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38 results for “Liquid chromatography mass spectrometry”
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)
<p>Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications.</p> <p>In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against <em>in silico</em> libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later).</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.</em></p>
Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry
<p>Data set of the Publication: </p> <div> <div>Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. <em>Environment International</em> 183: 108426. <a href="https://doi.org/10.1016/j.envint.2024.108426">https://doi.org/10.1016/j.envint.2024.108426</a>.</div> </div> <p>- raw LC-HRMS data in mzML format for positive and negative mode.</p> <p>- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.</p> <p> </p>
Liquid chromatography mass spectrometry data of HMCES SRAP domain
<p>Liquid chromatography mass spectrometry data for the HMCES SRAP domain alone (Apo_SRAPd) and for the SRAP domain incubated with abasic site containing DNA (SRAPd_DPC). All LC/MS data were acquired according to the previously published protocol (Chalk R., Springer New York, 2017).</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>
MS data set: Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
<p>Data set consisting of raw LC-MS2 data, LC-MS1 peak data and a description</p> <p>For unreviewed publication preprint: <strong>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS<sup>1</sup>) and <em>in silico </em>Peptide Mass Data</strong></p> <p>ABSTRACT</p> <p>Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS<sup>2</sup>) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS<sup>2</sup> data are tested against sequence databases covering a broad panel of different microbiological taxa.</p> <p>In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS<sup>1</sup> data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089</em></p> <p> </p>
Example run of the Biognosys iRT standard for liquid chromatography - mass spectrometry
<p>Depending on the composition of QC samples the LC performance can be monitored using peptides that elute over the entire gradient, and the dynamic range can be monitored if peptides are present in varying concentrations. Depicted here is the Biognosys iRT standard which consists of eleven peptides with varying chromatographic retention.</p>
Ishikawa diagram of sources of variability impacting a liquid chromatography - mass spectrometry experiment
<p>An Ishikawa diagram (non-exhaustively) highlighting some of the major sources of variability in each of the stages of an LC-MS experiment. These and other sources of variability will impact the results and should be considered in a comprehensive quality control workflow.</p>
Liquid chromatography - mass spectrometry workflow
<p>A typical LC-MS experiment consists of a sample preparation, a liquid chromatography, a mass spectrometry, and a bioinformatics stage. The sample preparation includes the proteolytic digestion of proteins into peptides. Next, consecutively the peptides are separated through liquid chromatography and measured through mass spectrometry. Finally, the acquired spectra are interpreted through bioinformatics means.</p>
Liquid Chromatography Mass Spectrometry Data (LCMS-1) from Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum
<p>The mzML files in this dataset are the Liquid Chromatography Mass Spectrometry Data (LCMS-1) data files, derived from the RAW MS files using GNPS file convertor. These files contain unprocessed features acquired from the monocultures (single species: <em>Skeletonema marinoi</em> and <em>Prymnesium parvum</em> separately) and co-culture conditions of (<em>Skeletonema marinoi</em> and <em>Prymnesium parvum</em>). The microalgae were grown in co-culture chambers, so the naming convention A, and B refer to the two sides of the chamber. So, 1a and 1b are <em>S. marinoi</em> monocultures, but 11a and 11b refer to s. marinoi and <em>P. parvum</em> respectively.</p> <p>The results of metabolomics data analysis are available on Zenodo with DOI: 10.5281/zenodo.10143554</p>
Result files (ONLYSTEREO): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ONLYSTEREO setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul>
Result files (ALLDATA): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data with LC-MS²Struct"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ALLDATA setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Top-k accuracies for the comparison methods (MS²+RT, ...)</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul> <p>Version history:</p> <ul> <li><strong>Version 1</strong>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>2D fingerprints</strong> (<a href="https://www.biorxiv.org/content/10.1101/2022.02.11.480137v1">preprint v1</a>)</li> <li><strong>Version 2</strong> <em>(this version)</em>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>3D fingerprints</strong></li> </ul>
Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling Dataset for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Data from: Liquid chromatography-mass spectrometry (LC-MS) data of a multi-epitope peptibody with bFGF/VEGFA
<p><span><span><span><span><span><span><span><span><span><span><span>The <span><span><span>protein </span></span></span><span><span><span>primary </span></span></span><span><span><span>structure of the recombinant </span></span></span>Peptibody were investigated systematically by Liquid Chromatography-Mass Spectrometry (LC-MS)<span><span><span>. T</span></span></span><span><span>he 15 amino acids of N-terminal were </span></span>Met-Gln-Lys-Arg-Lys-Arg-Lys-Lys-Ser-Arg-Tyr-Lys-Ser-Gly-Gly and <span><span>the C-terminal was Lys (K</span></span><span><span>), the same as</span></span> the theoretical sequence. <span><span>With more </span></span><a><span class="15"><span>protease</span></span></a><span><span>s, the whole sequence was detected at the coverage of </span></span>trypsin 87.5%, <span><span>c</span></span><span><span>hymotrypsin</span></span> 75.3% and <span><span>Glu-C</span></span> 76.7%<span><span>. The </span></span>peptide-mapping could be used as an valuable standard to certify the complete expression and primary structure of Peptibody. The pI and MW were 8.93 and 37.415 kDa, within the errors allowed . The binding specificity after production were analyzed using anti-VEGFA and anti-His antibodies.</span></span></span></span></span></span></span></span></span></span></span></p>
Raw data for the submitted manuscript entitled "Novel strategies for the determination of plastic additives derived from agricultural plastics in soil using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)"
Open the record for dataset details and reuse information.
Validation data Set: Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry
<p>Validation dataset for paper published in the Journal of Chromatography A.</p> <p> </p> <p>Clément Douillet, Mary Moloney, Melissa Di Rocco, Christopher Elliott, Martin Danaher,<br> Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry, Journal of Chromatography A, Volume 1665, 2022, 462793, ISSN 0021-9673,</p> <p><br> Abstract:</p> <p>The objective of this work was to develop a quantitative multi-residue method for analysing antiviral drug residues and their metabolites in poultry meat samples. Antiviral drugs are not licensed for the treatment of influenza in food producing animals. However, there have been some reports indicating their illegal use in poultry. In this study, a method was developed for the analysis of 15 antiviral drug residues in poultry muscle (chicken, duck, quail and turkey) using liquid chromatography coupled to tandem mass spectrometry. This included 13 drugs against influenza and associated metabolites, but also two drugs employed for the treatment of herpes (acyclovir and ganciclovir). The method required the development of a novel chromatographic separation using a hydrophilic interaction chromatographic (HILIC) BEH amide column, which was necessary to retain the highly polar compounds. The analytes were detected using a triple quadrupole mass spectrometer operating in positive electrospray ionization mode. A range of different sample preparation protocols suitable for polar compounds were evaluated. The most effective procedure was based on a simple acetonitrile-based protein precipitation step followed by a further dilution in a methanol/water solution. The confirmatory method was validated according to the EU 2021/808 guidelines on different species including chicken, duck, turkey and quail. The validation was performed using various calibration curves ranging from 0.1 µg kg−1to 200 µg kg−1, according to the analyte. Depending on the analyte sensitivity, decision limits achieved ranged from 0.12 µg kg−1 for arbidol to 34.7 µg kg−1 for ribavirin. Overall, the reproducibility precision values ranged from 2.8% to 22.7% and the recoveries from 84% to 127%. The method was applied to 120 commercial poultry samples from the Irish market, which were all found to be residue-free.<br> Keywords: Antiviral drug residues; Influenza; HILIC; LC-MS/MS; Poultry muscle</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>
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>
Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Liquid chromatography tandem mass spectrometry of AMPA receptor containing vesicles
<p>Regulated delivery of AMPA receptors (AMPARs) to the postsynaptic membrane is an essential step in synaptic strength modification, and in particular, long-term potentiation (LTP). While LTP has been extensively studied using electrophysiology and light microscopy, several questions regarding the molecular mechanisms of AMPAR delivery via trafficking vesicles remain outstanding, including the gross molecular make up of AMPAR trafficking organelles and identification and location of calcium sensors required for SNARE complex-dependent membrane fusion of such trafficking vesicles with the plasma membrane. Here, we isolated AMPAR containing vesicles (ACVs) from whole mouse brains via immunoisolation and characterized them using immunoelectron microscopy, immunoblotting, and liquid chromatography tandem mass spectrometry (LC-MS/MS). We identified several proteins on ACVs that were previously found to play a role in AMPAR trafficking, including synaptobrevin-2, Rabs, the SM protein Munc18-1, the calcium-sensor synaptotagmin-1, as well as several new candidates, including synaptophysin and synaptogyrin on ACV membranes. Here, we present three biological replicates of liquid chromatography tandem mass spectrometry of AMPA receptor containing vesicles.</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.