4. DISCUSSION
DEP is one of the most powerful methods that provides intrinsic
parameters of various cell types in a label-free manner.
Dielectrophoretic forces mostly do not alter cellular properties or
damage cells41-43. Hence, development of
dielectrophoretic characterization methods in conjunction with current
technologies will foster unrevealing cellular individuality and
consequently heterogeneity of populations to fight with diseases such as
GBM44-49.
GBM is highly resistive and invasive type of brain tumor, arising with
multiform structures that enormously enhances its
heterogeneity1-8. Precise and accurate
characterization of cells in the GBM microenvironment will lead better
diagnosis, prognosis, and personalized treatment
strategies9-13. In literature, there is still lack of
quantification about electrical and mechanical properties of glioma
cells at single-cell level16,19,27,29-31. In this
context, Memmel and co-workers reported one of the pioneering
interrogations that showed the migration pattern, actin cytoskeleton
organization and response to PI1K-, mTOR-, and Hsp90- inhibition of
glioblastoma cells with different invasive capacities. They used
super-resolution microscopy and tracked single cells. They presented
cell line specific polarized morphology and migration directionality of
glioma cells19. Another study from this group reported
that cell surface area and membrane folding parameters of five GBM lines
differing in mutational status for PTEN and p5350.
They implemented scanning electron microscopy imaging and single cell
electrorotation technique to measure membrane area and capacitance
values of the cells. Next, they associated these biophysical cellular
properties with the expression level of mTOR-dependent downstream
signaling pathway related to protein and lipid synthesis. The
area-specific capacitance values and membrane-folding factors they
measured can be directly used for dielectrophoretic simulation of glioma
cells, which is extremely limited in literature.
Here, we investigated dielectric parameters of U87 glioma cell line
using a gold microelectrode array located in a microfluidic channel.
First, we simulated the electric field distribution and determined the
strong nDEP and pDEP regions of the microfluidic chip, Figure
3 . We applied 3 Vpp, non-uniform electric filed both
for the simulations and experiments, we obtained consistent results for
the strength and distribution of the DEP forces (Figure 2 ). We
monitored clear pDEP response of the cells above 200 kHz, Figure
4 - Figure 5 . Figure 4b demonstrates that the
strength of the DEP forces amplified from 200 kHz to 500 kHz.Figure 4c shows the number of trapped U87 glioma cells
according to varying frequencies. In these experiments, we have never
observed clear nDEP responses of U87 glioma cells. However, conductivity
value of the DEP buffer, amplitude of the applied voltage, and geometry
of the electrodes might be optimized to obtain nDEP forces.
To the best of our knowledge no one has investigated heterogeneity of
U87 glioma cell line using DEP forces yet. Here, we showed heterogeneity
of the U87 glioma population when we applied DEP forces. Figure
5 exhibits that cells were distributed in the microfluidic channel
according to their intrinsic dielectric properties. Figure 5adisplays that when the applied frequency increased more than 300 kHz,
almost 50% of the cells in the population was experienced strong pDEP
forces and trapped by the electrodes (\(\sim 240\ \) cells, **p ≤ 0.01).Figure 5b and Figure 5c illustrates that the rest of
the cells were distributed in the microchannel according to the strength
of the DEP forces. At the weakest pDEP region, glioma cells mostly
positioned themselves in the middle of the microchannel and exhibited
significant pDEP behavior at 2 MHz (Figure 5c , *p ≤ 0.05). (The
results of the ANOVA Tukey’s multiple comparison test are presented in
the Supplementary Document ).
Finally, we compared viability of the cells between regular growth
medium and low-conductive DEP buffer using Students’ t-test
(Supplementary Document ). We did not obtain significant
viability difference (p = 0.256, Supplementary Document ),Figure 6a . Next, we performed DAPI and PI staining for the
glioma cell population to determine the percentage of the dead/damaged
(PI positive) cells before and after the electric field exposure.Figure 6b shows that there was no significant increase in the
percentage of PI-positive cells after the DEP experiment, (p
> 0.99, Supplementary Document ).
We showed that 50% of the U87 glioma cells has experienced strong pDEP
behavior when we suspended cells in the 20-µS/cm conductive buffer and
applied 3 Vpp and 200 kHz, 300 kHz, 400 kHz, 500 kHz, 1
MHz, 2 MHz, 5 MHz, 10 MHz frequencies. Almost 45% of the glioma cells
exhibited either weak or moderate pDEP responses and 5% of the cells
can be damaged or died in the population. We present that U87 glioma
cells had distinct subpopulations according to their dielectric
responses. The underlying reasons can rely on intrinsic properties
rather than the size difference among the glioma cells. When we measured
the areas of the U87 glioma cells, it showed gaussian distribution
(Supplementary Document ). Moreover, we observed that U87 glioma
cells can reversibly alter their morphology and organization of their
cytoskeleton in less than 30 second when we change the frequency of the
applied DEP forces.
We can speculate that these cells have different dielectric features.
Therefore, our further experiments will be performing downstream
analysis of these subpopulations upon their dielectrophoretic
separation. Notably, our results obtained by using 2-dimensional (2D)
electrode array, using 3D electrode arrays might provide wider spatial
distribution for the cells according to strength of the DEP forces.
Besides, as most of the studies in literature, we also manually
determined the positions of the cells under the influence of pDEP forces
(Figure 2 ), which is tedious, time-consuming and error
prone44-49. We plan to perform automated single-cell
analysis to quantify DEP responses of U87 glioma cell lines.