Cell Dynamics

This Application Note illustrates the use of Spatial Light Interference Microscopy (SLIM) for studying intra- and inter-cellular dynamics by tracking the movement of small organelles or analyzing a region of interest as a whole. Sub-nanometer sensitivity and diffraction limited resolution of SLIM provide ideal means to study the cell dynamics.

The quantitative data yield actual mass transport information, in terms of diffusion coefficients and velocity distributions. This has clear applications in basic biological sciences, such as cell viability and function, and clinical studies, such as drug testing.


Spatial Light Interference Microscopy (SLIM)

Phi Optics SLIM is a non-invasive phase imaging technology that quantifies the physical properties of live cells and tissues providing meaningful information regarding cell dynamics. 

The output is a live quantitative image (SLIM map) of the specimen: the intensity of every pixel in the frame is a measure of a phase shift map (in radians) or the optical path length difference (in nanometers) through the sample, which is measured with sensitivity better than 0.5 nanometers [1].

The phase shift map is converted on-the-fly to other SLIM maps, with their respective pixel intensity: thickness (in microns), dry mass area density (pictograms per square micron) and refractive index. Moreover, the phase shift map can be used for reconstructing the scattering measurement. More detailed description of SLIM can be found in the reference pages in the “How it works” page of this website.

Organelle tracking

SLIM images reveal the locations of small organelles within the cell. These dense deposits of protein, high in dry mass, can easily be detected, and tracked over a time-lapse measurement [2]. Phi Optics provides a set of plugins based on the NIH ImageJ and the Particle Tracker plugin by Sbalzarini and Koumoutsakos [3] for image analysis.

Particle detection and tracking

Particle tracking can be setup and performed using the Particle Tracker plugin. The steps to use the plugin are:

  1. Load a SLIM time sequence for tracking the particles (organelles). Particle contrast can also be enhanced by applying Laplacian to the image.
  2. Run the Particle Tracker plugin in the Dynamics menu
  3. Adjust the parameters (Radius, Cutoff, Per/Abs, Link Range, Displacement) and use “Preview Detected” to confirm the detection (Figure 2).
  4. Plugin will show a result window.
Picture shows drop-down menu for SLIM CellVista software with particle tracking parameter selected.

Figure. Particle tracking plugin configuration


Once the plugin finishes particle detection, it shows a result window with a variety of options to display the result.

The trajectories of the detected particles can be visualized by “Visualize All Trajectories” in the result window. The trajectories can be saved to a table, which contains the coordinate of each detected particle for each frame, by “All Trajectories to Table” in the result window. Using the coordinates, the Mean Square Displacement (MSD) can be plotted, and MSD vs. time plot yields diffusion coefficient.

Particle tracker result window showing the trajectories of detected particles

Figure. Particle tracking plugin results


Organelle movement within a beating heart cell

SLIM was used to track the organelle movement within a beating heart cell [2]. The tracking of organelle allowed for the measurement of the diffusion coefficient. The small diffusion coefficient indicates that the movement of the organelles are restricted by the cell

SLIM measurement for particles within the cardiac myocytes

Figure 4. SLIM measurement for particles within the cardiac myocytes

MSD measurement for particles within the cardiac myocytes in Fig. 2. (a) Zoom into the selected area shown in (d); (b) Displacement in Y direction; (c) Displacement in X direction. (d) Laplacian of the phase map; (e) MSD for the particle shown in (a). (f) MSD ensemble-averaged over 15 particles in (d). [2]

Intercellular transport between neurons

Intercellular transport has also been studied using SLIM, by analyzing the transport between neurons [2]. The diffusion coefficient in the horizontal and vertical directions are calculated. Because the neurites are mostly extending horizontally within the field of view, the spatial confinement along the vertical direction resulted in a very small diffusion coefficient along the vertical direction.

SLIM tracks particle transport in hippocampal neural networks

Figure. Tracking particle transport in a hippocampal neuron processor network (40X magnification) (a) SLIM map of the neuron network. The arrows 1 to 5 show the time-traces of the corresponding points along the dashed line. The whole field of view is 100 um×75 um. (b) Optical path length change in time for the five points indicated in (a). Peaks in the point traces correspond to phase shifts associated with (fast) organelle traffic. (c) Laplacian of the selected area in (a). The scale bar is 5 um. (d) Phase map of the same area as in (c), with some particle traces shown in fine lines. (e) Log–log plot of the MSD for 70 individual particles in (d). Since the particles are confined in the Y direction, the diffusion coefficient for this direction is 2 orders of magnitude smaller than for the other direction. The inset shows the same MSD curves in linear representation and two Y axes. [2]

Dispersion-relation Phase Spectroscopy (DPS)

SLIM provides a way to study cellular dynamics even when the dynamic structures are continuous and cannot be tracked as particles. DPS is a method for analyzing SLIM time lapse images. DPS provides a relation between the spatial and temporal fluctuations on the sample, and yields information about the diffusion coefficient, velocity distributions, as well as the mode of transport: random or deterministic.

A. Obtaining DPS data
  1. Load a SLIM time sequence for DPS analysis
  2. Run the DPS plugin in the Dynamics menu
  3. Adjust the parameters (time step and pixel size), select a square ROI to be analyzed and run the plugin.
  4. The result will show a DPS map and a DPS plot

Figure. DPS measurement selection


Figure. Dispersion plugin setup window and results

B. Dispersion-relation phase spectroscopy of inter- and intra-cellular transport

DPS is applied to a variety of cells, including glia, microglia and neurons, to infer the intercellular and intracellular transport of these cells [4]. The output provides information regarding the diffusion coefficient, advection velocity and the mode of transport.

Quantitative phase image of a culture of glia (a, g), microglia (c) and hippocampal neurons (e). (b) Dispersion curve measured for the cell in a. The green and red lines indicate directed motion and diffusion, respectively, with the results of the fit as indicated in the legend.

Figure. SLIM map of a culture of glia (a) and (g), microglia (c) and hippocampal neurons (e). (b) Dispersion curve measured for the cell in (a). The green and red lines indicate directed motion and diffusion, respectively, with the results of the fit as indicated in the legend. Inset shows the Γ (qx, qy) map in (d) and (f). (h) Dispersion curves, Γ(q), associated with the white box regions in (c) (,e) and (g), respectively. The corresponding fits and resulting D and Δv values are indicated. [4]



[1] G. Popescu (2011) Quantitative phase imaging of cells and tissues (McGrow-Hill, New York)

[2] Z. Wang, L. Millet, V. Chan, H. Ding, M. U. Gillette, R. Bashir, and G.Popescu, Label-free intracellular trans- port measured by spatial light interference microscopy, J. Biomed. Opt., 16(2), 026019 (2011).

[3] I. F. Sbalzarini and P. Koumoutsakos. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol., 151(2): 182–195, 2005

[4] R.Wang, Z. Wang, L. Millet, M. U. Gillette, A.J. Levine, and G.Popescu, Dispersion-relation phase spectros- copy of intracellular transport, Opt. Exp. 19(21), 20571 (2011).

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