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Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Quantifying microbial robustness in dynamic environments using microfluidic single-cell cultivation.

Luisa Blöbaum1,2, Luca Torello Pianale3, Lisbeth Olsson3

  • 1Multiscale Bioengineering, Technical Faculty, Bielefeld University, Bielefeld, Germany.

Microbial Cell Factories
|February 9, 2024
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Summary

Microbial robustness to environmental changes was assessed using dynamic microfluidic single-cell cultivation (dMSCC) and a novel pipeline. Longer glucose cycles increased ATP levels but reduced stability and increased cell heterogeneity.

Keywords:
BiosensorsDynamic environmentsLive-cell imagingMicrofluidic single-cell cultivationNutrient oscillationPopulation heterogeneitySaccharomyces cerevisiaeScale-down

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Area of Science:

  • Microbiology
  • Biotechnology
  • Systems Biology

Background:

  • Microbial function stability is crucial for laboratory and industrial applications.
  • Assessing robustness to dynamic environmental changes is challenging.
  • Dynamic microfluidic single-cell cultivation (dMSCC) offers precise environmental control and single-cell tracking.

Purpose of the Study:

  • To develop and apply a pipeline combining dMSCC and robustness quantification for microbial function stability analysis.
  • To investigate the performance and robustness of Saccharomyces cerevisiae under dynamic glucose feast-starvation cycles.
  • To assess microbial responses at population, subpopulation, and single-cell levels.

Main Methods:

  • Integration of dMSCC with a robustness quantification method.
  • Development of a semi-automated image and data analysis pipeline.
  • Exposure of Saccharomyces cerevisiae to glucose oscillations ranging from 1.5 to 48 minutes over 20 hours.

Main Results:

  • Specific growth rate decreased with longer oscillation intervals.
  • Intracellular ATP levels increased with longer oscillation intervals.
  • 48-minute oscillations yielded highest average ATP but lowest stability and highest heterogeneity.

Conclusions:

  • The developed pipeline enables robust investigation of microbial function stability in dynamic environments.
  • The strategy is automatable, parallelizable, and adaptable to various organisms and conditions.
  • Findings will guide microbial strain development and bioprocess optimization.