Jeroen Sebastiaan van Zon

NuclearIDTracker uses nuclear phenotypic signatures to classify intestinal cell types and track lineage dynamics without fluorescent reporters.

This paper introduces an algorithm that assigns error probabilities to cell tracks, enabling automated analysis and reducing manual curation needs in cell tracking.

The paper shows that BEST4/CA7+ cells and goblet cells depend on each other for survival and differentiation in the intestinal mucus layer.

The paper identifies sequential transcriptomic states of tuft cells along the intestinal crypt-villus axis and shows cytokine-specific differentiation into distinct tuft cell phenotypes.

The paper shows that reduced insulin signaling in C. elegans delays cell divisions more than molting, increasing desynchrony and triggering a compensatory delay in the molting program.

The paper investigates temporal scaling in C. elegans larval development, showing that timing of cellular events scales with developmental duration, even in mutants lacking a core timing regulator.

The paper identifies a mechanism by which C. elegans ASE neurons maintain their identity despite molecular fluctuations through the reservoir effect of the transcription factor CHE-1.

OrganoidTracker automates cell tracking in organoids using machine learning and manual error correction, achieving high accuracy with minimal manual intervention.

The paper introduces a density-based clustering method using percolation theory to identify functionally related gene modules from noisy, high-dimensional RNA sequencing data across different organisms.

The paper examines variability in β-catenin pulse dynamics during a stochastic cell fate decision in C. elegans and links BAR-1 pulse timing to cell fate outcome.

The paper investigates how molecular noise and birth order influence the stochastic AC/VU cell fate decision in C. elegans using time-lapse microscopy and single molecule FISH.