Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density

Richard M. Baker, Megan E. Brasch, Mary Elizabeth Manning, James H Henderson

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Understanding single andcollective cellmotility inmodel environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-basedmetrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer- based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.

Original languageEnglish (US)
Article number20140386
JournalJournal of the Royal Society Interface
Volume11
Issue number97
DOIs
StatePublished - Aug 6 2014

Fingerprint

Cell Tracking
Cell Count
Cells
Trajectories
Substrates
Shape memory effect
Polymers
Physics
Experiments
Benchmarking
Bioengineering
Cell Communication
Cell Division
Cell Movement
Transfection

Keywords

  • Automated cell tracking
  • Collective behaviour
  • Contour method
  • Mechanobiology
  • Shape-memory polymers

ASJC Scopus subject areas

  • Biophysics
  • Biotechnology
  • Bioengineering
  • Biomedical Engineering
  • Biomaterials
  • Biochemistry
  • Medicine(all)

Cite this

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abstract = "Understanding single andcollective cellmotility inmodel environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-basedmetrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43{\%} compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer- based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.",
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