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Related Experiment Video

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Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
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Quantifying differences in cell line population dynamics using CellPD.

Edwin F Juarez1,2, Roy Lau3, Samuel H Friedman3

  • 1Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA. juarezro@usc.edu.

BMC Systems Biology
|September 23, 2016
PubMed
Summary
This summary is machine-generated.

CellPD is an open-source tool that simplifies quantitative cell phenotype analysis for researchers without computational expertise. It enables accurate modeling of cell population dynamics and drug effects from high-throughput data.

Keywords:
Cell population dynamicsComputational modelingGrowth rateMathematical modelsMultiCellDSNet birth rateOpen sourceParameter estimationPhenotype comparisonPhenotype digitizerUser friendly

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Area of Science:

  • Quantitative biology
  • Computational biology
  • High-throughput screening

Background:

  • High-throughput datasets necessitate reproducible analyses linking molecular changes to phenotypes.
  • Current quantitative analysis tools often demand specialized computational expertise.

Purpose of the Study:

  • To introduce CellPD (cell phenotype digitizer), an accessible tool for quantitative cell phenotype analysis.
  • To enable users to fit mathematical models of cell population dynamics without prior training.

Main Methods:

  • CellPD processes a single spreadsheet input to generate parameter estimates, plots, and XML outputs.
  • Validation involved comparison with existing tools (cellGrowth, Excel) and usability testing with biologists.
  • A synthetic high-content screening dataset was used to demonstrate drug effect analysis.

Main Results:

  • CellPD accurately estimates cell culture growth rates and is robust to data sparsity.
  • Biologists successfully used CellPD on sample data within 30 minutes.
  • CellPD quantifies drug-dependent birth, death, and net growth rates, distinguishing cytostatic and cytotoxic effects.

Conclusions:

  • CellPD is an open-source tool for quantifying cell phenotypes, accessible to scientists regardless of modeling background.
  • Potential applications include drug effect quantification, gene knockout analysis, and data integration.