Junjing Xue, Huizhen Yue, Weilai Lu, Yanying Li, Guanghua Huang, Yu Vincent Fu
{"title":"拉曼光谱和机器学习在白色念珠菌鉴定和表征中的应用","authors":"Junjing Xue, Huizhen Yue, Weilai Lu, Yanying Li, Guanghua Huang, Yu Vincent Fu","doi":"10.1128/aem.01025-24","DOIUrl":null,"url":null,"abstract":"<p><p><i>Candida auris,</i> 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 <i>C. auris</i> remain a challenge. Here, we employed Raman spectroscopy combined with machine learning to identify <i>C. auris</i> 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 <i>Candida</i> species was 93.33%, with an accuracy of 98% for the clinically simulated samples. The drug susceptibility of <i>C. auris</i> to fluconazole and amphotericin B was 99% and 94%, respectively. Furthermore, the phenotypic prediction of <i>C. auris</i> yielded an accuracy of 100% for aggregating cells and 97% for filamentous cells. This proof-of-concept methodology not only precisely identifies <i>C. auris</i> 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.</p><p><strong>Importance: </strong>Currently, combating <i>Candida auris</i> 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 <i>C. auris</i> at the singe-cell level. It can accurately identify a single cell from the four <i>C. auris</i> clades. Additionally, we developed machine learning models designed to detect antifungal resistance in <i>C. auris</i> 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 <i>C. auris</i>, both of which are closely linked to its virulence. These results underscore the merits of Raman spectroscopy in the identification and characterization of <i>C. auris</i>, promising improved outcomes in the battle against <i>C. auris</i> infections and transmission.</p>","PeriodicalId":8002,"journal":{"name":"Applied and Environmental Microbiology","volume":" ","pages":"e0102524"},"PeriodicalIF":3.9000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Raman spectroscopy and machine learning for <i>Candida auris</i> identification and characterization.\",\"authors\":\"Junjing Xue, Huizhen Yue, Weilai Lu, Yanying Li, Guanghua Huang, Yu Vincent Fu\",\"doi\":\"10.1128/aem.01025-24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Candida auris,</i> 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 <i>C. auris</i> remain a challenge. Here, we employed Raman spectroscopy combined with machine learning to identify <i>C. auris</i> 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 <i>Candida</i> species was 93.33%, with an accuracy of 98% for the clinically simulated samples. The drug susceptibility of <i>C. auris</i> to fluconazole and amphotericin B was 99% and 94%, respectively. Furthermore, the phenotypic prediction of <i>C. auris</i> yielded an accuracy of 100% for aggregating cells and 97% for filamentous cells. This proof-of-concept methodology not only precisely identifies <i>C. auris</i> 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.</p><p><strong>Importance: </strong>Currently, combating <i>Candida auris</i> 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 <i>C. auris</i> at the singe-cell level. It can accurately identify a single cell from the four <i>C. auris</i> clades. Additionally, we developed machine learning models designed to detect antifungal resistance in <i>C. auris</i> 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 <i>C. auris</i>, both of which are closely linked to its virulence. These results underscore the merits of Raman spectroscopy in the identification and characterization of <i>C. auris</i>, promising improved outcomes in the battle against <i>C. auris</i> infections and transmission.</p>\",\"PeriodicalId\":8002,\"journal\":{\"name\":\"Applied and Environmental Microbiology\",\"volume\":\" \",\"pages\":\"e0102524\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied and Environmental Microbiology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1128/aem.01025-24\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Environmental Microbiology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1128/aem.01025-24","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/29 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
白色念珠菌是一种新出现的真菌病原体,其特点是具有多重耐药性和高死亡率的院内感染,对全球健康构成严重威胁。然而,如何精确、快速地识别和鉴定白色念珠菌仍是一项挑战。在这里,我们采用拉曼光谱与机器学习相结合的方法来鉴定 Cullis 及其近缘种,并在单细胞水平上预测抗真菌耐药性和关键毒力因子。所有念珠菌物种的平均鉴定准确率为 93.33%,临床模拟样本的准确率为 98%。念珠菌对氟康唑和两性霉素 B 的药物敏感性分别为 99% 和 94%。此外,对阿氏杆菌的表型预测结果显示,聚集细胞的准确率为 100%,丝状细胞的准确率为 97%。这一概念验证方法不仅能在支系特异性水平上精确鉴定念珠菌,还能快速预测其抗真菌耐药性和生物学特征,有望成为未来对抗这种耐多药病原体的重要医疗诊断工具:目前,由于缺乏有效的鉴定和表征方法,抗击白色念珠菌感染和传播具有挑战性。为了应对这些挑战,我们的研究提出了一种新方法,利用拉曼光谱和人工智能在单细胞水平上实现对念珠菌的精确鉴定和表征。它可以从四个 C. auris 支系中准确识别单细胞。此外,我们还开发了机器学习模型,旨在检测 C. auris 细胞的抗真菌耐药性,并根据单细胞拉曼光谱区分两种不同的表型。我们还构建了用于检测 C. auris 中聚集细胞和丝状细胞的预测模型,这两种细胞都与 C. auris 的毒力密切相关。这些结果凸显了拉曼光谱在鉴定和表征法氏囊虫方面的优势,有望在抗击法氏囊虫感染和传播的斗争中取得更好的成果。
Application of Raman spectroscopy and machine learning for Candida auris identification and characterization.
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.
期刊介绍:
Applied and Environmental Microbiology (AEM) publishes papers that make significant contributions to (a) applied microbiology, including biotechnology, protein engineering, bioremediation, and food microbiology, (b) microbial ecology, including environmental, organismic, and genomic microbiology, and (c) interdisciplinary microbiology, including invertebrate microbiology, plant microbiology, aquatic microbiology, and geomicrobiology.