High content imaging for monitoring signalling dynamics in single cells

Kathryn L Garner

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)
27 Downloads (Pure)

Abstract

All living cells are sensors of their environment: they sense signals, hormones, cytokines, and growth factors, among others. Binding of these signals to cell surface receptors initiates the transmission of messages along intracellular signalling pathways through protein-protein interactions, enzymatic modifications and conformational changes. Typically, the activation of signalling pathways are monitored in whole populations of cells, giving population average measures, often using experimental methods that destroy and homogenise the cell population. High content imaging is an automated, high-throughput fluorescence microscopy method that enables measurements of signal transduction pathways to be taken from live cells. It can be used to measure signalling dynamics, how the abundance of particular proteins of interest change over time, or to record how particular proteins move and change their localisation in response to a signal from their environment. Using this, and other single cell methods, it is becoming increasingly clear that cells are in fact very variable in their response to a given stimulus and in the quantities of cellular components they express, even in clonal (isogenic) cell lines. This review will discuss how high content imaging has contributed to our growing understanding of cellular heterogeneity. It will discuss how data generated has been combined with information theoretic approaches to quantify the amount of information transferred through noisy signalling pathways. Lastly, the relevance of heterogeneity to our understanding and treatment of disease will be considered, highlighting the importance of single cell measurements.

Original languageEnglish
Pages (from-to)R91-R100
Number of pages10
JournalJournal of Molecular Endocrinology
Volume65
Issue number4
Early online date21 Sept 2020
DOIs
Publication statusPublished - 1 Nov 2020

Keywords

  • heterogeneity
  • noise
  • high-throughput
  • microscopy

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