title: Nuclear Magnetic Resonance Spectroscopy-Based Identification of Yeast.
abstract: Rapid and robust high-throughput identification of environmental, industrial, or clinical yeast isolates is important whenever relatively large numbers of samples need to be processed in a cost-efficient way. Nuclear magnetic resonance (NMR) spectroscopy generates complex data based on metabolite profiles, chemical composition and possibly on medium consumption, which can not only be used for the assessment of metabolic pathways but also for accurate identification of yeast down to the subspecies level. Initial results on NMR based yeast identification where comparable with conventional and DNA-based identification. Potential advantages of NMR spectroscopy in mycological laboratories include not only accurate identification but also the potential of automated sample delivery, automated analysis using computer-based methods, rapid turnaround time, high throughput, and low running costs.We describe here the sample preparation, data acquisition and analysis for NMR-based yeast identification. In addition, a roadmap for the development of classification strategies is given that will result in the acquisition of a database and analysis algorithms for yeast identification in different environments.
authors.affiliation: Biomedical MRI Unit/MoSAIC, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, Herestraat 49, O&N 1, Box 505, Leuven, 3000, Belgium. uwe.himmelreich@med.kuleuven.be.
authors.forename: Uwe
authors.initials: U
authors.lastname: Himmelreich
authors.affiliation: Westmead Millennium Institute, Centre for Infectious Diseases and Microbiology, University of Sydney, Sydney, NSW, Australia.|Department of Infectious Diseases, Westmead Hospital, Westmead, NSW, Australia.
authors.forename: Tania C
authors.initials: TC
authors.lastname: Sorrell
authors.affiliation: Laboratory of Mycology, Applied Microbiology, Earth and Life Institute, Mycothèque de l'Université catholique de Louvain (BCCM/MUCL), Université catholique de Lovain, Louvain-la-Neuve, Belgium.
authors.forename: Heide-Marie
authors.initials: HM
authors.lastname: Daniel
mesh_terms: D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D009181:Mycoses / Q000175:diagnosis* / Q000382:microbiology; D015003:Yeasts / Q000302:isolation & purification*
keywords: Classification; Identification; NMR spectroscopy; Nuclear magnetic resonance; Yeast