Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling

Abstract

Nowadays control of the growth of Saccharomyces to obtain biomass or cellular wall components is crucial for specific industrial applications. The general aim of this contribution is to deal with experimental data obtained from yeast cells and from yeast cultures to attempt the integration of the two levels of information, individual and population, to progress in the control of yeast biotechnological processes by means of the overall analysis of this set of experimental data, and to assist in the improvement of an individual-based model, namely, INDISIM-Saccha. Populations of S. cerevisiae growing in liquid batch culture, in aerobic and microaerophilic conditions, were studied. A set of digital images was taken during the population growth, and a protocol for the treatment and analyses of the images obtained was established. The piecewise linear model of Buchanan was adjusted to the temporal evolutions of the yeast populations to determine the kinetic parameters and changes of growth phases. In parallel, for all the yeast cells analyzed, values of direct morphological parameters, such as area, perimeter, major diameter, minor diameter, and derived ones, such as circularity and elongation, were obtained. Graphical and numerical methods from descriptive statistics were applied to these data to characterize the growth phases and the budding state of the yeast cells in both experimental conditions, and inferential statistical methods were used to compare the diverse groups of data achieved. Oxidative metabolism of yeast in a medium with oxygen available and low initial sugar concentration can be taken into account in order to obtain a greater number of cells or larger cells. Morphological parameters were analyzed statistically to identify which were the most useful for the discrimination of the different states, according to budding and/or growth phase, in aerobic and microaerophilic conditions. The use of the experimental data for subsequent modeling work was then discussed and compared to simulation results generated with INDISIM-Saccha, which allowed us to advance in the development of this yeast model, and illustrated the utility of data at different levels of observation and the needs and logic behind the development of a microbial individual-based model.

Economical lipid production from Trichosporon oleaginosus via dissolved oxygen adjustment and crude glycerol addition

“…Intermediate products including NADPH and monosaccharides generated during respiration process are utilized to synthesize lipids and polysaccharides with the consumption of ATP in anabolism. Studies have revealed that oxygen role in the production of energy storage products (lipids and polysaccharides) (Atashrouz et al 2018, Ginovart et al 2018. It indicates that oxygen is essential in the cell division and lipid accumulation of oleaginous microorganisms.…”

Yeast Morphology Assessment through Automated Image Analysis during Fermentation

“…The automated analysis of images facilitates the examination of microorganisms at specific times throughout a process, providing instantaneous information concerning the parameters assessed. Typically, automated image analysis is divided into three stages: image processing, variable acquisition and statistical analysis [12]. Automated analysis of yeast cell images is not a novel concept and is most commonly used to assess the number of cells suspended within media.…”

Synthetic Pesticides Used in Agricultural Production Promote Genetic Instability and Metabolic Variability in Candida spp.

“…Observed cell heterogeneity may also be a result of disorders in the cell cycle of the analyzed strains after treatment with selected concentrations of pesticides [ 60 ]. In addition, the heterogeneity of cells to oxidative stress responses and increased levels of ROS can enhance the potential of interacting with DNA and cause genotoxicity leading to recombination and diverse mutations [ 61 , 62 ].…”