Katharina Anna Frings, Rumjhum Mukherjee, Vivien Schulze, Nils Heine, Nicolas Debener, Janina Bahnemann, Szymon Piotr Szafrański, Meike Stiesch, Katharina Doll-Nikutta, Maria Leilani Torres-Mapa, Alexander Heisterkamp
{"title":"Differentiation and identification of commensal and pathogenic oral bacteria at strain level using ATR-FTIR spectroscopy","authors":"Katharina Anna Frings, Rumjhum Mukherjee, Vivien Schulze, Nils Heine, Nicolas Debener, Janina Bahnemann, Szymon Piotr Szafrański, Meike Stiesch, Katharina Doll-Nikutta, Maria Leilani Torres-Mapa, Alexander Heisterkamp","doi":"10.1039/d5an00165j","DOIUrl":null,"url":null,"abstract":"The correct identification of different bacteria is a critical task in clinical applications and basic research especially in the oral cavity which has a complex bacterial community. Complementary to a variety of phenotyping and genotyping methods, we propose FTIR spectroscopy as a fast and non-destructive technique for accurate bacterial identification. This technique can be used to investigate the chemical makeup of a given sample and also allows for bacterial classification at strain level. In this work, we investigate the ability of ATR-FTIR spectroscopy to identify different oral bacteria from known laboratory stains as well as strains from patient-derived samples. Using this technique, six measured species could be classified with high accuracy (> 97 %) using chemometric models. Furthermore, the model which was only trained with laboratory strains could still correctly identify the patient-derived strains at the genus level. These results open the possibility of constructing a simplified tailored classification model based only on a target species and few other representative species, while still being able to distinguish the target species from a much larger number of other bacterial species for application to oral microbial communities.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"92 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analyst","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1039/d5an00165j","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The correct identification of different bacteria is a critical task in clinical applications and basic research especially in the oral cavity which has a complex bacterial community. Complementary to a variety of phenotyping and genotyping methods, we propose FTIR spectroscopy as a fast and non-destructive technique for accurate bacterial identification. This technique can be used to investigate the chemical makeup of a given sample and also allows for bacterial classification at strain level. In this work, we investigate the ability of ATR-FTIR spectroscopy to identify different oral bacteria from known laboratory stains as well as strains from patient-derived samples. Using this technique, six measured species could be classified with high accuracy (> 97 %) using chemometric models. Furthermore, the model which was only trained with laboratory strains could still correctly identify the patient-derived strains at the genus level. These results open the possibility of constructing a simplified tailored classification model based only on a target species and few other representative species, while still being able to distinguish the target species from a much larger number of other bacterial species for application to oral microbial communities.