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9 results for “Ion mobility spectrometry”
A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.
<p><strong>Contents of the dataset</strong></p> <p>The dataset includes the set of urine samples in .mea format, which can be<br> read using the GCIMS R package.</p> <p>It also contains analytical standards in the same format, used for quality<br> control of the equipment and as a retention time alignment reference.</p> <p>If you want to preview the data, you do not need to download the full Urines.zip<br> and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and<br> AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole<br> dataset.</p> <p>Besides the actual measurements, you will find the annotations.csv and<br> reference_peaks.csv files, with sample annotations and some reference peaks<br> identified in the samples.</p> <p>See further details below.</p> <p><br> <strong>Sample collection</strong></p> <p>Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects<br> were diagnosed with colorectal cancer, 14 subjects were controls. The study<br> protocol was approved by the Ethics Committee of Hospital de Reus (study<br> approval no. 074/2018).</p> <p>Samples were aliquoted and frozen at -80ºC for storage.</p> <p><strong>Sample preparation</strong><br> </p> <p>Sample preparation improves urine preservation by blocking bacterial growth in<br> the urine, and favours volatile extraction. It also adds an internal standard<br> for verification of instrument variability.</p> <p><em>Stock solution preparation</em></p> <p>Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium<br> azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with<br> deionized water until the final volume is 50mL. The HCl 5M is used to obtain<br> an acid pH. The pH is controlled with a pH test paper. The final pH level must<br> be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits<br> the bacterial growth in the urine.</p> <p><em>Internal standard solution preparation</em><br> </p> <p>The 4-flurobenzaldehyde is located in retention time around 200 seconds and<br> can be used as an internal standard.</p> <p>Prepare a methanol stock solution using 100 ml of methanol grade for<br> preparative chromatography and 200 ml of distilled water.</p> <p>Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution.</p> <p>Dilute the previous mixture in 400 mL of mili-Q water.</p> <p><br> <em>Sample preparation</em><br> </p> <p>Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution<br> were added to the urine sample, and 1.5 ml of the acidified urine sample were<br> transferred into a 20ml vial, ensuring only the supernatant of the sample<br> is transferred.</p> <p>Finally, 20 mL of the internal standard solution is added to the sample.</p> <p><strong>GC-IMS Analysis</strong></p> <p>Samples were analyzed with a GC-IMS FlavourSpec® instrument from<br> G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes<br> at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier<br> gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC<br> and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes.</p> <p>Besides the urines, a set of measurements of a ketone mixture was also analyzed<br> at least once per day as an analytical standard control of the equipment. The mixture<br> included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone,<br> 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as<br> the urine samples.</p> <p>Samples are provided in the native instrument format (.mea format), that can be<br> read with the GCIMS R package or with the instrument software.</p> <p><strong>Sample annotations</strong></p> <p>The dataset includes a CSV file with sample annotations.</p> <p>The annotations include the following information:</p> <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> <p><br> <strong>Reference peaks</strong></p> <p>Some peaks were manually annotated to ease the alignment of the samples and explore<br> alignment solutions. While manual peak labelling is not generally required, we<br> attach those reference peaks as well and their locations, in case they are of<br> interest.</p> <p>These reference peaks are found at reference_peaks.csv.</p> <p> </p>
Trapped ion mobility spectrometry-guided molecular discrimination between plasmalogens and other ether lipids in lipidomics experiments
<p>Supplementary Dataset for the manuscript "Trapped ion mobility spectrometry-guided molecular discrimination between plasmalogens and other ether lipids in lipidomics experiments" under preparation for bioRxiv submission.<br>Data is packed into a zip archive according to ZENODO upload limitations.<br>in the top folder the actual analysis and respective files, as described in the publications supplementary materials can be found as <em>PLOP2 </em>folder (including raw data etc.).<br>The additional images explain the repo layout in a sketched form: <br>Additionally also for data readout the used <em>docker-compose.yml </em>and a github clone of the important <em>timsr </em>package is included.<br>PLEASE READ the README!.<br><br>Additionally the file `Calibration-2021-08-31_11-44-22.pdf` contains the calibration report, and `PLOP4_method.pdf` a hystar report of the used methodology.<br><br>For LSI Reporting Checklist see DOI: 10.5281/zenodo.13963972</p>
Raw data for evaluation of Floral Volatile-Patterns in the Genus Narcissus using Gas Chromatography coupled Ion Mobility Spectrometry
<p>We used a commercial gaschromatography coupled ion mobility spectrometer, equipped with an integrated in-line enrichment system for fast, sensitive and automated analysis of floral volatile patterns in the genus <em>Narcissus</em>. The raw data of individual measurements are stored together with corresponding telemetry data as data matrices. The determined retention times and ion mobilities (series and columns) can be used for the identification of substances. The measured values (intensities) are used for a (semi)-quantoitative determination of individual substances.Based on these raw data, heatmaps can be generated in this way, which allow a comparison and potentially identification of floral volatiles. </p> <p>This data set is part of a proof of concept study for the use of GC-IMS in the investigation of flower volatiles.</p>
Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Products Mixtures - cytoscape files
<p>Cytoscape files associated with Figure 5 from the manuscript: Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Products Mixtures</p>
Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions
<p>Lipidomics studies suffer from analytical and annotation challenges due to the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision induced dissociation and MS (LC-IMS-CID-MS) may be employed. While LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we utilize the freely available, vendor-neutral, and open-source software Skyline to process and annotate the multidimensional lipidomic data. While Skyline was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small molecule data as well as data containing the IMS dimension. This protocol utilizes Skylines’ recently expanded capabilities, including small molecule spectral libraries, indexed retention time (iRT), and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results, and editing our manually validated 500+ lipid library. While the time required to complete the steps outlined here varies based on multiple factors such as dataset size and familiarity with Skyline, this protocol takes approximately 5.5 hours to complete when annotations are rigorously verified for maximum confidence.</p>
Breath Analysis by Ion Mobility Spectrometry
ClinicalTrials.gov study NCT00632307. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Fast Identification of Pathogen in the Setting of Hospital-acquired Pneumonia Using Ion Mobility Spectrometry
ClinicalTrials.gov study NCT01624181. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Non-invasive Detection of Pneumonia in Context of Covid-19 Using Gas Chromatography - Ion Mobility Spectrometry (GC-IMS)
ClinicalTrials.gov study NCT04329507. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Differences in Exhaled Breath by Using Ion Mobility Spectrometry (IMS) in Subjects Tested for SARS-CoV-2 Infection (COVID-19 Disease)
ClinicalTrials.gov study NCT04649931. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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