Abstract
SummaryEssential characteristics of cellular signaling networks include a complex interconnected architecture and temporal dynamics of protein activity. The latter can be monitored by Förster resonance energy transfer (FRET) biosensors at a single-live-cell level with high temporal resolution. However, these experiments are typically limited to the use of a couple of FRET biosensors. Here, we describe a FRET-based multi-parameter imaging platform (FMIP) that allows simultaneous high-throughput monitoring of multiple signaling pathways. We apply FMIP to monitor the crosstalk between epidermal growth factor receptor (EGFR) and insulin-like growth factor-1 receptor signaling, signaling perturbations caused by pathophysiologically relevant EGFR mutations, and the effects of a clinically important MEK inhibitor (selumetinib) on the EGFR network. We expect that in the future the platform will be applied to develop comprehensive models of signaling networks and will help to investigate the mechanism of action as well as side effects of therapeutic treatments.
Highlights
Intracellular signaling networks are complex machineries that reliably receive and process extracellular information to adjust the physiological state of cells to environmental changes
Design of a Forster resonance energy transfer (FRET)-Based Multi-parameter Imaging Platform FRET biosensors are used to measure the conformational change of proteins reflecting protein-protein interaction, posttranslational modification, concentration of second messengers and, most importantly, protein activities, not merely abundance (Newman et al, 2011)
By combining time-resolved single-live-cell imaging with advances in cell microarrays (Piljic et al, 2011; Ziauddin and Sabatini, 2001) and FRET biosensor technologies, we developed an FRET-based multi-parameter imaging platform (FMIP) to analyze the dynamics of various signaling pathways in real-time
Summary
Intracellular signaling networks are complex machineries that reliably receive and process extracellular information to adjust the physiological state of cells to environmental changes. It was established that signaling networks have a remarkable ability to encode the identity and quantity of a given stimulus by temporal patterns and/or dynamics of individual signaling components within the network architecture (Kholodenko et al, 2010; Kubota et al, 2012; Purvis and Lahav, 2013; Toettcher et al, 2013) These signaling network characteristics result in precise control over cellular responses and the ability to adapt to various perturbations. To understand how cellular signaling networks integrate information from the extracellular environment, how they evoke specific cellular responses and, most importantly, how normal signaling is rewired under the course of a disease, a method is needed that allows comprehensive system-level analysis of multiple signaling pathway dynamics under identical conditions Such a toolbox should be able to detect second messengers as well as key protein activities of the network with high temporal resolution, and clarify the interplay between signaling events and phenotypic changes
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