Airyscan Sr
Airyscan Sr - Related Videos
07:42
Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection
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2026
This protocol details the advanced imaging applications, and shared-resource management of a laser-scanning confocal microscope system integrated with a multiplexed array detector for high-resolution cellular and subcellular imaging. The workflow enables multimodal imaging from confocal to super-resolution (~120 nm) within a single platform, making nanoscale visualization more accessible.
07:28
Imaging Cell Viability on Non-transparent Scaffolds — Using the Example of a Novel Knitted Titanium Implant
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Cited by 6 •
2016
Here we present a fluorophore based imaging technique to detect cell viability on a non-transparent titanium scaffold as well as to detect glimpses of the scaffold impurities. This protocol troubleshoots the drawback of imaging cell-cell or cell-metal interactions on non-transparent scaffolds.
09:56
In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
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Cited by 7 •
2018
Here, we present the usefulness of longitudinal in vivo imaging in the follow-up of morphological changes of laser-induced choroidal neovascularization in mice.
10:44
In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing
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Cited by 1 •
2023
Here, we present a protocol for the in vitro selection of engineered transcriptional repressors (ETRs) with high, long-term, stable, on-target silencing efficiency and low genome-wide, off-target activity. This workflow allows for reducing an initial, complex repertoire of candidate ETRs to a short list, suitable for further evaluation in therapeutically relevant settings.
06:25
Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
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Cited by 17 •
2020
A protocol is proposed to capture natural hand function of individuals with hand impairments during their daily routines using an egocentric camera. The goal of the protocol is to ensure that the recordings are representative of an individual's typical hand use during activities of daily living at home.
FAQs
Instead of treating fluorescence as a single undifferentiated detector signal, the system records where emitted light reaches multiple detector elements. Those spatial measurements preserve information about the fluorescence distribution. Computational reassignment then places signal according to the recorded information, and the contributions are combined into an image with sharper spatial detail than conventional confocal detection.
Optical sectioning helps distinguish fluorescence originating from different regions of a specimen rather than combining information from the entire illuminated volume. Retaining this capability while improving spatial resolution makes it possible to examine fine structures within cells with greater positional clarity. This is especially relevant when subcellular features are distributed through complex biological samples.
The key distinction is how emitted fluorescence is collected and processed. Conventional confocal detection does not use the same multichannel spatial recording and computational reassignment described for Airyscan SR. By preserving detector-position information and combining reassigned signals, the super-resolution mode produces sharper images while maintaining the optical sectioning associated with confocal microscopy.
Computational reassignment converts the spatial information recorded by the detector elements into a more sharply organized image. The software does not merely increase display magnification; it uses the distribution of detected fluorescence to reposition and combine signal. This processing is central to obtaining improved spatial resolution from the multichannel detection scheme.
A specimen is prepared with fluorescent labels and examined using a confocal microscope configured for Airyscan SR acquisition. Emitted fluorescence is recorded across the multichannel detector array, preserving information from individual detector elements. Computational processing then reassigns and combines those signals to generate the sharper image used for biological interpretation.
Researchers can choose this mode when they need to examine fine subcellular structures, map protein distributions, or follow dynamic cellular events in fluorescently labeled specimens. Its combination of improved resolution, sensitivity, optical sectioning, and compatibility with live-cell imaging supports investigations that connect cellular organization with function rather than showing only broad specimen morphology.
The resulting images can provide information about the arrangement of subcellular features, the distribution of fluorescently labeled proteins, and changes occurring during cellular dynamics. These observations help relate the spatial organization of cell components to biological function. The method is therefore useful for studying both structural detail and events that develop over time in cells.