Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation

Background Microorganisms must respond to changes in their environment. Analysing the robustness of functions
(i.e. performance stability) to such dynamic perturbations is of great interest in both laboratory and industrial settings.
Recently, a quantification method capable of assessing the robustness of various functions, such as specific growth
rate or product yield, across different conditions, time frames, and populations has been developed for microorgan-
isms grown in a 96-well plate. In micro-titer-plates, environmental change is slow and undefined. Dynamic micro-
fluidic single-cell cultivation (dMSCC) enables the precise maintenance and manipulation of microenvironments,
while tracking single cells over time using live-cell imaging. Here, we combined dMSCC and a robustness quantifica-
tion method to a pipeline for assessing performance stability to changes occurring within seconds or minutes.
Results Saccharomyces cerevisiae CEN.PK113-7D, harbouring a biosensor for intracellular ATP levels, was exposed
to glucose feast-starvation cycles, with each condition lasting from 1.5 to 48 min over a 20 h period. A semi-auto-
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mated image and data analysis pipeline was developed and applied to assess the performance and robustness
of various functions at population, subpopulation, and single-cell resolution. We observed a decrease in specific
growth rate but an increase in intracellular ATP levels with longer oscillation intervals. Cells subjected to 48 min oscil-
lations exhibited the highest average ATP content, but the lowest stability over time and the highest heterogeneity
within the population.
Conclusion The proposed pipeline enabled the investigation of function stability in dynamic environments,
both over time and within populations. The strategy allows for parallelisation and automation, and is easily adaptable
to new organisms, biosensors, cultivation conditions, and oscillation frequencies. Insights on the microbial response
to changing environments will guide strain development and bioprocess optimisation.
Keywords Saccharomyces cerevisiae, Population heterogeneity, Dynamic environments, Scale-down, Biosensors, Live-
cell imaging, Microfluidic single-cell cultivation, Nutrient oscillation