Multidimensional and multimodal light microscopy combined with GFP (Green Fluorescence Protein) tagging has taken a prominent role in life science research due to its ability to study in vitro and in vivo biomolecules in the cell compartments and cell domains.
The main objective of SERPICO team is to decipher the dynamic coordination and organization of molecular complexes at the single cell level. Our first aim is to foster dedicated technological and methodological developments to build an integrated imaging approach that bridges the resolution gaps between the molecule and the whole cell, including their temporal behavior. While we will focus on particular biological models of endo-membrane biogenesis and trafficking in the endosomal-recycling pathway of specialized cells, most of results and developments will apply in different fields of cell and integrative biology. A global and pluridisciplinary (applied mathematics, computer science, biology, physics …) approach is necessary to identify the molecular processes resulting in pathological situations (cancer, degenerative diseases …), as well as to validate future therapeutic agents.
Scientific context and motivations
Light microscopy, especially fluorescence microscopy, has taken a prominent role in life science research due to its ability to investigate the 3D interior of cells and organisms. It enables to visualize, in vitro and in vivo, particular biomolecules and proteins (gene expression) with high specificity through fluorescent labeling (GFP – Green Fluorescence Protein probes) both at the microscopic and nanoscopic scales. Nevertheless, the mechanisms of life are very complex and driven by multimolecular interactions: mitotic spindle, cell signaling complexes, intracellular transport, cell morphogenesis and motility… A dynamical quantitative and integrated description of molecular interactions and coordination within macromolecular complexes at different scales appears essential today for the global understanding of live mechanisms. A long-term research consists in inferring the relationships between the dynamics of macromolecules and their functions. This constitutes one of the challenges of modern biology. The proposed mathematical models and algorithms are mainly developed to identify molecular processes in fundamental biology but they have also a strong potential for applications in biotechnology and medicine: disease diagnosis, detection of genomic instabilities, deterioration of cell cycle, epigenetic mechanisms and cancer prevention.
Objectives in cell imaging
Facing the amount of information provided by high-throughput multidimensional microscopy, the SERPICO team investigates computational and statistical models to better elucidate the role of specific proteins inside their multiprotein complexes and to help to decipher the dynamic coordination and organization of molecular complexes at the single cell level. We investigate image processing methods, mathematical models, and algorithms to build an integrated imaging approach that bridges the resolution gaps between the molecule and the whole cell, in space and time. We address the following topics:
- Image superresolution/image denoising required to preserve cell integrity (photo-toxicity versus exposure time) and image analysis in multidimensional microscopy;
- Motion analysis and computation of molecule trajectories in live-cell imaging to study molecular interactions in space and time);
- Computational simulation and modelling of molecule trafficking at different spatial and temporal scales (e.g. biophysical model assimilation for dynamic representation in video-microscopy and prediction in biology).
We focus on the cellular and molecular mechanisms involved in membrane transport and trafficking at the scale of a single cell.
Main challenges in image processing for multimodal and multidimensional microscopy
In most cases, modern microscopy in biology is characterized by a large number of dimensions that fits perfectly with the complexity of biological features: two or three spatial dimensions, at macro to nano-scales, and one temporal dimension, sometimes spectrally defined and often corresponding to one particular bio-molecular species. Dynamic microscopy is also characterized by the nature of the observable objects (cells, organelles, single molecules, …), by the large number of small size and mobile elements (chromosomes, vesicles, …), by the complexity of the dynamic processes involving many entities or group of entities sometimes interacting, by particular phenomena of coalescence often linked to image resolution problems, finally by the association, dissociation, recomposition or constitution of those entities (such as membrane fusion and budding). Thus, the corpus of data to be considered for a comparative analysis of multiple image series acquisitions is massive (up to few GigaBytes per hour). Therefore, it becomes necessary to facilitate and rationalize the production of those multidimensional data, to improve post acquisition analysis (i.e. image processing) which are limiting factors in front of the data, and to favor the organization and the interpretation of the information associated to this data corpus. It motivates and requires innovative mathematical tools and concepts: data fusion, image registration, superresolution, data mining, life dynamics modelling, …
Organization and collaborations
In collaboration with UMR 144 CNRS-Institut Curie (“Space Time imaging of Endomembranes and organelles Dynamics” (STED) team) and PICT-IBiSA (Cell and Tissue Imaging Facilities), the members of the SERPICO team participate in several projects (PhD and post-doc supervision, contracts…) with biologists in the field of cell biology and microscopy. We have promoted and designed non-parametric methods since prior knowledge cannot be easily taken into account for extracting unattended but desired information from image data. We have proposed user-friendly algorithms for processing 3D or 4D data. The scientific projects of the SERPICO team are complementary to the other on-going and planned projects of the UMR 144 CNRS-Institut Curie Unit. A subset of projects is related to instrumentation in electronic and photonic microscopy (PICT-IBiSA platform) including computational aspects on the reconstruction and enhancement of images related to sub-diffraction light microscopy and multimodal approaches. Our projects rely partially on the results and advances of these instrumental projects and a positive synergy is foreseen.
CytoDI (Inria Associated-Team): Quantitative Imaging of Cytoskeleton Dynamics in 3D
The main scientific goal of the CytoDI Associated-Team is the spatiotemporal characterization and comparison of cytoskeleton networks involved in cell migration and observed through live cell imaging in three dimensions (3D). Those networks include the cytoskeleton (microtubules (MT), intermediate filaments (IF)) dynamically resolvable by Bessel Beam Light Sheet fluorescent microscopy. The goal will be achieved through design of local and global descriptors of the spatial conformation and deformation of the cytoskeleton. Subsequently, general metrics to compare and classify the MT and IF networks will be investigated. This study will be carried out on oncogenically transformed lung cancer epithelial cells.
DALLISH (ANR project): Data Assimilation and Lattice LIght SHeet imaging for endocytosis/exocytosis pathway modeling in the whole cell
Lattice Light Sheet Microscopy (LLS-M) represents at present the novel generation of 3D fluorescence microscopes dedicated to single cell analysis, generating extraordinarily sharp, 3D images and videos. However, the usual conventional image processing algorithms developed for fluorescence microscopy are likely to fail to process the deluge of voxels generated by LLS-M instruments. The DALLISH project aims at improving the core of 3D image processing and quantification methods to face this computational challenge. The proposed methods will be used to decipher mechanisms involved in protein transport observed in LLS-M experiments.
NAVISCOPE (Inria Project lab (IPL): Image-guided NAvigation and VIsusalization of large data sets in live cell imaging and microSCOPy
Nowadays, the detection and visualization of important localized events and process in multidimensional and multi-valued images, especially in cell and tissue imaging, is tedious and inefficient. The objective of NAVISCOPE project is to develop original and cutting-edge visualization and navigation methods to assist scientists, enabling semi-automatic analysis, manipulation, and investigation of temporal series of multi-valued volumetric images, with a strong focus on live cell imaging and microscopy application domains. We address the three following challenges and issues:
Novel machine learning methods able to detect the main regions of interest, and automatic quantification of sparse sets of molecular interactions and cell processes during navigation to save memory and computational resources.
Novel visualization methods able to encode 3D motion/deformation vectors and dynamics features with color/texture-based and non-sub-resolved representations, abstractions, and discretization, as used to show 2D motion and deformation vectors and patterns.
Effective machine learning-driven navigation and interaction techniques for complex functional 3D+Time data enabling the analysis of sparse sets of localized intra-cellular events and cell processes (migration, division, etc.).
Finally, we will have also to overcome the technological challenge of gathering up the software developed in each team to provide a unique original tool for users in biological imaging, and potentially in medical imaging.
CATLAS: Cell ATLAS in cryo-electron tomography
In the CATLAS project, we are currently studying deep-learning and convolution neural networks (CNN) to count macromolecules (e.g., ribosomes) in 3D cell cryo-electron tomography images. Cryo-ET allows to image cells in situ, with enough resolution to locate macromolecules without sample-altering bio-markers. Developing classification methods is a way of obtaining an efficient counting technique, while being a powerful tool for geo-localization and macro-molecule reconstruction with sub-tomogram averaging. Final results demonstrate that our 3D CNN architecture (DeepFinder) outperforms state-of-the-art machine learning algorithms, as confirmed in the SHREC 2019 challenge dedicated to classification in cryo-electron tomography.