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.