Single-cell transcriptomics of complex bacterial communities

项目摘要 细菌中的基因表达是异质的，即使在遗传上相同的细胞内，由于随机性， 许多基因调控程序的激活。由此产生的表型多样性往往起着重要的作用 细菌群落的功能作用，例如促进水平基因转移。这从根本 单细胞行为不能用群体水平测量来解决，到目前为止，已经在 使用基于低通量PCR的测定的遗传上易处理的生物体的纯培养物。但 自然界中的大多数细菌存在于空间上组织成生物膜的复杂微生物群落中， 由多个相互作用的成员组成。在这样的社区中， 嘈杂的基因表达状态和对异质微环境的反应的动态相互作用。 缺乏测量复杂多微生物群落内表型状态的方法 同时在系统规模和单细胞分辨率导致缺乏对 细菌生态学，因此是微生物学和微生物组研究领域的关键障碍。期间 我的博士后，我开发了一种高通量细菌单细胞转录组学技术microSPLiT （微生物分裂池连接转录组学），其允许测量数十个细胞中的基因表达状态。 成千上万的单个细胞只使用普通的实验室设备。在我的实验室里，我的目标是进一步扩展 microSPLiT用于生物膜的单细胞转录组学，以及复杂的细菌聚生体。具体到 我的第一个项目，我们将创建一个单细胞基因表达图的单和双物种的生物膜， 铜绿假单胞菌和金黄色葡萄球菌的空间单细胞RNA联合测序 和延时成像。我们将描述在哪里和如何出现专门的表型亚群 在生物膜发展的不同阶段，以及它们如何响应竞争物种而变化。在第二 项目，我们将询问的功能作用的间歇性和异质性激活的不同 代谢和应激反应途径，我们甚至在同基因细胞中观察到， 外部线索具体来说，我们将测试这样的假设，即异质抽样的国家可能会促进 与细菌伙伴的物种间相互作用进化为在同一环境中共存。在这个项目中，我 旨在揭示来自人类肠道微生物群的关键物种的表型亚群， 单独或与其他共存物种成对组合。有了这些数据，我们将使用基因调控 网络建模，以预测肠道微生物群物种之间的高阶相互作用， 具有可预测行为特征的复杂性联盟。结果将为建立系统铺平道路- 水平的表型结构和更高的复杂性自然的涌现特性的理解 微生物群总的来说，开发的方法将成为微生物研究的广泛适用的工具 所获得的数据将为微生物群和生物膜的高分辨率功能分析提供基础。

Project Summary Gene expression in bacteria is heterogeneous even within genetically identical cells due to the stochastic activation of many gene regulatory programs. The resulting phenotypic diversity often plays an important functional role for bacterial communities, for example, facilitating horizontal gene transfer. This fundamentally single-cell behavior cannot be resolved with population level measurements and, so far, has been studied in pure cultures of genetically tractable organisms using low-throughput reporter-based assays. However, the majority of bacteria in nature reside in complex microbial communities spatially organized into biofilms and composed of multiple interacting members. Within such communities, a multitude of behaviors emerge from the dynamic interplay of noisy gene expression states and responses to the heterogeneous microenvironment. Absence of approaches for measuring phenotypic states within complex polymicrobial communities simultaneously at systems scale and with single-cell resolution results in a lack of mechanistic understanding of bacterial ecology and is therefore a critical barrier for the fields of microbiology and microbiome studies. During my postdoc, I developed a high-throughput bacterial single-cell transcriptomics technology, microSPLiT (microbial Split-Pool Ligation Transcriptomics), that allows to measure gene expression states in tens of thousands of individual cells using only common laboratory equipment. In my lab, I aim to further extend microSPLiT for single-cell transcriptomics of biofilms, as well as of complex bacterial consortia. Specifically, in my first project we will create a single-cell gene expression map of single- and dual-species biofilms of Pseudomonas aeruginosa and Staphylococcus aureus by a combination of spatial single-cell RNA sequencing and time-lapse imaging. We will characterize where and how the specialized phenotypic subpopulations emerge at different stages of biofilm development and how they change in response to competing species. In the second project, we will interrogate the functional role of the intermittent and heterogeneous activation of diverse metabolic and stress response pathways which we have observed even in isogenic cells and in absence of external cues. Specifically, we will test the hypothesis that heterogeneous sampling of such states may promote inter-species interactions with bacterial partners evolved to coexist in the same environment. In this project, I aim to uncover the phenotypic subpopulations arising in key species from human gut microbiota grown either solo or in pair-wise combinations with other co-occurring species. With these data, we will use gene regulatory network modeling to predict the higher-order interactions between gut microbiota species and engineer higher complexity consortia with predictable behavioral traits. The results will pave the way toward building systems- level understanding of the phenotypic structure and the emergent properties of a higher complexity natural microbiota. Overall, the developed approaches will become widely applicable tools for microbiological research and the acquired data will provide a foundation for high-resolution functional analyses of microbiota and biofilms.