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

Autores
Bosch, María Alejandra Nieves
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
La lista completa de autores que integran el documento puede consultarse en el archivo.
Centro de Investigación y Desarrollo en Fermentaciones Industriales
Materia
Biología
MALDI-ToF MS
Highly pathogenic bacteria
Spectral databases
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/189484
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteriaBosch, María Alejandra NievesBiologíaMALDI-ToF MSHighly pathogenic bacteriaSpectral databasesToday, 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.La lista completa de autores que integran el documento puede consultarse en el archivo.Centro de Investigación y Desarrollo en Fermentaciones Industriales2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/189484enginfo:eu-repo/semantics/altIdentifier/issn/2052-4463info:eu-repo/semantics/altIdentifier/doi/10.1038/s41597-025-04504-zinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-01-07T13:36:27Zoai:sedici.unlp.edu.ar:10915/189484Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-01-07 13:36:28.215SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
title A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
spellingShingle A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
Bosch, María Alejandra Nieves
Biología
MALDI-ToF MS
Highly pathogenic bacteria
Spectral databases
title_short A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
title_full A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
title_fullStr A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
title_full_unstemmed A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
title_sort A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria
dc.creator.none.fl_str_mv Bosch, María Alejandra Nieves
author Bosch, María Alejandra Nieves
author_facet Bosch, María Alejandra Nieves
author_role author
dc.subject.none.fl_str_mv Biología
MALDI-ToF MS
Highly pathogenic bacteria
Spectral databases
topic Biología
MALDI-ToF MS
Highly pathogenic bacteria
Spectral databases
dc.description.none.fl_txt_mv 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.
La lista completa de autores que integran el documento puede consultarse en el archivo.
Centro de Investigación y Desarrollo en Fermentaciones Industriales
description 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.
publishDate 2025
dc.date.none.fl_str_mv 2025-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/189484
url http://sedici.unlp.edu.ar/handle/10915/189484
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2052-4463
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41597-025-04504-z
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
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dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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