Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification

Giuseppe Pezzotti; Miyuki Kobara; Tenma Asai; Tamaki Nakaya; Nao Miyamoto; Tetsuya Adachi; Toshiro Yamamoto; Narisato Kanamura; Eriko Ohgitani; Elia Marin; Wenliang Zhu; Ichiro Nishimura; Osam Mazda; Tetsuo Nakata; Koichi Makimura

doi:10.3389/fmicb.2021.769597

Raman Imaging of Pathogenic Candida auris: Visualization of Structural Characteristics and Machine-Learning Identification

Front Microbiol. 2021 Nov 12:12:769597. doi: 10.3389/fmicb.2021.769597. eCollection 2021.

Authors

Giuseppe Pezzotti^{1

2

3

4

5}, Miyuki Kobara⁶, Tenma Asai^{1

2}, Tamaki Nakaya^{1

4}, Nao Miyamoto⁴, Tetsuya Adachi⁴, Toshiro Yamamoto⁴, Narisato Kanamura⁴, Eriko Ohgitani², Elia Marin^{1

4}, Wenliang Zhu¹, Ichiro Nishimura⁷, Osam Mazda², Tetsuo Nakata⁶, Koichi Makimura⁸

Affiliations

¹ Ceramic Physics Laboratory, Kyoto Institute of Technology, Kyoto, Japan.
² Department of Immunology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
³ Department of Orthopedic Surgery, Tokyo Medical University, Shinjuku-ku, Tokyo, Japan.
⁴ Department of Dental Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
⁵ The Center for Advanced Medical Engineering and Informatics, Osaka University, Suita, Osaka, Japan.
⁶ Division of Pathological Science, Department of Clinical Pharmacology, Kyoto Pharmaceutical University, Kyoto, Japan.
⁷ Division of Advanced Prosthodontics, The Jane and Jerry Weintraub Center for Reconstructive Biotechnology, UCLA School of Dentistry, Los Angeles, CA, United States.
⁸ Medical Mycology, Graduate School of Medicine, Teikyo University, Itabashi-ku, Tokyo, Japan.

Abstract

Invasive fungal infections caused by yeasts of the genus Candida carry high morbidity and cause systemic infections with high mortality rate in both immunocompetent and immunosuppressed patients. Resistance rates against antifungal drugs vary among Candida species, the most concerning specie being Candida auris, which exhibits resistance to all major classes of available antifungal drugs. The presently available identification methods for Candida species face a severe trade-off between testing speed and accuracy. Here, we propose and validate a machine-learning approach adapted to Raman spectroscopy as a rapid, precise, and labor-efficient method of clinical microbiology for C. auris identification and drug efficacy assessments. This paper demonstrates that the combination of Raman spectroscopy and machine learning analyses can provide an insightful and flexible mycology diagnostic tool, easily applicable on-site in the clinical environment.

Keywords: Candida auris; Raman imaging; Raman spectroscopy; ergosterol; glucans; machine-learning.