EMBL Practical Course 2010, Advanced Microscopy

Lecture Notes: Measuring movement of vesicles, virus and cells

 Kota Miura (Centre for Molecular and Cellular Imaging, EMBL), 11.March.2010





Time series of digital images, usually called ‘a stack’, contains temporal dynamics of position and intensity. By analyzing these dynamics, we can extract numerical parameter which then enables us to characterize the biological system. There are three types of dynamics. (i) Position does not change but intensity changes over time. (ii) Position changes but the intensity does not change. (iii) Both Position and Intensity change over time. Since (iii) is a combination of (i) and (ii), I will explain the basics of the measurement of type (i). Some additional information on kymograph and optical flow estimation is also planned.  




Single Particle Tracking (SPT, Individual Movement)


An excellent review on SPT that also discusses about mean-square-displacement plot and interpretations can be found in à (Saxton and Jacobson, 1997). A frequently referred SPT analysis paper is (Kusumi et al., 1993). Concept of micro-diffusion coefficient was introduced in this paper. Theoretical Comparison of SPT and FRAP can be found in (Qian et al., 1991). Review on tracking techniques in cell biology with additional details on optical flow estimation is in elsewhere (Miura, 2005).   


Segmentation techniques


- Manual tracking (-> ImageJ “manual tracker plugin”, see link below)

- Manual contour tracing and centroid

- Thresholding

- Local intensity maxima

- Gaussian-fitting

            many applications, you often see articles in Biophysical Journal

            Sub-pixel resolution

            Vaccinia Virus tracking example

- Active Contour (SNAKES), Level-set

            c.f. http://iacl.ece.jhu.edu/projects/gvf/

- Pattern matching (cross-correlation or sum-of-difference technique)

            3D tracking of macrophage-like cells à (Grabher et al., 2007)





            à see (Meijering et al., 2008)


Quantitative comparison of segmentation techniques:


à See (Cheezum et al., 2001)

Kymograph Tools


à Multiple Kymograph Plugin http://www.embl.de/eamnet/html/kymograph.html

à Kymoquant: http://cmci.embl.de/downloads/kymoquant

            quantitative analysis of ambiguous patterns in kymograph







Cytoplasmic Architecture


Diffusion within cytoplasm is not a simple pure-diffusion. Cytoskeletons, organelle and molecular complexes become obstacles to the movement of proteins. In a very small scale, the vacant spaces between these structures allow the molecule to move around without encountering these structures. In this vacant space, the cytoplasmic viscosity is said to be similar to water, or 2-3 folds higher than water. Measurement of small scale diffusion needs special techniques. On the other hand, we also can measure the movement of molecules in a larger scale. In this case, diffusing molecules encounters steric hindrances and bindng/reaction with other molecules. Diffusion coefficient that includes this slowing factor is thus an apparent diffusion. More specifically when the molecule mobility is slowed down due to binding/reactions, this type pf diffusion is called effective diffusion.


To know more about microscopic diffusion and macroscopic diffusion inside cell, refer to Luby-Phelps papers (Luby-Phelps, 1994; Luby-Phelps, 2000).



ImageJ website

Free and powerful software for quantitative image analysis.



For a list of software used in cell biology, consult -> (Hamilton, 2009)


3D tracking software




target object


Bitplane Imaris (spot tracker) 


spots (shape detection difficult)




spots (not really used)


ImageJ plugin (manual track)  


any object, without segmentation module, tracking by clicking in XY and finiding highest intensity along z


ImageJ plugin (particle tracker 3D)


tracking virus, spherical shape is assumed


QUIA        (no public access)

no public access

tracking shape-changing cells using active contour, shape changes allowed. Not evaluated.




CMCI (Centre for Molecular and Cellular Imaging) @EMBL







            Cheezum, M. K., Walker, W. F. and Guilford, W. H. (2001). Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys J 81, 2378-88.

            Grabher, C., Cliffe, A., Miura, K., Hayflick, J., Pepperkok, R., Rorth, P. and Wittbrodt, J. (2007). Birth and life of tissue macrophages and their migration in embryogenesis and inflammation in medaka. J Leukoc Biol 81, 263-71.

            Hamilton, N. (2009). Quantification and its applications in fluorescent microscopy imaging. Traffic 10, 951-61.

            Kusumi, A., Sako, Y. and Yamamoto, M. (1993). Confined lateral diffusion of membrane receptors as studied by single particle tracking (nanovid microscopy). Effects of calcium-induced differentiation in cultured epithelial cells. Biophys J 65, 2021-40.

            Luby-Phelps, K. (1994). Physical properties of cytoplasm. Curr Opin Cell Biol 6, 3-9.

            Luby-Phelps, K. (2000). Cytoarchitecture and physical properties of cytoplasm: volume, viscosity, diffusion, intracellular surface area. Int Rev Cytol 192, 189-221.

            Meijering, E., Smal, I., Dzyubachyk, O. and Olivo, J. C. (2008). Time-Lapse Imaging. In Microscope Image Processing, pp. 401-440. Burlington, MA: Elsevier Academic Press.

            Miura, K. (2005). Tracking Movement in Cell Biology. In Advances in Biochemical Engineering/Biotechnology, vol. 95 (ed. J. Rietdorf), pp. 267. Heidelberg: Springer Verlag.

            Qian, H., Sheetz, M. P. and Elson, E. L. (1991). Single particle tracking. Analysis of diffusion and flow in two-dimensional systems. Biophys J 60, 910-21.

            Saxton, M. J. and Jacobson, K. (1997). Single-particle tracking: applications to membrane dynamics. Annu Rev Biophys Biomol Struct 26, 373-99.