Biology subjects

Lyudchik, J.

Publications and source records attributed to Lyudchik, J..

4 recordsLinked to original sources

bioRxiv2025

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context

Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cellfilling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by >750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a [~]10 million {micro}m3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information, we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISMs molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports selfcorrecting neuron reconstruction with molecular context.

neuroscience↗

bioRxiv2025

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↗

bioRxiv2024

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↗

bioRxiv2022

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↗