Abstract
Stochastic gene expression leads to inherent variability in expression outcomes even in isogenic single-celled organisms grown in the same environment. The Drop-Seq technology facilitates transcriptomic studies of individual mammalian cells, and it has had transformative effects on the characterization of cell identity and function based on single-cell transcript counts. However, application of this technology to organisms with different cell size and morphology characteristics has been challenging. Here we present yeastDrop-Seq, a yeast-optimized platform for quantifying the number of distinct mRNA molecules in a cell-specific manner in individual yeast cells. Using yeastDrop-Seq, we measured the transcriptomic impact of the lifespan-extending compound mycophenolic acid and its epistatic agent guanine. Each treatment condition had a distinct transcriptomic footprint on isogenic yeast cells as indicated by distinct clustering with clear separations among the different groups. The yeastDrop-Seq platform facilitates transcriptomic profiling of yeast cells for basic science and biotechnology applications.
Highlights
Stochastic gene expression leads to inherent variability in expression outcomes even in isogenic single-celled organisms grown in the same environment
After the initial formation of oil droplets by feeding into the microfluidic chip yeast cells together with the microbeads resuspended in this solution, the oil droplets were incubated for 30 min at 30 °C to ensure that Zymolyase breaks the cell walls and cell lysis occurs (Supplementary Fig. 1)
Despite the use of isogenic yeast cells across our experiments, we identified subclusters in the Uniform Manifold Approximation and Projection (UMAP) plot corresponding to transcriptomic heterogeneity within the DMSO, Guanine and Mycophenolic acid (MPA) + Guanine-treated yeast populations; differently from these observations, the MPA treatment led to a relatively homogeneous population (Fig. 2c)
Summary
Stochastic gene expression leads to inherent variability in expression outcomes even in isogenic single-celled organisms grown in the same environment. The Drop-Seq technology facilitates transcriptomic studies of individual mammalian cells, and it has had transformative effects on the characterization of cell identity and function based on single-cell transcript counts. Application of this technology to organisms with different cell size and morphology characteristics has been challenging. The original Drop-Seq platform has been developed and optimized for mammalian cells with the goal of measuring mRNA counts at the single-cell level across genetic backgrounds and/or growth conditions. As a proof-of-principle application of yeastDrop-Seq, we measure how Mycophenolic acid (MPA) and guanine impact mRNA counts globally at the single-cell level. Our work uncovers the global transcriptomic effects of MPA and guanine in a pathway-specific manner at single-cell resolution, providing novel insights about how MPA extends the lifespan in yeast
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