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

追踪细菌生物膜中的单细胞基因表达异质性及其后果

• 批准号: 10242439
• 负责人: Jing Yan
• 金额: $ 150.75万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242439
• 关键词: 3-Dimensional; Address; Algorithms; Antibiotic Therapy; Architecture; Bacteria; Biomechanics; Cells; Chemicals; Clinical; Complex; Coupled; Development; Disease; Extracellular Matrix; Gene Expression; Gene Expression Profile; Genes; Growth; Health; Heterogeneity; Image; Individual; Industrialization; Knowledge; Life Style; Methods; Microbial Biofilms; Nature; Pattern; Population; Procedures; Production; Regulation of Cell Shape; Reporter; Resolution; Shapes; Signal Transduction; Surface; Variant; bacterial community; cell behavior; cell motility; chronic infection; deep learning algorithm; imaging platform; innovation; live cell imaging; operation; programs; response; spatiotemporal

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.

项目摘要 细菌生物膜是包围在细胞外基质中的细菌细胞的表面附着群落。 生物膜是健康和工业操作中的一个问题，因为持续的感染，流动的堵塞， 和表面结垢。单细胞活细胞成像的最新进展揭示了在细胞内明确的细胞排序， 个体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 了解哪些基因在每个生物膜居住细胞中被激活，以及细胞组织是如何被激活的。 由单细胞水平的基因表达模式决定。我们不知道基因 表达谱在生物膜中因细胞而异，以及这种异质性对生物膜的影响 发展生物膜中细胞间的差异可能是消除生物膜中臭名昭著的困难的基础。 慢性感染，因为生物膜细胞对抗生素治疗的不同反应。本提案中 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 单细胞水平的生长动力学和相关基因表达模式。利用这些图像 平台，我们将揭示单细胞基因表达，单个细胞行为， 以及构成细菌生物膜发育程序基础的局部细胞组织。具体来说，我们将 使用深度学习算法来推动单细胞生物膜成像的时间和空间分辨率限制， 并开发一种创新的，耦合分割跟踪方法，以生成一个强大的三维 生长生物膜的谱系追踪算法。通过结合谱系追踪和荧光报告，我们将 在整个生物膜发育过程中遵循每个单独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， 单细胞水平的表达异质性和3D生物膜中的相关后果。此外，本发明还提供了一种方法， 我们将研究参与细胞内和细胞间信号传导、运动和 附着和细胞形状调节来扩大我们的发现。通过这些努力，我们将揭示个人 基因表达、细胞行为和局部细胞有序性相互作用，以定义生物膜 建筑与发展。本提案中获得的知识为以下方面提供了新的战略： 操纵复杂的细菌群落，并有可能改变临床治疗程序， 生物膜相关疾病，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。

项目成果

Mechanical forces drive a reorientation cascade leading to biofilm self-patterning.

• DOI: 10.1038/s41467-021-26869-6
• 发表时间: 2021-11-17
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Nijjer J;Li C;Zhang Q;Lu H;Zhang S;Yan J
• 通讯作者: Yan J

Agent-based modeling of stress anisotropy driven nematic ordering in growing biofilms.

基于代理的生物膜生长中应力各向异性驱动的向列排序建模。

• DOI: 10.1039/d3sm01535a
• 发表时间: 2024
• 期刊: Soft matter
• 影响因子: 3.4
• 作者: Li,Changhao;Nijjer,Japinder;Feng,Luyi;Zhang,Qiuting;Yan,Jing;Zhang,Sulin
• 通讯作者: Zhang,Sulin

New Insights into Vibrio cholerae Biofilms from Molecular Biophysics to Microbial Ecology.

从分子生物物理学到微生物生态学对霍乱弧菌生物膜的新见解。

• DOI: 10.1007/978-3-031-22997-8_2
• 发表时间: 2023
• 期刊: Advances in experimental medicine and biology
• 作者: Tai,Jung-ShenB;Ferrell,MicahJ;Yan,Jing;Waters,ChristopherM
• 通讯作者: Waters,ChristopherM

Social evolution of shared biofilm matrix components.

• DOI: 10.1073/pnas.2123469119
• 发表时间: 2022-07-05
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Tai, Jung-Shen B.;Mukherjee, Saikat;Nero, Thomas;Olson, Rich;Tithof, Jeffrey;Nadell, Carey D.;Yan, Jing
• 通讯作者: Yan, Jing