 8 to 4 h while maintaining precision (intra and inter-run coefficient of variation <7%) and accuracy (±17%). Recent improvements in our GC/MS method have enhanced our assay sensitivity and specificity, resulting in longer analytical column life and maintaining or reducing the limit of detection for all six analytes. Indigo ASCENTTM software (3.7.1, Indigo BioAutomation, Inc.) is used for peak integration, calibration and quantification. A streamlined sample data flow was created in parallel with the automated method, in which samples can be tracked from receiving to final laboratory information management system output with minimal human intervention, minimizing potential human error. This newly validated, automated method and sample data flow are currently applied in biomonitoring of AAs in the US noninstitutionalized population NHANES 2013-2014 cycle.
| S-EPMC6330153 | biostudies-literature
Project description:Glomus sp. MS strain:MS Genome sequencing
| PRJNA210520 | ENA
Project description:Methanosarcina barkeri MS strain:MS Raw sequence reads
| PRJNA613292 | ENA
Project description:Urine samples Metataxonomic analysis by 16S rRNA gene inventory
| PRJNA208706 | ENA
Project description:16S rRNA gene targeted metagenome sequencing of urine microbiome in kidney stone disease
| PRJNA398193 | ENA
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels