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Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering

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TGF-Beta Signaling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2488))

Abstract

Fluorescent live cell time-lapse microscopy is steadily contributing to our better understanding of the relationship between cell signaling and fate. However, large volumes of time-series data generated in these experiments and the heterogenous nature of signaling responses due to cell-cell variability hinder the exploration of such datasets. The population averages insufficiently describe the dynamics, yet finding prototypic dynamic patterns that relate to different cell fates is difficult when mining thousands of time-series. Here we demonstrate a protocol where we identify such dynamic phenotypes in a population of PC-12 cells that respond to a range of sustained growth factor perturbations. We use Time-Course Inspector, a free R/Shiny web application to explore and cluster single-cell time-series.

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Acknowledgments

We acknowledge the financial support of the Swiss National Science Foundation (grants 31003A-163061 and 51PHPO-163583), and the Swiss Cancer League.

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Authors and Affiliations

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

    Maciej DobrzyƄski, Marc-Antoine Jacques & Olivier Pertz

Authors
  1. Maciej DobrzyƄski
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  2. Marc-Antoine Jacques
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  3. Olivier Pertz
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Corresponding author

Correspondence to Maciej DobrzyƄski .

Editor information

Editors and Affiliations

  1. Department of Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment, Berlin, Germany

    Zhike Zi

  2. Department of Biochemistry, University of Colorado, Boulder, CO, USA

    Xuedong Liu

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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DobrzyƄski, M., Jacques, MA., Pertz, O. (2022). Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering. In: Zi, Z., Liu, X. (eds) TGF-Beta Signaling. Methods in Molecular Biology, vol 2488. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2277-3_13

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  • DOI: https://doi.org/10.1007/978-1-0716-2277-3_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2276-6

  • Online ISBN: 978-1-0716-2277-3

  • eBook Packages: Springer Protocols

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