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Mining single-cell time-series datasets with Time Course Inspector.

Maciej Dobrzyński1, Marc-Antoine Jacques1, Olivier Pertz1

  • 1Institute of Cell Biology, University of Bern, Bern 3012, Switzerland.

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|December 14, 2023
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Summary

Recent advances in live cell imaging generate vast single-cell time-series data. Time Course Inspector (TCI) is a new R/Shiny tool that combines time-series analysis with clustering to identify cell sub-populations with distinct temporal behaviors.

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Area of Science:

  • Cell biology
  • Computational biology
  • Bioinformatics

Background:

  • Live cell imaging and biosensor technology enable high-throughput single-cell time-series data generation.
  • Identifying distinct cell sub-populations and their dynamic behaviors is crucial for understanding cell fate decisions.

Purpose of the Study:

  • To develop a novel computational tool for analyzing large single-cell time-series datasets.
  • To facilitate the identification of sub-populations with unique temporal dynamics.

Main Methods:

  • Development of Time Course Inspector (TCI), a tool implemented in R/Shiny.
  • Integration of time-series analysis with hierarchical clustering and cluster validation methods.
  • Interactive data exploration, visualization, and outlier removal capabilities.

Main Results:

  • TCI enables convenient inspection and analysis of single-cell time-series data.
  • The tool facilitates interactive exploration of various clustering approaches.
  • Demonstrated utility of TCI in analyzing a fluorescent biosensor-based single-cell signaling dataset.

Conclusions:

  • Time Course Inspector (TCI) is an effective tool for analyzing complex single-cell time-series data.
  • TCI aids in identifying cell sub-populations with distinct temporal behaviors, crucial for cell fate determination.
  • The tool enhances the analysis of microscopy-derived biosensor data.