A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast

Elife. 2020 May 18:9:e55320. doi: 10.7554/eLife.55320.

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.

Keywords: S. cerevisiae; chromosomes; gene expression; heterogeneity; infectious disease; lag phase; metabolic shift; microbiology; single-cell RNA-seq; tools & methods.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Carbon / metabolism
  • Energy Metabolism / genetics*
  • Energy Metabolism / physiology
  • Gene Expression / genetics
  • Gene Expression Regulation, Fungal / genetics
  • Glucose / metabolism*
  • Maltose / metabolism*
  • RNA / genetics
  • RNA-Seq / methods*
  • Saccharomyces cerevisiae / genetics
  • Saccharomyces cerevisiae / growth & development
  • Single-Cell Analysis / methods*
  • Spheroplasts
  • Transcription, Genetic / genetics
  • Transcriptome / genetics

Substances

  • RNA
  • Maltose
  • Carbon
  • Glucose

Associated data

  • GEO/GSE144820
  • GEO/GSE116246