Description du projet
Renforcer la résolution de la microscopie sans marqueurs mettra en avant les mitochondries de manière plus naturelle
Une considération fondamentale dans toute expérience biologique consiste à savoir si le paradigme expérimental, notamment la préparation de l’échantillon et les méthodes d’observation, modifie les conclusions de manière importante pour l’interprétation des résultats. Analyser les éléments sous-cellulaires, dont les protéines, ou les organelles, comme les mitochondries, dépend souvent de techniques d’amplification ou de marquage pour identifier le signal parmi le «bruit». Le projet MitoQuant développe un système d’imagerie de cellules vivantes à haute résolution et forte spécificité, capable d’améliorer la qualité des images dans des préparations sans marqueurs. En exploitant l’apprentissage automatique et l’autofluorescence (l’émission naturelle de lumière par les protéines endogènes), cette technologie de microscopie quantitative promet de faire la lumière sur les processus liés aux mitochondries ayant d’importantes applications dans des centaines de maladies associées à un dysfonctionnement mitochondrial.
Objectif
Mitochondria play a vital role in the cellular machinery, hence it is little surprising that their dysfunction has been linked to many diseases, from diabetes to neurodegeneration. However, as many studies on the interplay of organelles and molecular dynamics often employ fluorescence microscopy, a continued worry overshadowing findings and deductions is the possibility that the transfection-induced overexpression of fluorescent proteins skews the obtained results. A recent approach, the gene editor CRISPR-CAS9, which modifies rather than adds DNA sequences, circumvents this issue, but in turn often reduces the available signal levels. To counter low signals and yet offer highest resolution and specificity, MitoQuant aims to image contextual mitochondrial information with label-free superresolution, while simultaneously enhance image quality of specific but sparse fluorescently labelled proteins of interest through recently presented de-noising routines based on machine learning. Therefore, the development of a novel instrument to provide adequate resolution and contrast, matching label-based live-cell superresolution techniques like structured illumination microscopy, is the first main goal of this project. The proposed microscope will work in the deep UV range and employ dedicated optics originally developed for material science to provide high numerical apertures at short wavelengths, thus enabling live-cell imaging in the 100nm range. Concurrently, a neural network will be compiled and trained to enhance signals under low-light conditions and to extract and classify cellular organelles based on their quantitative phase and autofluorescence information. Building on an excellent track record of developing application-tailored microscopes as well as advanced image reconstruction and processing algorithms particularly suited for live-cell superresolution, the researcher strives to start with first live-cell experiments in good time after establishing the technique.
Champ scientifique
- natural sciencesbiological sciencesneurobiology
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programme(s)
Régime de financement
MSCA-IF-EF-ST - Standard EFCoordinateur
9019 Tromso
Norvège