Resolving Oral Bacteria Interactions with a High-Throughput Low-Cost Single-Cell Transcriptomics Approach

采用高通量低成本单细胞转录组学方法解决口腔细菌相互作用

基本信息

批准号：
10678379
负责人：
Anna Kuchina
金额：
$ 27.15万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10678379
关键词：
Acute Address Affect Architecture Bacteria Bacterial Adhesins Bar Codes Binding Biological Biological Models Bypass Cell Communication Cell Separation Cell Size Cell Wall Cells Coculture Techniques Communities Complex Disease Environment Eukaryota Fusobacterium nucleatum Future Gene Expression Gene Expression Regulation Genes Genetic Transcription Genome Goals Homeostasis Human Microbiome In Vitro Life Style Ligation Measures Messenger RNA Methodology Methods Microbe Microbiology Microfluidics Modeling Modernization Morphology Noise Oral Oral cavity Organism Outcome Parasites Pathogenicity Pathway interactions Peptidoglycan Porphyromonas gingivalis Probability Prokaryotic Cells Protocols documentation RNA Radiation Research Resolution Sampling Science Signal Transduction Stress Study models System Techniques Technology Testing Time Viral Virulence Virulence Factors cell type clinically relevant combinatorial cost dental biofilm design differential expression experimental study extracellular fungus human microbiota in vivo Model innovation insight instrumentation interest laboratory equipment member microbial microbiota microorganism interaction novel oral bacteria oral microbial community periodontopathogen periopathogen single-cell RNA sequencing transcriptome transcriptome sequencing transcriptomics

项目摘要

1 Abstract 2 Interactions among the microbiota within human microbiome are what define the community function during 3 healthy homeostasis and changes in these relationships can result in increased virulence leading to disease. 4 Bacteria from different species in the oral cavity make up the diverse plaque biofilms and are well known to 5 recognize and bind each other when grown together or artificially mixed (aggregation). Many studies rely on this 6 model to determine how cell-cell interactions affect each member. However, a major limitation to understanding 7 how direct binding between microbes may regulate expression within a simple dual bacteria:bacteria interaction 8 is the fact that there are mixed cell states within the co-culture. In reality, there is not a controlled 1:1 relationship, 9 and not all cells within the culture are directly bound to another cell. Many cells will remain free but still influenced 10 by soluble metabolites and signals in the shared media environment. When bulk transcriptomics are applied to 11 measure the gene expression of such mixed cultures, the average expression of all the cell states together 12 generates noise that interferes with the true signal of interest – what is occurring in specific biological states, 13 such as cells that are attached to each other. This results in a high probability that a true positive will be 14 missed and the inability to confidently attribute observed changes to the cells of interest. Ultimately this problem 15 represents a universal issue in all microbiology that has largely been ignored to date mainly due to the 16 technological limitations of physically capturing only the cells of interest as well as the biological limitation of low 17 bacterial mRNA content. Here we propose to leverage a recent breakthrough in single-cell transcriptomics 18 (scRNAseq) that has been developed for prokaryotes and successfully applied to monocultures and artificially 19 mixed non-interacting bacteria (MPI Kuchina et al., Science 2021), designated MicroSPLiT (microbial split-pool 20 ligation transcriptomics). MicroSPLiT was achieved through overcoming major challenges specific to bacteria, 21 such as their low mRNA content, diversity in cell size, and cell wall architecture. This technique is designed to 22 be high throughput, profiling tens of thousands of cells in a single experiment, low-cost, and importantly, an 23 approach achievable by any lab with only basic laboratory equipment. The goal of this study is to leverage and 24 expand upon this very recent, highly innovative breakthrough to: 1) Overcome a major universal problem in 25 microbiology by developing a comprehensive approach to determine true cell-cell binding interactions at the 26 single-cell level, and 2) apply this technique to gain insight into several important interactions between oral 27 microbial species including known periopathogens and those between recently discovered ultrasmall, reduced 28 genome parasitic oral bacteria and their bacterial host. Overall, the outcomes of this project are expected to 29 directly advance our understanding of the regulation of genes between physically interacting microbiota and 30 develop a protocol for the research community to be able to utilize this new widely accessible approach. 31

1 摘要 2 人类微生物组内微生物群之间的相互作用决定了微生物群落功能 3 健康的体内平衡和这些关系的变化可能导致毒力增加，从而导致疾病。 4 口腔中不同物种的细菌构成了不同的菌斑生物膜，并且众所周知 5 当生长在一起或人工混合（聚合）时相互识别并结合。许多研究依赖于此。 6 模型来确定细胞间相互作用如何影响每个成员然而，这是理解的一个主要限制。 7 微生物之间的直接结合如何调节简单双细菌内的表达：细菌相互作用 8 是共培养中存在混合细胞状态的事实。实际上，不存在受控的 1:1 关系。 9 并且并非培养物中的所有细胞都直接与另一个细胞结合。许多细胞将保持游离状态，但仍会受到影响。 10 当批量转录组学应用于共享介质环境中时，可溶性代谢物和信号。 11 测量此类混合培养物的基因表达，所有细胞状态在一起的平均表达 12 产生的噪声会干扰真正感兴趣的信号——特定生物状态下发生的情况， 13 例如细胞相互附着，这导致真阳性的可能性很高。 14 错过了并且无法自信地将观察到的变化归因于感兴趣的细胞最终这个问题。 15 代表了所有微生物学中的一个普遍问题，迄今为止，该问题在很大程度上被忽视，主要是由于仅物理捕获感兴趣的细胞的 16 种技术限制以及低浓度的生物限制 17 细菌 mRNA 含量在此我们建议利用单细胞转录组学的最新突破。 18 (scRNAseq) 已针对原核生物开发并成功应用于单一培养和人工培养 19 种混合的非相互作用细菌（MPI Kuchina 等人，Science 2021），指定为 MicroSPLiT（微生物分池） MicroSPLiT 是通过克服细菌特有的重大挑战而实现的， 21 例如它们的低 mRNA 含量、细胞大小和细胞壁结构的多样性。 22 具有高通量、可在一次实验中对数万个细胞进行分析、低成本，而且重要的是， 23 任何实验室只需基本实验室设备即可实现的方法本研究的目标是利用和利用。 24 将这一最新的、高度创新的突破扩展到： 1) 克服一个重大的普遍问题 25 微生物学，通过开发一种综合方法来确定真实的细胞与细胞结合相互作用 26 单细胞水平，以及 2) 应用该技术来深入了解口腔细胞之间的几个重要相互作用 27 种微生物物种，包括已知的周围病原体和最近发现的超小型、减少的微生物物种总体而言，该项目的结果预计将是28种基因组寄生口腔细菌及其细菌宿主。 29 直接增进了我们对物理微生物群与基因之间基因调节的理解 30 为研究界制定一项协议，以便能够利用这种新的广泛使用的方法。 31