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AAV-based dual-reporter circuit for monitoring cell signaling in living human cells

Overview of attention for article published in Journal of Biological Engineering, June 2017
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Title
AAV-based dual-reporter circuit for monitoring cell signaling in living human cells
Published in
Journal of Biological Engineering, June 2017
DOI 10.1186/s13036-017-0060-9
Pubmed ID
Authors

Zhiwen Zhang, Zachary Stickney, Natalie Duong, Kevin Curley, Biao Lu

Abstract

High-throughput methods based on molecular reporters have greatly advanced our knowledge of cell signaling in mammalian cells. However, their ability to monitor various types of cells is markedly limited by the inefficiency of reporter gene delivery. Recombinant adeno-associated virus (AAV) vectors are efficient tools widely used for delivering and expressing transgenes in diverse animal cells in vitro and in vivo. Here we present the design, construction and validation of a novel AAV-based dual-reporter circuit that can be used to monitor and quantify cell signaling in living human cells. We first design and construct the AAV-based reporter system. We then validate the versatility and specificity of this system in monitoring and quantifying two important cell signaling pathways, inflammation (NFκB) and cell growth and differentiation (AP-1), in cultured HEK293 and MCF-7 cells. Our results demonstrate that the AAV reporter system is both specific and versatile, and it can be used in two common experimental protocols including transfection with plasmid DNA and transduction with packaged viruses. Importantly, this system is efficient, with a high signal-to-background noise ratio, and can be easily adapted to monitor other common signaling pathways. The AAV-based system extends the dual-reporter technology to more cell types, allowing for cost-effective and high throughput applications.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 19%
Researcher 3 19%
Student > Master 2 13%
Student > Doctoral Student 1 6%
Student > Ph. D. Student 1 6%
Other 3 19%
Unknown 3 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 31%
Agricultural and Biological Sciences 4 25%
Neuroscience 3 19%
Engineering 1 6%
Unknown 3 19%