Application of TOF-SIMS with Chemometrics To Discriminate between Four Different Yeast Strains from the Species Candida glabrata and Saccharomyces cerevisiae

Abstract

We present a TOF-SIMS analysis of the cell surface differences between four yeast strains from two species, Candida glabrata and Saccharomyces cerevisiae (haploid strains BY4742 and BY4741 and the derived diploid BY4743). The study assesses the suitability of TOF-SIMS analysis in combination with statistical methods (principal component analysis, Fisher's discriminant analysis, and cluster analysis) for the discrimination between the four yeast strains. We demonstrate that a combination of these statistical methods identifies 34 ions, from a total data set of 1200, which can be used to distinguish between the four yeasts. The study discusses the assignments of surface cell membrane phospholipids for the identified ions and the resulting differences in the phospholipid pattern between the four yeasts, particularly in relation to ploidy and budding pattern. The method shows that fatty acids, phosphatidylglycerols, phosphatidylethanolamines, phosphatidylserines, and phosphatidylcholines, as well as cardiolipins, are of diagnostic importance.

Metabolomic biomarkers correlating with hepatic lipidosis in dairy cows

“…First, each sample was standardized to the mean of the control (set to 100%) and normalized using z-score values. Then, a MANOVA (multivariate analysis of variance) was used for compound selection [24,25]. All 29 variables that showed a significant group difference (p ≤ 0.006) were selected for a principal component analysis-linear discriminant function model.…”

Imaging and differentiation of mouse embryo tissues by ToF-SIMS

“…Wagner and Castner have used PCA and singular value decomposition to successfully cluster ToF-SIMS mass spectra generated from samples of single proteins and from samples of alkanethiol self-assembled monolayers, adsorbed onto gold substrates [22,[27][28][29]. Statistical analysis of ToF-SIMS spectra has also been employed to distinguish three species of freeze-dried yeasts based on membrane phospholipids [11] and to discriminate four yeast strains based on composite spectra from samples of yeast cultures [30]. Vegetative Bacillis cells were discriminated from spores based on ToF-SIMS analysis of phospholipid fragments [25].…”

Spectroscopy in the analysis of bacterial and eukaryotic cell footprints on implant surfaces

“…In contrast to XPS, this method seems to have potential for separation of mammalian and bacterial footprints. Some previous experiments have applied TOF-SIMS for cellular material and have successfully discriminated, for example, between different yeast strains (Jungnickel et al, 2005), different breast cancer cell types (Kulp et al, 2006) and living and dead bacterial cells (Tyler et al, 2006). These studies, however, are significantly different from ours as they studied the actual cellular materials whereas we used TOF-SIMS to discriminate the infected surfaces from the non-infected surfaces after careful cleaning and removal of the cellular material (cells) from these surfaces.…”

“…In the field of microbiology, ToF-SIMS has been applied to imaging colonies of microbes (Tyler et al, 2006, Esquenazi et al, 2009, to characterise intact, individual bacterial spores in 3D (Ghosal et al, 2008) and to detect antibiotics within intact bacterial colony biofilms (Gasper et al, 2008). Some previous experiments have applied TOF-SIMS to cellular material and have successfully discriminated between different yeast strains (Jungnickel et al, 2005) and different breast cancer cell types (Kulp et al, 2006). Here, we extend these earlier findings further, attempting to distinguish between two different cell types based on their footprints, following detachment of the cells from their matrix, rather than trying to distinguish the cells themselves.…”