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通过大面积自动原子力显微镜分析生物膜组装

Analysis of biofilm assembly by large area automated AFM.

作者信息

Millan-Solsona Ruben, Brown Spenser R, Zhang Lance, Madugula Sita Sirisha, Zhao HuanHuan, Dumerer Blythe, Bible Amber N, Lavrik Nickolay V, Vasudevan Rama K, Biswas Arpan, Morrell-Falvey Jennifer L, Retterer Scott, Checa Martí, Collins Liam

机构信息

Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.

Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.

出版信息

NPJ Biofilms Microbiomes. 2025 May 8;11(1):75. doi: 10.1038/s41522-025-00704-y.

DOI:10.1038/s41522-025-00704-y
PMID:40341406
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12062311/
Abstract

Biofilms are complex microbial communities critical in medical, industrial, and environmental contexts. Understanding their assembly, structure, genetic regulation, interspecies interactions, and environmental responses is key to developing effective control and mitigation strategies. While atomic force microscopy (AFM) offers critically important high-resolution insights on structural and functional properties at the cellular and even sub-cellular level, its limited scan range and labor-intensive nature restricts the ability to link these smaller scale features to the functional macroscale organization of the films. We begin to address this limitation by introducing an automated large area AFM approach capable of capturing high-resolution images over millimeter-scale areas, aided by machine learning for seamless image stitching, cell detection, and classification. Large area AFM is shown to provide a very detailed view of spatial heterogeneity and cellular morphology during the early stages of biofilm formation which were previously obscured. Using this approach, we examined the organization of Pantoea sp. YR343 on PFOTS-treated glass surfaces. Our findings reveal a preferred cellular orientation among surface-attached cells, forming a distinctive honeycomb pattern. Detailed mapping of flagella interactions suggests that flagellar coordination plays a role in biofilm assembly beyond initial attachment. Additionally, we use large-area AFM to characterize surface modifications on silicon substrates, observing a significant reduction in bacterial density. This highlights the potential of this method for studying surface modifications to better understand and control bacterial adhesion and biofilm formation.

摘要

生物膜是在医学、工业和环境背景中至关重要的复杂微生物群落。了解它们的组装、结构、基因调控、种间相互作用以及环境响应是制定有效控制和缓解策略的关键。虽然原子力显微镜(AFM)能在细胞乃至亚细胞水平上提供关于结构和功能特性的极其重要的高分辨率见解,但其有限的扫描范围和劳动密集型性质限制了将这些较小尺度特征与生物膜的功能宏观组织联系起来的能力。我们通过引入一种自动化大面积AFM方法来解决这一限制,该方法能够在毫米尺度区域上捕获高分辨率图像,并借助机器学习实现无缝图像拼接、细胞检测和分类。大面积AFM显示出在生物膜形成早期阶段提供了以前被掩盖的空间异质性和细胞形态的非常详细的视图。使用这种方法,我们研究了泛菌属YR343在PFOTS处理的玻璃表面上的组织情况。我们的研究结果揭示了表面附着细胞之间的一种优选细胞取向,形成了独特的蜂窝状图案。鞭毛相互作用的详细图谱表明,鞭毛协调在生物膜组装中除了初始附着之外还发挥作用。此外,我们使用大面积AFM来表征硅基片上的表面修饰,观察到细菌密度显著降低。这突出了该方法在研究表面修饰以更好地理解和控制细菌粘附及生物膜形成方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/ec39fa6baac2/41522_2025_704_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/14ec1bfe39f1/41522_2025_704_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/406d504fc111/41522_2025_704_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/a41517c5652c/41522_2025_704_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/74965d682eee/41522_2025_704_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/c000e5a790cb/41522_2025_704_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/ec39fa6baac2/41522_2025_704_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/14ec1bfe39f1/41522_2025_704_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/406d504fc111/41522_2025_704_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/a41517c5652c/41522_2025_704_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/74965d682eee/41522_2025_704_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/c000e5a790cb/41522_2025_704_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d2/12062311/ec39fa6baac2/41522_2025_704_Fig6_HTML.jpg

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本文引用的文献

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Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis.机器学习与人工智能:助力基于原子力显微镜的生物标志物在癌症诊断中的临床转化。
Comput Struct Biotechnol J. 2024 Oct 5;24:661-671. doi: 10.1016/j.csbj.2024.10.006. eCollection 2024 Dec.
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Spatially structured exchange of metabolites enhances bacterial survival and resilience in biofilms.代谢物的空间结构化交换增强了细菌在生物膜中的生存能力和弹性。
Nat Commun. 2024 Aug 31;15(1):7575. doi: 10.1038/s41467-024-51940-3.
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A comparative analysis of pairwise image stitching techniques for microscopy images.
显微镜图像的两两图像拼接技术的比较分析。
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AEcroscopy: A Software-Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation.AEcroscopy:一个软硬件框架,助力显微镜实现自动化和自主实验。
Small Methods. 2024 Oct;8(10):e2301740. doi: 10.1002/smtd.202301740. Epub 2024 Apr 19.
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On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition.基于原子力显微镜图像的机器学习分析进行图像分类、样本表面识别。
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