Descripción del proyecto
La mejora de la resolución de la microscopía sin marcado pondrá el foco en las mitocondrias de una forma más natural
Una consideración crítica en cualquier experimento biológico es si el paradigma experimental, en particular, la preparación de la muestra y los métodos de observación, alteran o no los resultados de formas importantes para la interpretación de los resultados. El análisis de los componentes subcelulares, incluidas las proteínas u orgánulos como las mitocondrias, a menudo se basan en técnicas de marcado o amplificación para identificar la señal en el «ruido». El proyecto MitoQuant está desarrollando un sistema de imagenología de células vivas de alta especificidad y alta resolución capaz de mejorar la calidad de la imagen en preparaciones sin marcado. La tecnología microscópica cuantitativa, que aprovecha el aprendizaje automático y la autofluorescencia (la emisión natural de luz de las proteínas endógenas), promete arrojar luz sobre procesos vinculados con las mitocondrias con aplicaciones importantes para cientos de enfermedades relacionadas con la disfunción mitocondrial.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
Régimen de financiación
MSCA-IF-EF-ST - Standard EFCoordinador
9019 Tromso
Noruega