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, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans 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 C. albicans 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.
Highlights
The rise of high-throughput single-cell mRNA sequencing has led to a greater understanding of the functional and phenotypic heterogeneity present in our body on a cellular level
Initial experiments were performed using S. cerevisiae, a popular model organism that is amenable to technology development due to easier lysis of the species’ relatively thinner cell wall
Using differential expression (DE) analysis on the combined dataset, we identified the following genes of interest: DE genes that show higher expression in the fluconazole treated datasets, e.g., CHT2, INO1, POL30, TNA1, RHD3, HXK2 (Figure 4E) that included several antigenic genes and genes upregulated during a host immune response; DE genes that show increased expression in the control data that decreased with time under fluconazole treatment, e.g., ASR1, ASR2, WH11, HSP70, AHP1 (Figure 4F) associated with core and heat shock specific stress responses; and DE genes that show the highest expression transiently in the 1.5 h fluconazole treated sample, e.g., MRV5, ADH2, SOD5, CAR1 (Figure 4G) associated with acid, osmotic and alkaline stress responses
Summary
The rise of high-throughput single-cell mRNA sequencing (scRNA-seq) has led to a greater understanding of the functional and phenotypic heterogeneity present in our body on a cellular level. While variation between different cell types (in a multicellular organism) or cells of different species may be expected, scRNA-seq techniques have shown that there is significant cell-tocell heterogeneity even between otherwise identical cells [4]. High-throughput techniques can examine thousands of cells at once, adding statistical power to determine variability between cells [1,5]. Technological challenges, such as the tough cell walls, small size, and concomitantly smaller amounts of transcripts per cell [6] have prevented similar applications in unicellular microbial organisms [7]. Microbes have significantly less mRNA compared to animal cells, with estimates ranging up to two orders of magnitude less for bacterial cells [11,12]
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