Sensitive, high-throughput single-cell RNA-Seq reveals within-clonal transcript-correlations in yeast populations

Single-cell RNA-seq (scRNA-seq) has revealed extensive cellular heterogeneity within many organisms but few methods have been developed for microbial clonal populations. The yeast genome displays an unusually dense transcript spacing with interleaved and overlapping transcription from both strands, resulting in a minuscule but complex pool of RNA protected by a resilient cell wall. Here, we developed a sensitive, scalable and inexpensive yeast single-cell RNA-seq (yscRNA-seq) method that digitally counts transcript start sites (TSS) in a strand- and isoform-specific manner with unique molecular identifiers (UMI). YscRNA-Seq detects expression of low-abundant, non-coding RNAs and at least half of the protein-coding genome in each cell. From just one single-cell transcriptome experiment of a bulk population, enough heterogeneity is uncovered between individual cells to identify biological associations without the need for perturbation experiments necessary to derive correlations from bulk data. Within cells of a single clonal population, we observe negative expression correlation of sense/antisense pairs while duplicated gene pairs and divergent transcripts co-express. By combining yscRNA-Seq with index sorting, which allows phenotypic characterization, we uncover a linear cell size-dependent change in absolute RNA content. Although we detect an average of ~3.5 molecules per gene, a single cell tends restrict the number of expressed isoforms. Remarkably, there is a highly variable expression within metabolic genes, whose stochastic expression primes cells for fitness benefit towards the corresponding environmental challenge. These findings suggest functional transcript diversity as a mechanism for providing a selective advantage to individual cells within otherwise transcriptionally heterogeneous microbial populations. Overall design: Yeast single cell RNA-seq (yscRNA-seq) data for S288c and YJM789 strains

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Nadal-Ribelles M, Islam S, Wei W, Latorre P, Nguyen M, de Nadal E, Posas F, Steinmetz LM