Biology subjects

Görlitz, F.

Publications and source records attributed to Görlitz, F..

3 recordsLinked to original sources

bioRxiv2024

Image processing tools for petabyte-scale light sheet microscopy data

Light sheet microscopy is a powerful technique for high-speed 3D imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are memory and performance-optimized. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution, and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

bioinformatics↗

bioRxiv2021

Robust optical autofocus system utilizing neural networks trained for extended range and time-course and automated multiwell plate imaging including single molecule localization microscopy

We present a robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, e.g. due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilise a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/- 100 m. We characterise the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localisation microscopy.

bioengineering↗

bioRxiv2020

Impact of N-terminally substituted glucagon family receptor agonists on signal bias, trafficking and downstream responses

Receptors for the peptide hormones glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and glucagon (GCG) are important regulators of insulin secretion and energy metabolism. Recently described GLP-1 receptor agonists showing signal bias in favour of cyclic AMP over {beta}-arrestin-2 recruitment have delivered promising results in preclinical studies. Here we first sought to establish the role of {beta}-arrestins in the control of intracellular signalling and trafficking responses at the closely related GLP-1, GIP and GCG receptors, through studies performed in cells depleted of both {beta}-arrestin isoforms. We also generated analogues of GLP-1, GCG and GIP which in some cases showed selective reduction in {beta}-arrestin-2 recruitment versus cAMP signalling compared to the parent peptide. Despite reduced acute signalling potency and/or efficacy, some biased GLP-1 and GIP analogues increased maximal sustained insulin secretion from INS-1 832/3 clonal beta cells, although only at high agonist concentrations. Biased GCG analogues did not affect maximal insulin release, or glucose output in hepatocytes.

pharmacology and toxicology↗