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Author Notes:

Hang Lu, Email: hang.lu@gatech.edu

We would like to acknowledge funding from the National Institutes of Health (R01GM088333, R01AG056436, R01NS115484, R21DC015652, R21NS117066, R21NS117067), National Science Foundation (DBI1707401 and DMS1764406) and Simons Foundation/SFARI 594594 to FHL. JW would like to acknowledge support from the National Science Foundation Graduate Research Fellowship (DGE-1650044).

There are no conflicts to declare.

Subjects:

Keywords:

  • RNA-sequencing
  • scRNA-seq

Enabling high-throughput single-animal gene-expression studies with molecular and micro-scale technologies

Tools:

Journal Title:

LAB ON A CHIP

Volume:

Volume 20, Number 24

Publisher:

, Pages 4528-4538

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Gene expression and regulation play diverse and important roles across all living systems. By quantifying the expression, whether in a sample of single cells, a specific tissue, or in a whole animal, one can gain insights into the underlying biology. Many biological questions now require single-animal and tissue-specific resolution, such as why individuals, even within an isogenic population, have variations in development and aging across different tissues and organs. The popular techniques that quantify the transcriptome (e.g. RNA-sequencing) process populations of animals and cells together and thus, have limitations in both individual and spatial resolution. There are single-animal assays available (e.g. fluorescent reporters); however, they suffer other technical bottlenecks, such as a lack of robust sample-handling methods. Microfluidic technologies have demonstrated various improvements throughout the years, and it is likely they can enhance the impact of these single-animal gene-expression assays. In this perspective, we aim to highlight how the engineering/method-development field have unique opportunities to create new tools that can enable us to robustly answer the next set of important questions in biology that require high-density, high-quality gene expression data.
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