This Application Note illustrates the use of Spatial Light Interference Microscopy (SLIM) for neuroscience research. The non-invasive live cell imaging provides a viable environment for fragile neurons and neuronal stem cells, which are very susceptible to damages from temperature, chemicals, and light. The speed of SLIM acquisition is capable of detecting the transport between neurons, and the wide field of view is capable of imaging the formation of a neuronal network. Therefore, SLIM provides an environment where neurons can be studied at both single cell level and population level.


Spatial Light Interference Microscopy

Phi Optics SLIM is a non-invasive phase imaging technology that quantifies the physical properties of live cells and tissues. The output is a live quantitative image (SLIM map) of the specimen on the microscope stage. The intensity of every pixel in the frame is a measure of the optical path length difference (in radians) through the sample, i.e. a phase shift map, which is measured with better than 0.5 nanometers sensitivity (Figure 1) [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 (picograms per square micron) and refractive index. More detailed description of SLIM can be found in the reference pages in the “How it works” page of this website.

CellVista software phase shift map

Figure 1. Phi Optics CellVista acquisition program

Simple measurements – NeuronJ

NeuronJ is an ImageJ plugin developed by Meijering et al. [2]. The plugin can be found in the 2D analysis menu, and once run, a new toolset on the main ImageJ window loads. NeuronJ allows the user to easily trace along neurites and performs measurements for parameters such as the length, mean value along the trace, and many more. Moreover, the traces can be saved as a text le consists of the coordinate and values of the points along the trace, and allows the user to treat the data as a list of values instead of a complex structure.

  1. Convert the SLIM map to an 8-bit image and save
  2. Run NeuronJ found in 2D analysis menu and load the 8-bit image using NeuronJ “Load images”
  3. Trace neurites using “Add tracings” in NeuronJ
  4. Run measurements using “Measure tracings” in NeuronJ (Figure 2)

SLIM interfaces easily with NeuronJ

Figure 2. Tracing and measuring neurites using NeuronJ


Rapid exchanges of material among neurons appear in the form of vesicle transport along the neurites that connect neighboring neurons. These vesicles are higher in density compared to the neurites and appears as a particle moving along the neurites. Therefore, by applying a particle tracking method, the movement of these vesicles can be tracked and the transport or “traffic” along the neuronal highway can be studied (Figure 3) [3]. For more detail about the particle tracking, please see AN 02 – Cell Dynamics.

SLIM tracks particle transport in hippocampal neural networks

Figure 3. Particle transport in neurites of a hippocampal neuron processor network. The objective is ZEISS Plan-Neofluar 40X/0.75.

Figure 3 demonstrates plotting of a neurites phase map over time. (a) Phase 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 ?m×75 ?m. The objective used is Zeiss Plan-Neofluar 40×/0.75. (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 ?m. (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.



Because neurons are very fragile and vulnerable to many invasive factors, such as chemicals, light, and heat, it is difficult to study them live and study their growth. SLIM provides a solution for this type of problem by providing an imaging environment that is both temperature and CO2 controlled, and also by exposing the cells to minimal light damage. The growth of neurons have been studied and the emergence of neuronal network have been shown using SLIM (Figure 4) [4].Dry-mass-growth-treated-vs-untreated

Figure 4 illustrates Dry Mass Growth over time and the effect of a growth inhibitor, lithium chloride (LiCl.)  (A) Dry mass density maps acquired at 0, 10, and 24 hours for both the untreated and LiCl treated cultures; the yellow scale bar corresponds to 200 ?m. (B) Total dry mass vs. time of the entire field of view for both conditions. Round markers are raw mass data, solid lines are the average over 1 hour, and error bars indicate standard deviation. For the untreated culture it can be seen that majority of the mass growth occurs between 0 and 10 hours, the time during which the cells are extending processes most actively. In the LiCl treated culture neurite outgrowth is severely retarded and no significant increase in mass is observed during any period.



The non-invasive environment that SLIM provides is suitable for studying not only mature neuron cells but also progenitor cells, which are even more fragile than mature neurons. The structural change of the neural progenitor cells and the change in dynamics can easily be studied using SLIM to show the development of mature cells from the progenitor cells, while keeping the cell alive and well (Figure 5)

SLIM image of freshly plated neural progenitor cells

Neural progenitor cells plated, day 0

SLIM provides a non-invasive environment for natural growth and formation of neurons

Figure 5. Mature neuron cells after 14 days growth


[1] G. Popescu (2011) Quantitative phase imaging of cells and tissues (McGrow-Hill, New York)
[2] E. Meijering, M. Jacob, J.-C. F. Sarria, P. Steiner, H. Hirling and M. Unser, Design and validation tool for neurite tracing and analysis in uorescence microscopy images, Cytometry Part A, 58 (2), 167-176 (2004)
[3] 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, Journal of Biomedical Optics, 16 (2), 026019 (2011)
[4] M. Mir, T. Kim, A. Majumder, M. Xiang, R. Wang, S. C. Liu, M. U. Gillette, S. Stice and G. Popescu, Label-free characterization of emerging human neuronal networks, Scienti c Reports, 4, 4434 (2014)

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