Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Abstract

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

Paralogs in the PKA regulon traveled different evolutionary routes to divergent expression in budding yeast

“…Breakthroughs at a few levels might soon yield the technology necessary to read bioinformatic regulatory signals in these yeast at a much greater level of precision. A screen of pathway perturbations linked to high throughput gene expression readout, such as single cell or single colony RNA-seq (Jackson et al, 2019;Liu et al, 2019), combined with machine learning approaches would make great strides towards identifying such a bioinformatic regulatory signal for the PKA pathway. This would be strengthened by a tighter link between pathway mutants and transcription factor binding via traditional assays such as ChIP-seq and complementary methods such as the calling card method (Mayhew and Mitra, 2016).…”

Candida albicans exhibits distinct cytoprotective responses to anti-fungal drugs that facilitate the evolution of drug resistance

“…Yeast ( S. cerevisiae ) has previously been examined by single cell sequencing using the Fluidigm C1 system but this handles less than 100 cells 41 . Jackson et al 42 sequenced~40K S. cerevisiae cells with the commercial Chromium (10X Inc.) system. We opted to build a fungal DROP-seq modified from the original approach presented in Macosko et al 43,44 , to address issues of cost and flexibility in comparison with commercial alternatives.…”

“…Whereas Macosko et al recommends a ratio of 100K cells to 120K beads for DROP-seq, we found that a ratio of 1M cells for 120K beads generated a sufficient yield of cDNA as per the Agilent Tapestation. Jackson et al 42 used 5M cells as input to the Chromium (10X Inc.) system. Furthermore, whereas Macosko et al use 1ml of lysis buffer, we used 1.2 ml, and instead of 13 PCR cycles, we used 17 ( Jackson et al used 10 cycles).…”

A Bright IDEA

“…Defining a functional map of the cell that connects regulators with their targets is essential for understanding, and engineering, cellular behavior (Chasman et al , ). The budding yeast Saccharomyces cerevisiae is an ideal organism for defining and studying gene regulatory networks (Hughes & de Boer, ) using global methods from DNA microarrays (Hughes et al , ) to single‐cell RNA sequencing (Jackson et al , ).…”