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Lyudchik, J.

Publications and source records attributed to Lyudchik, J..

3 recordsLinked to original sources

Image-based 3D active sample stabilization on the nanometer scale for optical microscopy

Super-resolution microscopy often entails long acquisition times of minutes to hours. Since drifts during the acquisition adversely affect data quality, active sample stabilization is commonly used for some of these techniques to reach their full potential. While drifts in the lateral plane can often be corrected after acquisition, this is not always possible or may come with drawbacks. Therefore, it is appealing to stabilize sample position in three dimensions during acquisition. Various schemes for active sample stabilization have been demonstrated previously, with some reaching sub-nm stability in three dimensions. However, these high-performance implementations significantly added to the complexity of the hardware and/or sample preparation. Here, we present a scheme for active drift correction that delivers the nm-scale 3D stability demanded by state-of-the-art super-resolution techniques and is straightforward to implement. Using a refined algorithm that does not depend on sparse peaks typically provided by fiducial markers added to the sample, we stabilized our sample position to [~]1 nm in 3D using objective lenses both with high and low numerical aperture. Our implementation requires only the addition of a standard widefield imaging path and we provide an open-source control software with graphical user interface to facilitate easy adoption of the module. Finally, we demonstrate how this has the potential to enhance data collection for diffraction-limited and super-resolution imaging techniques using single-molecule localization microscopy and cryo-confocal imaging as showcases. Why it mattersSuper-resolution light microscopy has enabled the visualization of biological structures down to the nm-scale. However, uncorrected drifts during often extended acquisition times may adversely affect data quality. Active drift correction in three dimensions has achieved sub-nm stabilization, but state-of-the-art techniques come with considerable overhead on sample preparation and/or hardware. Here, we demonstrate an image-based stabilization scheme which allows for flexibility regarding structures used for stabilization and is straightforward to adopt. Using a maximally simple implementation, we stabilized the position of our sample to around 1 nm over extended acquisition times and demonstrated usefulness in two example imaging settings where sample drifts are critical, super-resolution single-molecule localization microscopy and confocal imaging at cryogenic temperatures.

biophysics↗

Light-microscopy based dense connectomic reconstruction of mammalian brain tissue

The information-processing capability of the brains cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner. One-Sentence SummaryHydrogel expansion enables molecularly informed reconstruction of brain tissue at synaptic resolution with light microscopy.

neuroscience↗

Uncovering brain tissue architecture across scales with super-resolution light microscopy

Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanoscopic synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS leverages fixation-compatible extracellular labeling and advanced optical readout, in particular stimulated-emission depletion and expansion microscopy, to comprehensively delineate cellular structures. It enables 3D-reconstructing single synapses and mapping synaptic connectivity by identification and tailored analysis of putative synaptic cleft regions. Applying CATS to the hippocampal mossy fiber circuitry, we demonstrate its power to reveal the systems molecularly informed ultrastructure across spatial scales and assess local connectivity by reconstructing and quantifying the synaptic input and output structure of identified neurons.

neuroscience↗