mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of Saccharomyces cerevisiae and Candida albicans

Abstract

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, <i>Saccharomyces cerevisiae</i>, a popular model organism, and <i>Candida albicans</i>, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 <i>S. cerevisiae</i> cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock <i>S. cerevisiae</i> using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of <i>S. cerevisiae</i> under heat shock. We also applied mDrop-seq to <i>C. albicans</i> cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to <i>S. cerevisiae</i>. Single-cell transcriptomes in 39,705 <i>C. albicans</i> cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between <i>C. albicans</i> cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.