Gregor Neuert

TrueSpot is an automated tool for quantifying signal puncta in fluorescent imaging, outperforming 2D methods in 3D spot detection.

TrueProbes improves RNA detection by designing high-specificity smRNA-FISH probes using BLAST analysis and thermodynamic modeling, outperforming existing tools in computational and experimental metrics.

The paper investigates how transcriptional stochasticity influences the regulation of Xist and Tsix at the XCI locus through different mechanisms based on nascent transcription levels.

The paper investigates how dynamic stress gradients affect cell growth and signaling in yeast, finding that gradual stress enhances growth with minimal volume reduction through a logarithmic MAPK signaling mechanism.

The paper shows that stimulation rate, along with concentration, determines MAPK signaling and cell survival in budding yeast through a rate threshold mechanism.

The paper uses live single-cell imaging and computational modeling to show that diverse kinetic stimulations improve prediction of signaling dynamics by better constraining model parameters.

The paper shows that gradual hyperosmotic stress allows human cells to survive by preventing caspase activation and accumulating proline, unlike acute stress.

This paper develops and validates an FSP-FIM method to optimize single-cell experiments for parameter estimation in yeast stress response models.

The paper introduces CellDissect, a method for automated cell and nuclear segmentation in suspension cells using widefield images, and evaluates its accuracy against manual segmentation.

The paper develops experimental methodologies to control temporal changes in extracellular stimuli for studying dynamic cellular processes in single cells.

The paper describes a smFISH protocol to quantify mRNA levels and localization in single yeast cells under osmotic stress and provides raw data and processed counts for further analysis.

The paper develops a computational approach to model gene regulation by addressing distributional challenges in single-cell mRNA data, leading to predictive insights into transcription mechanisms.