Single-cell transcriptomics of complex bacterial communities

项目摘要 由于随机性，即使在遗传相同的细胞中，细菌中的基因表达也是异质性的 激活许多基因调控程序。由此产生的表型多样性通常起着重要的作用 细菌群落的功能作用，例如，促进水平基因转移。这从根本上说 单个细胞的行为不能用种群水平测量来解决，到目前为止，已经在 使用低通量的基于报告的分析方法对遗传易驯化的生物进行纯培养。然而， 自然界中的大多数细菌生活在复杂的微生物群落中，这些微生物群落在空间上被组织成生物膜和 由多个相互作用的成员组成的。在这样的社区中，出现了许多行为 噪声基因表达状态和对异质微环境的反应的动态相互作用。 缺乏测量复杂多菌群落中表型状态的方法 同时在系统规模和单细胞分辨率上导致缺乏对 细菌生态学，因此是微生物学和微生物组研究领域的一个关键障碍。在.期间 在我的博士后，我开发了一种高通量细菌单细胞转录技术，microSPLiT (微生物裂解连接转录学)，它允许测量数十个 数以千计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 用于生物膜以及复杂细菌联合体的单细胞转录的MicroSPLiT。具体而言，在 我的第一个项目我们将创建单细胞基因表达图谱，包括单种和双种生物膜 铜绿假单胞菌和金黄色葡萄球菌空间单细胞RNA联合测序 和延时成像。我们将描述特化表型亚群出现的地点和方式 在生物膜发展的不同阶段，以及它们如何随着竞争物种的变化而变化。在第二个 项目，我们将询问间歇性和异质性激活不同的功能作用 我们观察到的代谢和应激反应途径，即使在同基因细胞中也是如此，并且在缺乏 外部线索。具体地说，我们将测试这样一个假设，即这种状态的不同抽样可能会促进 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 目的揭示人类肠道微生物区系中关键物种的表型亚群。 单独或与其他共生物种成对组合。有了这些数据，我们将使用基因调控 预测肠道微生物区系物种与工程师之间高阶相互作用的网络模型 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- 对一种复杂程度较高的自然界的表型结构和紧急性质有较深的理解 微生物区系。总体而言，所开发的方法将成为微生物研究的广泛适用工具 所获得的数据将为微生物区系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.