Application of Raman spectroscopy and machine learning for Candida auris identification and characterization.

Autor:	Xue J; Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, China.; State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China., Yue H; Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.; Beijing Key Laboratory of Basic Research With Traditional Chinese Medicine on Infectious Diseases, Beijing Institute of Chinese Medicine, Beijing, China., Lu W; State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China., Li Y; State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China., Huang G; Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China., Fu YV; State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.; Savaid Medical School, University of Chinese Academy of Sciences, Beijing, China.
Jazyk:	angličtina
Zdroj:	Applied and environmental microbiology [Appl Environ Microbiol] 2024 Nov 20; Vol. 90 (11), pp. e0102524. Date of Electronic Publication: 2024 Oct 29.
DOI:	10.1128/aem.01025-24
Abstrakt:	Candida auris, an emerging fungal pathogen characterized by multidrug resistance and high-mortality nosocomial infections, poses a serious global health threat. However, the precise and rapid identification and characterization of C. auris remain a challenge. Here, we employed Raman spectroscopy combined with machine learning to identify C. auris isolates and its closely related species as well as to predict antifungal resistance and key virulence factors at the single-cell level. The average accuracy of identification among all Candida species was 93.33%, with an accuracy of 98% for the clinically simulated samples. The drug susceptibility of C. auris to fluconazole and amphotericin B was 99% and 94%, respectively. Furthermore, the phenotypic prediction of C. auris yielded an accuracy of 100% for aggregating cells and 97% for filamentous cells. This proof-of-concept methodology not only precisely identifies C. auris at the clade-specific level but also rapidly predicts the antifungal resistance and biological characteristics, promising a valuable medical diagnostic tool to combat this multidrug-resistant pathogen in the future. Importance: Currently, combating Candida auris infections and transmission is challenging due to the lack of efficient identification and characterization methods for this species. To address these challenges, our study presents a novel approach that utilizes Raman spectroscopy and artificial intelligence to achieve precise identification and characterization of C. auris at the singe-cell level. It can accurately identify a single cell from the four C. auris clades. Additionally, we developed machine learning models designed to detect antifungal resistance in C. auris cells and differentiate between two distinct phenotypes based on the single-cell Raman spectrum. We also constructed prediction models for detecting aggregating and filamentous cells in C. auris , both of which are closely linked to its virulence. These results underscore the merits of Raman spectroscopy in the identification and characterization of C. auris , promising improved outcomes in the battle against C. auris infections and transmission. Competing Interests: The authors declare no conflict of interest.
Databáze:	MEDLINE
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