ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

Li, Pai; Zhang, Ze; Tong, Yiying; Foda, Bardees M.; Day, Brad

doi:10.1083/jcb.202203024

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…A new filament segregation algorithm, Implicit Laplacian of Enhanced Edge (ILEE), was created to provide accurate and robust analyses of cytoskeleton 2D and 3D images, thus eliminating the traditional limitation and user bias of the approach involving manual global thresholding. Indeed, ILEE can process plant MT images with satisfying performance [ 216 ]. Because cortical MT organization is a determining factor for division plane formation [ 53 ], a computer simulation method was developed to capture the effects of cell geometry on MT organization.…”

Section: New Techniques For Studying Plant Mtsmentioning

confidence: 99%

Microtubule Regulation in Plants: From Morphological Development to Stress Adaptation

Hsiao

Huang

2023

Biomolecules

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Microtubules (MTs) are essential elements of the eukaryotic cytoskeleton and are critical for various cell functions. During cell division, plant MTs form highly ordered structures, and cortical MTs guide the cell wall cellulose patterns and thus control cell size and shape. Both are important for morphological development and for adjusting plant growth and plasticity under environmental challenges for stress adaptation. Various MT regulators control the dynamics and organization of MTs in diverse cellular processes and response to developmental and environmental cues. This article summarizes the recent progress in plant MT studies from morphological development to stress responses, discusses the latest techniques applied, and encourages more research into plant MT regulation.

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Section: New Techniques For Studying Plant Mtsmentioning

confidence: 99%

Microtubule Regulation in Plants: From Morphological Development to Stress Adaptation

Hsiao

Huang

2023

Biomolecules

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“…This procedure is fundamental and critical to analyze the cytoskeleton and organelles. In images with fluorescently labeled cytoskeletons, cytoskeletal regions are typically defined based on fluorescence intensity, where regions with intensity above a predetermined threshold are identified as cytoskeleton (Higaki et al 2010; Li et al 2022; Hembrow et al 2023). Two primary methods are used to determine this threshold.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density

Horiuchi,

Kamimura,

Hanaki

et al. 2024

Preprint

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Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal micrographs of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

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“…Quantitative image analysis is a key tool that allows one to capture the complex organization of actin filaments and better understand cellular processes involving the cytoskeleton. Over the years, a number of image-based analysis tools have been developed to characterize different actin filament architectures found in both plant and animal cells (Gan et al, 2016; Jacques et al, 2013; Kimori et al, 2016; P. Li et al, 2023; Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Morphometric Analysis of Actin Networks

Akenuwa,

Gu,

Nebenführ

et al. 2024

Preprint

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The organization of cytoskeletal elements is pivotal for coordinating intracellular transport in eukaryotic cells. Several quantitative measures based on image analysis have been proposed to characterize morphometric features of fluorescently labeled actin networks. While helpful in detecting differences in actin organization between treatments or genotypes, the accuracy of these measures could not be rigorously assessed due to a lack of ground-truth data to which they could be compared. To overcome this limitation, we utilized coarse-grained computer simulations of actin filaments and crosslinkers to generate synthetic actin networks with varying levels of bundling. We converted the simulated networks into pseudo-fluorescence images similar to images obtained using confocal microscopy. Using both published and novel analysis procedures, we extracted a series of morphometric parameters and benchmarked them against analogous measures based on the ground-truth actin configurations. Our analysis revealed a set of parameters that reliably reports on actin network density, orientation, ordering, and bundling. Application of these morphometric parameters to root epidermal cells ofArabidopsis thalianarevealed subtle changes in network organization between wild-type and mutant cells. This work provides robust measures that can be used to quantify features of actin networks and characterize changes in actin organization for different experimental conditions.

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ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images

Cited by 5 publications

References 45 publications

Microtubule Regulation in Plants: From Morphological Development to Stress Adaptation

Microtubule Regulation in Plants: From Morphological Development to Stress Adaptation

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density

Morphometric Analysis of Actin Networks

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