An ultra high‐throughput, massively multiplexable, single‐cell RNA‐seq platform in yeasts

• Published in: Yeast (Chichester, England) England), 2024-04, Vol.41 (4), p.242-255
• Main Authors: Brettner, Leandra, Eder, Rachel, Schmidlin, Kara, Geiler‐Samerotte, Kerry
• Format: Article
• Language: English
• Subjects: Bar codes; Candida albicans; Cell cycle; Cell membranes; Diploids; Environmental conditions; Genotypes; Growth conditions; high‐throughput; low‐cost; Phenotypes; Ploidy; Ribonucleic acid; RNA; RNA processing; Saccharomyces cerevisiae; single‐cell RNA sequencing; Strains (organisms); Transcriptomes; Transcriptomics; Yeast
• ISSN: 0749-503X; 1097-0061
• DOI: 10.1002/yea.3927

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high‐throughput single‐cell RNA sequencing (scRNAseq) platform, SPLiT‐seq (Split Pool Ligation‐based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage “the awesome power of yeast” by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT‐seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single‐cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell‐to‐cell heterogeneity across the entire transcriptome. Yeast serves as a workhorse model organism for uncovering the basic tenants of biology. Here we present a cost‐effec