Biology subjects

Hörl, D.

Publications and source records attributed to Hörl, D..

5 recordsLinked to original sources

bioRxiv2025

Local Mitochondrial Physiology Defined by mtDNA Quality Guides Purifying Selection

The mitochondrial genome (mtDNA) encodes essential subunits of the electron transport chain and ATP synthase. Mutations in these genes impair oxidative phosphorylation, compromise mitochondrial ATP production and cellular energy supply, and can cause mitochondrial diseases. These consequences highlight the importance of mtDNA quality control (mtDNA-QC), the process by which cells selectively maintain intact mtDNA to preserve respiratory function. Here, we developed a high-throughput flow cytometry assay for Saccharomyces cerevisiae to track mtDNA segregation in cell populations derived from heteroplasmic zygotes, in which wild-type (WT) mtDNA is fluorescently labeled and mutant mtDNA remains unlabeled. Using this approach, we observe purifying selection against mtDNA lacking subunits of complex III (COB), complex IV (COX2) or the ATP synthase (ATP6), under fermentative conditions that do not require respiratory activity. By integrating cytometric data with growth assays and simulations, we show that the decline of {Delta}atp6 mtDNA reflects both intracellular selection for intact genomes and a proliferative disadvantage of cells lacking ATP synthase. In contrast, loss of {Delta}cob and {Delta}cox2 mtDNA is primarily driven by selective maintenance of functional genomes. In heteroplasmic cells containing both intact and mutant mtDNA, fluorescent reporters revealed local reductions in ATP levels and membrane potential ({Delta}{Psi}) near mutant genomes, indicating spatial heterogeneity in mitochondrial physiology that reflects local mtDNA quality. Disruption of the respiratory chain by deletion of nuclear-encoded subunits (RIP1, COX4) abolished these physiological gradients and impaired mtDNA-QC, suggesting that local bioenergetic differences are required for selective recognition. Together, our findings support a model in which yeast cells assess local respiratory function as a proxy for mtDNA integrity, enabling intracellular selection for functional mitochondrial genomes.

cell biology↗

bioRxiv2025

A flexible framework for automated STED super-resolution microscopy

Super-resolution microscopy enables the observation of cells at unprecedented detail but usually entails high light exposure and slow imaging. Thus, often only a few manually selected regions are imaged, limiting the ability to capture the distribution of quantitative features in a population of cells in an unbiased fashion. An exciting strategy to circumvent these limitations are imaging pipelines in which informative regions are detected on-the-fly by software and imaged automatically. Point-scanning methods like STimulated Emission Depletion (STED), in particular, can be sped up by selective imaging of small regions. Here, I present autoSTED, a flexible Python-based framework to construct automated imaging pipelines for STED microscopy. Instead of fixed acquisition loops defined at the beginning of an experiment, autoSTED employs a priority queue of acquisition tasks. After each image acquisition, callback functions can trigger actions like adding new tasks based on data, enabling dynamic and adaptive imaging. Complex experimental pipelines can be built from easily exchangeable building-blocks or expanded through custom code, facilitating integration of state-of-the-art computer vision methods. autoSTED can drastically speed up super-resolved imaging of subcellular structures and enables autonomous operation of a microscope for days with minimal hands-on time and bias.

bioinformatics↗

bioRxiv2021

YeastMate: Neural network-assisted segmentation of mating and budding events in S. cerevisiae

Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends. The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer packaged installers for our whole software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.

bioinformatics↗

bioRxiv2020

Deciphering the regulatory logic of a Drosophila enhancer through systematic sequence mutagenesis and quantitative image analysis

Transcriptional enhancers are short DNA sequences controlling the spatial activity, timing and levels of eukaryotic gene transcription. Their quantitative transcriptional output is thought to result from the number and organization of transcription factor binding sites (TFBSs). Yet, how the various aspects of regulatory information are encoded in enhancer sequences remains elusive. We addressed this question by quantifying the spatial activity of the yellow spot enhancer active in developing Drosophila wings. To identify which enhancer DNA sequence contributes to enhancer activity, we introduced systematic mutations along the enhancer. We developed an analytic framework that uses comprehensive descriptors to quantify reporter assay in transgenic flies and measure spatial variations in activity levels across the wing. Our analysis highlights an unexpected density of regulatory information in the spot enhancer sequence. Furthermore, it reveals an unanticipated regulatory logic underlying the activity of this enhancer, and how it reads the wing trans-regulatory landscape to encode a spatial pattern.

genetics↗

bioRxiv2018

BigStitcher: Reconstructing high-resolution image datasets of cleared and expanded samples

New methods for clearing and expansion of biological objects create large, transparent samples that can be rapidly imaged using light-sheet microscopy. Resulting image acquisitions are terabytes in size and consist of many large, unaligned image tiles that suffer from optical distortions. We developed the BigStitcher software that efficiently handles and reconstructs large multi-tile, multi-view acquisitions compensating all major optical effects, thereby making single-cell resolved whole-organ datasets amenable to biological studies.

bioinformatics↗