Characterizing the temporal dynamics of gene expression in single cells with sci-fate

Gene expression is a dynamic process on multiple scales, e.g. the cell cycle, response to stimuli, normal differentiation and development, etc. However, nearly all techniques for profiling gene expression in single cells fail to directly capture these temporal dynamics, which limits the scope of biology that can be effectively investigated. Towards addressing this, we developed sci-fate, a new technique that combines S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), in order to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. As a proof-of-concept, we applied sci-fate to a model system of cortisol response, and characterized expression dynamics in over 6,000 single cells. From these data, we quantify the dynamics of the cell cycle and of glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data to develop a framework for estimating cell state transition probabilities, and to identify factors whose dynamic expression potentially regulates these transitions. The experimental and computational methods described here may be broadly applicable to quantitatively characterize cell state dynamics in in vitro systems. Overall design: sci-fate profiling for HEK293T cells, NIH/3T3 cells, A549 cells across different treatment conditions (DEX 0 hour, 2 hour, 4 hour, 6 hour, 8 hour and 10 hour treatment). Please note that [1] the fastq files are generated from combined samples of different treatment samples [2] the processed cell information and transcriptome barcode are listed in the cell annotation file of processed data [3] the *gene_annotate.txt and *gene_annotate_newly_synthesised.txt files are identical, yet provided in duplicate as they are reference data for two different data sets.基因表达是一种多尺度的动态过程，例如细胞周期、应激响应、正常分化与发育等。然而，几乎所有用于单细胞基因表达谱分析的技术均无法直接捕获这些时序动态过程，这限制了可有效开展研究的生物学范畴。为解决这一问题，我们开发了sci-fate技术，该技术将新合成mRNA的S4U标记（S4U labeling）与单细胞组合索引（single cell combinatorial indexing，sci-）相结合，可同时对大量单个细胞中的全部转录组与新合成转录组进行谱分析。作为概念验证，我们将sci-fate应用于皮质醇响应模型系统，并对超过6000个单细胞的表达动态进行了表征。基于这些数据，我们量化了细胞周期与糖皮质激素受体（glucocorticoid receptor）激活的动态过程，同时探究了二者的交叉关联。此外，我们利用这些数据构建了用于预测细胞状态转换概率的框架，并鉴定出那些动态表达可能调控此类转换的因子。本文所述的实验与计算方法可广泛应用于体外系统中细胞状态动态的定量表征。 整体实验设计：针对HEK293T细胞、NIH/3T3细胞与A549细胞，在不同处理条件下（地塞米松DEX处理0小时、2小时、4小时、6小时、8小时及10小时）开展sci-fate谱分析。 请注意： 1. FASTQ文件由不同处理组的混合样本生成； 2. 已处理的细胞信息与转录组条形码均列于已处理数据的细胞注释文件中； 3. *gene_annotate.txt与*gene_annotate_newly_synthesised.txt两份文件内容完全一致，因二者分别作为两个不同数据集的参考数据，故以副本形式提供。