Biophysical Society Bulletin | January 2022

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Know the Editor Yoav Shechtman

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Technion - Israel Institute of Technology

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Editor Biophysical Reports

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Yoav Shechtman

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At a cocktail party of non-scientists, how would you explain what you do? I ruin perfectly good microscopes for a living. My group utilizes the fact that in modern microscopy, there is an extra layer between the human observer and the sample being observed—the computer. This relatively new fact (relative to the history of microscopy) allows for designing novel optical systems that distort images cleverly to encode information in them. This results in images that to the naked eye would seem distorted. The information is then decoded, yielding im- aging systems with super-capabilities compared to conven- tional microscopes. The capabilities we focus on are mainly volumetric and multicolor fluorescence microscopy. What are you currently working on that excites you? For the last several years, my group has been using deep learning to solve various microscopy challenges. These in- clude applications in image analysis, such as super-resolution localization microscopy at high density in two dimensions and three dimensions, single-particle diffusion characteri- zation, multicolor imaging, and more; but perhaps the most exciting is neural-net–based optical system design—namely, using algorithms to design the optical system itself such that the measurements are maximally informative. In principle, automated system design frees us from the boundaries of our own human imagination and from falling into local design minima that are sometimes the result of tradition. Of course, algorithmic design has its own limitations, and a human in the loop is still necessary for sanity checks and for some direction of the design process, although we try to let the algorithms “run loose” as much as possible. We are currently pursuing various ways to algorithmically design different applica- tion-specific optical systems. 0 0.25 0.5 0.75 1 Control +M β CD Normalised intensity Wavelength (n ) C E 0 0.25 0.5 0.75 1 Lo Ld Normalised intensity Wavelength (nm) Model membrane Plasma membrane

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NR4A NR12A “Stimulated emission depletion super-resolution microsco- py can induce photobleaching (i.e., irreversible fluorophore destruction), limiti g long-term sample observation. Ex- changeable dyes circumvent this problem by only temporarily binding to their target. Here, we used NR4A, an exchangeable dye that also reports on membrane packing, to image model and live-cell plasma membranes. We demonstrate that NR4A enables long-term quantification of membrane biophysical properties, even at the highest stimulated emission depletion resolution, without photobleaching-induced signal loss. NR4A can also be used to simultaneously measure membrane pack- ing and dynamics. By combining the benefits of super-reso- lution microscopy, fluorescence spectroscopy, and long-term imaging, exchangeable dyes open new possibilities to image dynamic processes with high temporal and spatial resolu- tion, which we demonstrate by imaging lipid exchange during membrane fusion.” -1 0 Pablo Carravilla , Anindita Dasgupta , Gaukhar Zhurgenbayeva , Dmytro I. Danylchuk , Andrey S. Klymchenko , Erdinc Sezgin , Christian Eggeling GP Laurdan Control +M β CD -0.5 -0.4 -0.3 -0.2 -0.1 0 GP 0.2 -0.9 0.5 -0.9 0.5 -0.9 +M β CD Control Ld Lo G F Experiment 1 Experiment 2 Experiment 3 Long-term STED imaging of membrane packing and dynamics by exchangeable polarity-sensitive dyes -0.5

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Version of Record Published September 13, 2021 DOI:https:/doi.org/10.1016/j.bpr.2021.100023

January 2022

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