Hyperstack Imaging
Hyperstack Imaging - Related Videos
11:48
Closed System Cell Culture Protocol Using HYPERStack Vessels with Gas Permeable Material Technology
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Cited by 10 •
2010
An Introduction into the technology, protocol and handling of the Corning HYPERStack Vessels and accessories used for high yield adherent cell culture. The protocol will show how to use the closed system vessels for increasing cell harvesting over current stacked plate products.
02:52
Visualizing Bacterial Protein Distribution in Three Dimensions by Fluorescence Imaging
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2026
Source: Bratton, B. P., et al., Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization. J. Vis. Exp. (2019)This video demonstrates the method for acquiring and analyzing three-dimensional fluorescence images of bacterial cells to study protein organization at the single-cell level.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
06:09
Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging
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2016
The social amoebae Dictyostelium discoideum has recently been established as a system to study protein misfolding and proteostasis. Here, we describe a new imaging-based methodology to study temperature-induced protein aggregation and the cellular stress response in D. discoideum.
09:08
Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
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Cited by 59 •
2011
Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.
FAQs
Hyperstack Imaging preserves the correspondence among x-y position, depth, time point, and fluorescence channel. This organization lets a researcher interpret a signal in relation to its three-dimensional location and temporal state rather than as an isolated image. The result is a dataset suited to reconstructing structure and following changes without losing channel relationships.
Depth and time answer different biological questions within the same analysis. A z-stack reveals how structures are arranged through three-dimensional space, whereas repeated time points show how that arrangement changes. Combining them allows neural structures or signals to be compared across both location and time, which is important when a process is spatially organized and dynamic.
Multiple fluorescence channels add interpretive context by allowing different labels to be examined together. Because the channels remain linked to the same spatial and temporal framework, measurements can be compared across labeled features instead of treated as unrelated images. In neuroscience, this supports examination of neuronal morphology, synaptic organization, activity-linked signals, and cell interactions within one dataset.
Researchers first assemble x-y planes into z-stacks, then associate those depth series with repeated time points, multiple fluorescence channels, or both. The linked dimensions are kept together during visualization and measurement. This workflow supports reconstruction of three-dimensional structures and quantitative comparison of changes across the recorded series without separating the relevant spatial, temporal, or labeling information.
In neuronal morphology studies, the approach helps visualize and quantify structures whose form extends through depth. Researchers can inspect a reconstructed three-dimensional arrangement and compare measurements across time or labeled features when those dimensions are present. This is useful for examining structural organization while retaining the spatial relationships needed to interpret morphology.
For synaptic organization and cell interactions, linked channels provide a way to examine labeled features in their shared spatial context. In intact tissue or culture, the dataset can preserve how cells and structures are positioned relative to one another. That context strengthens visualization and quantitative comparison when neural organization or interactions change over time.
Activity-linked signals can be analyzed alongside spatial depth and other fluorescence labels, rather than being viewed only as separate two-dimensional observations. Repeated time points make it possible to follow signal-associated changes, while the z dimension relates them to three-dimensional structures. This combination helps investigators connect dynamic neural events with morphology or neighboring cellular features.