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59 results for “MALDI-TOF”
Version 4.2 (20230306) 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>20230306<em>, </em>btmsp files modified May 31, 2023, additional taxonomic information added Dec 27, 2024<em>) </em></p> <p>Version 4.2 (20230306) of the RKI MALDI-ToF mass spectra database represents the third update of the original database (version 20161027, <a href="http://doi.org/10.5281/zenodo.163517">https://doi.org/10.5281/zenodo.163517</a>). The RKI Database v.4.2 now contains a total of 11055 MALDI-ToF mass spectra from 1601 microbial strains of highly pathogenic (i.e. biosafety level 3, BSL-3) bacteria such as <em>Bacillus anthracis</em>, <em>Brucella melitensis</em>, <em>Yersinia pestis</em>, <em>Burkholderia mallei / pseudomallei</em> and <em>Francisella tularensis</em> as well as a selection of spectra of their close and distant relatives. The database can be used as a reference for the diagnosis of BSL-3 bacteria using proprietary and free software packages for MALDI-ToF MS-based microbial identification. The spectral data are provided as a zip archive (<a href="https://zenodo.org/records/14562231/files/zenodo%20db%20230306.zip?download=1&preview=1">zenodo db 230306.zip</a>) containing the original mass spectra in their native data format (Bruker Daltonics). Please refer to the pdf file (<a href="https://zenodo.org/records/14562231/files/230306-ZENODO-Metadata.pdf?download=1&preview=1">230306-ZENODO-Metadata.pdf</a>) for information on cultivation conditions, sample preparation and details of the spectra acquisition. Please do not try to print this document (>1600 pages!).</p> <p>Version 20230306 of the RKI database contains for the first time files in the <em>btmsp</em> format (e.g. <a href="https://zenodo.org/records/14562231/files/2023-May-23-Bacillus-RKI-Database-568.btmsp?download=1&preview=1">2023-May-23-Bacillus-RKI-Database-568.btmsp </a> <a href="https://zenodo.org/api/files/35e90a0c-653d-4ba4-bf93-50b2bd80d073/2023-May-23-Bacillus-RKI-Database-570.btmsp"> </a>and others). These files were generated using the MALDI Biotyper software (Bruker Daltonics) and contain a total of 1601 main spectra (msp) from the BSL-3 database in the proprietary data format of the MALDI Biotyper software. *.<em>btmsp </em>files can be imported and used for identification with this software solution. Please refer to the manufacturer's manual for details on importing <em>btmsp </em>files. Note that the btmsp file available in database version 4 is broken and cannot be imported.</p> <p>The pkf files (<a href="https://zenodo.org/records/14562231/files/230306_ZENODO_30Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_30Peaks_0.75.pkf</a>, <a href="https://zenodo.org/records/14562231/files/230306_ZENODO_45Peaks_0.75.pkf?download=1&preview=1">230306_ZENODO_45Peaks_0.75.pkf</a>) represent two versions of the MS peak list data in a Matlab compatible format. The latter data can be imported into MicrobeMS, a free Matlab-based software solution developed at the RKI. MicrobeMS can be used for the identification of microorganisms by MALDI-ToF MS and is available at <a href="https://wiki-ms.microbe-ms.com">https://wiki-ms.microbe-ms.com</a>.</p> <p>The Excel file <a href="https://zenodo.org/records/14562231/files/Taxonomy%20information%20-%20RKI%20MALDI-ToF%20MS%20database%20of%20HPB%20at%20ZENODO%20v.4.xlsx?download=1&preview=1">Taxonomy information - RKI MALDI-ToF MS database of HPB at ZENODO v.4.xlsx</a> contains additional taxonomic information such as a detailed list of bacterial MALDI-ToF mass spectra (sheet #1), overviews on the number of spectra per strain, species or bacterial genus (sheet #2), numbers of strains per species, or genus (sheet #3), etc.</p> <p>The RKI mass spectrometry database is updated regularly.</p> <p>The author would like to thank the following individuals for providing microbial strains and species or mass spectra thereof. Without their help, this work would not have been 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, Nosocomial Pathogens and Antibiotic Resistances (FG13), Wernigerode, Germany</li> <li><strong>Alejandra Bosch</strong> - CINDEFI, CONICET-CCT 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, Daniela Jacob, Silke Klee, Susann Dupke </strong>and <strong>Holger Scholz</strong> - Robert Koch-Institute, Highly Pathogenic Microorganisms (ZBS2), Berlin, Germany</li> <li><strong>Jörg Rau </strong>- Chemisches und Veterinäruntersuchungsamt Stuttgart, Fellbach, Germany</li> <li><strong>Jens Jacob</strong> - Robert Koch-Institute, Hospital Hygiene, Infection Prevention and Control (FG14), Berlin, Germany</li> <li><strong>Martin Mielke</strong> - Robert Koch-Institute, Department 1 - Infectious Diseases, 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> <li><strong>Herbert Tomaso</strong><strong> </strong>– Friedrich-Löffler-Institut (FLI), Federal Research Institute for Animal Health, Jena, Germany</li> <li><strong>Gabriel Karner</strong><strong> </strong>- Karner Düngerproduktion GmbH, Research & Development, Neulengbach, Austria</li> <li><strong>Rainer </strong><strong>Borriss</strong><strong> </strong>- Institute of Marine Biotechnology e.V. (IMaB), Greifswald, Germany</li> <li><strong>Le Thi Thanh Tam</strong><strong> </strong>- Division of Plant Pathology and Phyto-Immunology, Plant Protection Research Institute, Hanoi, Socialist Republic of Vietnam</li> <li><strong>Xuewen</strong><strong> Gao</strong><strong> </strong>- College of Plant Protection, Nanjing Agricultural University, Key Laboratory of Integrated Management of Crop Diseases and Pests, Nanjing, People’s Republic of China</li> </ul> <p>For a detailed description of the database see: Lasch, P., Beyer, W., Bosch, A. <em>et al.</em> A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria. <em>Sci Data</em> <strong>12</strong>, 187 (2025). <a href="https://doi.org/10.1038/s41597-025-04504-z">https://doi.org/10.1038/s41597-025-04504-z</a></p>
MALDI-TOF spectra of bovine, caprine and ovine milk and their mixtures
<p>In this dataset we provide MALDI-TOF spectra of bovine milk in caprine and ovine milk. The dataset contains normalized spectra of pure milk samples and their mixtures. <strong>Different level of adulteration (0.5%, 1%, 5%, 10%, 20%, 40%, 60%, 80%)</strong> were analyzed throughout the lactation period of goat and sheep. Two different ranges of peptide-protein spectra <strong>(500–4000 Da; 4–20 kDa)</strong> were used. Exported spectra are in TXT files (<em>m/z</em> and intensity) for processing in other software (ex. mMass). Acquisition methods are included.</p> <p>MALDI-TOF MS analysis was performed using an Autoflex Speed (Bruker Daltonics, Germany) equipped with a SmartBeam<sup>TM</sup> II laser (355 nm) and flexControl software (version 3.4 Build 135, Bruker Daltonics, Germany).</p> <p> </p> <p>The dataset is a part of Supplementary file for the manuscript:</p> <p><em><strong>Evaluation of MALDI‐TOF MS technology in small ruminants’ milk adulteration using raw bovine milk</strong></em> by L. Rysova, P. Cejnar, O. Hanus, V. Legarova, J. Havlik, H. Nejeschlebova, I. Nemeckova, R. Jedelska, M. Bozik, submitted to <em>Journal of Dairy Science</em> (Manuscript ID JDS.2021-21396) on 08-Oct-2021.</p>
Evaluation of MALDI‐ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures - MALDI-ToF Mass Spectra
<p>Datasets in support of the <em>bioRxiv </em>submitted paper Doellinger et al. (<strong>2019</strong>) "<em>Evaluation of MALDI‐ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures"</em> - MALDI-ToF Mass Spectra.</p> <p>The experiment and sample description and spectra numbering is consistent with the publication. Mass spectral data files are provided as unprocessed raw data in the manufacturer's original data format (Bruker Daltonics). Data is compressed using the freely available 7zip software.</p> <p><strong>Content:</strong></p> <p><em><strong>Figure 1.zip</strong></em>: Cereulide detection in <em>B. cereus</em> samples cultivated using different cultivation media and different sample preparation, or cereulide extraction methods.</p> <p><em><strong>Figure 2.zip</strong></em>: Effectivity of cereulide extraction by different solvents from <em>B. cereus</em> F4810/72 colony material.</p> <p><em><strong>Figure 3.zip</strong></em>: MALDI LIFT-ToF /ToF MS spectrum of cereulide.</p> <p><em><strong>Figure 4.zip</strong></em>: Determination of the limit of detection (LOD) of cereulide by MALDI- and LDI-ToF</p> <p><em><strong>Table 1.zip</strong></em>: Analysis of cereulide in <em>B. cereus</em> strains by MALDI-ToF MS.</p> <p><em><strong>Fig.SI.01.zip: </strong></em> Ultraperformance Liquid Chromatography – Mass Spectrometry (UPLC-MS/MS) analysis of ethanolic washing solutions of <em>B. cereus</em> F4810/72.</p> <p><em><strong>Fig.SI.02.zip:</strong></em> A selection of MALDI-ToF and LDI-ToF technical replicate mass spectra obtained from a commercial cereulide standard.</p> <p><em><strong>Fig.SI.03.zip:</strong></em> Limit of detection (LOD) of cereulide determined by MALDI- and LDI-ToF MS of ethanol wash solutions from <em>B. cereus</em> ATCC 10987 spiked by a cereulide standard. </p> <p><em><strong>Fig.SI.04.zip:</strong></em> Direct cereulide detection by means of MALDI- (panels <strong>A</strong>-<strong>F</strong>) and LDI-ToF MS (panels <strong>G</strong>-<strong>M</strong>) in linear and reflectron measurement mode.</p>
Supplementary files - Evaluation of MALDI-TOF MS technology in small ruminant milk adulteration using raw bovine milk
<p>The dataset is a part of Supplementary file for the manuscript:</p> <p><strong>Evaluation of MALDI-TOF MS technology in small ruminant milk adulteration using raw bovine milk</strong> by L. Rysova, P. Cejnar, O. Hanus, V. Legarova, J. Havlik, H. Nejeschlebova, I. Nemeckova, R. Jedelska, M. Bozik, submitted to <em>Journal of Dairy Science</em> (Manuscript ID JDS.2021-21396), Received October 8, 2021, Accepted January 31, 2022, Corresponding author: bozik@af.czu.cz, <a href="https://doi.org/10.3168/jds.2021-21396">https://doi.org/10.3168/jds.2021-21396</a></p> <p><strong>File 1:</strong> Detailed MALDI-TOF method description</p> <p><strong>File 2: </strong>Quantification of milk adulteration – calibration of the model Quantification of milk adulteration – calibration of the model</p> <p><strong>Table S1: </strong>Baseline characteristics of pure bovine milk which was used as an adulterant of caprine milk<strong> </strong></p> <p><strong>Table S2: </strong>Baseline characteristics of pure bovine milk which was used as an adulterant of ovine milk</p> <p><strong>Table S3: </strong>Root mean squared error (RMSE) of predicted caprine and ovine adulterated milk samples using set A as the training set and set B as the test set.</p> <p><strong>Table S4: </strong>Root mean squared error (RMSE) of predicted caprine and ovine adulterated milk samples using both, set A and set B , as the one training set and set C as the test set.</p> <p><strong>Table S5: </strong>Root mean squared error (RMSE) of predicted caprine and ovine adulterated milk samples using set AB as the training set and set C as the test set.</p> <p>In this version <strong>SD values in Table S2 were corrected</strong>.</p>
MALDI-TOF spectra of archaeological (Oncorhynchus) and modern (Salmo salar) bone collagen
<p>SPECIES INFORMATION<br> csv containing information about the samples that links the information about the species and files</p> <p><br> MALDI TOF-MS</p> <p>MALDI Spectra from a Bruker Ultraflex II range m/z 800-3500<br> Three technical replicates were averaged in mMass<br> Each of these spectra a tab delimited .txt file are uploaded</p> <p><br> SEQUENCE DATA<br> An aligned FASTA file containing the bovine reference collagen sequence and both versions of S. salar and O. mykiss sequences. The sequences are concatenated with COL1A1, COL1A2, and COL1A3 for the two fish and COL1A1, COL1A2, COL1A1 for bovine.</p> <p>Three annotated gff files containing the sequence from version 1 of S. salar annotated with the locations of the published mammal markers and the biomarkers presented in this paper. Each gff file corresponds to one of the three collagen proteins COL1A1, COL1A2, and COL1A3.</p>
MALDI-TOF MS spectra data included in Dumolin et al. 2019
<p>MALDI-TOF MS and genome assembly data used for benchmarking of the SPeDE dereplication program.</p> <p> </p>
MS-UMG: MALDI-TOF Mass Spectra and Resistance Information on Antimicrobials from University Medical Center Göttingen
<p>During routine diagnostic procedures, we aggregated MALDI-TOF MS data of organisms isolated from clinical specimens from the University Medical Center Göttingen (UMG) in 2020 / 2021. We integrated these with corresponding antimicrobial susceptibility profiles. This amounted to 26,961 mass spectra and 26,961 corresponding metadata entries for the year 2020, and 50,381 mass spectra and 50,381 corresponding metadata entries for 2021, respectively. The dataset reflects 348 different species of bacterial and fungal organisms and 72 different antimicrobial susceptibility testing (AST) results.</p> <p> </p> <p>Please cite: </p> <div> <div>Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profiles on Direct Antimicrobial Resistance Prediction through Machine Learning</div> </div> <div><span><span><span>Youngjun</span> <span>Park</span></span>, <span><span>Michael</span> <span>Weig</span></span>, <span><span>Christine</span> <span>Noll</span></span>, <span><span>Oliver</span> <span>Bader</span></span>, <span><span>Anne-Christin</span> <span>Hauschild</span></span></span></div> <div><span>bioRxiv </span><span>2024.10.18.617592; </span><span><span>doi:</span> https://doi.org/10.1101/2024.10.18.617592</span></div>
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>
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>
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>
Data from: Comparison of rapid biodiversity assessment of meiobenthos using MALDI-TOF MS and metabarcoding
<p>Nowadays, most biodiversity assessments involving meiofauna are mainly carried out using very time-consuming, specimen-wise morphological identifications, which demands comprehensive taxonomic knowledge. Animals have to be examined for minor differences of setae compositions, mouthpart morphology or number of segments for various extremities. DNA-based methods such as metabarcoding as well as recently emerged rapid analyses using MALDI-TOF mass spectrometry to identify specimens based on a proteome fingerprint could vastly accelerate the process of specimen identification in biodiversity assessments. However, these techniques depend on reference libraries to connect collected data to morphologically described species. In this study the success rate of both approaches have been tested based on reference libraries constructed using part of the samples from a new study area to identify unknown samples. Using MALDI-TOF MS we found, that species which do not exist in an incomplete mass spectra reference library only have minor impact on the results, when employing a post hoc test for Random Forest classifications. This test reveals specimens that demand morphological re-examination for the final species assignment. Metabarcoding however strongly demands a rich reference library to provide correct MOTU assessments in congruence with morphological determination. Nevertheless, with a complete library and a suitable data transformation [herein log(x + 1)], the number of reads per MOTU reflects relative species abundances in metabarcoding inference. The results of this study facilitate specimen identification by using MALDI-TOF MS, which is incomparably cheap for specimen-by specimen identification, but when it comes to sample-wise analyses, metabarcoding outperforms other techniques by far.</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>
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>
Design and high-throughput implementation of MALDI-TOF/MS-based assays for Parkin E3 ligase activity
<p><strong>Summary</strong></p> <p><span>Parkinson’s disease (PD) is a progressive neurological disorder that manifests clinically as alterations in movement as well as multiple non-motor symptoms including but not limited to cognitive and autonomic abnormalities. Loss-of-function mutations in the gene encoding the ubiquitin E3 ligase Parkin are causal for familial and juvenile PD. Among several therapeutic approaches being explored to treat or improve PD patient’s prognosis, the use of small molecules able to reinstate or boost Parkin activity represents a potential pharmacological treatment strategy. A major barrier is the lack of high throughput platforms for the robust and accurate quantification of Parkin activity <em>in vitro</em>. Here we present two different and complementary Matrix Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF/MS) based approaches for the quantification of Parkin E3 ligase activity<em> in vitro</em>. Both approaches are scalable for high-throughput primary screening to facilitate the identification of Parkin modulators.</span></p>
DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra
<p>Early administration of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques can take up to 72 hours to generate results. We have developed a novel machine learning approach to predict antimicrobial resistance directly from MALDI-TOF mass spectra profiles of clinical samples. We trained calibrated classifiers on a newly-created publicly available database of mass spectra profiles from clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. The dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation against a panel of clinically important pathogens, including Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, resulting in AUROC values of 0.80, 0.74, and 0.74 respectively, demonstrated the potential of using machine learning to substantially accelerate antimicrobial resistance determination and change of clinical management. Furthermore, a retrospective clinical case study found that implementation of this approach would have resulted in a beneficial change in the clinical treatment in 88% (8/9) of cases. MALDI-TOF mass spectra based machine learning may thus be an important new tool for treatment optimization and antibiotic stewardship.</p>
MALDI-TOF MS data: Species delimitation of Hexacorallia and Octocorallia around Iceland using nuclear and mitochondrial DNA and proteome fingerprinting
<p>Cold-water corals build up reef structures or coral gardens and play an important role for many organisms in the deep sea. Climate change, deep-sea mining, and bottom trawling are severely compromising these ecosystems, making it all the more important to document the diversity, distribution, and impacts on corals. This goes hand in hand with species identification, which is morphologically and genetically challenging for Hexa- and Octocorallia. Morphological variation and slowly evolving molecular markers both contribute to the difficulty of species identification. In this study, a fast and cheap species delimitation tool for Octocorallia and Scleractinia of the Northeast Atlantic was tested based on 49 specimens. Two nuclear markers (ITS2 and 28S rDNA) and two mitochondrial markers (COI and mtMutS) were sequenced. The sequences formed the basis of a reference library for comparison to the results of species delimitation based on proteomic analysis using the MALDI-TOF MS method. The genetic methods were able to distinguish 17 of 18 presumed species. The MALDI-TOF MS method was able to distinguish 7 species. Species that could not be distinguished from one another still achieved good signals but were not represented by enough specimens for comparison. Therefore, it is predicted that with an extensive reference library of proteome spectra for Scleractinia and Octocorallia, MALDI-TOF MS may provide a rapid and cost-effective alternative for species discrimination in corals.</p>
Direct prediction for carbapenemase-producing and colistin-resistant Klebsiella pneumoniae isolates from routine MALDI-TOF mass spectrum using machine learning
<p>The emergence of carbapenem-nonsusceptible K. pneumoniae (CnSKP) leads a serious threat to patient survival and colistin resistance makes the treatment of CnSKP more difficultly. To make treatment strategy properly and quickly, we aimed to develop a rapid prediction method for CnSKP and colistin-resistant K. pneumoniae (ColRKP) based on the spectra of routine matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI–TOF MS). The machine learning (ML) model for differentiating CnSKP and carbapenem-susceptible K. pneumoniae (CSKP) showed accuracy of 0.8869 and AUC of 0.9551; the model for ColRKP and colistin-intermediate K. pneumoniae (ColIKP) showed accuracy of 0.8361 and the AUC of 0.8447.</p>
Rapid pathogen identification in aqueous humor samples by combining Fc-MBL@Fe3O4 enrichment and MALDI-TOF MS profiling
<p>Prompt clinical diagnosis and antimicrobial therapy are key to managing infective endophthalmitis. The small volume of aqueous humor, low bacterial counts, and empirical medication by physicians make existing diagnostic methods time-consuming and imprecise. Here, we investigated the feasibility of combining Fc-MBL@Fe3O4 enrichment with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling to identify pathogens in aqueous humor. Aqueous humor aspirated from freshly enucleated porcine eyes was inoculated with different inocula of <em>Staphylococcus aureus</em> (<em>S. aureus</em>), <em>Staphylococcus </em><em>epidermidis</em> (<em>S. epidermidis</em>), and <em>Klebsiella pneumoniae</em> (<em>K. pneumoniae</em>). We performed identification directly in aqueous humor samples and after short-term culture of micro-LB broth. Aqueous humor endophthalmitis samples were enriched with Fc-MBL@Fe3O4 and analyzed with MALDI-TOF MS. The identification time and minimum bacterial concentration required for identification were determined. The enrichment efficiency of Fc-MBL@Fe3O4 for different bacteria was > (87.5±5.0)%. The objects of direct identification include live bacteria and bacteria treated with antibiotics, which can be completed within 1.5 hours. The minimum number of bacteria needed for positive identification was 2.20×10<sup>6 </sup>CFU. For micro-LB broth culture, the identification of bacteria can be completed within 6.5-9.5 h for aqueous humor samples with an initial bacterial count of tens to hundreds.</p>
A comparison of minimally-invasive sampling techniques for ZooMS analysis of bone artifacts: MALDI-TOF mass spectra
<p><span></span></p> <p>Bone and antler are important raw materials for tool manufacture in many cultures, past and present. The modification of osseous features which take place during artifact manufacture frequently makes it difficult to identify either the bone element or the host animal, which can limit our understanding of the cultural, economic, and/or symbolic factors which influence raw material acquisition and use. While biomolecular approaches can provide taxonomic identifications of bone or antler artifacts, these methods are frequently destructive, raising concerns about invasive sampling of culturally-important artifacts or belongings. Collagen peptide mass fingerprinting (Zooarchaeology by Mass Spectrometry or ZooMS) can provide robust taxonomic identifications of bone and antler artifacts. While the ZooMS method commonly involves destructive subsampling, minimally-invasive sampling techniques based on the triboelectric effect have also been proposed. In this paper, we compare three previously proposed minimally-invasive sampling methods (forced bag, eraser, and polishing film) on an assemblage of 15 bone artifacts from the pre-contact site EjTa-4, a large midden complex located on Calvert Island, British Columbia, Canada. We compare the results of the minimally-invasive methods to 10 fragmentary remains sampled using the conventional destructive ZooMS method. We assess the reliability and effectiveness of these methods by comparing MALDI-TOF spectral quality, the number of diagnostic and high molecular weight peaks as well as the taxonomic resolution reached after identification. We find that coarse fiber-optic polishing films are the most effective of the minimally-invasive techniques compared in this study, and that the spectral quality produced by this minimally-invasive method was not significantly different from the conventional destructive method. Our results suggest that this minimally-invasive sampling technique for ZooMS can be successfully applied to culturally significant artifacts, providing comparable taxonomic identifications to the conventional, destructive ZooMS method.</p>
MALDI-TOF MS spectra of archaeological whale bone specimens from Atlantic Europe
<p class="MsoNormal"><span>Whale bones are regularly found during archaeological excavations. Identification of these specimens to taxonomic levels is problematic due to their fragmented state. This lack of taxonomic resolution limits understanding of the past spatiotemporal distributions of whale populations and reconstructions of early whaling activities. To overcome this challenge, we performed Zooarchaeology by Mass-Spectrometry on an unprecedented selection of 719 archaeological and palaeontological specimens of probable whale bone from Atlantic European contexts, from the Middle to Late Neolithic (c.3500–2500 BCE) to the eighteenth century CE.</span></p> <p class="MsoNormal"><span>The results show high numbers of Balaenidae (most likely North Atlantic right whale (<em>Eubalaena glacialis</em>)) and grey whale (<em>Eschrichtius robustus</em>) specimens, two species no longer present in the eastern North Atlantic. Many of these specimens derive from contexts associated with the known medieval whaling cultures of the Basques, northern Spaniards, Normans, Flemish, Frisians, Anglo-Saxons, and Scandinavians. This association raises the likelihood that pre-industrial whaling impacted these taxa, contributing to their extinction and extirpation respectively. Much lower numbers of other large whale taxa were identified, suggesting that it was once abundant and accessible whales that suffered the greatest long-term impact. The pattern of natural abundance leading to over-exploitation, well-documented for other taxa, is thus applicable to early whaling. </span></p>
ScienceDex guides
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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.