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

用于分析和注释单细菌 RNA-Seq 数据的计算框架

基本信息

批准号：
10444669
负责人：
ITAI YANAI
金额：
$ 25.43万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-08 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10444669
关键词：
Address Adopted Algorithms Antibiotic Resistance Architecture Bacteria Bacterial Infections Bacterial Physiology Biological Biology Cell Count Cells Cellular biology Communities Data Data Analyses Data Set Development Disease Ensure Environment Eukaryota Eukaryotic Cell Gene Expression Gene Expression Profiling Gene Structure Genes Genetic Genome Genomics Growth Heterogeneity Individual Infection Knowledge Machine Learning Measurement Measures Methodology Methods Pathogenesis Pathogenicity Pathway interactions Phenotype Physiological Physiological Processes Physiology Population Process Prokaryotic Cells RNA Processing Regulation Regulator Genes Replication Origin Research Personnel Ribosomal RNA Technology Time Transcript Untranslated RNA Variant Virulence Factors Work antibiotic tolerance base cell growth cell type computer framework computerized tools denoising design experimental study flexibility innovation insight pathogen pathogenic bacteria programs response single cell technology single-cell RNA sequencing tool transcriptome transcriptome sequencing transcriptomics

项目摘要

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.

概括传染性细菌疾病的发病机制取决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细菌种群所援引的常见策略，特别是在病毒因素的表达中。学习异质基因表达，设计用于真核生物的单细胞技术，已经是在这种环境中的影响很大，但是使用这些技术在细菌中受到了挑战。最近的进步已导致单细菌RNA-seq的显着改善，因此现在可以衡量在一个实验中，数十万个细胞中的基因表达。但是，分析此类数据集是出于技术和生物学原因，每个细胞中检测到的转录本的数量极少。因此，细菌发病机理的研究需要新的计算和概念框架才能实现单细菌RNA-Seq数据集的分析。在这里，我们提出了一套创新的工具，可以利用独特的细菌生理学的各个方面，以应对这些数据的挑战特征。在我们的第一个目标中，我们提出了第一种专门针对单细菌数据量身定制的RNA-Seq denoising方法。这种方法使用高细胞数量识别共同基因表达曲线模块的功率，然后使用来自模块表达的细胞潜在空间，以使相邻细胞平滑，以使其高度高已解决的转录组轮廓。在我们的第二个目标中，我们寻求根据细胞的复制性注释细胞，并通过整合直接衍生出细菌细胞生物学的参数来增长速率。具体来说，我们将采取细菌单一复制起源的一般趋势的优势，以更高的表达基因更接近复制的起源与复制基因组。此外，我们将推断一个单元的增长率迅速生长的细胞具有较高的非编码RNA的原理的基础他们成熟的对手。在我们的最终目标中，我们将发布并维护一个分析的计算包致病社区使用的工具。单细菌RNA-seq场是快速增长的，需要计算支持将在阐明抗生素耐受性和细菌发病机理。