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377 results for “Mass spectrometry”
Spatially mapping the baseline and bisphenol-A exposed Daphnia magna lipidome using desorption electrospray ionisa-tion - mass spectrometry
<p>Data from desorption electrospray ionisation - mass spectrometry of <em>Daphnia magna</em> tissue section from control and daphnids exposed to 5 ppm of bisphenol A over their 7<sup>th</sup> adult instar, specifically four sampling points; 8, 24, 48 and 72 h after the 6<sup>th</sup> brood, as well as data acquired from a blank DESI slide.</p> <p>Data is provided in the form of *imzML files with the associated *.ibd of the same name.</p>
Imaging of Organic Samples with Megaelectron Volt Time-of-Flight Secondary Ion Mass Spectrometry Capillary Microprobe
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) with MeV primary ions offers a fine balance between secondary ion yield for molecules in the mass range from 100 to 1000 Da and beam spot size, both of which are critical for imaging applications of organic samples. Using conically shaped glass capillaries with an exit diameter of a few micrometers, a high energy heavy primary beam can be collimated to less than 10 μm. In this work, imaging capabilities of such a setup are presented for some organic samples (leucine-evaporated mesh, fly wing section, ink deposited on paper). Lateral resolution measurement and molecular distributions of selected mass peaks are shown. The negative influence of the beam halo, an unavoidable characteristic of primary beam collimation with a conical capillary, is also discussed. A new start trigger for TOF measurements based on the detection of secondary electrons released by the primary ion is presented. This method is applicable for a continuous primary ion beam, and for thick targets that are not transparent to the primary ion beam. The solution preserves the good mass resolution of the thin target setup, where the detection of primary ions with a PIN diode is used for a start trigger, reduces the background, and enables a wide range of samples to be analyzed.</p>
Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging
<p>Data & Code release for publication:</p> <p>Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, <a href="https://doi.org/10.1093/mtomcs/mfac013">https://doi.org/10.1093/mtomcs/mfac013</a></p> <p>Python code for LA ICP MS imaging data segmentation</p> <p>Code & Data also on <a href="https://github.com/sekro/la-icp-msi_segmentation">github</a></p> <p>Thresholding based segmentation of LA-ICP-MS imaging data</p> <p>Description</p> <p><a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/main.py">src/main.py</a> - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/laicpms_data_handler.py">src/laicpms_data_handler.py</a> - contains object to import, handle and segment (shimadzu) raw data</p> <p>Dependencies</p> <p>Python 3.8.1 or newer</p> <p>For packages see <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/requirements.txt">requirements.txt</a></p> <p>Data</p> <p>LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in <a href="https://doi.org/10.1093/mtomcs/mfac013">publication</a>. LA-ICP-MS imaging data as plain text files (comma-separated values)</p> <ul> <li>Condition 1 = fresh frozen (FF)</li> <li>Condition 2 = room temperature vacuum dried and sealed (RTV)</li> <li>Condition 3 = formalin fixed (FFix)</li> <li>Condition 4 = formalin fixed, paraffin sealed (FFPS)</li> </ul> <p>3 replicate sectioning sets named A, B, C</p> <p>File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set</p> <p>License</p> <p>Data</p> <p>CC-BY 4.0 - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/data/LICENSE">LICENSE</a> file in data folder</p> <p>Source code</p> <p>MIT - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/LICENSE">LICENSE</a> file in src folder</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere (dataset)
<p>Dataset accompanying the journal article titled "The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere". Preprint: doi.org/10.5194/egusphere-2022-33</p>
Data from: Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis
<p>Seven top-level folders</p> <p>GC-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of GC-PTR-TOF-MS data</p> <p>LSV-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under linear sweep voltammetry</p> <p>MSCP-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under multi-step chronopotentiometry</p> <p>PTR-TOF-MS-Calibration<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS calibration data</p> <p>SEM<br> - Raw images from scanning electron microscope</p> <p>Stability<br> - Raw data of electrochemical stability</p> <p>TEM<br> - Raw images from transmission electron microscopy</p>
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology
<p>Contact prediction data generated by EPC-map used in the paper "Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology" by Rappsilber et al.</p>
Development of a desorption electrospray ionization –multiple-reaction-monitoring mass spectrometry (DESI-MRM) workflow for spatially mapping oxylipins in pulmonary tissue
<p>Data from desorption electrospray ionization mass spectrometry – multiple-reaction-monitoring mass spectrometry (DESI-MRM) analysis of oxylipins in guinea pig lung tissue following<em> in vivo</em> exposure to house dust mite extract.</p> <p>Data are provided as Waters *.raw data folders, each incuding an 'Analyte .txt' file, which is generated from processing within MassLynx (Waters). The 'ion_library.txt' file includes details about the MRM transitions and is required for processing the data with quantMSImageR (<span><a href="https://github.com/targeted-lipidomics/quantMSImageR"><span>https://github.com/targeted-lipidomics/quantMSImageR</span></a></span><span>).</span></p>
DATASET - Mass Spectrometry - Snake venom proteomics of three subspecies of the North African mountain viper (Vipera monticola, Saint-Girons 1954) from Morocco
<p><strong>This DATASET collection includes the mass spectrometry files for proteomics venom investigation of three subspecies of the North African mountain viper (<em>Vipera monticola</em>, Saint-Girons 1954) from Morocco.</strong></p> <p><strong>Species list:</strong></p> <ol> <li>Vipera monticola monticola</li> <li>Vipera monticola atlantica</li> <li>Vipera monticola saintgironsi</li> </ol> <p><strong>Folders 01-03 - BOTTOM-UP PROTEOMICS</strong>: The venom pools were investigated by the bottom-up "snake venomics" (labled as SVX) approach and in short: separated by RP-HPLC, followed by SDS-PAGE separation and the single bands were in-gel processed by DTT, IAC and finally o/n tryptic digested. Samples submitted to HPLC-MS/MS. Early peptidic fractions of the first HPLC run were directly submitted to HPLC-MS/MS analytic w/o further gel procession. Folders 01 to 03 include the MS and MS/MS spectra of the snake species 1-3, respectively. Files are included as RAW and MZML format.</p> <p>Used instrument: LTQ Orbitrap XL mass spectrometer (Thermo, Bremen, Germany) with an Agilent 1260 HPLC system (Agilent Technologies, Waldbronn, Germany) using a reversed-phase Grace Vydac 218MS C18 (2.1 × 150 mm; 5 μm particle size) column.</p> <p>Modifications: UNIMOD:4 - \"Iodoacetamide derivative.\"</p> <p>Used protein database: Uniprot_8570_serpentes_reviewed_CandIso_2747_entries_230398.fasta</p>
Results of elemental analyses of brain and liver human tissue samples performed by inductively coupled plasma mass spectrometry
<p>Human tissue samples of brain and liver were obtained after min. 24 h postmortem from the Department of Forensic Medicine, University of Lublin. Tissue samples were collected from typical anatomical locations intended for histopathological examination: A—polus frontalis (frontal pole), B—gyrus precentralis (precentral gyrus), C—gyrus postcentralis (postcentral gyrus), D—cortex cingularis (gyrus cinguli cingulate gyrus), E—hippocampus (hippocampus), F—caput nuclei caudati (head of caudate nucleus), G—fasciculus longitudinalis superior cerebri (superior longitudinal fasciculus of brain, SLF), H—fasciculus longitudinalis inferior cerebri (inferior longitudinal fasciculus of brain, ILF), I—thalamus dorsalis (dorsal thalamus), J—nucleus accumbens septi (nucleus accumbens septi, NAc), K—insula (insula), L—hepar (liver). Samples were taken with the consent of the prosecutor and the Local Bioethics Committee (Medical University of Lublin, Poland, KE-0254/152/2021, approval date 24 June 2021). The study was conducted in accordance with the World Medical Association Code of Ethics, Declaration of Helsinki, for experiments involving human subjects. The samples were mineralized to remove the organic matrix using microwave minerali-zation with nitric acid (69% suprapur HNO3, Baker, Radnor, PA, USA) in the microwave mineralization system Multiwave 5000 (Anton Paar, Graz, Austria). After mineralization step, HCl (Merck, Darmstadt, Germany) was added and diluted by ultrapure water. The elemental analysis was performed using the inductively coupled plasma mass spectrometer Agilent 8900 ICP-MS Triple Quad (Agilent, Santa Clara, CA, USA). </p>
Mass spectrometry dataset for: "Discovery of Nostatin A, an azole containing sactipeptide with prominent cytostatic activity and pro-apoptotic activity"
<p>MSn mass spectrometry dataset used in: <strong>Discovery of nostatin A, an azole-containing sactipeptide with prominent cytostatic and pro-apoptotic activity.</strong> Kateřina Delawská*, Jan Hájek*, Kateřina Voráčová*, Marek Kuzma, Jan Mareš, Kateřina Vicková, Alan Kádek, Dominika Tučková, Filip Gallob, Petra Divoká, Martin Moos, Stanislav Opekar, Lukas Koch, Kumar Saurav, David Sedlák, Petr Novák, Petra Urajová, Jason Dean, Radek Gažák, Timo J.H. Niedermeyer, Zdeněk Kameník, Petr Šimek, Andreas Villunger and Pavel Hrouzek. <em>Org. Biomol. Chem.</em> (2025). doi:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4OB01395F">10.1039/D4OB01395F</a></p> <p> </p> <p><strong>Description:</strong></p> <p>MSn mass spectrometry elucidation of the molecular structure of nostatin A, a bioactive peptide isolated from Nostoc sp. cyanobacteria.</p> <p><strong>Sample and data processing:</strong></p> <p><em><strong>1) FTICR data:</strong></em><br>Experiments were performed using a 15T SolariX XR Fourier-transform ion cyclotron resonance mass spectrometer (ESI-FTICR MS; Bruker Daltonics, Billerica, MA, USA) equipped with infrared multiple photon dissociation (IRMPD). All data were acquired using 2 µl/min direct infusion of NosA dissolved at 10 µM in 60% acetonitrile acidified with 0.1% formic acid. Ion fragmentation was performed using IRMPD inside the ICR cell. For this a Diamond C-30A CO2 laser (Coherent, Santa Clara, CA, USA) resonating at 10.6 µm was custom-coupled to the SolariX FTICR MS and laser pulses were precisely timed in synchronization with the ICR pulse sequence. Further MS3 fragmentation experiments were performed using in source collisional fragmentation (isCID) followed by quadrupole isolation and subsequent collision induced fragmentation of particular fragment ions of interest. Detailed parameters (ESI and MS settings) are stored inside the metadata of the individual data files as well as described in the resulting publication. Spectral peaks were also exported in plain m/z vs intensity XY text files from Bruker Data Analysis 5.1.</p> <p><em><strong>2) qTOF data:</strong></em><br>HPLC-HRMS experiment was performed using a Dionex UltiMate 3000 HPLC system (Thermo Scientific, Sunnyvale, CA, USA) coupled with a diode array detector (DAD) connected to the Bruker Impact HD mass spectrometer equipped with an electrospray ionization (ESI) source (ESI-HRMS; Bruker, Billerica, MA, USA). The separations were performed on a C18 column (Phenomenex Kinetex C18, 150 × 4.6 mm, 2.6 μm) eluted with water (A)/acetonitrile (B) gradient (0 min 15%, 1 min 15%, 20 min 100%, 25 min 100%, 30 min 15%, 33min 15% of B) at a constant flow rate of 0.6 mL/min. Both solvents were acidified with 0.1% formic acid. Fragmentation energy was set to 87eV and 35eV for 1+ and 2+ Nostatin A, respectively. Detailed parameters (ESI and MS settings) are stored in the metadata of the individual data file as well as described in the resulting publication.</p> <p>*other variants of Nostatin bearing different acyl chains on proline residue were detected in crude extract only (C33_nosA_LCMS_2217.d) and not purified.</p> <p>Raw Bruker DataAnalysis .d files, which otherwise behave as folders, were compressed with the TAR algorithm implemented in the 64-bit Total Commander 11.02. </p>
Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.
Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.
Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.
Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.
DATASET - Mass Spectrometry - Snake venom proteomics of island and mainland V. ammodytes populations from North Macedonia
<p><strong><span>This DATASET collection includes the mass spectrometry files for proteomics venom investigation of island and mainland <em>V. ammodytes</em> populations from North Macedonia.</span></strong></p> <p><strong><span>Sample list:</span></strong></p> <ol> <li><span>Island - adult - male</span></li> <li><span>Island - adult - female</span></li> <li><span>Island - juvenile</span></li> <li><span>Island - subadult</span></li> <li><span>Mainland - adult</span></li> <li><span>Mainland - subadult</span></li> <li><span>Mainland - juvenile</span></li> </ol> <p><strong><span>Folders 01-07 - BOTTOM-UP PROTEOMICS</span></strong><span>: The venom pools were investigated by the bottom-up "snake venomics" (labelled as SVX) approach and in short: separated by RP-HPLC, followed by SDS-PAGE separation and the single bands were in-gel processed by DTT, IAC and finally o/n tryptic digested. Samples submitted to HPLC-MS/MS. Early peptidic fractions of the first HPLC run were directly submitted to HPLC-MS/MS analytic w/o further gel procession. Folders 01 to 07 include the MS and MS/MS spectra of the <em>V. ammodytes</em> sample pools from different populations. Files are included as RAW and MZML format.</span></p> <p><span>Used instrument: LTQ Orbitrap XL mass spectrometer (Thermo, Bremen, Germany) with an Agilent 1260 HPLC system (Agilent Technologies, Waldbronn, Germany) using a reversed-phase Grace Vydac 218MS C18 (2.1 × 150 mm; 5 </span><span>μ</span><span>m particle size) column.</span></p> <p><span>Modifications: UNIMOD:4 - \"Iodoacetamide derivative.\"</span></p> <p><span>Used protein database: Uniprot_8750_serpentes_CanNIso_2674_entries_220210_cRAP_220210.fasta</span></p>
Characterization of heteroatom distributions in the polar fraction of North Sea oils using high-resolution mass spectrometry
<p>Supplementary data for <a href="https://doi.org/10.1016/j.petrol.2019.106563">10.1016/j.petrol.2019.106563</a></p> <p>Mass spectra were measured on a Q Exactive HF at 240k@200 m/z resolution in nanospray-ESI direct infusion using a Advion TriVersa NanoMate source. Broadband mass spectra were generated from SIM-segments using dimspy (https://github.com/computational-metabolomics/dimspy). Peaks were annotated using Formularity v.1.0.0 (10.1021/acs.analchem.7b03318) after internal calibration using a homologous CHN series (identified from preliminary KMD/KM plots). All plots were generated using python 3.6.6 and the plotly graphing library (https://plot.ly/python/).</p> <p> </p>
Dataset related to article "On the extension of the use of a standard operating procedure for nicotine, glycerol and propylene glycol analysis in e-liquids using mass spectrometry"
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Mass Spectrometry Imaging Raw Data Files
<p>The enclosed ZIP file contains raw data generated using the Waters MALDI SYNAPT G2-Si High-Definition MS System. Imaging experiments were performed on fresh-frozen human carotid plaque sections and fresh-frozen rabbit aorta sections. Data were collected in both positive and negative modes for each type of tissue.</p>
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