Poster by M. Granfors at the Protein Folding in Real Time conference, Stockholm, 11 March 2026

Fluorescence microscopy image of yeast cells, with Hsp104-GFP marking protein aggregates, making them visible as bright spots. (Image by J. Masaryk.)
Machine learning based tracking of protein aggregates in yeast
Mirja Granfors, Jakub Masaryk, Carlo Manzo, Markus Tamas, Giovanni Volpe
Date: 11th March 2026
Time: 18:00 – 20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden

Arsenic is a toxic metal linked to serious diseases like cancer and neurodegeneration. One proposed mechanism of toxicity is that arsenic causes proteins to misfold and aggregate inside cells, but the dynamics and regulation of this process remain poorly understood. Using fluorescence microscopy data from living yeast cells, we are developing a machine learning approach to automatically detect, track, and analyze protein aggregate movement over time.

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