More diversity at the top A detailed knowledge of cell differentiation hierarchies is important for understanding diverse biological processes such as organ development, tissue regeneration, and cancer. Single-cell RNA sequencing can help elucidate these hierarchies, but it requires reliable computational methods for predicting cell lineage trajectories. Gulati et al. developed CytoTRACE, a computational framework based on the simple observation that transcriptional diversity—the number of genes expressed in a cell—decreases during differentiation. CytoTRACE outperformed other methods in several test cases and was successfully applied to study cellular hierarchies in healthy and tumor tissue. Science, this issue p. 405 The number of expressed genes per cell is the foundation of a method that can resolve cell differentiation hierarchies. Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential—the number of expressed genes per cell—and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

中文翻译：

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单细胞转录多样性是发育潜力的标志

更多多样性在顶部 细胞分化层次的详细知识对于理解器官发育、组织再生和癌症等不同的生物过程非常重要。单细胞 RNA 测序可以帮助阐明这些层次结构，但它需要可靠的计算方法来预测细胞谱系轨迹。古拉蒂等。开发了 CytoTRACE，这是一种基于简单观察的计算框架，即转录多样性——细胞中表达的基因数量——在分化过程中会减少。CytoTRACE 在几个测试案例中优于其他方法，并成功应用于研究健康和肿瘤组织中的细胞层次结构。科学，这个问题 p。405 每个细胞表达的基因数量是解析细胞分化层次的方法的基础。单细胞 RNA 测序 (scRNA-seq) 是一种重建细胞分化轨迹的强大方法。然而，推断分化的状态和方向具有挑战性。在这里，我们展示了一个简单但稳健的发育潜力决定因素——每个细胞表达基因的数量——并利用这种转录多样性测量方法开发了一个计算框架 (CytoTRACE)，用于根据 scRNA-seq 数据预测分化状态。当应用于不同的组织类型和生物时，CytoTRACE 优于以前的方法和近 19,000 个注释基因集，用于解析 52 个实验确定的发育轨迹。此外，它促进了静止干细胞的鉴定，并揭示了有助于乳腺肿瘤发生的基因。