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377 results for “Mass spectrometry”
MALDI-TOF-MS spectra of modern Australian marsupials for ZooMS (Zooarchaeology by Mass Spectrometry)
<p>MALDI-TOF-MS spectra of extracted collagen from modern Australian marsupials. These spectra were used to develop peptide markers for Zooarchaeology by Mass Spectrometry (ZooMS). All spectra are uploaded in .mzml format.</p> <p>One sample per species was also analyzed with LC-MS/MS (indicated in the metadata file). The LC-MS/MS data is available at PXD027107 through MassIVE (doi:10.25345/C5TC2H). Information about the species and sample numbers can be found in the metadata file.</p>
MALDI-TOF Spectra for Zooarcheology by Mass Spectrometry (ZooMS) for Borić et al. (2021)
<p>This dataset contains MALDI-TOF spectral data in .mzML format for zooarcheology by mass spectrometry (ZooMS) samples referenced in Borić et al. (2021).</p> <p>Folder names correspond to the ZooMS sample names referenced in the article. Files in the same folder are technical replicates.</p>
Rapid identification of MRSA using mass spectrometry and machine learning from over 20000 clinical isolates
<p>Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a MALDI-TOF MS based machine learning model for rapidly MRSA prediction, the model was evaluated on a prospective test and four external clinical sites. On the dataset comprising 20359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78–0.88. Our MALDI–TOF MS-based ML model for the rapid MRSA identification can be easily integrated into the current clinical workflows and can further support physicians prescribe proper antibiotic treatments. </p>
Version 3 (20181130) of the MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)
<p><em>(Version </em>20181130<em>) </em></p> <p><strong><em>Edit #1 (Mar 06, 2023): New database version (v.4.2 - 20230306) - available</em>: </strong><a href="https://zenodo.org/records/14562231">https://zenodo.org/records/14562231</a></p> <p>Version 3 (20181130) of the RKI’s MALDI-TOF mass spectral database represents the second update of the original database (version 20161027, https://doi.org/10.5281/zenodo.163517). The RKI database v.3 contains altogether 6264 mass spectra from highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as <em>Bacillus anthracis</em>, <em>Yersinia pestis</em>, <em>Burkholderia mallei</em>, <em>Burkholderia pseudomallei</em> and <em>Francisella tularensis</em> as well as a selection of spectra from their close and more distant relatives. The database can be used as a reference for the diagnostics of BSL-3 bacteria using proprietary and free software packages for MALDI-TOF MS-based microbial identification. Spectral data are distributed as a 7-zip archive that contains the original mass spectra in its native data format (Bruker Daltonics). Please refer to the pdf file (181130-ZENODO-Metadata.pdf) to obtain information on cultivation condition, sample preparation and details of spectra acquisition. Do not try to print this document (~1000 pages!)</p> <p>The pkf-file (181130_ZENODO_Peaklist_30Peaks_1.6.pkf) contains the MS peak list data in a Matlab compatible format. The latter data file can be imported into MicrobeMS, a Matlab-based free-of-charge software solution developed at RKI. MicrobeMS is available from <a href="https://wiki-ms.microbe-ms.com">https://wiki-ms.microbe-ms.com</a>.</p> <p>The RKI mass spectral database will be updated on a regular basis.</p> <p>The author's grateful thanks are given to the following persons for providing microbial strains and species, or mass spectra. Without their help this work would not be possible.</p> <ul> <li><strong>Wolfgang Beyer</strong> - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany</li> <li><strong>Guido Werner</strong> - Robert Koch-Institute, <em>Nosocomial Pathogens and Antibiotic Resistances</em> (FG13), Wernigerode, Germany</li> <li><strong>Alejandra Bosch</strong> - <em>CINDEFI, CONICET-CCT</em> La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina</li> <li><strong>Michal Drevinek</strong> - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic</li> <li><strong>Roland Grunow</strong> - Robert Koch-Institute, <em>Highly Pathogenic Microorganisms</em> (ZBS2), Berlin, Germany</li> <li><strong>Daniela Jacob</strong> - Robert Koch-Institute, <em>Highly Pathogenic Microorganisms</em> (ZBS2), Berlin, Germany</li> <li><strong>Silke Klee</strong> - Robert Koch-Institute, <em>Highly Pathogenic Microorganisms</em> (ZBS2), Berlin, Germany</li> <li><strong>Jörg Rau</strong> - Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li><strong>Jens Jacob</strong> - Robert Koch-Institute, <em>Hospital Hygiene, Infection Prevention and Control </em>(FG14), Berlin, Germany</li> <li><strong>Martin Mielke</strong> - Robert Koch-Institute, <em>Department 1 - Infectious Diseases</em>, Berlin, Germany</li> <li><strong>Monika Ehling-Schulz</strong> - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria</li> <li><strong>Armand Paauw</strong> - Department of Medical Microbiology, CBRN protection, Universitair Medisch Centrum Utrecht, TNO, Rijswijk, The Netherlands</li> </ul>
Secondary ion mass spectrometry, a powerful tool for revealing ink formulations and animal skins in medieval manuscripts
<p>Book production by medieval scriptoria has gained growing interest in recent studies. In this context, identifying ink compositions and parchment animal species from illuminated manuscripts is of great importance. Here, we introduce time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a non-invasive tool to identify both inks and animal skins in manuscripts, at the same time. For this purpose, both positive and negative ion spectra in inked and non-inked areas were recorded. Chemical compositions of pigments (decoration) or black inks (text) were determined by searching for characteristic ion mass peaks. Animal skins were identified by data processing of raw ToF-SIMS spectra using principal component analysis (PCA). In illuminated manuscripts from the fifteenth to sixteenth century, malachite (green), azurite (blue), cinnabar (red) inorganic pigments, as well as iron-gall black ink, were identified. Carbon black and indigo (blue) organic pigments were also identified. Animal skins were identified in modern parchments of known animal species by a two-step PCA procedure. We believe the proposed method will find extensive application in material studies of medieval manuscripts, as it is non-invasive, highly sensitive and able to identify both inks and animal skins at the same time, even from traces of pigments and tiny scanned areas.</p>
Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues
<p><strong>Thermal evaporation as sample preparation </strong><strong>for </strong><strong>silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues</strong></p> <p>MSI datasets in SCiLS Lab SL File (*.sl) or as flexImaging sequence (*.mis)</p>
Inductively coupled plasma mass spectrometry of ions released from the coating into LB and DMEM medium
<p>several coatings from the ternary alloy of Zr-Cu-Ag metallic glasses (fabricated by PVD magnetron co-sputtering) were tested by the mass spectroscopy to evaluate their ion release in LB and DMEM. LB is the common medium for bacterial culture while DMEM is common for cells. coated samples have the name SP in them, while the non-coated one is PBT. They were in orbital shaker at 37˚C, 120 rpm for 1 day, 3 days and 7 days. </p>
LipidQuant 1.0: Automated data processing in lipid class separation - mass spectrometry quantitative workflows
Open the record for dataset details and reuse information.
Data from: SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms
Open the record for dataset details and reuse information.
Secondary ion mass spectrometry, a powerful tool for revealing ink formulations and animal skins in medieval manuscripts
Open the record for dataset details and reuse information.
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>
Online Aerosol Chemical Characterization by Extractive Electrospray Ionization − Ultrahigh-Resolution Mass Spectrometry (EESI-Orbitrap)
<p>These datasets are the raw data presented in the presented work of "Online Aerosol Chemical Characterization by Extractive Electrospray Ionization − Ultrahigh-Resolution Mass Spectrometry (EESI-Orbitrap)" in <em>Environmental Science & Technology Journal</em>, doi:10.1021/acs.est.9b07090.</p> <p>Abstract: Current mass spectrometry techniques for the online measurement of organic aerosol (OA) composition are subjected to either thermal/ionization-induced artifacts or limited mass resolving power, hindering accurate molecular characterization. Here, we combined the soft ionization capability of extractive electrospray ionization (EESI) and the ultrahigh mass resolution of Orbitrap for real-time, near-molecular characterization of OAs. Detection limits as low as tens of ng m<sup>−3</sup> with linearity up to hundreds of μg m<sup>−3</sup> at 0.2 Hz time resolution were observed for single- and mixed-component calibrations. The performance of the EESI-Orbitrap system was further evaluated with laboratory-generated secondary OAs (SOAs) and filter extracts of ambient particulate matter. The high mass accuracy and resolution (140 000 at <em>m/z</em> 200) of the EESI-Orbitrap system enable unambiguous identification of the aerosol components’ molecular composition and allow a clear separation between adjacent peaks, which would be significantly overlapping if a medium-resolution (20 000) mass analyzer was used. Furthermore, the tandem mass spectrometry (MS<sup>2</sup>) capability provides valuable insights into the compound structure. For instance, the MS<sup>2</sup> analysis of ambient OA samples and lab-generated biogenic SOAs points to specific SOA precursors in ambient air among a range of possible isomers based on fingerprint fragment ions. Overall, this newly developed and characterized EESI-Orbitrap system will advance our understanding of the formation and evolution of atmospheric aerosols.</p> <p>Structure of the datasets: The raw data are separated into individual file of excel format for the figures. Simulation data from Figure 4 can be generated using the matlab code named <em>Figure4PeakSimulation.m.</em> </p>
Raw mass spectrometry data for publication "Vertebrate cellular endolysosome modulating pore-forming protein is negatively regulated by its homologue under environmental oxidative conditions"
<p><strong>Abstract</strong>: Endolysosomes are key players in cell physiology, including material exchange, immunity and environmental adaptation etc. Bacterial pore-forming toxin aerolysin-like proteins (ALPs) are widely distributed in animals and plants. βγ-CAT is a complex of an ALP (BmALP1) and a trefoil factor (BmTFF3) in the frog <em>Bombina maxima</em>. It is the first example that a secreted endogenous pore-forming protein modulates the biochemical properties of endolysosomes via pore formation in these vesicles. Here, we report the identification of BmALP3, a homologue of BmALP1 that lacks membrane pore formation capacity. Both BmALP3 and BmALP1 contain a conserved cysteine in their C-terminal regions. BmALP3 was readily oxidized to disulfide bond linked homodimer, and the homodimer could then oxidize BmALP1 via disulfide bond exchange, resulting in the dissociation of βγ-CAT subunits and elimination of its biological activity. Consistent with its behavior <em>in vitro</em>, BmALP3 senses environmental oxygen tension <em>in vivo</em>, leading to modulation of βγ-CAT activity. Interestingly, this C-terminal cysteine site is well conserved in numerous vertebrate ALPs. These findings, for the first time, uncovered the existence of a regulatory ALP (BmALP3) and its modulating action on a cell executive ALP (BmALP1) in a redox-dependent manner, which is completely different from that of bacterial toxin aerolysins.</p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 2 - anti-HLA-A29 antibody DK1G8
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 WT versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 2 - anti-HLA-ABC antibody W6/32
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 WT versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications from HLA-A29-negative (DK1G8-negatively selected) fractions with anti-HLA-ABC antibody W6/32. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.
<p>The spectra were provided as unprocessed raw data in the manufacturers data format (Bruker), as labelled two zip archives with SARS CoV 2 positives and negative, according to the reviewer's recommendation.</p> <p>This information belongs to the publication (in review in <em>Journal of Virological Methods</em>)<br> "A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs"<br> All the information belongs to the National Reference Institute, INEI-ANLIS DR CARLOS G MALBRAN, BUENOS AIRES, ARGENTINA.</p>
MALDI-TOF-MS archaeological spectra and for African bovid collagen for Zooarchaeology by Mass Spectrometry (ZooMS) from Zambia
<p>The MALDI data for archaeological samples from Zambia. The spectra are all in the folder in .mzml format. The samples are labeled the same as in the corresponding manuscript. The modern African bovid spectra that were used to determine markers can be found at Zenodo doi:10.5281/zenodo.3964709.</p>
3D metabolic profiles acquired by a Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS)
<p>Dataset for "Spatially resolved 3D metabolomic profiling in tissues"</p> <p>The Dataset has 6 subdata of TOF-SIMS signal raw data:<br> * Inside germinal center dataset separate into 2<br> * Outside germinal center dataset<br> * Border germinal center dataset<br> * Unlabeled dataset separate into 2<br> * Labeled tonsil tissue dataset<br> * Unlabeled tonsil tissue dataset</p> <p>1 processed data for python numpy version: 3D metabolites npy Data.zip</p> <p>Each dataset is a folder that has 191 txt files corresponding to the signal of 189 mass channels, the sum of all signals and the rest of signals not captured. <br> In each folder, the signal corresponding to a channel is named: name - molecular compound<br> </p>
Supplemtary Data 2 - Panga ya Saidi Averaged Spectra for Zooarchaeology by Mass Spectrometry Analysis (ZooMS)
<p>Averaged ZooMS spectra from Iron Age deposits at Panga ya Saidi, Kenya 2020.</p>
Cross-linked Mass Spectrometry (MALDI) data of GR C3 NTD and TSG101cc
<p>The zip file contains three folders from three separate datasets. The proteins were cross-linked with DST and then run on SDS-PAGE to separate unlinked protein. Bands were cut from the gels and then proteolyses overnight before conducting MS. Most of the datasets were collected using trypsin to produce protein fragments or in one case trypsin + chymotrypsin. Each folder contains a large number of control samples including: blank portions of the gel, uncross-linked protein samples, cross-linked GR without TSG101, cross-linked TSG101 without GR (four different bands because of much self-cross-linking). The files were hand curated for analysis.</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.