at Google ScholarTitle data
Pistiki, Aikaterini ; Monecke, Stefan ; Shen, Haodong ; Ryabchykov, Oleg ; Bocklitz, Thomas ; Rösch, Petra ; Ehricht, Ralf ; Popp, Jürgen:
Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates.
In: Microbiology Spectrum.
Vol. 10
(2022)
Issue 5
.
- e00763-22.
ISSN 2165-0497
DOI: https://doi.org/10.1128/spectrum.00763-22
Abstract in another language
Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | MRSA; Raman spectroscopy; 785 nm fiber probe; 532-nm single-cell analysis; Raman microscopy; UV resonance Raman spectroscopy; single-cell analysis |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 31 May 2023 11:57 |
| Last Modified: | 31 May 2023 11:57 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/81081 |