Team description

MORPHEME is a joint research team between Inria, CNRS, Inserm and Université Côte d’Azur (UniCA), affiliated with Inria Centre at Université Côte d’AzurComputer Science+Signals+Systems Laboratory (I3S) and Institute of Biology Valrose (iBV)

Its creation process started in 2010, and in 2013 it got the status of « Equipe-Projet Commune (EPC) ». It was renewed in 2023.

Key Facts

  • Objectives: Characterize and model the morphological properties of biological structures from the cell to the supra-cellular scale with the intent of providing a better understanding of the development of normal tissues and a characterization at the supracellular level of pathologies such as the Fragile X syndrome, Alzheimer or diabetes.
  • Motivation: The understanding of morphological and topological aspets in mesoscopic structures have a key influence on the functional behavior of organs and living entities.
  • Positioning: At the interface between computational sciences and biology.
  • Framework:
    • Scales: from cell to supra-cellular scale;
    • Modalities: microscopy imaging (confocal, fluorescence, 2-photon, phase-contrast, video), tomography;
    • Data: in vitro and in vivo images (2D, 2D+t, 3D or 3D+t);
    • Tools: image processing, statistical learning and computational modeling.

Research Axes

Imaging, Feature extraction, Interpretation/Classification, Modeling.

1. Imaging:

  • Design of the suitable experimental conditions and the adequate preparation of samples
  • Optimization of the acquisition protocol (staining, imaging…) and definition of relevant quantitative characteristics of interest
  • Reconstruction/restoration of native data from noisy, under-sampled measurements to improve the image readability and interpretation

2. Feature extraction:

  • Detection and delineation of the biological structures of interest in images by incorporating them into the previously defined models for improving the detection quality
  • Observing feature evoluation to address morphogenesis and structure development.
  • The main challenges are the variability of biological structures and the huge size of datasets

3. Interpretation/Classification:

  • Inference of (quantitative/qualitative) parameters of the model considered  used to extract the biological structure under study
  • Classification schemes for characterizing the different populations based either on the model parameters or on some specific metric between the extracted structures
  • The final goal is to provide biological information characterizing and discriminating between the different populations

4. Modeling: 

  • Forward modeling: modeling biological phenomena such as axon growth or network topology in different contexts using the expertise of the biologists/biophysicists in our team  to calibrate/validate the models
  • Inverse modeling: using a priori information to design a suitable model and extract relevant information from images

Representative publications

  • Léo Guignard, Ulla-Maj Fiuza, Bruno Leggio, Julien Laussu, Emmanuel Faure, Gaël Michelin, Kilian Biasuz, Lars Hufnagel, Grégoire Malandain, Christophe Godin, Patrick Lemaire, Contact area-dependent cell communication and the morphological invariance of ascidian embryogenesisScience, American Association for the Advancement of Science (AAAS), 2020, 369 (6500), pp.158. PDF
  •  V. Stergiopoulou, L. Calatroni, H. Goulart, S. Schaub, L. Blanc-Féraud, COL0RME: Super-resolution microscopy based on sparse blinking fluorophore localization and intensity estimation, Biological Imaging, Cambridge University Press, 2022, 2. PDF
  • Agustina Razetti, Caroline Medioni, Grégoire Malandain, Florence Besse, Xavier Descombes, A stochastic framework to model axon interactions within growing neuronal populations,  PLoS Computational Biology, 2018, 14 (12). PDF
  • Fabienne de Graeve, Eric Debreuve,  Somia Rahmoun,  Szilvia Ecsedi, Alia Bahri, Arnaud Hubstenberger,  Xavier Descombes,  Florence Besse, Detecting and quantifying stress granules in tissues of multicellular organisms with the Obj.MPP analysis tool, Traffic, Wiley, 2019. PDF
  • Georgios Efthymiou, Agata Radwanska, Anca-Ioana Grapa, Stéphanie Beghelli-de La Forest Divonne, Dominique Grall, et al.. Fibronectin Extra Domains tune cellular responses and confer topographically distinct features to fibril networksJournal of Cell Science, Company of Biologists, 2021. PDF

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