3D Quantification of Vascular-Like Structures in z Stack Confocal Images

Bonda, Ulrich; Jaeschke, Anna; Lighterness, Anthony; Baldwin, Jeremy; Werner, Carsten; De‐Juan‐Pardo, Elena M.; Bray, Laura J.

doi:10.1016/j.xpro.2020.100180

Cited by 7 publications

(7 citation statements)

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“…Processing a 1-GB nd2 file with Qiber3D on an Intel Core i7-6700 machine with 16 GB RAM running a Windows 10 (64-bit) operating system took ∼7.5 minutes. Manually analyzing a similar image takes ∼8.5 min, not considering the time to switch between various software packages [ 12 ]. While this is a slight decrease in processing time of 1 image, Qiber3D can be applied to numerous images without user interaction, making it suitable for analyzing large datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Besides carrying the risk of user-based subjectivity, this also limits the throughput of samples for experiments with large image datasets. In some cases, switching between multiple existing software packages is necessary [ 12 ], making the image processing time- and resource-consuming and, therefore, again, not feasible for large datasets.…”

Section: Introductionmentioning

confidence: 99%

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Qiber3D—an open-source software package for the quantitative analysis of networks from 3D image stacks

2022

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Background Optical slice microscopy is commonly used to observe cellular morphology in 3D tissue culture, e.g., the formation of cell-derived networks. The morphometric quantification of these networks is essential to study the cellular phenotype. Commonly, the quantitative measurements are performed on 2D projections of the image stack, resulting in the loss of information in the third dimension. Currently available 3D image analysis tools rely on manual interactions with the software and are therefore not feasible for large datasets. Findings Here we present Qiber3D, an open-source image processing toolkit. The software package includes the essential image analysis procedures required for image processing, from the raw image to the quantified data. Optional pre-processing steps can be switched on/off depending on the input data to allow for analyzing networks from a variety of sources. Two reconstruction algorithms are offered to meet the requirements for a wide range of network types. Furthermore, Qiber3D’s rendering capabilities enable the user to inspect each step of the image analysis process interactively to ensure the creation of an optimal workflow for each application. Conclusions Qiber3D is implemented as a Python package, and its source code is freely available at https://github.com/theia-dev/Qiber3D. The toolkit was designed using a building block principle to enable the analysis of a variety of structures, such as vascular networks, neuronal structures, or scaffolds from numerous input formats. While Qiber3D can be used interactively in the Python console, it is aimed at unsupervised automation to process large image datasets efficiently.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Qiber3D—an open-source software package for the quantitative analysis of networks from 3D image stacks

2022

Self Cite

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“…All images were acquired under a similar excitation intensity, detector sensitivity, and pinhole settings to minimize cross‐laser excitation and bleed‐through and to allow for between‐sample intensity comparisons. Images used for analysis were obtained by taking a Z‐axis stack of image planes (1024 × 1024 pixels) with 2 μm steps encompassing the entire cell structure and combining image planes into a maximum intensity projection stack as described (Bonda et al, 2020; Shihan et al, 2021). The maximum fluorescence intensity was quantitated for each channel using Fiji software (NIH).…”

Section: Methodsmentioning

confidence: 99%

Prolonged ethanol exposure modulates constitutive internalization and recycling of 5‐HT1A receptors

Wang

Liu

Roberts

et al. 2021

Journal of Neurochemistry

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Alcohol exposure alters the signaling of the serotoninergic system, which is involved in alcohol consumption, reward, and dependence. In particular, dysregulation of serotonin receptor type 1A (5‐HT1AR) is associated with alcohol intake and withdrawal‐induced anxiety‐like behavior in rodents. However, how ethanol regulates 5‐HT1AR activity and cell surface availability remains elusive. Using neuroblastoma 2a cells stably expressing human 5‐HT1ARs tagged with hemagglutinin at the N‐terminus, we found that prolonged ethanol exposure (18 h) reduced the basal surface levels of 5‐HT1ARs in a concentration‐dependent manner. This reduction is attributed to both enhanced receptor internalization and attenuated receptor recycling. Moreover, constitutive 5‐HT1AR internalization in ethanol naïve cells was blocked by concanavalin A (ConA) but not nystatin, suggesting clathrin‐dependent 5‐HT1AR internalization. In contrast, constitutive 5‐HT1AR internalization in ethanol‐treated cells was blocked by nystatin but not by ConA, indicating that constitutive 5‐HT1AR internalization switched from a clathrin‐ to a caveolin‐dependent pathway. Dynasore, an inhibitor of dynamin, blocked 5‐HT1AR internalization in both vehicle‐ and ethanol‐treated cells. Furthermore, ethanol exposure enhanced the activity of dynamin I via dephosphorylation and reduced myosin Va levels, which may contribute to increased internalization and reduced recycling of 5‐HT1ARs, respectively. Our findings suggest that prolonged ethanol exposure not only alters the endocytic trafficking of 5‐HT1ARs but also the mechanism by which constitutive 5‐HT1AR internalization occurs.

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“…However, in these processes, organoid size, shape, and other factors were usually not being continuously tracked, and there is no reliable automatic segmentation technology that can be used for locating and quantifying organoids in a 3D culture environment [ 31 ]. Considering that organoids are usually cultured in a 3D environment, it is hard to locate organoids with a single Z -axis image [ 32 ]. Scientists usually have to change the Z -axis during imaging by adjusting the position and focal plane manually, recording the corresponding image data, using an optical microscope, and performing analysis based on subjective measurements—essentially hand-drawing the organoid’s boundary.…”

Section: Organoids a New Approach To Disease Modelingmentioning

confidence: 99%

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Chen

et al. 2023

Bio-des. Manuf.

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In modern terminology, “organoids” refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions. Graphic abstract

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3D Quantification of Vascular-Like Structures in z Stack Confocal Images

Cited by 7 publications

References 5 publications

Qiber3D—an open-source software package for the quantitative analysis of networks from 3D image stacks

Qiber3D—an open-source software package for the quantitative analysis of networks from 3D image stacks

Prolonged ethanol exposure modulates constitutive internalization and recycling of 5‐HT1A receptors

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

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