ArticleMicrobial cell factories2012
Physiological heterogeneities in microbial populations and implications for physical stress tolerance.
Article in Microbial cell factories, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 74 citations in OpenAlex.
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- Functional insights from recombinant production of bacterial proteases in Saccharomyces cerevisiae.Microbial cell factories · 2025Article
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- microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.PloS one · 2022Article
- The CArchives of microbiology · 2021Article
- Menaquinone-mediated regulation of membrane fluidity is relevant for fitness of Listeria monocytogenes.Archives of microbiology · 2021Article
- Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses With a Toolbox of Biosensors.Frontiers in microbiology · 2021Article
- Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses.Microbial cell factories · 2020Article
- Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats.Frontiers in bioengineering and biotechnology · 2020Article
- Quantitative Flow Cytometry to Understand Population Heterogeneity in Response to Changes in Substrate Availability inFrontiers in bioengineering and biotechnology · 2019Article
- Diverse genetic error modes constrain large-scale bio-based production.Nature communications · 2018Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditionally average values of the whole population are considered when analysing microbial cell cultivations. However, a typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level. There are indications that such heterogeneity may be unfavourable on the one hand (reduces yields and productivities), but also beneficial on the other hand (facilitates quick adaptation to new conditions--i.e. increases the robustness of the fermentation process). Understanding and control of microbial population heterogeneity is thus of major importance for improving microbial cell factory processes.
resultsIn this work, a dual reporter system was developed and applied to map growth and cell fitness heterogeneities within budding yeast populations during aerobic cultivation in well-mixed bioreactors. The reporter strain, which was based on the expression of green fluorescent protein (GFP) under the control of the ribosomal protein RPL22a promoter, made it possible to distinguish cell growth phases by the level of fluorescence intensity. Furthermore, by exploiting the strong correlation of intracellular GFP level and cell membrane integrity it was possible to distinguish subpopulations with high and low cell membrane robustness and hence ability to withstand freeze-thaw stress. A strong inverse correlation between growth and cell membrane robustness was observed, which further supports the hypothesis that cellular resources are limited and need to be distributed as a trade-off between two functions: growth and robustness. In addition, the trade-off was shown to vary within the population, and the occurrence of two distinct subpopulations shifting between these two antagonistic modes of cell operation could be distinguished.
conclusionsThe reporter strain enabled mapping of population heterogeneities in growth and cell membrane robustness towards freeze-thaw stress at different phases of cell cultivation. The described reporter system is a valuable tool for understanding the effect of environmental conditions on population heterogeneity of microbial cells and thereby to understand cell responses during industrial process-like conditions. It may be applied to identify more robust subpopulations, and for developing novel strategies for strain improvement and process design for more effective bioprocessing.
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