A dataset for developing proteomic tools for pathogen detection via differential cell lysis of whole blood samples

This data descriptor presents a curated dataset for pathogen detection and identification ( Staphylococcus aureus , Pseudomonas aeruginosa , and Candida albicans ) directly from whole-blood samples. The dataset was created using differential cell lysis combined with rapid extraction, digestion, and...

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Veröffentlicht in:Scientific data 2024-10, Vol.11 (1), p.1105-8, Article 1105
Hauptverfasser: de Oliveira Veloso Rezende, Jéssica, Batista, Michel, Machado, Kelly Cavalcanti, Bandini, Thiago Bousquet, de Menezes, Igor Alexandre Côrtes, do Carmo De Stefani, Fernanda, Dias Mariano Santos, Marlon, Carvalho, Paulo Costa, Kurt, Louise Ulrich, Brant, Rodrigo Soares Caldeira, Morello, Luis Gustavo, Marchini, Fabricio Klerynton
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Sprache:eng
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Zusammenfassung:This data descriptor presents a curated dataset for pathogen detection and identification ( Staphylococcus aureus , Pseudomonas aeruginosa , and Candida albicans ) directly from whole-blood samples. The dataset was created using differential cell lysis combined with rapid extraction, digestion, and mass spectrometry-based proteomics. Our method offers a rapid diagnostic alternative to traditional culture, enabling timely disease management, such as sepsis. Highlighting our dataset’s uniqueness, it features a three-tier structure: Spectral Libraries of Pathogens for identifying peptide peaks for putative biomarkers; Spiked pathogen in blood MS data for biomarker panel optimization through varied concentration samples; and Parallel Reaction Monitoring (PRM) data from sepsis patients for validating our biomarker panel, achieving 83.3% sensitivity within seven hours without microbial enrichment culture. This dataset serves as a comprehensive reference for bioinformatic tool development and biomarker panel proposals, advancing microbial detection, antimicrobial resistance, and epidemiological studies.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03834-8