Gupta Shreyan, Cai James J
Department of Veterinary Integrative Biosciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
CPRIT Single Cell Data Science Core, Texas A&M University, College Station, TX, USA.
Commun Biol. 2025 Jan 19;8(1):88. doi: 10.1038/s42003-025-07530-0.
Gene expression is a dynamic and stochastic process characterized by transcriptional bursting followed by periods of silence. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to measure transcriptional bursting and silencing at the individual cell level. In this study, we introduce the single-cell Stochastic Gene Silencing (scSGS) method, which leverages the natural variability in single-cell gene expression to decipher gene function. For a target gene g under investigation, scSGS classifies cells into transcriptionally active (g + ) and silenced (g-) samples. It then compares these cell samples to identify differentially expressed genes, referred to as SGS-responsive genes, which are used to infer the function of the target gene g. Analysis of real data demonstrates that scSGS can reveal regulatory relationships up- and downstream of target genes, circumventing the survivorship bias that often affects gene knockout and perturbation studies. scSGS thus offers an efficient approach for gene function prediction, with significant potential to reduce the use of genetically modified animals in gene function research.
基因表达是一个动态且随机的过程，其特征为转录爆发，随后是沉默期。单细胞RNA测序（scRNA-seq）是在单个细胞水平上测量转录爆发和沉默的强大工具。在本研究中，我们介绍了单细胞随机基因沉默（scSGS）方法，该方法利用单细胞基因表达中的自然变异性来解读基因功能。对于所研究的目标基因g，scSGS将细胞分类为转录活跃（g +）和沉默（g-）样本。然后比较这些细胞样本以鉴定差异表达基因，即所谓的SGS反应基因，这些基因用于推断目标基因g的功能。对真实数据的分析表明，scSGS可以揭示目标基因上下游的调控关系，规避了通常影响基因敲除和扰动研究的生存偏差。因此，scSGS为基因功能预测提供了一种有效方法，具有在基因功能研究中减少转基因动物使用的巨大潜力。
