Confocal Dataset Analysis
Confocal Dataset Analysis - Related Videos
10:56
A User-friendly and Powerful R Analysis of Large-scale Datasets
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2025
This report describes a method involving an R script in the open-source software RStudio to analyze large-scale datasets obtained from time series experiments.
10:16
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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2025
This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene expression.
09:49
Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope
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Cited by 13 •
2022
High-resolution cryo-EM maps of macromolecules can be also achieved by using 200 kV TEM microscopes. This protocol shows the best practices for setting accurate optics alignments, data acquisition schemes, and selection of imaging areas that are all essential for the successful collection of high-resolution datasets using a 200 kV TEM.
09:13
Protocol for Three-dimensional Confocal Morphometric Analysis of Astrocytes
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Cited by 11 •
2015
Astrocytes in the CNS change their functional and structural properties in response to harmful stimuli. This report presents a protocol for assessment of three-dimensional astrocyte morphology in diseased conditions or after therapeutic interventions.
08:32
Extracellular Vesicle Uptake Assay via Confocal Microscope Imaging Analysis
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Cited by 3 •
2022
Extracellular vesicles (EVs) contribute to cellular biology and intercellular communications. There is a need for practical assays to visualize and quantify EVs uptake by the cells. The current protocol proposes the EV uptake assay by utilizing three-dimensional fluorescence imaging via confocal microscopy, following EV isolation by a nano-filtration-based microfluidic device.
FAQs
Each section records fluorescence at a successive depth in the specimen, creating a stack rather than a single image. Analysis aligns these depth-specific views for visualization and three-dimensional reconstruction. This makes it possible to examine how cells, tissues, or biomaterial features are arranged through depth, not only across one plane.
Segmentation identifies regions corresponding to meaningful biological or material features, while background separation limits measurements to the relevant fluorescence signal. This distinction affects estimates of structure and organization because unseparated background can obscure boundaries or alter measured quantities. Applying these steps together converts image information into interpretable measurements.
Quantitative measurements can characterize cell morphology, spatial organization, scaffold architecture, and cellular interactions within engineered environments. These outputs extend analysis beyond visual inspection by providing structural information that supports comparisons among experimental conditions. The same dataset can therefore describe both cellular features and the organization of the surrounding engineered material.
A practical workflow begins with visualization of the fluorescence stack, followed by separation of relevant signals from background. Segmentation then isolates cells, tissues, scaffold features, or other regions of interest, after which measurements are extracted and three-dimensional features can be reconstructed. This sequence links image organization to quantitative structural analysis.
Researchers can apply the approach when they need to examine engineered biological environments, including cell morphology, spatial organization, scaffold architecture, or cellular interactions. It is relevant to tissue-engineering systems, diagnostic models, and other biomedical platforms because confocal datasets can provide structural and quantitative information about features within those systems.
Reliable quantification enables researchers to compare structural features across experimental conditions rather than relying only on visual impressions. Those comparisons can describe changes in cells, tissues, biomaterials, or their interactions within engineered environments. The resulting evidence can help guide the design of tissue-engineering systems, diagnostic models, and other biomedical platforms.