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, 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.

Phenotypic Heterogeneity in the DNA Replication Stress Response Revealed by Quantitative Protein Dynamics Measurements

“…The time-scale of cellular responses to stress, including DNA replication, proteotoxic, and heat-induced stress, is well-characterized at the transcriptional level for cell populations (Dohn et al, 2021; Gasch et al, 2017; Rendleman et al, 2018). High-resolution analysis of the relocalization of 110 proteins in yeast revealed that the timing of re-localization events can be different in different stresses, can take place over extended periods of time, and can be transient (Dénervaud et al, 2013).…”

Integration of silicon chip microstructures for in-line microbial cell lysis in soft microfluidics

“…5f). As the gene with the highest mRNA expression in S. cerevisiae [21], we expect TDH3 to have the lowest Cq value of the three genes However, the cDNA for these genes are consistently detected at higher PCR cycle numbers, compared to S. cerevisiae, across multiple experiments (data not shown). C. albicans possess thicker cell walls than S. cerevisiae [27] and is therefore more difficult to lyse.…”

“…Nuclease-free water was tested as an independent negative control We also used qRT-PCR to detect mRNA for three C. albicans genes: LSC2, TDH3 and ACT1. TDH3 is one of the highest expressed genes in C. albicans [21].…”

Estimation of Carbon Metabolism in Saccharomyces cerevisiae Acclimatized to Glycerol Assimilation with Quantitative PCR

“…In this study, to precisely estimate the metabolic shifts of wildtype and no.7 under each culture condition in view of transcriptomics, the transcription levels of TDH3, TDH2 (glyceraldehyde-3-phosphate dehydrogenase), RDN18 (structural constituent of ribosome/translation), KRE11 (unknown/ER to Golgi vesicle-mediated transport), and SGA1 (glucan 1,4-alpha-glucosidase) as the candidate housekeeping genes [25][26][27] were evaluated by qPCR (Figure 4). In the results, excluding SGA1, delta Rn values of TDH3, TDH2, RDN18, KRE11, and SGA1 were logistically increased even with the differences in wildtype and no.7 under different conditions, meaning those of transcription.…”