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

Abstract

Yeasts are naturally diverse, genetically tractable, and easy to grow in a myriad of experimental conditions such that researchers have the ability to investigate any number of genotypes, strains, environments, or the interaction thereof. However, studies of variation in the yeast transcriptome have been limited by the processing capabilities of available RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform for yeasts. 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 we use 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 samples 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 report transcriptionally distinct cell states related to cell cycle, growth rate, metabolic strategies, stress responses, etc. 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.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

“…Problematically, these strategies rely on knowledge about the diversity of mutants and tradeoffs that exist (or that can emerge) within an infectious population. While information about population heterogeneity, heteroresistance, and substructure is expensive and arduous to obtain (Andersson et al 2019b; Bottery et al 2021), new methods, in addition to the one presented in this study, are emerging (Kuchina et al 2021; Aissa et al 2021; Nagasawa et al 2021; Forsyth et al 2021; Hsieh et al 2022; Brettner et al 2022a). This type of richer data dovetails with emerging population genetic models that predict the likelihood of resistance to a given drug regimen (Read and Huijben 2009; Day et al 2015; Wilson et al 2016; Cannataro et al 2018; Somarelli et al 2020; Feder et al 2021; King et al 2022).…”

The rise of single‐cell transcriptomics in yeast

“…The SPLIT‐seq rationale uses cells as “physically isolated entities,” thereby avoiding the need for single‐cell isolation. Cells are subjected to chemical fixation with formaldehyde before cell wall digestion and permeabilization (Brettner et al, 2022). During combinatorial indexing, cells are reiteratively split and pooled into barcode‐containing plates for three rounds.…”

“…The SPLIT-seq rationale uses cells as "physically isolated entities," thereby avoiding the need for single-cell isolation. Cells are subjected to chemical fixation with formaldehyde before cell wall digestion and permeabilization (Brettner et al, 2022) Conversely, for profiling large cell quantities offer cost-effective solutions and are often sequenced at lower depth. Despite these methods do not track single-cell phenotypes, they can be paired with previous cell sorting enrich for specific populations.…”

“…Although the number of transcripts per cell remains relatively constant, the identity of these genes varies, and total mRNA scales linearly with size. Highly variable genes are enriched in the cell cycle, metabolic strategies, and stress response (Brettner et al, 2022; Gasch et al, 2017; Jariani et al, 2020; Nadal‐Ribelles et al, 2019b; Wang et al, 2022).…”

“…Indeed, stochastic expression of other glucose‐repressed genes has been reported to be partially dependent on the stress‐responsive Msn2/4 transcription factor and to coordinate faster induction during a shift from glucose to maltose media (Jariani et al, 2020). In addition to galactose and iron regulon, the mild expression of genes related to the environmental stress response (ESR) has been observed in a small percentage of an exponentially growing population in both S. cerevisiae and S. pombe (Brettner et al, 2022; Gasch et al, 2017; Saint et al, 2019).…”