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Related Concept Videos

Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
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Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy.

Jonathan W Young1, James C W Locke, Alphan Altinok

  • 1Division of Biology, California Institute of Technology, Pasadena, USA.

Nature Protocols
|December 20, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a quantitative single-cell time-lapse microscopy protocol for analyzing bacterial gene circuits. The method enables detailed tracking of gene expression dynamics and cell behavior in individual bacteria like Escherichia coli.

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Area of Science:

  • Microbiology
  • Molecular Biology
  • Biophysics

Background:

  • Quantitative single-cell microscopy is crucial for understanding gene circuit dynamics.
  • Bacterial cell behavior and gene regulation exhibit significant heterogeneity.
  • Automated microscopy offers a powerful approach to capture dynamic cellular processes.

Purpose of the Study:

  • To describe and validate a protocol for quantitative single-cell time-lapse microscopy of bacteria.
  • To enable detailed analysis of gene regulation and cell differentiation in microbial systems.
  • To provide a method for generating quantitative expression data for mathematical modeling.

Main Methods:

  • Utilizing automated microscopy systems for bacterial imaging.
  • Employing agarose pads for culturing and imaging bacterial microcolonies.
  • Applying custom MATLAB code for cell segmentation, tracking, and lineage analysis.
  • Acquiring time-lapse image series of bacterial growth and gene expression.

Main Results:

  • Successfully applied the protocol to Bacillus subtilis and Escherichia coli.
  • Quantified gene regulation and fluctuations in individual bacterial cells.
  • Generated cell lineage data revealing dynamic expression profiles.
  • Demonstrated the feasibility of image acquisition within 1 day for fast-growing bacteria.

Conclusions:

  • The described protocol provides a robust method for quantitative single-cell analysis in bacteria.
  • This approach facilitates the study of complex gene circuits and cellular heterogeneity.
  • The generated data supports the development and validation of mathematical models for gene regulation.