High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing.

Area of Science:

• Microbial Engineering
• Synthetic Biology
• Genomics

Background:

• Yeast is a valuable platform for genetic engineering due to advanced genome editing tools.
• Screening millions of genetically distinct yeast strains for desired phenotypes is a significant bottleneck.
• Current high-throughput screening methods often lack comprehensive data or are limited in scope.

Purpose of the Study:

• To develop a scalable and high-throughput method for screening genetically engineered yeast strains.
• To overcome the limitations of existing screening techniques in microbial engineering.
• To enable rapid phenotype inference and pathway analysis in large yeast strain libraries.

Main Methods:

• Adaptation of single-cell RNA sequencing (scRNA-seq) for analyzing isogenic yeast colonies.
• Culturing yeast colonies within hydrogels and preparing spheroplasts for scRNA-seq.
• Utilizing scRNA-seq data to infer cellular phenotypes and identify engineered pathways.

Main Results:

• Demonstrated a method to accelerate strain screening in microbial engineering.
• Successfully applied scRNA-seq to picoliter yeast colonies.
• RNA sequencing data effectively inferred yeast phenotypes and sorted engineered pathways.

Conclusions:

• The developed method significantly enhances the throughput of yeast strain screening.
• This approach addresses a critical obstruction in microbial engineering, enabling faster development of engineered yeast.
• Scalable scRNA-seq offers a powerful tool for phenotype-driven microbial engineering.