Issue 2, 2020

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

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

Supplementary files

Article information

Article type
Paper
Submitted
09 Sep 2019
Accepted
22 Nov 2019
First published
22 Nov 2019

Lab Chip, 2020,20, 266-273

Author version available

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, 20, 266 DOI: 10.1039/C9LC00896A

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