Dynamic single‐cell analysis of Saccharomyces cerevisiae under process perturbation: comparison of different methods for monitoring the intensity of population heterogeneity

Abstract

BACKGROUND Single cell biology has attracted a lot of attention in recent years and has led to numerous fundamental results pointing out the heterogeneity of clonal cell populations. In this context, microbial phenotypic heterogeneity under bioprocessing conditions needs to be further investigated. In this study, yeast based processes have been investigated by using on‐line flow cytometry (FC) in combination with a fluorescent transcriptional reporter (GFP) and viability fluorescence tags (propidium iodide, PI). Methods aiming at expressing the dispersion of these fluorescence tags among the yeast populations have been investigated for different bioreactor operating conditions. RESULTS Yeast viability was determined on the basis of PI uptake. Segregation between PI negative and positive subpopulations could be efficiently quantified on the basis of the mean‐to‐median ratio or the amplitude of the interquartile range. On the other hand, the same quantification could not be made for the segregation occurring at the level of GFP synthesis. Indeed, when cells were exposed to sub‐lethal or mild stresses (such as in scale‐down reactors) two GFP subpopulations could be visualized by real‐time FC, but quantification by one of the above‐mentioned methods was not possible. CONCLUSIONS Yeast population heterogeneity was observed in representative bioreactor operating conditions. Difficulties for the determination of segregation at the level of GFP synthesis point out the fact that one needs to understand the segregation mechanisms for the applied fluorescent reporters, to judge whether simple mathematical tools may be applied or if more sophisticated computational tools are needed for quantification of the microbial population segregation. © 2014 Society of Chemical Industry

Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity

“…Over recent years these reporters have been refined to highlight the metabolic potential of microbial cell factories (Box 2). From a process control perspective, data analysis must be addressed more specifically because it is difficult to quantify the degree of segregation of a microbial population on the basis of on-line parameters [34,35].…”

Phenotypic variability in bioprocessing conditions can be tracked on the basis of on‐line flow cytometry and fits to a scaling law

“…Since flow cytometry allows single cell analysis technique, cell‐to‐cell variability and noise in gene expression were also considered. However, our previous work pointed out that finding appropriate parameters for the quantification of the cell‐to‐cell variability in GFP synthesis is not a straightforward task [6, 7]. On the other hand, a lot of fundamental researches has been dedicated to the investigation of phenotypic noise for several model organisms, including microbial cells (mainly based on Escherichia coli , Bacillus subtilis and Saccharomyces cerevisiae ), and most of the results, as well as mathematical and statistical tools for the inference of noise, are available as genome‐scale database of biological noise comprising the degree of stochasticity of thousands of genes [8–11].…”

Investigation of cell line specific responses to pH inhomogeneity and consequences for process design

“…Since the temperature shift was performed at 50 h, delayed base addition affected the cells only after the exponential growth phase. It is well established for microbial cells, that stress resistance and growth are inversely correlated [39–41]. Since the decrease in temperature results in a decrease in growth rate, the stress resistance of the cells could be improved.…”