Tracking single-cell gene expression heterogeneity and its consequences in bacterial biofilms

项目摘要 细菌生物膜是包裹在细胞外基质中的细菌细胞的表面附着群落。 生物膜在健康和工业运营中是一个令人担忧的问题，因为持续的感染、流动堵塞、 和表面污垢。单细胞活体成像的最新进展揭示了细胞有序的明确 单个生物膜簇和塑造它们的基本生物力学原理。然而，我们几乎没有 了解在每个生物膜细胞中哪些基因被激活，以及细胞是如何组织的 由单细胞水平上的基因表达模式决定。我们不知道基因是否以及如何 生物膜中不同细胞的表达谱不同，以及这种异质性对生物膜有什么影响 发展。生物膜中细胞间的变异可能会掩盖根除生物膜的臭名昭著的困难 慢性感染，因为生物膜细胞对抗生素治疗的反应不同。在本建议书中 我们提出了通过开发新的成像平台来捕获生物膜来应对这一挑战的想法 单细胞水平上的生长动态和相关基因表达模式。使用这些成像 平台，我们将揭示单细胞基因表达、个体细胞行为、 以及构成细菌生物膜发育程序的局部细胞组织。具体来说，我们将 使用深度学习算法推动单细胞生物膜成像的时间和空间分辨率极限， 并开发一种创新的、耦合的分割-跟踪方法来生成健壮的三维 生物膜生长中的谱系追踪算法。通过结合血统追踪和荧光记者，我们将 在整个生物膜发育过程中，遵循每个单个细胞的时空表达模式。通过 重点是基质的产生、降解和细胞分散，我们将创建基因的具体例子 3D生物膜中单细胞水平的表达异质性及其相关后果。此外, 我们将研究参与细胞内和细胞间信号传递的基因的异质性，运动性和 依附，并在细胞形状调节方面扩大了我们的发现。通过这些努力，我们将揭示个人是如何 基因表达、细胞行为和局部细胞排序相互作用，从而定义生物膜 建筑和发展。目前提案中获得的知识为以下方面提供了新战略 操纵复杂的细菌群落，有可能改变临床治疗程序 与生物膜有关的疾病，例如通过开发专门诱导扩散或靶向的新化学品 顽固的细胞群体。

Project Summary Bacterial biofilms are surface-attached communities of bacterial cells enclosed in an extracellular matrix. Biofilms are a concern in health and in industrial operations because of persistent infections, clogging of flows, and surface fouling. Recent advances in single-cell live imaging have revealed well-defined cell ordering in individual biofilm clusters and the underlying biomechanical principles that shape them. However, we have little understanding of which genes are activated in each biofilm-dwelling cell and how cell organization is determined by the gene expression pattern at the single-cell level. We do not know whether and how gene expression profiles vary from cell to cell in biofilms, and what consequences such heterogeneity has on biofilm development. Cell-to-cell variation in biofilms could underly the notorious difficulty in eradicating biofilms in chronic infections because of the differential response of biofilm cells to antibiotic treatment. In this proposal we put forward ideas to address this challenge by developing new imaging platforms to capture the biofilm growth dynamics and associated gene expression pattern at the single-cell level. Using these imaging platforms, we will uncover the intricate interplay between single-cell gene expression, individual cell behavior, and local cell organization that underlies the developmental program of bacterial biofilms. Specifically, we will use deep learning algorithms to push the temporal and spatial resolution limits in single-cell biofilm imaging, and develop an innovative, coupled segmentation-tracking method to generate a robust three-dimensional lineage tracing algorithm in growing biofilms. By combining lineage tracing and fluorescent reporters, we will follow the spatiotemporal expression pattern of each individual cell throughout biofilm development. By focusing on matrix production, degradation, and cell dispersion, we will create concrete examples of gene expression heterogeneity at the single-cell level and the associated consequences in 3D biofilms. In addition, we will investigate heterogeneity in genes involved in intra- and intercellular signaling, in motility and attachment, and in cell shape regulation to broaden our finding. With these efforts, we will reveal how individual gene expression, cell behavior, and local cell ordering reciprocally interact with each other to define biofilm architecture and development. The knowledge obtained in the current proposal offers new strategies for manipulating complex bacterial communities and has the potential to change the clinical procedure of treating biofilm-related diseases, for example by developing new chemicals that specifically induce dispersal or target the recalcitrant cell populations.