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

Abstract 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 bacte...

Full description

Saved in:
Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04504-z
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract 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.
ISSN:2052-4463