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377 results for “Mass spectrometry”
Systematic reanalysis of co-fractionation mass spectrometry data: predicted interactomes
<p>This upload contains predicted interactomes for 27 species or clades: 17 individual organisms with at least three published CF-MS experiments, and 9 phylogenetic groupings of those organisms. </p> <p>The following individual organisms are represented:</p> <ul> <li>Arabidopsis thaliana</li> <li>Brassica oleracea</li> <li>Caenorhabditis elegans</li> <li>Chaetomium thermophilum</li> <li>Chlamydomonas reinhardtii</li> <li>Dictyostelium discoideum</li> <li>Drosophila melanogaster</li> <li>Nematostella vectensis</li> <li>Homo sapiens</li> <li>Mus musculus</li> <li>Oryza sativa</li> <li>Plasmodium berghei</li> <li>Plasmodium falciparum</li> <li>Plasmodium knowlesi</li> <li>Strongylocentrotus purpuratus</li> <li>Triticum aestivum</li> <li>Trypanosoma brucei</li> <li>Xenopus laevis</li> </ul> <p>The following clades are also represented:</p> <ul> <li>BOP clade</li> <li>Deuterostomia</li> <li>Eucharontoglires</li> <li>Eukaryota</li> <li>Mesangiospermae</li> <li>Opiskothonta</li> <li>Plasmodium</li> <li>Tetrapoda</li> <li>Viridaplantae</li> </ul> <p>The interactomes are provided in two forms. Files in the 'All interactions' directory include the complete classifier scores for every possible protein pair (sorted in descending order). Files in the '50% precision' directory include only those interactions identified above 50% precision, for convenience. </p> <p>Proteins were mapped to orthogroups using eggNOG. Maps from eggNOG orthogroups to UniProt accessions are available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/eggNOG. The phylogenetic tree used to group species into clades is also available from https://github.com/skinnider/CF-MS-analysis/tree/master/data/resources/TimeTree/species.nwk.</p> <p>The third and final directory, 'Human', contains the consensus CF-MS human interactome, in which proteins were merged across 46 human experiments by their gene names, rather than based on eggNOG orthogroups. The directory contains both complete classifier scores (file 'classifier-scores.tsv.gz') and the consensus CF-MS interactome, at 50% precision (file 'CF-MS-interactome.tsv').</p>
Data from: Long-lived metabolic enzymes in the crystalline lens identified by pulse-labeling of mice and mass spectrometry
<p>The lenticular fiber cells are comprised of extremely long-lived proteins while still maintaining an active biochemical state. Dysregulation of these activities has been implicated in age-related cataracts, and other lens diseases. However, the lenticular protein dynamics underlying health and disease is unclear. We sought to measure the global protein turnover rates in the eye using dietary nitrogen-15 (15N)-labeling of mice between 3 and 15 weeks of age. By performing mass spectrometry we measured the 14N- to 15N-peptide ratios of 248 lens proteins, including Crystallin, Aquaporin, Collagen and Laminin of the lens capsule, and enzymes that catalyze glycolysis as well as oxidation and reduction reactions. Unexpectedly, like the crystallin proteins, many of these enzymes are also exceedingly long-lived. The slow replacement of these enzymes in spite of young age of the mice suggests their potential roles in age-related metabolic changes in the lens.</p>
Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry
<p>Carbon stable isotope ratios (δ<sup>13</sup>C) for protein fractions extracted from a mixture of cultured cells of <em>Allochromatium vinosum </em>DSM 180 and <em>Synechocystis</em> sp. PCC6803, as well as from extracts of each pure culture.</p> <p>Citation: Mohr W, Tang T, Sattin SR, Bovee RJ, Pearson A. (2014) Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry. Analytical Chemistry 86, 8514-8520.</p> <p>Contact: Ann Pearson (pearson@eps.harvard.edu)</p>
Protocols for mass spectrometry imaging
<p>Protocols for metabolomic profiling using GC-MS, semi-thin cryosectioning of tissues for MALDI-IMS, and the application of a matrix on semi-thin cryosections for MALDI-IMS.</p>
Mass spectrometry imaging of metabolites in symbiont containing tissues of Bathymodiolus sp. mussels from a hydrothermal vent
<p>Molecules in <em>Bathymodiolus </em>sp. tissue. Distribution of five lipid metabolites in symbiont containing gill tissues was visualized using MALDI mass spectrometry imaging (red: high amounts, blue: low amounts of lipids).</p>
A MALDI-TOF Mass Spectrometry Database for Identification and Classification of Highly Pathogenic Microorganisms from the Robert Koch-Institute (RKI)
<p><em>(Version 20161027) </em></p> <p><strong><em>Edit #1 (May 23, 2017): New database version (v.2 - 20170523) - available</em>: </strong> <a href="https://doi.org/10.5281/zenodo.582602">10.5281/zenodo.582602</a></p> <p><strong><em>Edit #2 (Nov 30, 2018): New database version (v.3 - 20181130) - available</em>: </strong> <a href="https://doi.org/10.5281/zenodo.1880975">10.5281/zenodo.1880975</a></p> <p><strong><em>Edit #3 (Mar 06, 2023): New database version (v.4.2 - 20230306) - available</em>: </strong> <a href="https://zenodo.org/records/14562231">10.5281/zenodo.7702375</a></p> <p> </p> <p>The Robert Koch-Institute (RKI) database of microbial MALDI-TOF mass spectra contains mass spectral entries from highly pathogenic (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 RKI mass spectral 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. The database itself is distributed as a zip archive that contains the original mass spectra in its native data format (Bruker Daltonics). Please refer to the pdf file (161027-ZENODO-Metadata.pdf) to obtain information on the metadata of the spectra. Do not try to print this document (~1000 pages!)</p> <p>The pkf-file (161027_zenodo_Peaklist_(30Peaks1,6).pkf ) contains <em>so-called</em> database spectra in a Matlab compatible format. The latter data file can be imported into MicrobeMS, a Matlab-based free-of-charge software solution developed at the RKI. MicrobeMS is available from http://www.microbe-ms.com.</p> <p>For the future it is intended to update the RKI database of MALDI-TOF mass spectra on a regular basis.</p> <p>The author's grateful thanks are given to the following persons for providing microbial strains and species. Without their help this work would not be possible.</p> <ul> <li>Wolfgang Beyer - University of Hohenheim, Faculty of Agricultural Sciences, Stuttgart, Germany</li> <li>Guido Werner - Robert Koch-Institute, <em> Nosocomial Pathogens and Antibiotic Resistances</em> (FG13), Wernigerode, Germany</li> <li>Alejandra Bosch - CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina</li> <li>Michal Drevinek - National Institute for Nuclear, Biological and Chemical Protection, Milin, Czech Republic</li> <li>Roland Grunow - Robert Koch-Institute, <em>Highly Pathogenic Microorganisms</em> (ZBS2), Berlin, Germany</li> <li>Daniela Jacob - Robert Koch-Institute, <em>Highly Pathogenic Microorganisms</em> (ZBS2), Berlin, Germany</li> <li>Silke Klee - Robert Koch-Institute, <em>Highly Pathogenic Microorganisms</em> (ZBS2), Berlin, Germany</li> <li>Jörg Rau - Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li>Jens Jacob - Robert Koch-Institute, <em>Hospital Hygiene, Infection Prevention and Control </em>(FG14), Berlin, Germany</li> <li>Martin Mielke - Robert Koch-Institute, <em>Department 1 - Infectious Diseases</em>, Berlin, Germany</li> <li>Monika Ehling-Schulz - Functional Microbiology, Institute of Microbiology, University of Veterinary Medicine, Vienna, Austria</li> </ul> <p> </p>
Mass spectrometry analysis of MSI2 interacting partners in K562 cells
<p>Mass spectrometry analysis of MSI2 interacting partners in K562 cells for manuscript "<strong>Functional screen of MSI2 interactors identifies an essential role for SYNCRIP in myeloid leukemia stem cells"</strong> by Vu et al. 2017. </p>
Version 2 (20170523) 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>20170523<em>) </em></p> <p><strong><em>Edit #1 (Nov 30, 2018): New database version (v.3 - 20181130) - available</em>: </strong> <a href="https://doi.org/10.5281/zenodo.1880975">10.5281/zenodo.1880975</a></p> <p><strong><em>Edit #2 (Mar 06, 2023): New database version (v.4.2 - 20230306) - available</em>: </strong> <a href="https://zenodo.org/records/14562231">10.5281/zenodo.7702375</a></p> <p>Version 2 (20170523) of the RKI’s MALDI-TOF mass spectral database is an update of the original database (version 20161027, https://doi.org/10.5281/zenodo.163517). The RKI database contains mass spectral entries from highly pathogenic (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 (170523-ZENODO-Metadata.pdf) to obtain information on the metadata of the spectra. Do not try to print this document (~1100 pages!)</p> <p>The pkf-file (170523_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 http://www.microbe-ms.com.</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. 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>
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>
Mass spectrometry data for: A small protein coded within the mitochondrial canonical gene nd4 regulates mitochondrial bioenergetics
<p><span><strong>Background</strong>:</span> <span>Mitochondria have a central role in cellular functions, aging and in certain diseases. They possess their own genome, a vestige of their bacterial ancestor. Over the course of evolution, most of the genes of the ancestor have been lost or transferred to the nucleus. In humans, the mtDNA is a very small circular molecule with a functional repertoire limited to only 37 genes. Its extremely compact nature with genes arranged one after the other and separated by short non-coding regions suggests that there is little room for evolutionary novelties. This is radically different from bacterial genomes, which are also circular but much larger, and in which we can find genes inside other genes. These sequences, different from the reference coding sequences, are called alternative open reading frames or altORFs, and they are involved in key biological functions. </span><span>However, whether altORFs exist in mitochondrial protein-coding genes or elsewhere in the human mitogenome has not been fully addressed.</span></p> <p><span><strong>Results</strong>:</span> <span>We found a downstream alternative ATG initiation codon in the +3 reading frame of the human mitochondrial <em>nd4</em> gene. This newly characterized altORF encodes a 99-amino acids long polypeptide, MTALTND4, which is conserved in primates. Our custom antibody, but not the pre-immune serum, was able to immunoprecipitate MTALTND4 from HeLa cell lysates, confirming the existence of an endogenous MTALTND4 peptide. The protein is localized in mitochondria and cytoplasm and is also found in the plasma, </span><span>and it impacts cell and mitochondrial physiology. </span></p> <p><span><strong>Conclusions</strong>:</span> <span>Many human-mitochondrial-translated ORFs might have so far gone unnoticed. By ignoring mtaltORFs, we have underestimated the coding potential of the mitogenome.</span> <span>Alternative mitochondrial peptides such as MTALTND4 may offer </span><span>a new framework for the investigation of mitochondrial functions and diseases.</span></p>
Quantification of phosphorylated metabolites, organic acids, and intermediates of the TCA cycle using capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) following treatment of Escherichia coli with ciprofloxacin
<p>Capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) was used to quantify phosphorylated metabolites, organic acids, and intermediates of the TCA cycle of Escherichia coli treated with ciprofloxacin, BTP-001 (a novel antimicrobial peptide), and a combination of the two . Metabolite extracts were analyzed with a Xevo TQ-XS triple quadrupole mass spectrometer (Waters, USA).</p><p>Samples were gathered from E. coli cultures grown in batch cultivations using 1 liter bioreactors. Briefly, intracellular metabolites were extracted by cycling samples between −20 °C EtOH and N2 (<i>l</i>) in three consecutive freeze–thaw cycles, with vortexing every 10 min during the thawing phase. Filters were removed and the cell debris was pelleted (4500 rcf, 10 min, -9 °C). The supernatants were transferred to a new tube, snap frozen in N2 (<i>l</i>), and lyophilized. Lyophilized extracts were reconstituted in 500 µL cold Milli-Q H2O and cleared by spin-filtration with a 10 kDa molecular cutoff (20817 rcf, 10 min, 0 °C). A mix of 80 µL centrifuged sample and 20 µL 13C-labeled ISTD extract from yeast was sent to analysis. </p><p>Data processing and absolute quantification was performed as earlier described using the TargetLynx application manager of MassLynx v 4.1 (Waters) to interpolate calibration curves made with appropriate dilutions of analytical grade standards (Sigma-Aldrich). The response factor of the corresponding U13C-isotopologues were used to correct the standard and sample extract response factors. Extract concentrations were normalized to the CDW, which was calculated from interpolation of the OD600 vs. CDW (g/L) curve. </p><p>Further statistical analysis in MetaboAnalyst v 5.0 replaced missing values with 1/5 of the minimum value of the respective metabolite. An unpaired T-test with unequal variance determined differential enriched metabolites with a false discovery rate (FDR) < 0.05 which are presented as log2 fold-change compared to control. </p>
Tandem Mass Spectrometry Data (LCMS-2) from Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum
<p>The mzML files in this dataset are the Liquid Chromatography Tandem Mass Spectrometry (LCMS-2) data files, derived from the RAW MS-2 files using GNPS file convertor. These files contain unprocessed features fragmented features from the MS-1 data files available as <10.5281/zenodo.10143127> 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>). These files are used for structure annotations. </p> <p>The results of metabolomics annotation are available on Zenodo with DOI: 10.5281/zenodo.10143554</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>
DATASET - Mass Spectrometry - Snake venom proteomics of seven taxa of the genera Vipera, Montivipera, Macrovipera and Daboia across Türkiye
<p><strong>Publication: Damm <em>et al.</em> 2024 - <a title="DOI URL" href="https://doi.org/10.1021/acs.jproteome.4c00171">https://doi.org/10.1021/acs.jproteome.4c00171</a></strong></p> <p> </p> <p><strong>This DATASET collection includes the mass spectrometry files for proteomics venom investigation of seven taxa of the genera <em>Vipera</em>, <em>Montivipera</em>, <em>Macrovipera</em> and <em>Daboia </em>across Türkiye.</strong></p> <p><strong>Species list:</strong></p> <ol> <li>Vipera berus barani</li> <li>Vipera darevskii</li> <li>Montivipera bulgardaghica bulgardaghica </li> <li>Montivipera bulgardaghica albizona</li> <li>Montivipera xanthina</li> <li>Macrovipera lebetinus obtusa</li> <li>Daboia palaestinae</li> </ol> <p><strong>Folders 01-07 - 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 07 include the MS and MS/MS spectra of the snake species 1-7, 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_canonical_2640_entries_cRAP_210408.fasta</p> <p><strong>Folders 10-11 - TOP-DOWN PROTEOMICS</strong>: The venom pools were investigated by the non-reduced and TCEP reduced top-down (labled as TD) approach and in short: untreated or TCEP reduced samples submitted to HPLC-MS/MS. Folders 10 and 11 include the MS and MS/MS spectra of the snake species 1-7 as labled. Files are included as RAW and MZML format.</p> <p>Used instrument: Q Exactive HF mass spectrometer (Thermo, Bremen, Germany) with a Vanquish ultra-high performance liquid chromatography (UHPLC) system (Agilent Technologies, Waldbronn, Germany) using a reversed-phase Supelco Discovery BIO wide C18 (2.0 × 150 mm; 3 μm particle size; 300 Å pore size).</p> <p>Modifications: none (either red. or non-red. disulfide bridges)</p> <p>Used protein database for TopPIC analysis: Uniprot_8570_serpentes_reviewed_ISOandCAN_2749_entries_NOcRAP_231011.fasta</p> <p> </p>
Tandem Mass Spectrometry Dataset for Machine Learning in Metabolomics
<p>This dataset contains tandem mass spectrometry data cleaned and processed from the publicly available GNPS Spectral Library. We aim to continuously update this dataset with new data points as the spectral libraries expand.</p>
Mass spectrometry of axonemes from Tetrahymena thermophila CU428 and acetylation mutants
<p>Acetylation of α-tubulin at the lysine 40 residue (αK40) by the ATAT1/MEC-17 acetyltransferase influences the properties of microtubules and is a widespread phenomenon in eukaryotic cells. Previous research indicates that microtubules that undergo acetylation at αK40 are more stable and resilient to damage. Notably, αK40 acetylation represents the sole identified post-translational modification site within the microtubule lumen, suggesting its role in regulating the lateral interactions among protofilaments within the microtubule structure. This investigation focuses on evaluating the impact of tubulin acetylation on doublet microtubules present in the cilia of <em>Tetrahymena thermophila</em>, employing mass spectrometry analysis. Cilia samples derived from <em>Tetrahymena</em> wild type, acetylation mutants (K40R and MEC17-Knockout), and non-acetylation mutants (RIB72B-Knockout and RIB72AB-Knockout) underwent comparative mass spectrometry analysis. The results from mass spectrometry revealed a correlation between αK40 acetylation and phosphorylation within the ciliary structures.</p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Complementary aerosol mass spectrometry elucidates sources of wintertime sub-micron particle pollution in Fairbanks, Alaska during ALPACA 2022
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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.