High throughput gene expression profiling of yeast colonies with microgel-culture Drop-seq

Abstract

We describe isogenic colony sequencing (ICO-seq), a massively-parallel strategy to assess the gene expression profiles of large numbers of genetically distinct yeast colonies.

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

“…By overlaying WH11 expression on the tSNE (Figure 4C, upper panel), we determined that cluster 1 likely contained white colonies. We also found that STF2 expression increased in cluster 1 (Figure 4C, lower panel), consistent with previously obtained data 17 . In clusters 0 and 2, WH11 and STF2 were significantly downregulated compared with cluster 1 (Figure 4D).…”

“…A potential concern with our method is that microgel culture of yeast may significantly alter gene expression. Previous work investigating yeast gene expression in microgels and on agar demonstrate differences in gene expression averages but overall a positive correlation 17 , though further investigation of this claim on a variety of yeast strains is prudent. The method also has limited cell capture efficiency due to stochastic loading of mRNA capture beads following Poisson statistics 14 .…”

“…We present a detailed protocol of our recently developed method for sequencing yeast cells using high-throughput droplet microfluidics 17 . To overcome the challenge of limited RNA, we encapsulate and culture single yeast cells in picoliter hydrogel spheres.…”

“…We obtained a set of gene expression profiles using the workflow and used them for analysis of colonies expressing at least 300 unique genes. As a reference dataset, we used C. Albicans expression data obtained from a previously published study 17 and filtered out colonies expressing fewer than 600 unique genes. After performing principal component (PC) analysis and a t-stochastic neighbor embedding (tSNE) dimensionality reduction 21 , we found general concordance between our sample dataset and the reference (Figure 4A).…”

Paralogs in the PKA regulon traveled different evolutionary routes to divergent expression in budding yeast

“…Breakthroughs at a few levels might soon yield the technology necessary to read bioinformatic regulatory signals in these yeast at a much greater level of precision. A screen of pathway perturbations linked to high throughput gene expression readout, such as single cell or single colony RNA-seq (Jackson et al, 2019;Liu et al, 2019), combined with machine learning approaches would make great strides towards identifying such a bioinformatic regulatory signal for the PKA pathway. This would be strengthened by a tighter link between pathway mutants and transcription factor binding via traditional assays such as ChIP-seq and complementary methods such as the calling card method (Mayhew and Mitra, 2016).…”