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Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie

Athanasios D Balomenos1, Victoria Stefanou1, Elias S Manolakos2,3

  • 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Ilissia, Greece.

BMC Bioinformatics
|October 30, 2021
PubMed
Summary

This study introduces ViSCAR, a new R package for analyzing bacterial single-cell movies. ViSCAR enables detailed visualization and analytics of bacterial communities, aiding in understanding complex dynamics and biological noise.

Keywords:
Bacterial cell community dynamicsCell cytometryGeneration treesLineage treesLive-cell imagingSingle-cell analyticsStochasticity modelingTime-lapse microscopyVisualization

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Area of Science:

  • Microbiology
  • Computational Biology
  • Bioinformatics

Background:

  • Time-lapse microscopy live-cell imaging is crucial for single-cell resolution studies of bacterial community evolution.
  • Analysis of bacterial 'single-cell movies' generates big data, essential for understanding growth dynamics and heterogeneity.
  • Accurate automated bacterial bioimage analysis and single-cell analytics are needed to fully exploit this data.

Purpose of the Study:

  • To present ViSCAR (Visualization and Single-cell Analytics using R), a novel set of methods for exploring bacterial single-cell movie data.
  • To enable visual exploration and correlation of single-cell attributes from complex bacterial datasets.
  • To facilitate the modeling and visualization of spatiotemporal attribute evolution within microbial communities.

Main Methods:

  • Development of R functions for visualization and single-cell analytics.
  • Application to complex bacterial single-cell movies with thousands of cells.
  • Methods for modeling spatiotemporal attribute evolution and inferring stochastic phenomena.

Main Results:

  • ViSCAR allows visual exploration and correlation of single-cell attributes from bacterial movies.
  • The tool facilitates modeling of attribute evolution at various community organization levels.
  • ViSCAR can identify and auto-correct errors in bioimage analysis of crowded cell populations.

Conclusions:

  • ViSCAR empowers researchers to characterize stochasticity and uncover mechanisms behind cellular phenotypes.
  • The software aids in deciphering the dynamic behavior of large, heterogeneous microbial communities.
  • ViSCAR provides accessible source code for broader research application.