Biology subjects

Kroeschell, G.

Publications and source records attributed to Kroeschell, G..

4 recordsLinked to original sources

bioRxiv2026

Fluorescently-labeled split-QF hemidrivers: simplifying and enhancing methods enabling intersectional targeting of discrete cell types

Single cell transcriptomics data predict greater cell type diversity than previously appreciated, defined by combinatorial gene expression "codes". These codes can enable intersectional targeting strategies for selectively labeling and manipulating predicted cell types, thereby facilitating functional tests to resolve cell-specific roles and refine cell-type definitions. One such approach cleaves driver components of binary expression systems (e.g., Gal4/UAS and QF/QUAS) into two co-dependent halves, an Activation Domain (AD) and a DNA Binding Domain (DBD). The transcriptional activity of these "split-drivers" (aka, hemidrivers) is only reconstituted in cells that co-express them. While widely used in invertebrates, these systems have yet to be systematically deployed in vertebrates. Here, we developed a series of fluorescently labeled split-QF and QF2 hemidrivers via direct reporter fusions or the 2A viral peptide co-expression system. Fluorescent labeling serves to 1) simplify hemidriver transgenic line creation and maintenance, 2) allow AD and DBD intersects to be visualized directly, and 3) enable robust quality control assessments of on-target efficacy and off-target activity. These resources can enhance transgenic targeting specificity, enabling functional dissections of cell types revealed by single cell transcriptomics. SUMMARYWe engineered fluorescently labeled split-QF and -QF2 hemidrivers in zebrafish to simplify intersectional targeting tool generation and enable robust quality control assessments of split-driver function.

genetics↗

bioRxiv2026

Threshold-Free Neural Network Models for Swim Bout Detection of Larval Zebrafish

Accurate detection of swim bouts is essential for decoding motor behavior in larval zebrafish, yet conventional threshold-based methods rely on subjective cutoffs and per-experiment tuning. To address this limitation, we developed threshold-free deep learning models operating on tail-curvature time series: an offline model for post hoc analysis and an online model for real-time detection. Both improved precision over threshold-based methods, particularly for low-amplitude bouts. Using the offline model, we show that a head-fixed, closed-loop preparation largely preserves naturalistic swimming kinematics. Repeated trials reduced bout frequency and increased interbout interval, whereas genetic background primarily affects bout duration and maximum amplitude; notably, casper larvae lacked the late-trial decline observed in wildtype and nacre. We further show that detector choice can alter inferred behavioral outcomes. Together, these results establish a high-throughput framework for robust and reproducible analysis of zebrafish swimming behavior across experimental conditions.

bioengineering↗

bioRxiv2025

Versatile high-speed volumetric imaging from microscopic to macroscopic scale by self-adaptive oblique plane microscopy

There is an increasing need for large-scale high-speed volumetric recording in complex multi-cellular model systems to define dynamic processes. Oblique plane microscopy (OPM) provides a solution that features oblique illumination, rapid optical scanning, and remote focusing to achieve real-time 4D microscopy. OPM implements light sheet imaging via a single primary objective lens, making the entire space below the objective accessible for large specimens, such as living mouse brain. Yet it is challenging to adopt OPM beyond a microscopic scale (i.e. size < 1mm), limiting its broad applications. Here we present a self-adaptive OPM that leverages Abbes sine condition to unlock its flexibility across a range of field-of-views (FOVs) (up to 8 mm2) and resolutions (down to 2.2 {micro}m3). This versatility enables brain-wide single neuron volumetric calcium imaging in behaving larval zebrafish (1x0.4 mm2 FOV at 5 Hz) and capillary blood cell tracking in living mouse brain (>3x3 mm2 FOV) with a sweeping 0.32 mm wide volume section at 100 Hz. In optically cleared mouse brain, the flexibility allows a screen-and-zoom capability by sequentially imaging the whole brain at low-and-high magnifications to locate and resolve subcellular structures such as dendritic tress and spines. By offering a switchable imaging resolution, volume, and speed, the self-adaptive OPM achieves a versatile platform for studying a wide range of multi-cellular model system, whether in vivo or fixed and optically cleared.

bioengineering↗

bioRxiv2023

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained de-aberration networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

bioengineering↗