Publisher Correction: Quantitative image analysis of microbial communities with BiofilmQ

Hartmann, Raimo; Jeckel, Hannah; Jelli, Eric; Singh, Praveen; Vaidya, Sanika; Bayer, Miriam; Rode, D; Vidakovic, Lucia; Díaz-Pascual, Francisco; Fong, Jiunn C. N.; Dragoš, Anna; Lamprecht, Olga; Thöming, Janne G.; Netter, Niklas; Häußler, Susanne; Nadell, Carey D.; Sourjik, Victor; Kovács, Ákos T.; Yildiz, Fitnat H.; Drescher, Knut

doi:10.1038/s41564-021-00863-6

Cited by 9 publications

(9 citation statements)

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“…Our classification results provide an automated alternative for experts to analyze different coated areas and provide additional inputs to the domain experts to further control their experimental parameters. Several tools have been employed to extract and evaluate the geometric properties of biofilm microstructures including deep neural network (Buetti-Dinh et al, 2019 ), BioFilm Analyzer (Bogachev et al, 2018 ), BiofilmQ (Hartmann et al, 2021 ), and ImageJ (Rueden et al, 2017 ). However, these tools characterize the smooth, homogeneous, and non-overlaying geometric structures and are not generic enough to classify the congested biofilm microstructures and byproducts as done in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…Images lacking sufficient resolution make it difficult for experts to manually detect certain class objects in such images with certainty. Tools such as BiofilmQ (Hartmann et al, 2021 ), ImageJ (Rueden et al, 2017 ) that have been used for microscopy image analyses are not effective in analyzing biofilm images where the class objects appear in bursts of high density and separating cells from clusters and byproducts is often ambiguous (Ragi et al, 2021 ). On the other hand, deep learning methods can be used to build models that can accurately and automatically detect each of the above three class objects in each image patch and these results can then be used to automatically build heatmaps depicting the distribution of these class objects in each image.…”

Section: Introductionmentioning

confidence: 99%

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An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms

et al. 2022

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Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels—cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms

et al. 2022

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“…Images of the biofilms were obtained on a Zeiss LSM 880 confocal microscope at 20× magnification for image generation and subsequent biofilm parameters analysis. Imaris (Oxford Instruments) was used to process the images, and BiofilmQ ( 90 ) was used to measure the global biofilm parameters of the biofilms that formed in each test condition.…”

Section: Methodsmentioning

confidence: 99%

Outer membrane vesicles and the outer membrane protein OmpU govern Vibrio cholerae biofilm matrix assembly

Potapova,

Garvey,

Dahl

et al. 2024

mBio

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Biofilms are matrix-encased microbial communities that increase the environmental fitness and infectivity of many human pathogens including Vibrio cholerae . Biofilm matrix assembly is essential for biofilm formation and function. Known components of the V. cholerae biofilm matrix are the polysaccharide Vibrio polysaccharide (VPS), matrix proteins RbmA, RbmC, Bap1, and extracellular DNA, but the majority of the protein composition is uncharacterized. This study comprehensively analyzed the biofilm matrix proteome and revealed the presence of outer membrane proteins (OMPs). Outer membrane vesicles (OMVs) were also present in the V. cholerae biofilm matrix and were associated with OMPs and many biofilm matrix proteins suggesting that they participate in biofilm matrix assembly. Consistent with this, OMVs had the capability to alter biofilm structural properties depending on their composition. OmpU was the most prevalent OMP in the matrix, and its absence altered biofilm architecture by increasing VPS production. Single-cell force spectroscopy revealed that proteins critical for biofilm formation, OmpU, the matrix proteins RbmA, RbmC, Bap1, and VPS contribute to cell-surface adhesion forces at differing efficiency, with VPS showing the highest efficiency whereas Bap1 showing the lowest efficiency. Our findings provide new insights into the molecular mechanisms underlying biofilm matrix assembly in V. cholerae , which may provide new opportunities to develop inhibitors that specifically alter biofilm matrix properties and, thus, affect either the environmental survival or pathogenesis of V. cholerae . IMPORTANCE Cholera remains a major public health concern. Vibrio cholerae , the causative agent of cholera, forms biofilms, which are critical for its transmission, infectivity, and environmental persistence. While we know that the V. cholerae biofilm matrix contains exopolysaccharide, matrix proteins, and extracellular DNA, we do not have a comprehensive understanding of the majority of biofilm matrix components. Here, we discover outer membrane vesicles (OMVs) within the biofilm matrix of V. cholerae . Proteomic analysis of the matrix and matrix-associated OMVs showed that OMVs carry key matrix proteins and Vibrio polysaccharide (VPS) to help build biofilms. We also characterize the role of the highly abundant outer membrane protein OmpU in biofilm formation and show that it impacts biofilm architecture in a VPS-dependent manner. Understanding V. cholerae biofilm formation is important for developing a better prevention and treatment strategy framework.

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“…Z-stack images were captured from a total of 12 representative fields per mutant from three independent experiments. Images were analyzed using BiofilmQ ( 74 ). Global biofilm properties and individual microcolony measurements were calculated from preprocessed files with and without segmentation by cube dissection, respectively.…”

Section: Methodsmentioning

confidence: 99%

Type IV pilus retraction is required for Neisseria musculi colonization and persistence in a natural mouse model of infection

Rhodes,

Rendón,

et al. 2024

mBio

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Retraction of the pathogenic Neisseria Type IV pilus (Tfp) requires PilT, the primary retraction motor, and its paralogs PilU and PilT2. The importance of Tfp retraction for natural infection is unknown. Using our natural animal model of Neisseria -host interaction, we have examined the role of these proteins in the ability of commensal Neisseria musculi (Nmus) to colonize and persist in its native host, the mouse. We report that Nmus Δ pilT cannot colonize mice; Δ pilU and Δ pilT2 can colonize and persist in mice, but in lower numbers. Microcolonies formed by Δ pilT , Δ pilTU , and pilT L201C , expressing PilT with a point mutation in its ATP hydrolysis domain, are sensitive to removal by fluid shear forces. Thus, PilT promotes Nmus colonization while PilU and PilT2 influence persistence. We present a non-exclusive model for how these Tfp retraction motor proteins contribute to Nmus persistent colonization. Our findings have implications for the roles of these motor proteins in mediating interactions of human-adapted pathogenic and commensal Neisseria with their human host. IMPORTANCE We describe the importance of Type IV pilus retraction to colonization and persistence by a mouse commensal Neisseria, N. musculi, in its native host. Our findings have implications for the role of Tfp retraction in mediating interactions of human-adapted pathogenic and commensal Neisseria with their human host due to the relatedness of these species.

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Publisher Correction: Quantitative image analysis of microbial communities with BiofilmQ

Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41564-021-00863-6.

Cited by 9 publications

References 0 publications

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms

An AI-based approach for detecting cells and microbial byproducts in low volume scanning electron microscope images of biofilms

Outer membrane vesicles and the outer membrane protein OmpU govern Vibrio cholerae biofilm matrix assembly

Type IV pilus retraction is required for Neisseria musculi colonization and persistence in a natural mouse model of infection

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