Abstract
Cellular morphodynamics can be used as markers for many physiological and pathological processes. This protocol provides a step-by-step guide to identify variations in motility and morphology within (or across) cell populations using non-invasive live imaging and reproducible image analysis techniques such as segmentation and tracking. Detailed instructions cover all the way from cell culturing and labelling to automatic image and statistical analyses, including the definition of multiple descriptors that characterise the shape and movement of cells in a quantitative manner. All methods are available as free open-source software and illustrated by video tutorials.
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Chenouard, N., Bloch, I., & Olivo-Marin, J. C. (2013). Multiple hypothesis tracking for cluttered biological image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2736–3750. https://doi.org/10.1109/TPAMI.2013.97.
De Chaumont, F., Dallongeville, S., Chenouard, N., Hervé, N., Pop, S., Provoost, T., … & Lagache, T. (2012). Icy: an open bioimage informatics platform for extended reproducible research. Nature methods, 9(7), 690.
Diamond, L. S., Harlow, D. R., & Cunnick, C. C. (1978). A new medium for the axenic cultivation of Entamoeba histolytica and other Entamoeba. Transactions of the Royal Society of Tropical Medicine and Hygiene, 72(4), 431–432. https://doi.org/10.1016/0035-9203(78)90144-X.
Ducroz, C., Olivo-Marin, J. C., & Dufour, A. (2012). Characterization of cell shape and deformation in 3d using spherical harmonics. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 848–851). IEEE. Barcelona. https://doi.org/10.1109/isbi.2012.6235681.
Dufour, A., Meas-Yedid, V., Grassart, A., & Olivo-Marin, J. C. (2008, December). Automated quantification of cell endocytosis using active contours and wavelets. In 2008 19th International Conference on Pattern Recognition (pp. 1–4). IEEE, Tampa, FL. https://doi.org/10.1109/icpr.2008.4761748.
Dufour, A. C., Olivo-Marin, J. C., & Guillén, N. (2015). Amoeboid movement in protozoan pathogens. Seminars in Cell & Developmental Biology, 46, 128–134. https://doi.org/10.1016/j.semcdb.2015.10.010.
Manich, M. (2020). Online data of “A protocol to quantify cellular morphodynamics: from cell labelling to automatic image analysis” (Version http://icy.bioimageanalysis.org). Zenodo. https://doi.org/10.5281/zenodo.3594363.
Olivo-Marin, J. C. (2002). Extraction of spots in biological images using multiscale products. Pattern Recognition, 35, 1989–1996. https://doi.org/10.1016/S0031-3203(01)00127-3.
Petropolis, D. B., Faust, D. M., Deep Jhingan, G., & Guillen, N. (2014). A New Human 3D-Liver Model Unravels the Role of Galectins in Liver Infection by the Parasite Entamoeba histolytica. PLoS Pathogens, 10(9), e1004381. https://doi.org/10.1371/journal.ppat.1004381.
Wiesmann, V., Franz, D., Held, C., Munzenmayer, C., Palmisano, R., & Wittenberg, T. (2015). Review of free software tools for image analysis of fluorescence cell micrographs. Journal of Microscopy, 257, 39–53. https://doi.org/10.1111/jmi.12184.
Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., & Olivo-Marin, J. C. (2002). Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Transactions on Medical Imaging, 21(10), 1212–1221. https://doi.org/10.1109/TMI.2002.806292.
Acknowledgements
We acknowledge Marion Louveaux for advice on image analysis reproducibility and data online availability. We are grateful to the technical unit of BioImagerie Photonique of Institut Pasteur for their help with microscopy experiments. A.B.P. is part of the Pasteur-Paris University (PPU) International Ph.D. Program.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 665807, the Institut Carnot Pasteur Microbes & Sante (ANR 16 CARN0023-01), the Labex IBEID (ANR-10-LABX-62-IBEID), France-BioImaging infrastructure (ANR-10-INBS-04) and the program PIA INCEPTION (ANR-16-CONV-0005).
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Movie 1: Fluorescent cells moving freely.
Movie 2: Segmented cells with Active Contours.
Movie 3: Segmented cells with Active Contours and centroid tracks.
Tutorial 1: Using Time Stamp Overlay plugin.
Tutorial 2: HK-Mean segmentation on frame 0 and color ROIs affectation.
Tutorial 3: Approximative ROIs manual design and automatic segmentation with Active Contours.
Tutorial 4: Automatic ROIs detection with HKM and segmentation with Active Contours.
Tutorial 5: Segmentation with Active Contours and cell tracks analysis with Track Manager and Track Processors.
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Manich, M., Boquet-Pujadas, A., Dallongeville, S., Guillen, N., Olivo-Marin, JC. (2020). A Protocol to Quantify Cellular Morphodynamics: From Cell Labelling to Automatic Image Analysis. In: Guillen, N. (eds) Eukaryome Impact on Human Intestine Homeostasis and Mucosal Immunology. Springer, Cham. https://doi.org/10.1007/978-3-030-44826-4_25
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DOI: https://doi.org/10.1007/978-3-030-44826-4_25
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