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Real-time Deconvolution Microscope System

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Microvolution® software delivers nearly instantaneous deconvolution by combining intelligent software programming with the power of a GPU

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Developed by Stanford scientists, Microvolution software will improve your research:

• Work more effectively in dim light and realize greater success rates with live-cell and time-lapse experiments

• Create cleaner measurements after deconvolution (e.g., colocalization, FRET data, neurite lengths, fluorescence intensities)

• Make adjustments to your microscopy experiments on the fly and collect more data from the same sample

The software deconvolves images from widefield, confocal, two photon, light sheet, and HCA microscopes.

Blind deconvolution option improves noisy data, such as deep tissue imaging. Multi-GPU options enable even giant images to be processed in seconds.

*Bruce MA, Butte MJ. Real-time GPU-based 3D Deconvolution, Optics Express, 2013; 21(4): 4766.

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Speed

Deconvolve your fluorescence images, from small to very large, in a fraction of the time

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Accuracy

Thin Filaments Are Preserved with Microvolution Software

Microvolution’s method starts with the proven Richardson-Lucy algorithm that is used by most software programs. Other vendors take mathematical shortcuts to speed up iterations, resulting in imprecise images after deconvolution. Microvolution takes no shortcuts. Our software delivers accurate images, up to 200 times faster.

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Image courtesy of Molecular Devices.

Clarity

Increased resolution

When collected under the right conditions, deconvolution can help break the diffraction barrier. Pictured below are 180 nm separated lines on an Argo-SIM slide, imaged with widefield microscopy. Deconvolution brings a √2 improvement in visual resolution.

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Consist of ; 

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Microvolution® Deconvolution Software

• Delivers almost instantaneous deconvolution

• Easy to use ImageJ plugin

• Unlike other vendors, deconvolves your images with accuracy

• Enables both 2-D and 3-D Deconvolution

• Works on computers that have NVIDIA GPU boards incorporated into the computer hardware. 

 

 Blind Deconvolution Option

• Algorithm modifies the PSF during the run to correct for extra aberrations

• Ideal for deep tissue imaging and other noisy applications

• Point spread functions are determined by computer for optimal imaging

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Batch Option

• Simplifies processing of large datasets, e.g. multi-channel timelapse experiments

• Reduced memory needs for big image

 

Multi-GPU Option

• Enables faster computation of very large images

• Processes multiple channels or time points in parallel

• Operates with 2 or more NVIDIA GPU cards. 

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Plugin for µManager 2.0 Option     New!!

• This plugin to µManager 2.0, enables hands-off deconvolution of your images as they are acquired.

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Microscopy for Multi-positions, Multi-channels, Z-stacks and Time-series acquisition.

• Olympus IX 83 Motorised with Z-focus Drive

• Nikon Eclipse Ti2-E Motorised with Z-focus Drive

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XY Motorised Stages

• Mot. XY Stages (Märzhäuser, Applied Scientific Instrumentation)

• Piezo Z-Stage option

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Microscope Light Sources

• LED Light source

• Arc Light source

• Laser Light source

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Microscope Incubator for Live-cell Imaging

• Temperature, CO2, O2 and Humidity controll

• Objective Heater option

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TTL Device Controller

• High-speed TTL Device Controller for hardware synchronization

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Cameras

• sCMOS

• EMCCD 

 

and PC Workstation with Micro-Manager ver.2 Software

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Citations

Microvolution has been cited in the following publications

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Vogt, E.-J. et al. Anchoring cortical granules in the cortex ensures trafficking to the plasma membrane for post-fertilization exocytosis. Nature Communications 10, 2271 (2019) doi:10.1038/s41467-019-10171-7

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Ozel, M.N. et al. Serial synapse formation through filopodial competition for synaptic seeding factors. Developmental Cell 50, 447-461.E8 (2019) doi:10.1016/j.devcel.2019.06.014

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Kim, C., Seedorf, G.J., Abman, S.H., & Shepherd, D.P. High throughput imaging identifies a spatially localized response of primary fetal pulmonary artery endothelial cells to insulin-like growth factor 1 treatment. bioRxiv (preprint) , (2019) doi:10.1101/674499

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Sancer, G. et al. Modality-specific circuits for skylight orientation in the fly visual system. Current Biology 29, 1-14 (2019) doi:10.1016/j.cub.2019.07.020

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Mohan, A. et al. Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1-P29S Melanoma Cells. Developmental Cell 43, 444-460.e9 (2019) doi:10.1016/j.devcel.2019.04.007

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Chakraborty, T. et al. Light-sheet microscopy with isotropic, sub-micron resolution and solvent-independent large-scale imaging. bioRxiv (preprint) , (2019) doi:10.1101/605493

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Condon, N. et al. Macropinosome formation by tent pole ruffling in macrophages. Journal of Cell Biology 217, 3873-3885 (2018) doi:10.1083/jcb.201804137

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Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968-981 (2018) doi:10.1016/j.cell.2018.07.010

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Wall, A., Condon, N., Luo, L., & Stow, J. Rab8a localisation and activation by Toll-like receptors on macrophage macropinosomes. Philosophical Transactions of the Royal Society B: Biological Sciences 374, (2018) doi:10.1098/rstb.2018.0151

 

Kashekodi, A., Meinert, T., Michiels, R. & Rohrbach, A. Miniature scanning light-sheet illumination implemented in a conventional microscope. Biomedical Optics Express 9, 4263-4274 (2018) doi:10.1364/BOE.9.004263

 

Kwak, B., Lee, Y., Lee, J., Lee, S. & Lim, J. Mass fabrication of uniform sized 3D tumor spheroid using high-throughput microfluidic system. Journal of Controlled Release 275, 201-207 (2018) doi:10.1016/j.jconrel.2018.02.029

 

Jin, E. et al. Live Observation of Two Parallel Membrane Degradation Pathways at Axon Terminals. Current Biology 28, 1027-1038.e4 (2018) doi:10.1016/j.cub.2018.02.032

 

Donnelly, S. K. et al. Rac3 regulates breast cancer invasion and metastasis by controlling adhesion and matrix degradation. Journal of Cell Biology (2017) doi:10.1083/jcb.201704048

 

Ryan, D. P. et al. Automatic and adaptive heterogeneous refractive index compensation for light-sheet microscopy. Nature Communications 8, 612 (2017) doi:10.1038/s41467-017-00514-7

 

McNamara, G., Difilippantonio, M., Ried, T. & Bieber, F. R. in Current Protocols in Human Genetics 4.4.1-4.4.89 doi:10.1002/cphg.42

 

Singh, J., Nowlin, T., Seedorf, G., Abman, S., & Shepherd, D. Quantifying three-dimensional rodent retina vascular development using optical tissue clearing and light-sheet microscopy. Journal of Biomedical Optics 22, 31753 (2017) doi:10.1117/1.JBO.22.7.076011

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and more.

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