Computational framework for analyzing and annotating single bacterium RNA-Seq data

总结 传染性细菌疾病的发病机制依赖于细菌部署基因表达程序的能力 快速灵活地适应新环境。这些遗传程序的异质表达是一种 细菌种群的共同策略，特别是在毒力因子的表达中。研究 由于基因表达的异质性，单细胞技术被设计用于真核生物， 在这种情况下非常有影响力，但在细菌中使用这些技术一直具有挑战性。最近 这些进展导致了单细菌RNA-Seq的显著改进，使得现在可以测量 在一次实验中，数十万个细胞的基因表达。然而，分析这些数据集是 由于技术和生物学原因，每个细胞中检测到的转录本数量极低。 因此，细菌致病机理的研究需要新的计算和概念框架， 单细菌RNA-Seq数据集的分析。在这里，我们提出了一套创新的工具，利用独特的 细菌生理学的方面，以解决这些数据的挑战性特征。在我们的第一个目标中，我们建议 第一种专门针对单细菌数据量身定制的RNA测序去噪方法。这种方法利用 高细胞数的力量，以确定模块的共同变化的基因表达谱，然后使用 从模块表达式导出的单元格的潜在空间，以平滑相邻单元格， 解析了转录组图谱。在我们的第二个目标中，我们试图根据细胞的复制和 通过整合直接来自细菌细胞生物学的参数来确定生长速率。具体来说，我们将采取 细菌的单一复制起点的一般倾向的优点是与以下基因的更高表达相关： 更接近复制起点的基因到复制基因组。此外，我们将推断细胞的生长速度， 快速生长的细胞相对于非编码前RNA具有更高丰度的原理的基础， 成熟的同行。在我们的最终目标，我们将发布和维护一个计算包与分析 供致病性研究界使用的工具。单细菌RNA-Seq领域发展迅速，需要 计算支持，这将有助于阐明抗生素耐受性的机制， 细菌致病

SUMMARY Pathogenesis of infectious bacterial disease relies on the ability of bacteria to deploy gene expression programs rapidly and flexibly to adapt to new environments. Heterogeneous expression of these genetic programs is a common strategy invoked by bacterial populations, particularly in the expression of virulence factors. To study heterogeneous gene expression, single-cell technologies were designed for use in eukaryotes and have been extremely impactful in that setting, but use of these technologies has been challenging in bacteria. Recent advances have led to significant improvement in single-bacterium RNA-Seq, making it possible to now measure gene expression in hundreds of thousands of cells in a single experiment. However, analyzing such datasets is challenged by the extremely low number of detected transcripts in each cell, for technical and biological reasons. The study of bacterial pathogenesis thus requires new computational and conceptual frameworks to enable analysis of single-bacterium RNA-Seq datasets. Here, we propose a set of innovative tools that exploit unique aspects of bacterial physiology to address the challenging features of these data. In our first Aim, we propose the first RNA-Seq denoising approach specifically tailored for single-bacterium data. This approach makes use of the power of high cell numbers to identify modules of co-varying gene expression profiles and then uses the latent space for cells derived from the module expression to smooth over neighboring cells for more highly- resolved transcriptome profiles. In our second Aim, we seek to annotate cells according to their replicative and growth rates by integrating parameters directly derived from bacterial cell biology. Specifically, we will take advantage of the general tendency of a bacterium’s single origin of replication to relate higher expression of genes closer to the origin of replication to a replicating genome. In addition, we will infer a cell’s growth rate on the basis of the principle that rapidly growing cells have a higher abundance of pre-noncoding RNA relative to their mature counterparts. In our final Aim we will release and maintain a computational package with analysis tools for use by the pathogenicity community. The single-bacterium RNA-Seq field is fast-growing and requires computational support that will enable progress in elucidating the mechanisms of antibiotic tolerance and bacterial pathogenesis.