Meso-Raman approach for rapid yeast cells identification

Martina Alunni Cardinali; Debora Casagrande Pierantoni; Silvia Caponi; Laura Corte; Daniele Fioretto; Gianluigi Cardinali

doi:10.1016/j.bpc.2019.106249

Meso-Raman approach for rapid yeast cells identification

Biophys Chem. 2019 Nov:254:106249. doi: 10.1016/j.bpc.2019.106249. Epub 2019 Aug 14.

Authors

Martina Alunni Cardinali¹, Debora Casagrande Pierantoni², Silvia Caponi³, Laura Corte², Daniele Fioretto⁴, Gianluigi Cardinali⁵

Affiliations

¹ Department of Physics and Geology, University of Perugia, via Pascoli, I-06123 Perugia, Italy.
² Department of Pharmaceutical Sciences, University of Perugia, via del Liceo 1, I-06123 Perugia, Italy.
³ Institute of Materials, National Research Council (IOM-CNR), Unit of Perugia, c/o Department of Physics and Geology, University of Perugia, Via A. Pascoli, I-06123 Perugia, Italy. Electronic address: silvia.caponi@cnr.it.
⁴ Department of Physics and Geology, University of Perugia, via Pascoli, I-06123 Perugia, Italy; CEMIN-Excellence Research Center, University of Perugia, via Pascoli, I-06123 Perugia, Italy.
⁵ Department of Pharmaceutical Sciences, University of Perugia, via del Liceo 1, I-06123 Perugia, Italy; CEMIN-Excellence Research Center, University of Perugia, via Pascoli, I-06123 Perugia, Italy.

PMID: 31454612
DOI: 10.1016/j.bpc.2019.106249

Abstract

An increasing effort is currently devoted to developing Raman spectroscopy for identification of microorganisms. Micro-Raman setups are typically used for this purpose with the limit that the intra-species and inter-species spectral variability are comparable, thus limiting the identification capability. To overcome this limit a meso-Raman approach is here implemented. Thin films of planktonic cells are analyzed throughout the collection of back-scattered light providing a Raman signal already averaged over tens of cells. The collecting of unpolarized (VU) and depolarized (HV) Raman signals increased the spectral information obtainable from the data, demonstrating the ability of the principal component analysis to differentiate the most common Candida species, namely C. glabrata, C. albicans, C. parapsilosis and C. tropicalis. The proposed method can contribute to bring Raman spectroscopy closer to its potential clinical use for fast identification of yeast cells.

Keywords: Candida yeasts; Light scattering; Phenotypic identification; Raman microscopy; Vibrational spectroscopy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Candida / chemistry*
Candida / isolation & purification
Principal Component Analysis
Spectrum Analysis, Raman / methods*