MicroscopyInnovations


US National Institute of Standards and Technology and at Brookhaven National Laboratory. Applications include development of devices based on


advanced functional materials: electronic (transistors, com- munication systems), ionic (batteries, supercapacitors, mem- ristors), magnetic (spintronics, magnetic memory), photonic (lasers), and electromechanical (NEMS/MEMS) materials and their hybrids. Recently discovered radiation damage mitigation using the Euclid stroboscopic technique should make this type of UTEM beneficial for biological and biomedical research, in particular, for cryo-EM studies of bio samples.


CX-A: Live Cell Imaging in Multi-Well Plates Nanolive SA Developer: Sebastien Equis


automates image data


Te Nanolive CX-A microscope acquisition


through an intuitive user interface that enables first-time users to set up experiments in just a few min- utes and walk away, while the CX-A automatically collects the images. In addition to improving data signifi- cance, multiple imaging regimens


can be programmed within the same multi-well plate, allowing users to run different applications in parallel. A real-time pre- view allows the user to navigate through the data at any time. Living cells can be imaged for long periods of time. Tanks to its harmless way of cell preparation and observation, hundreds of images can be collected every hour, transforming endpoint assays into continuous analysis. Experiments can last for days or weeks at a time, while cells remain unperturbed in a physi- ologically controlled environment. From the hardware point of view, the CX-A is the first and


only holo-tomographic microscope compatible with multi-well plates that can image living cells for extended times without sac- rifices in resolution. Te large field of view (up to 1 mm × 1 mm) of the CX-A allows monitoring of cells at the population level, in multiple dishes, and in parallel. Te Nanolive imag- ing technology (non-invasive,


label-free, three-dimensional,


high-resolution) provides contrast on live specimens with zero phototoxicity and zero photobleaching. Te combination of this imaging technology with the new CX-A delivers a walk- away solution for long-term live cell imaging of cell organelles, single cells, and cell populations. Tis next-generation micro- scope allows observation of living cell populations all the way down to individual organelles with a resolution of < 200 nm. Compatibility with multi-well plates (for example, 96-well plates) allows scientists to test multiple conditions in parallel, bringing statistical significance to every experiment. Results from such studies can provide insights into how cells interact, how organelle morphologies change, and how


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organelles interact. Long-term imaging allows researchers to visualize biological processes unfolding in real time at high spatial and temporal resolution. For example, the CX-A allows users in the pharmaceutical


industry to perform multiple


label-free drug perturbation assays, live and over long periods of time. Such capabilities can enhance drug screening, target identification, and mechanism of action studies.


Accelerating Iterative Deconvolution and Multiview Fusion


National Institute of Biomedical Imaging and Bioengineering


Developers: Min Guo, Yicong Wu, and Hari Shroff It is now possible to accu-


mulate massive amounts of light microscopy data with the click of a button—in a single day a light-sheet microscope can generate hundreds of tera- bytes of data. Tis capability


has led to a “data deluge” that impedes scientific discovery. Deconvolution is an important post-acquisition step; however, this can take much longer than data collection. Stitching and registration are even more time-consuming image processing operations. Algorithms and soſtware have been created for drastically reducing the time required for deconvolving, stitch- ing, and registering large light microscopy datasets. Tese innovations permit up to several thousand-fold faster decon- volution and multi-view fusion than previous soſtware. A number of


soſtware improvements were made to


achieve these speeds. First, methods were adapted from medi- cal imaging, where an unmatched back projector accelerates Richardson-Lucy deconvolution by at least 10-fold, in most cases requiring only a single iteration. Second, improvements in 3D image-based registration and stitching with a graphics processing unit (GPU) resulted in increased speeds of 10-fold to 100-fold over CPU processing. Tis performance advantage is important given that registration and stitching are more time- consuming than deconvolution. Tird, deep learning can pro- vide further accelerations, particularly for deconvolution with a spatially varying point spread function. A novel neural network architecture, “DenseDeconNet,” was created for this purpose, and the unmatched back projector method was used to train the neural network. Tese methods are effective on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, these methods facilitate the use of new computational imaging microscopes that would be dif- ficult to operate without these improvements in data processing speed. Te methods described here are freely available as open- source soſtware written in the MATLAB, CUDA, and Python programming languages. Two microscopy companies and ten labs across the world have tested these algorithms on their microscopy data, and


www.microscopy-today.com • 2020 September


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