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Related Experiment Video

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ScanLag: High-throughput Quantification of Colony Growth and Lag Time
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Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application.

Julian Bär1, Mathilde Boumasmoud1, Roger D Kouyos1,2

  • 1Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Scientific Reports
|October 1, 2020
PubMed
Summary
This summary is machine-generated.

Colony Time-lapse application (ColTapp) quantifies bacterial growth heterogeneity. This tool helps analyze metabolic states and improve personalized antibiotic therapies by correcting lag time overestimation.

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Area of Science:

  • Microbiology
  • Bacterial Physiology
  • Computational Biology

Background:

  • Genetically identical bacteria exhibit phenotypic heterogeneity, crucial for population survival, especially during antibiotic challenges.
  • Bacterial metabolic state distribution can be inferred by analyzing growth resumption dynamics on agar plates.
  • Current methods for analyzing bacterial growth from images may overestimate lag times, particularly in dense cultures.

Purpose of the Study:

  • To introduce ColTapp, a novel application for quantifying bacterial colony growth dynamics from time-lapse images.
  • To enable detailed analysis of colony size, color, morphology, and lag time estimation.
  • To address and correct biases in lag time estimation caused by colony density.

Main Methods:

  • Development of the Colony Time-lapse application (ColTapp) with a user-friendly graphical interface.
  • Analysis of Staphylococcus aureus time-lapse image datasets using ColTapp.
  • Implementation of a correction factor for lag time estimation based on available colony area.

Main Results:

  • ColTapp accurately quantifies bacterial colony growth parameters, including size, color, and morphology.
  • The study identified and quantified the overestimation of lag time in dense bacterial populations.
  • A method to correct lag time overestimation by considering colony area was successfully demonstrated.

Conclusions:

  • ColTapp provides a robust tool for analyzing bacterial phenotypic heterogeneity and metabolic states.
  • Accurate lag time estimation is critical and can be improved by accounting for colony density.
  • Enhanced analysis of bacterial growth dynamics holds potential for guiding personalized antibiotic treatment strategies.