Jacob Kæstel‐Hansen

This study uses live-cell imaging to show that enhancer-promoter interactions drive gene activation through transient contacts of ~25-42 nanometers.

The paper shows that LD-targeted proteins are confined by nanodomains and transported via ER-LD bridges, revealing mechanisms for their selective accumulation on lipid droplets.

RNASCAPE quantifies mRNA cytosolic escape using deep learning, achieving 78% accuracy with three timepoints and LNP parameters, revealing cholesterol's impact on mRNA release.

The paper uses machine learning and diffusion analysis to compare intracellular trafficking of insulin analogs and endogenous insulin, revealing differences in diffusion and lysosomal colocalization.

This paper introduces DeepSPT, a deep learning framework that automates the analysis of sub-cellular diffusion to infer functional information, achieving high accuracy in identifying endosomal structures during viral infections.

This study uses single-molecule assays to analyze how binding efficiency, processivity, and turnover affect protease activity in Savinase and its mutants.

This study tracks single nanoMOFs in live cells to understand their intracellular dynamics and internalization pathways using fluorescence and machine learning.

This paper evaluates how different performance metrics and generalization definitions affect the assessment of protein regression models.