Criteria for Development of MALDI‐TOF Mass Spectral Database

Kostrzewa, Markus; Maier, Τ.

doi:10.1002/9781118960226.ch2

Cited by 10 publications

(5 citation statements)

References 50 publications

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“…However, most of the peaks are unannotated, leaving these spectra as a black box. Theoretically, many of these peaks are believed to be ribosomal subunit proteins [2], and identification protocol using these proteins are being developed (the S10-GERMS method) [3]. However, the peaks of the ribosomal subunits are relatively small, and most of the key prominent peaks for species identification remain unidentified [4].…”

Section: Introductionmentioning

confidence: 99%

What Do We See in Spectra?: Assignment of High-Intensity Peaks of Cutibacterium and Staphylococcus Spectra of MALDI-TOF Mass Spectrometry by Interspecies Comparative Proteogenomics

et al. 2021

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Matrix-assisted laser-desorption/ionization time-of-flight (MALDI–TOF) mass spectrometry is a widely used and reliable technology to identify microbial species and subspecies. The current methodology is based on spectral fingerprinting, analyzing protein peaks, most of which are yet to be characterized. In order to deepen the understanding of these peaks and to develop a more reasonable identification workflow, we applied proteogenomic approaches to assign the high-intensity peaks of MALDI–TOF spectra of two bacterial genera. First, the 3–22 kD proteomes of 5 Cutibacterium strains were profiled by UPLC–MS/MS, and the amino acid sequences were refined by referring to their genome in the public database. Then, the sequences were converted to m/z (x-axis) values based on their molecular masses. When the interspecies comparison of calculated m/z values was well-fitted to the observed peaks, the peak assignments for the five Cutibacterium species were confirmed. Second, the peak assignments for six Staphylococcus species were performed by using the above result for Cutibacterium and referring to ribosomal subunit proteins coded on the S10-spc-alpha operon (the S10-GERMS method), a previous proteomics report by Becher et al., and comprehensive genome analysis. We successfully assigned 13 out of 15 peaks for the Cutibacterium species and 11 out of 13 peaks for the Staphylococcus species. DNA-binding protein HU, the CsbD-like protein, and 50S ribosomal protein L7/L12 were observed in common. The commonality suggests they consist of high-intensity peaks in the MALDI spectra of other bacterial species. Our workflow may lead to the development of a more accurate species identification database of MALDI–TOF mass spectrometry based on genome data.

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Section: Introductionmentioning

confidence: 99%

What Do We See in Spectra?: Assignment of High-Intensity Peaks of Cutibacterium and Staphylococcus Spectra of MALDI-TOF Mass Spectrometry by Interspecies Comparative Proteogenomics

et al. 2021

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“…However, the interoperability of the MALDI-TOF methodology requires a standardized procedure in the conservation of samples and the choice of the mosquito body part of adult specimens 41 . For MS identi cation, standardization of procedures for preparation and reproducibility between instruments and homemade databases will be desirable [42][43][44][45] . Contrary to MALDI-TOF or DNAbarcoding methodologies, our method is not designed to identify Aedes at larval stages.…”

Section: Discussionmentioning

confidence: 99%

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest

CANNET,

SIMON-CHANE,

HISTACE

et al. 2023

Preprint

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Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest. Large scales and proactive entomological surveys of Aedes mosquitoes need skilled technicians and/or costly technical equipment, further puzzled by the vast amount of named species. In this study, we developed an automatic classification system of Aedes species by taking advantage of the species-specific fingerprinting displayed by Wing Interferential Patterns. A database containing 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and >85% for two (Ochlerotatus and Stegomyia) out of three subgenera tested. Lastly, eight were accurately classified among the 10 Aedes sp. that have undergone a training process with an overall accuracy of>70%. Altogether these results demonstrate the potential of this methodology for Aedes species identification and will represent a tool for the future implementation of large-scale entomological surveys.

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“…MALDI‐TOF‐MS biotyping analysis elicits the characteristic mass and peak density distribution of ribosomal 16S proteins in the sample. Since this mass spectrum is species‐specific for many microorganisms, it represents a “molecular fingerprint” (Kostrzewa & Maier, 2016). Spectra was determined using the PCA method supported by external MATLAB software integrated into the MALDI Biotyper.…”

Section: Methodsmentioning

confidence: 99%

Contribution of MALDI‐TOF‐MS‐based principal component analysis for distinguishing foodborne pathogens

Cevik

Kaynar

2023

Journal of Food Safety

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Foodborne diseases are important to determine bacteria in strain level, which are analyzed by library‐based devices and bioinformatics‐enabled. The aim of this study was to investigate the contribution of principal component analysis (PCA) with matrix‐assisted laser desorption ionization time‐of‐flight mass spectrometry (MALDI‐TOF‐MS) to distinguish according to the differences of bacterial strains as rapidly screening of foodborne bacteria. The MALDI‐TOF‐MS‐based PCA analysis was used for differentiating bacterial strains isolated from ready‐to‐eat foods. According to the results of PCA analysis, the percentages of distance and proximity between species were evaluated by composite correlation indexes (CCI). Bacillus cereus were detected in burghul salad (BC1) and macaroni salad (BC2) taken from the SB2‐snack bar, and the similarity rate was determined as 97%. Three other B. cereus bacteria (BC3, BC4, and BC5) in the same cluster were also isolated from salads collected from SB4‐snack bar. The similarity of Klebsiella pneumoniae bacteria, which have the codes KP1 and KP2, isolated from macaroni salad and burghul salad taken from the SB2 snack bar respectively were 96%. Additionally, the CCI values of two E. coli strains in burghul (EC1) salad and Russian salad (EC2) in the same sampling point (SB1) were determined as 97%. In conclusion, this analysis with MALDI‐TOF‐MS based PCA has revealed the relationships between bacteria genera and species, beyond just the identification of bacteria and the rapid screening of bacteria in perishable foodstuffs. Similarities between bacterial strains identified for different samples from the same sampling point suggested that there were not adequate hygiene rules and storage requirements were not followed.

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Criteria for Development of MALDI‐TOF Mass Spectral Database

Cited by 10 publications

References 50 publications

What Do We See in Spectra?: Assignment of High-Intensity Peaks of Cutibacterium and Staphylococcus Spectra of MALDI-TOF Mass Spectrometry by Interspecies Comparative Proteogenomics

What Do We See in Spectra?: Assignment of High-Intensity Peaks of Cutibacterium and Staphylococcus Spectra of MALDI-TOF Mass Spectrometry by Interspecies Comparative Proteogenomics

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest

Contribution of MALDI‐TOF‐MS‐based principal component analysis for distinguishing foodborne pathogens

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