Project description
Boosting the resolution of label-free microscopy will put mitochondria in focus more naturally
A critical consideration in any biological experiment is whether or not the experimental paradigm, particularly the sample preparation and observation methods, alters the outcomes in ways important to interpretation of results. Analysing subcellular components including proteins or organelles like mitochondria often relies on labelling or amplification techniques to identify the signal in the 'noise'. The MitoQuant project is developing a high-resolution and high-specificity live-cell imaging system capable of enhancing image quality in label-free preparations. Exploiting machine learning and autofluorescence (the natural emission of light from endogenous proteins), the quantitative microscope technology promises to shine light on mitochondria-linked processes with important applications for hundreds of diseases associated with mitochondrial dysfunction.
Objective
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.
Fields of science
- 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)
Funding Scheme
MSCA-IF-EF-ST - Standard EFCoordinator
9019 Tromso
Norway