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Flow Cytometry01:23

Flow Cytometry

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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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Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
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Single-cell multi-parametric characterization of microbiota by flow cytometry.

Lisa Budzinski1, Andreas Radbruch2, Hyun-Dong Chang1

  • 1Deutsches Rheuma-Forschungszentrum Berlin, a Leibniz Institute, Berlin, Germany; Institute for Biotechnology, Technische Universität Berlin, Berlin, Germany.

Methods in Cell Biology
|May 5, 2024
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Summary

This study introduces a novel flow cytometry protocol for single-cell analysis of human microbiota. This method enables detailed characterization of gut bacteria, advancing microbiome research.

Keywords:
AntibodyFlow cytometryMicrobiotaSelf-organizing map

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Area of Science:

  • Microbiology
  • Immunology
  • Biotechnology

Background:

  • The human microbiota significantly impacts health and disease.
  • Current microbiota analysis methods (culturomics, sequencing, metaproteomics) have limitations in single-cell resolution.
  • Single-cell analysis of bacteria lags behind that of host immune cells.

Purpose of the Study:

  • To present a protocol for single-cell characterization of bacterial cells from complex microbiota samples.
  • To enable detailed analysis of the human microbiota at the single-cell level using multi-parametric flow cytometry.

Main Methods:

  • Development of a multi-parametric flow cytometry protocol for single-cell bacterial analysis.
  • Isotype-specific detection of host-antibody coating on intestinal bacteria ex vivo.
  • Quantitative DNA staining, light scatter detection, and cryoconservation for reliable data.
  • Automated analysis of multi-dimensional data using a self-organizing map (SOM) algorithm.

Main Results:

  • Establishment of a reproducible protocol for single-cell microbiota fingerprinting.
  • Demonstration of host-antibody coating detection on individual bacteria.
  • Automated data analysis enabling comparative microbiome studies.

Conclusions:

  • The developed protocol provides a powerful tool for single-cell characterization of the human microbiota.
  • This approach enhances our understanding of the host-microbe interactions.
  • The protocol is adaptable for integrating additional phenotypic markers and advancing analytical cytometry for bacteria.