Whole-body Light Sheet Imaging, Visualisation and Analysis of Tissue-cleared Specimens
The comparatively large size and optical opacity of mammalian models has limited researchers to imaging snapshots of cellular organisation on thin-sectioned tissue samples. This challenge has led to the development of CLARITY and derivative methods for tissue-, organ- and body clearing that made whole-body imaging possible. CLARITY has in turn enabled complex imaging with its own set of challenges related to image data acquisition and subsequent image visualisation, analysis and data mining.
Imaging cleared tissues by confocal microscopy is time-consuming, particularly when scanning a large field of view at depth, additionally, photobleaching can negatively impact image data quality and downstream reconstruction. These challenges can be addressed by light-sheet fluorescence microscopy, which is reliant on large field of view (FOV), sensitive (high quantum efficiency – QE) and fast sCMOS cameras. Experiments can generate terabyte-sized data sets, which poses new challenges for computational analysis of high-resolution image stacks. Therefore, software solutions must complement an sCMOS-based light-sheet microscope and must be able to process large data sets of cleared volumes. For example, assistance with automated segmentation and tracing of e.g. nerve tracts would be a powerful tool for many research groups
Scientific CMOS (sCMOS) camera technology with LightScan PLUS, featuring FlexiScan & CycleMax enables simple and flexible adaption of the Rolling Shutter scan mode to applications such as image and scanning light-sheet fluorescence microscopy. sCMOS cameras combine speed, resolution, large FOV, high QE (up to 95%) and modular software with advanced automatic and semi-automatic volume segmentation and nerve and fibre tracing including Autopath, Autodepth, spine detection and Torch™. These features make sCMOS cameras a powerful, streamlined solution for analysis and data mining of cleared brain or embryo volumes.
Andor and Bitplane camera and software solution for whole-body light sheet imaging, visualisation & analysis
Andor recommends the Zyla 4.2 Plus and Sona scientific CMOS cameras for light-sheet fluorescence microscopy. Zyla sCMOS is a well-established camera platform for fast live-cell imaging at high resolution. Sona is the most sensitive sCMOS camera with very large FOV and superior longevity and quantitative accuracy. Imaris is a comprehensive image data analysis suite enabling TB image stacks analysis with modular approach to cellular and neuroscience analysis including Torch™ algorithm for efficient and fast nerve tracing. Imaris’ Vantage module aides multi-dimensional data interpretation, plotting and presentation.
Whole-body light sheet imaging and visualisation: Zyla 4.2 Plus/Sona and Imaris
Fast, sensitive large FOV, light-sheet-optimised sCMOS
High QE and large FOV (95% and 32 mm sensor diagonal in Sona) and 100 fps and LightScan PLUS mode (Zyla 4.2 Plus 10-tap) mean that both sCMOS cameras are ideal choices for whole-body light sheet imaging of cleared samples. Result – sensitive, high-resolution and high-speed imaging of entire organs or embryos cleared with CLARITY and imaged with light-sheet fluorescence microscopy.
Image analysis tool handling TB images with advanced segmentation and tracing
Imaris offers a toolset for researchers studying neuronal networks’ architecture including detailed morphology and neuron tracing in TB-sized image data sets. Result – unravel complex structures in your data sets, analyse and present data mined from data-rich image stacks.
Seamless camera-to-data experience
Seamless camera-to-data experience Control your sCMOS in and export image files from µManager into Imaris for advanced image processing including visualisation, analysis and interactive interpretation. Result – a streamlined user experience of image analysis from camera to whisker plots
Treweek, Jennifer B et al. “Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping” Nature protocols vol. 10,11 (2015): 1860-1896.