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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Author Spotlight: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry
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Imaging Flow Cytometry to Study Biofilm-Associated Microbial Aggregates.

Michał Konieczny1,2, Peter Rhein2, Katarzyna Czaczyk1

  • 1Department of Biotechnology and Food Microbiology, Poznan University of Life Sciences, ul. Wojska Polskiego 28, 60-627 Poznan, Poland.

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Summary

This study introduces an advanced tool combining imaging flow cytometry and machine learning to analyze microbial biofilms on food processing surfaces. The method effectively characterizes microbial aggregates and their metabolic activity, aiding in detecting invasive biofilm forms.

Keywords:
biofilm dispersalbioimagingfood-processingmachine learningsingle-cell analysis

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Area of Science:

  • Microbiology
  • Food Science
  • Analytical Chemistry

Background:

  • Biofilms on food-processing surfaces pose significant contamination risks.
  • Understanding microbial aggregate structure and physiology is crucial for effective control.
  • Current methods for biofilm analysis lack high-throughput and detailed characterization capabilities.

Purpose of the Study:

  • To design an advanced analytical tool for precise characterization of microbial aggregates in food-processing biofilms.
  • To combine imaging flow cytometry with machine learning for biofilm analysis.
  • To evaluate the complexity and cellular metabolic activity of microbial aggregates.

Main Methods:

  • Biofilm samples were collected from food-processing lines at multiple points and time points.
  • Imaging flow cytometry was used to analyze microbial aggregates and singlets.
  • A machine learning protocol interpreted data on aggregate complexity and cellular metabolic activity (active, mid-active, nonactive cells).

Main Results:

  • Significant differences in microbial aggregate complexity were observed across different diagnostic points.
  • Bacterial aggregates predominantly consisted of active microbial cells (e.g., 75.3% on a mushroom crate).
  • The tool successfully discriminated between small and large microbial cell aggregates and assessed cell viability.

Conclusions:

  • The developed analytical tool provides detailed characterization of bacterial aggregates within biofilms.
  • Cellular aggregates play a protective role in the survival of active microbial cells.
  • This methodology offers high-throughput screening potential for detecting invasive biofilm forms in the food industry.