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Updated: Apr 19, 2026

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Characterization of Aquatic Biofilms with Flow Cytometry
Published on: June 6, 2018
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Biomass measurement by flow cytometry during solid-state fermentation of basidiomycetes
Susanne Steudler1, Ulrike Böhmer, Jost Weber
1Institut für Lebensmittel- und Bioverfahrenstechnik, Technische Universität Dresden, Dresden, Germany.
Summary
A new flow cytometry method rapidly quantifies fungal biomass during solid-state fermentation (SSF) by counting nuclei. This technique offers a faster, more accurate alternative to traditional biomass measurement methods in SSF processes.
Area of Science:
- Biotechnology
- Microbiology
- Bioprocess Engineering
Background:
- Solid-state fermentation (SSF) is crucial for cultivating basidiomycetes that produce lignocellulosic enzymes.
- Accurate fungal biomass quantification is vital for analyzing growth kinetics in SSF.
- Existing biomass determination methods for SSF are often laborious, time-consuming, or limited in applicability.
Purpose of the Study:
- To develop and optimize a rapid, reliable method for fungal biomass determination during SSF.
- To establish a flow cytometry-based approach for quantifying fungal nuclei.
- To validate the new method against established techniques like ergosterol determination.
Main Methods:
- Fungal biomass was cultivated using SSF on an organic substrate.
- Fungal nuclei were isolated after cell disruption.
- Nuclei were stained with SYTOX® Green and quantified using flow cytometry.
- A calibration curve correlating dry biomass to nuclei concentration was generated.
Main Results:
- The developed flow cytometry method provides rapid fungal biomass measurements.
- The method was optimized by testing various buffers and disruption techniques.
- Results showed good correlation with the ergosterol determination method.
- The technique is scalable from small cultures (15 mL) to larger reactors (10 L).
Conclusions:
- Flow cytometry-based fungal nuclei counting is a rapid and effective method for biomass determination in SSF.
- This approach overcomes limitations of traditional methods, offering broader applicability.
- The technique supports efficient monitoring and optimization of SSF processes for enzyme production.
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