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smFISH in chips: a microfluidic-based pipeline to quantify in situ gene expression in whole organisms

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Abstract

Gene expression and genetic regulatory networks in multi-cellular organisms control complex physiological processes ranging from cellular differentiation to development to aging. Traditional methods to investigate gene expression relationships rely on using bulk, pooled-population assays (e.g. RNA-sequencing and RT-PCR) to compare gene expression levels in hypo- or hyper-morphic mutant animals (e.g. gain-of-function or knockout). This approach is limited, especially in complex gene networks, as these genetic mutations may affect the expressions of related genes in unforseen ways. In contrast, we developed a microfluidic-based pipeline to discover gene relationships in a single genetic background. The microfluidic device provides efficient reagent exchange and the ability to track individual animals. By automating a robust microfluidic reagent exchange strategy, we adapted and validated single molecule fluorescent in situ hybridization (smFISH) on-chip and combined this technology with live-imaging of fluorescent transcriptional reporters. Together, this multi-level information enabled us to quantify a gene expression relationship with single-animal resolution. While this microfluidic-based pipeline is optimized for live-imaging and smFISH C. elegans studies, the strategy is highly-adaptable to other biological models as well as combining other live and end-point biological assays, such as behavior-based toxicology screening and immunohistochemistry.

Graphical abstract: smFISH in chips: a microfluidic-based pipeline to quantify in situ gene expression in whole organisms

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Publication details

The article was received on 09 Sep 2019, accepted on 22 Nov 2019 and first published on 22 Nov 2019


Article type: Paper
DOI: 10.1039/C9LC00896A
Lab Chip, 2020, Advance Article

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    smFISH in chips: a microfluidic-based pipeline to quantify in situ gene expression in whole organisms

    J. Wan, G. Sun, J. Dicent, D. S. Patel and H. Lu, Lab Chip, 2020, Advance Article , DOI: 10.1039/C9LC00896A

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