Center For Genomics and Systems Biology, New York University, New York, United States.
Department of Biology, New York University, New York, United States.
Elife. 2020 Jan 27;9:e51254. doi: 10.7554/eLife.51254.
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 scRNAseq in budding yeast (). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.
理解基因表达程序如何被控制需要确定转录因子和靶基因之间的调控关系。基因调控网络通常是根据遗传扰动或环境刺激后获得的基因表达数据构建的。单细胞 RNA 测序（scRNAseq）在单个实验中捕获了数千个单个细胞的基因表达状态，在组合实验设计、大量独立测量和获取细胞周期与环境反应之间的相互作用方面具有优势，而这些相互作用是通过对基因表达的群体水平分析隐藏的。为了利用这些优势，我们开发了一种用于芽殖酵母（）的 scRNAseq 方法。我们将 11 种不同环境条件下的多样化转录条形码基因缺失突变体混合在一起，并通过对 38285 个单个细胞进行测序来确定它们的表达状态。我们对从 scRNAseq 数据中学习基因调控网络的框架进行了基准测试，该框架包含多任务学习，并构建了一个包含 12228 个相互作用的全局基因调控网络。
