Periodic Reporting for period 1 - MitoQuant (Development of Deep-UV Quantitative Microscopy for the Study of Mitochondrial Dysfunction)
Période du rapport: 2019-07-01 au 2021-06-30
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.
During the first year of the project period, such a microscope was design and building started. Due to heavy delays caused by the pandemic and the associated start of the lockdown in month nine of the project, the microscope could only be finished half a year behind schedule. During the lockdown, focus was therefore shifted to software-related aspects of the project ahead of time. This led to a novel approach for illumination calibration in quantitative phase microscopy using deep learning. The developed neural network operates on the Fourier spectra of images, which makes it much more transferable than other networks. We managed to outperform the gold standard method in illumination calibration by a factor of 2 using our approach. Furthermore, progress on computational superresolution microscopy was achieved using the fluctuation imaging method MUSICAL. Eventually, these approaches will be used to enhance autofluorescence image data. In the last six months of the project, lab work was possible again and the microscope could be finished. Its image quality was evaluated on a range of samples and the microscope was found to provide far greater contrast than visible light brightfield microscopy. We investigated this finding and hypothesised that it is due to the strong absorbance of DUV wavelengths in biological samples. Research in this direction is ongoing.
Living up to the spirits of the Marie Curie program, the project leader has learned the local language Norwegian, successfully acquired prestigious young investigator funding from the Norwegian Research Council and initiated the patenting process for a new type of microscopy technique. Personal development was supported by the host university through the Aurora Outstanding program that involved individual mentoring as well as courses in research communication, innovation, and leadership.
Further, a full video series on optical alignment was produced: https://www.youtube.com/watch?v=L6dGgYd4DYE
The developed deep learning method delivers a two-fold improvement in accuracy over the current state of the art. The approach and software are published and openly available. Similarly, software was developed for computational nanoscopy, with both software and datasets openly available.
In total, four papers have been published linked to the research conducted in this project.