Mapping and classifying the topography of adhesive cell interface with high precision
Biological cells constantly process various input signals in order to live and act, one important trigger being interaction with the environment via adhesion. Adhesion is determined by the strong coupling of cell biochemical reactions to the spatial constraints imposed by its membrane and cytoskeleton, as well as the force sensitivity of all the players. To develop an interpretative and operative understanding of cell adhesion, an important step is to map the gap between the interacting cells with sufficient resolution. Reflection interference contrast microscopy (RICM) has been highly successful in filling this gap in soft matter systems. Recently, we extended the use of quantitative RICM to cells, completing a 40 year old challenge. Here we propose to develop RICM further to make it faster as well as more accessible to non-specialist users, including through an artificial intelligence (AI) based approach.
Cell Adhesion, Optical Microscopy, Deep learning
The aim is to develop new correlative approaches to surface sensitive optical imaging and image analysis, including deep learning, in order to map the topography of the cell membrane at the soft-interface with nanometric precision. To do so, RICM will be extended and combined with information on location and dynamics of specific cell surface proteins. The long-term goal is to use the knowledge of these parameters to develop a physical understanding of cell adhesion and to provide new label-free phenotypical and functional tests for biology and medicine. At the end of the project, we expect to have a user-friendly software to analyze images of spreading cells to yield high precision real-time topographical maps of the adhesive cell interface and to correlate that to maps of adhesion molecules.
Proposed approach (experimental / theoretical / computational)
Fundamental research in biology is more and more based on complex optical microscopy images which contain high amount of unexploited data. Among them, methods without labelling are particularly difficult to analyse. We have a recognized expertise in such a technique called reflection interference contrast microscopy (RICM) applied to soft matter [1-2] and biophysics [3-6]. The combination of engineered surfaces and RICM has already proved to be fruitful in understanding the interaction of T lymphocytes with their targets . We linked local chemical [5,6] or global mechanical [5,6] properties of the stimulating surface to cell-scale properties, as well as to local organization of receptors, membrane or actin. The general strategy will combine physically controlled structured surface with advanced surface optical microscopy (RICM as well as variable angle total internal reflection microscopy) with innovative image analysis strategies to establish a ground truth which will subsequently be used as the basis for AI based fast analysis.
The project is strongly interdisciplinary spanning topics from live cell manipulation, to advanced microscopy, image analysis and artificial intelligence methods. The two host groups have high experience at the interface of physics / biology / immunology. Additional interactions with specialists of deep-learning will also provide an added value to the project through collaborations with Stéphane Ayache and Thierry Artières (LIS). The project on one hand is interdisciplinary by its very nature and on the other hand will develop a new experimental/analysis technique that will be useful to the CENTURI community. In accordance with the Centuri criterion, the technique is expected to be available to biology groups in Luminy within the timeframe of the project.
The candidate should have an academic background in physics/engineering or biophysics. Candidates with previous experience in optical microscopy will be given preference. Reasonable competence in computer programming is expected. We look for highly motivated candidates willing to do experimental and computational work at the interface of physics and biology, in two interdisciplinary laboratories gathering physicists, biologists and medical doctors.
1. S. Fenz, T. Bihr, D. Schmidt, R. Merkel, U. Seifert, K. Sengupta, and A-S. Smith. Nat. Phys. 13: 906–913 (2017).
2. M-J. Dejardin, A. Hemmerle, A. Sadoun, Y. Hamon, P-H. Puech, K. Sengupta, and L. Limozin.
Nano Lett. 18(10):6544-6550 (2018)
3. F. Pi, P. Dillard, R. Alameddine, E. Benard, A. Wahl, I. Ozerov, A. Charrier, L. Limozin, and K. Sengupta, Nano Lett. 15 (8):5178-5184 (2015).
4. R. Alameddine, A. Wahl, F. Pi, K. Bouzalmate, L. Limozin, A. Charrier, and K. Sengupta. Nano Lett. 17 (7): 4284–4290 (2017).
5. A. Wahl, C. Dinet, P. Dillard, P-H. Puech, L. Limozin, and K. Sengupta. Proc. Natl. Acad. Sci.
6. P. Dillard, R. Varma, K. Sengupta, L. Limozin Biophys. J. 107:2629-2638 (2014)