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High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing
Author(s) -
Jesse Q. Zhang,
KaiChun Chang,
Leqian Liu,
Zev J. Gartner,
Adam R. Abate
Publication year - 2020
Publication title -
journal of visualized experiments
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.596
H-Index - 91
ISSN - 1940-087X
DOI - 10.3791/61014
Subject(s) - computational biology , yeast , rna , spheroplast , biology , dna sequencing , synthetic biology , genome , phenotype , model organism , genetics , gene , escherichia coli
The powerful tools available to edit yeast genomes have made this microbe a valuable platform for engineering. While it is now possible to construct libraries of millions of genetically distinct strains, screening for a desired phenotype remains a significant obstacle. With existing screening techniques, there is a tradeoff between information output and throughput, with high-throughput screening typically being performed on one product of interest. Therefore, we present an approach to accelerate strain screening by adapting single cell RNA sequencing to isogenic picoliter colonies of genetically engineered yeast strains. To address the unique challenges of performing RNA sequencing on yeast cells, we culture isogenic yeast colonies within hydrogels and spheroplast prior to performing RNA sequencing. The RNA sequencing data can be used to infer yeast phenotypes and sort out engineered pathways. The scalability of our method addresses a critical obstruction in microbial engineering.

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