Mitochondria are potentially the most well-known organelle; they are a fundamental part of the eukaryotic cellular machinery and their dysfunction has been linked to many diseases, ranging from diabetes to neurodegeneration. To gain information on these and other sub-cellular compartments, which contain features well below 200 nm, as well as their interactions, recent research focuses more and more on superresolution microscopy to study sub-cellular and even molecular dynamics. Superresolution microscopy, also called nanoscopy, can resolve details below 100 nm and is on its way to become an indispensable tool in biomedical research. However, almost all optical nanoscopy methods require the sample to be fluorescently labelled to break the diffraction barrier of optical imaging. This is often seen as a serious caveat as photobleaching limits the duration of time-lapse recordings, certain labels require the cell membrane to be damaged, and engineered fusion-proteins can alter the native proteins’ function. A further worry is overexpression, i.e. the cell ends up with far too many proteins of a certain type, which limits the meaningfulness of conclusions from those types of experiments. Therefore, label-free techniques with excellent resolution and specificity are of high interest to alleviate these drawbacks.
This is where the MitoQuant project comes into play. Seeing the need for label-free nanoscopy, this project aims to develop a microscopy solution capable of delivering quantitative sub-cellular resolution time-lapse imaging with adequate label-free specificity and contrast that is suitable for organelle imaging. Importantly, and in difference to electron microscopy, this microscope shall be applicable to largely “unprepared” samples and even living cells. Plenty of applications for such a microscope exist. One such example application area, which gives the project its name, is the label-free study of mitochondria and their interaction with other cellular organelles or potentially even individual proteins. The main route to achieving this feat is the exploration of deep ultra-violet (DUV) wavelengths for imaging in combination with quantitative phase microscopy and machine learning.
Therefore, the development of a novel instrument to provide adequate resolution and contrast, approaching label-based live-cell superresolution techniques like structured illumination microscopy, is the first main goal of this project. To counter low signals and yet offer highest resolution and specificity, MitoQuant’s second objective is to recover weak signal from mitochondrial by enhancing image quality through novel image processing based on machine learning.
