A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast
Abstract
Current methods for single-cell RNA sequencing (scRNA-seq) of yeast cells do not match the throughput and relative simplicity of the state-of-the-art techniques that are available for mammalian cells. In this study, we report how 10x Genomics' droplet-based single-cell RNA sequencing technology can be modified to allow analysis of yeast cells. The protocol, which is based on in-droplet spheroplasting of the cells, yields an order-of-magnitude higher throughput in comparison to existing methods. After extensive validation of the method, we demonstrate its use by studying the dynamics of the response of isogenic yeast populations to a shift in carbon source, revealing the heterogeneity and underlying molecular processes during this shift. The method we describe opens new avenues for studies focusing on yeast cells, as well as other cells with a degradable cell wall.
Data availability
Sequencing data have been deposited in GEO under accession code GSE144820
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Transition between fermentation and respiration determines historydependent behavior in fluctuating carbon sourcesNCBI Gene Expression Omnibus, , GSE116246.
Article and author information
Author details
Funding
Fonds Wetenschappelijk Onderzoek
- Lieselotte Vermeersch
- Bram Cerulus
Vlaams Instituut voor Biotechnologie
- Kevin J Verstrepen
European Research Council (Council CoG682009)
- Kevin J Verstrepen
AB-InBev-Baillet Latour Fund
- Kevin J Verstrepen
Human Frontier Science Program (246 RGP0050/2013)
- Kevin J Verstrepen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Antonis Rokas, Vanderbilt University, United States
Publication history
- Received: January 20, 2020
- Accepted: May 15, 2020
- Accepted Manuscript published: May 18, 2020 (version 1)
- Version of Record published: May 29, 2020 (version 2)
Copyright
© 2020, Jariani et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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