Data Assimilation and Lattice LIght SHeet imaging for endocytosis/exocytosis pathway modeling in the whole cell – DALLISH

Fluorescence imaging and microscopy has a prominent role in life science and medical research. It consists of imaging specific cellular and intracellular objects of interest at the diffraction limit (200nm), using wide field as well as confocal microscopy, after tagging them with genetically engineered proteins that emit fluorescence. In this context, the past decade has witnessed a tremendous interest in the concept of Super-Resolution Microscopy (SR-M) and 3D fluorescence Light Sheet Microscopy (LS-M). One of the main reasons explaining this enthusiasm lies in the discovery of methods to break the diffraction limit established in optics several decades ago. Since, variants of SR-M (awarded by Nobel Prize in 2014) and LS-M have been investigated in an increasing number of biological studies.

The so-called Lattice Light Sheet Microscopy (LLS-M) represents at present the novel generation of 3D fluorescence microscopes dedicated to single cell analysis, generating extraordinarily high-resolved and sharp, but huge 3D images and videos: one single live cell experiment in one single condition, imaging two molecular markers of the endocytosis pathway and using cutting-edge LLS-M can result into up to one Terabyte of data within an hour, at the spatial resolution of 100–200 nanometers in 3D. In such a situation, it is found the usual conventional image reconstruction algorithms and image analysis methods developed for 3D fluorescence microscopy are likely to fail to process a deluge of voxels generated by LLS-M instruments. As a consequence, it is necessary to develop new paradigms and computational strategies for image reconstruction and 3D molecule tracking/motion estimation. Furthermore, establishing correspondences between the image-based measurements/features (e.g motion vectors, trajectories), stochastic motion models and the underlying biological and biophysical information remains a challenging task. In order to tackle these challenges, the DALLISH project aims at improving the core of bioimage processing and quantification methods in 3D cell imaging. The strong interactions with biophysical and statistical models promoted in the project, will open the way to better quantifying the knowledge carried by biologists and the expertise of data processing experts. To reach the goals of the project, applied mathematics, image processing and analysis, and computer science have to be considered in association with biophysics and biology. A sustained synergy between all these scientific domains is mandatory.

The impact of the project will be three-fold. First, our new image processing paradigms and improved algorithms (allowing faster, more resolved and more accurate results) will have direct benefits in modern bioimaging, in general and in cell imaging in particular. Second, the methods and algorithms will apply to decipher molecular mechanisms of protein transports, here focused on endocytosis / exocytosis. Finally, in a larger perspective, the quantitative description of protein transport as described in the DALLISH project, will be a prerequisite for understanding the functioning of a cell in normal and pathological situations, as default in protein transport appeared over the years, as a major contributory factor to a number of diseases, including cancer, viral infection and neurodegenerative diseases.