A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria

Vorname: Peter Lasch; Wolfgang Beyer; Alejandra Bosch; Rainer Borriss; Michal Drevinek; Susann Dupke; Monika Ehling-Schulz; Xuewen Gao; Roland Grunow; Daniela Jacob; Silke R Klee; Armand Paauw; Jörg Rau; Andy Schneider; Holger C. Scholz; Maren Stämmler; Le Thi Thanh Tam; Herbert Tomaso; Guido Werner; Joerg Doellinger

doi:10.1038/s41597-025-04504-z

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Title (eng)

A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria

Author

Vorname: Peter Lasch

Wolfgang Beyer

Alejandra Bosch

Rainer Borriss

Michal Drevinek

Susann Dupke

Monika Ehling-Schulz

University of Veterinary Medicine Vienna

Xuewen Gao

Roland Grunow

Daniela Jacob

Silke R Klee

Armand Paauw

Jörg Rau

Andy Schneider

Holger C. Scholz

Maren Stämmler

Le Thi Thanh Tam

Herbert Tomaso

Guido Werner

Joerg Doellinger

Abstract (eng)

Today, MALDI-ToF MS is an established technique to characterize and identify pathogenic bacteria. The technique is increasingly applied by clinical microbiological laboratories that use commercially available complete solutions, including spectra databases covering clinically relevant bacteria. Such databases are validated for clinical, or research applications, but are often less comprehensive concerning highly pathogenic bacteria (HPB). To improve MALDI-ToF MS diagnostics of HPB we initiated a program to develop protocols for reliable and MALDI-compatible microbial inactivation and to acquire mass spectra thereof many years ago. As a result of this project, databases covering HPB, closely related bacteria, and bacteria of clinical relevance have been made publicly available on platforms such as ZENODO. This publication in detail describes the most recent version of this database. The dataset contains a total of 11,055 spectra from altogether 1,601 microbial strains and 264 species and is primarily intended to improve the diagnosis of HPB. We hope that our MALDI-ToF MS data may also be a valuable resource for developing machine learning-based bacterial identification and classification methods.

Keywords (eng)

Spectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationBacteria Isolation & PurificationBacteria ClassificationDatabasesFactualHumans

Type (eng)

journal article

Language

[eng]

Persistent identifier

https://phaidra.vetmeduni.ac.at/o:3916

DOI

10.1038/s41597-025-04504-z

Is in series

Title (eng)