David Hörl

The paper introduces autoSTED, a framework for automated STED microscopy that enables faster and more unbiased imaging of subcellular structures.

This study shows that yeast cells use local mitochondrial physiology to select for functional mtDNA, with purifying selection against specific mtDNA mutations under fermentative conditions.

The paper uses super-resolution microscopy to measure enhancer-promoter distances during pluripotency exit, finding minimal changes despite large transcriptional changes.

This paper introduces a method to generate densely labeled oligonucleotides for detecting small genomic elements using FISH in single cells.

This study uses a small molecule inhibitor to rapidly and synchronously induce senescence-like features in human cells, enabling analysis of its core regulatory program without paracrine effects.

YeastMate automates detection and segmentation of mating and budding events in S. cerevisiae cells using a deep learning model based on Mask R-CNN.

The paper quantifies the spatial activity of a Drosophila enhancer through sequence mutagenesis and image analysis, revealing dense regulatory information and novel regulatory logic.

BigStitcher reconstructs high-resolution image datasets from cleared and expanded biological samples, compensating optical distortions for single-cell analysis.