Research
The overall objective of our research is to provide a quantitative view of processes of biomedical interest, by combining imaging tools with single-molecule sensitivity and dynamic imaging capability, advanced image analysis algorithms, and cell biology.
Exploring Cell Biology with Single-Molecule Visualization
Our research concentrates on the study of molecular mechanisms in cellular biophysics, with a special focus on the spatiotemporal organization and dynamics of cell membrane components in health and disease.
We use single-molecule-based super-resolution light microscopy methods, such as photo-activated localization microscopy (PALM) and direct stochastic optical reconstruction microscopy (dSTORM) to obtain images with molecular-scale resolution. Complementary studies using single-particle tracking (SPT) allow us to detail molecular movement at high temporal resolution.
Currently, we are investigating the role of membrane proteins in:
- tight junctions, cell-cell interactions, and tissue permeability of the gastrointestinal tract (grant MICIN PID2021-125386NB-I00);
- cell migration, in relation to wound healing (grant MINECO BFU2017-85693-R);
- membrane-level interactions mediated by glycosylation and glycans.
Collaborations
- Raquel Martin, Universitat de Barcelona
- Masayoshi Fukasawa, National Institute of Infectious Diseases, Japan
- Maria Garcia-Parajo, ICFO, Castelldefels (Barcelona)
- Tomoya Isaji, Tohoku Medical and Pharmaceutical University, Japan
Data Science for BioImaging
We work at the development of algorithms and methods to extract knowledge and insights from images and data obtained through image-based techniques.
Currently, we use tools from both classical statistics and deep learning for the:
- quantification of protein copy number in super-resolution imaging;
- classification and segmentation of biomedical images;
- analysis of single particle trajectories undergoing anomalous diffusion.
We are deeply involved in the organization of the AnDi challenge.
Collaborations
- Giovanni Volpe, Gothenburg University, Sweden
- Oriol Gallego, Universitat Pompeu Fabra
- Maciej Lewenstein, ICFO, Castelldefels (Barcelona)
- Francesca Cella Zanacchi, Università di Pisa, Italy
Selected publications
- Geometric deep learning reveals the spatiotemporal features of microscopic motion (2023) [link]
- Objective comparison of methods to decode anomalous diffusion (2021) [link]
- Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (2021) [link]
- A machine learning method for single trajectory characterization (2020) [link][arXiv:1903.02850]
- Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering (2020) [link][arXiv:1909.13133]