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

Dobrzyński, Maciej; Jacques, Marc-Antoine; Pertz, Olivier (2022). Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering. Methods in molecular biology, 2488, pp. 183-206. Springer 10.1007/978-1-0716-2277-3_13

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Biology > Institute of Cell Biology

UniBE Contributor:

Dobrzynski, Maciej, Jacques, Marc-Antoine Frédéric Roméo, Pertz, Olivier

Subjects:

500 Science > 570 Life sciences; biology

ISSN:

1940-6029

Publisher:

Springer

Language:

English

Submitter:

Pubmed Import

Date Deposited:

30 Mar 2022 10:24

Last Modified:

09 May 2023 12:33

Publisher DOI:

10.1007/978-1-0716-2277-3_13

PubMed ID:

35347690

Uncontrolled Keywords:

Cell-cell heterogeneity Clustering Computational biology Data analysis Signal processing Signaling dynamics Single-cell data Time-series

BORIS DOI:

10.48350/168410

URI:

https://boris.unibe.ch/id/eprint/168410

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