Descrizione del progetto
Aumentare la risoluzione della microscopia senza marcature consentirà di mettere a fuoco i mitocondri in modo più naturale
Una considerazione critica in ogni esperimento biologico è se il paradigma sperimentale, in particolare i metodi di preparazione e osservazione del campione, altererà o meno gli esiti in maniera significativa per l’interpretazione dei risultati. L’analisi dei componenti subcellulari, tra cui proteine o organelli quali i mitocondri, spesso si basa su tecniche di marcatura o di amplificazione per identificare il segnale nel «rumore». Il progetto MitoQuant sta sviluppando un sistema di imaging a cellule vive ad alta risoluzione e specificità in grado di migliorare la qualità dell’immagine in preparati privi di marcatura. Sfruttando l’apprendimento automatico e l’autofluorescenza (l’emissione naturale di luce da proteine endogene), la tecnologia microscopica quantitativa promette di fare luce sui processi collegati ai mitocondri con importanti applicazioni per centinaia di malattie associate alla disfunzione mitocondriale.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF-EF-ST - Standard EFCoordinatore
9019 Tromso
Norvegia