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Updated: Jul 4, 2025
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High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing
Published on: May 21, 2020
7.0K
An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts
Leandra Brettner1, Rachel Eder1,2, Kara Schmidlin1,2
1Biodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA.
Yeast (Chichester, England)
|January 29, 2024
Summary
We optimized Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq) for yeast, enabling high-throughput, cost-effective single-cell RNA sequencing. This powerful tool allows researchers to study hundreds of yeast strains and environments simultaneously, revealing cellular heterogeneity.
Area of Science:
- * Molecular Biology
- * Genomics
- * Cell Biology
Background:
- * Yeast's genetic tractability and ease of growth make it an ideal model organism.
- * Traditional RNA sequencing methods have limitations in processing large numbers of yeast samples and analyzing transcriptomes at a single-cell level.
- * Investigating yeast transcriptomes across diverse genotypes and environments is crucial for understanding cellular function and adaptation.
Purpose of the Study:
- * To optimize and validate the Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq) platform for yeast research.
- * To enable high-throughput, cost-effective single-cell RNA sequencing (scRNAseq) in yeast.
- * To facilitate the study of transcriptional phenotypes across numerous yeast strains and environments, and to investigate cellular heterogeneity.
Main Methods:
- * Optimization of the SPLiT-seq platform for yeast, utilizing a combinatorial barcoding strategy.
- * Application of SPLiT-seq to 43,388 cells from multiple yeast species and ploidies.
- * Leveraging the cell membrane as a reaction container for in-situ barcoding, enabling mass parallelization without physical cell isolation.
Main Results:
- * Successfully applied SPLiT-seq to a large number of yeast cells, demonstrating its scalability and cost-effectiveness compared to traditional scRNAseq.
- * The method allows for the simultaneous analysis of hundreds of yeast genotypes or growth conditions.
- * Detected transcriptionally distinct cell states within clonal yeast populations, including variations related to cell cycle, ploidy, and metabolic strategies.
Conclusions:
- * The optimized SPLiT-seq platform significantly enhances the capacity for yeast transcriptome analysis at the single-cell level.
- * This technology democratizes high-throughput scRNAseq for yeast research, requiring no specialized equipment.
- * SPLiT-seq provides powerful applications for studying transcriptional phenotypes across diverse conditions and for dissecting cellular heterogeneity in yeast.
