Dielectric parameters are among the essential biophysical properties of cells and can be associated with various immune and blood diseases [1
]. Permeability and conductivity of the membrane and cytoplasm define dielectric properties of a cell in a specific microenvironment, which may change due to surface area of the cell as given by its size and shape; expression levels of surface proteins; form of cytoplasm; composition of cytos7ol; the surface charge density of the membrane; the morphologic complexity of membrane surfaces such as ruffles, microvilli, and blebs; as well as due to interfacial polarization of ions at the cell surfaces. Discovery of electrophysiological properties of cells, such as dielectrophoretic mobility, membrane relaxation period, crossover frequency difference, etc., relies on the phenomenon of dielectrophoresis (DEP), described by Herbert Pohl in 1951 [6
]. Yet, intensive research has been conducted to utilize dielectrophoretic properties of cells to be label-free biomarkers for immune and blood diseases [7
]. In this study, we interrogated whether dielectric movement and deformation measurements provide a specific, label-free, sensitive electromechanical biomarker for U937 monocytes and U937-differentiated macrophages.
Monocytes and macrophages can be considered as active machines that can immediately adapt to their microenvironment for pathogenesis and homeostasis through altering their electromechanical properties [9
]. They are highly heterogenetic cells with their morphology, location, tissue-specific relations, and functional capabilities [11
]. When examined by electron microscopy, the monocytes are spherical cells and they have microvilli and microcytotic vesicles, hence, their membrane surfaces have several ruffles and blebs, whereas macrophages have an irregular shape with electron-dense membrane-bound lysosomes. Besides, the microenvironment in which macrophages differentiate defines their shape, biochemistry and function [13
]. Although we have been still investigating and discovering their new functions, such as the roles of macrophages in the electrical conduction of heart [14
], in general, we know that monocytes enroll in tumor formation and invasion via metastasis and angiogenesis [15
], macrophages are employed in pathogen recognition, phagocytosis [17
], removal of dead cells and cellular debris [18
] and tissue homeostasis [19
]. Their diverse functions are continuously controlled by their dynamic microenvironment [21
A pioneering work, sharing the purpose of determining electrical properties of mammalian cells according to their life cycle, was presented by Eisenberg and Doljanski in 1962. They measured the electrokinetic properties of liver cells in growth processes [25
]. Next, Dr Petty’s research group reported heterogeneous distribution of electrophoretic mobilities of human monocyte subpopulations [26
], while Dr Bauer and Dr Hannig determined the changes of the electrophoretic mobility (EM) of human monocytes during in vitro maturation into macrophages [27
]. The current research direction, which investigates the change of cellular dielectrophoretic properties during the cell cycle, maturation or differentiation, mostly relies on determining the first crossover frequencies and measuring migration differences of cells [7
]. Along the same lines, our previous investigations have presented the dielectrophoretic characterization and separation of U937 monocytes and U937-differentiated macrophages using their crossover frequencies and dielectrophoretic mobility differences according to their membrane permittivity and conductivity in a low conductive DEP buffer [30
]. However, none of our previous studies have revealed dielectrophoresis-induced mechanical deformation of cells. Similarly, Tonin et al. interrogated electrophoretic mobility (EPM) during yeast growth and observed a nonmonotonic behavior during the cell cycle. They concluded that the maximal EPM occurred at the initial stage of the growth, and it strongly reduced at its final stage [33
]. Song et al. employed DEP to sort human mesenchymal stem cells and their differentiation progeny, osteoblasts. Their results showed that osteoblasts experienced stronger DEP forces that laterally migrated them, whereas human mesenchymal stem cells remained on their original trajectories [34
]. Dr. Salmanzadeh and his group used contactless DEP and observed that the trapping voltage of mouse ovarian surface epithelial cells increased as the cells progressed from a non-tumorigenic to a tumorigenic phenotype [35
On the other hand, DEP has been utilized as a tool to stretch cells for characterization of their mechanical properties. It has provided great potential to implement single-cell biomechanical tests with high-throughput, automation, low complexity and cost, high scalability and portability in comparison to conventional biomechanical techniques, such as atomic force microscopy [36
], optical tweezers [4
], magnetic twisting cytometry [38
], micropipette aspiration [39
], diffraction phase microscopy [40
] and microfluidic ektacytometry [41
]. In this concept, Guido et al. demonstrated the capability of this new technique by characterizing deformability of cancerous MCF7 and noncancerous MCF10A cells [44
]. Du and coworkers used this technique to reveal the biophysical properties of healthy, uninfected and infected red blood cells by Plasmodium falciparum malaria parasites [45
In this study, we utilized dielectrophoresis to study the electromechanical properties of monocytes and macrophages that might quantify their population heterogeneity [11
]. We measured the movement and calculated the deformation indexes of cells [47
] under the influences of dielectrophoretic forces when 10–20 Vpeak-to-peak
) voltage with frequencies ranging from 50 kHz to 1 MHz have been applied.
2. Materials and Methods
2.1. DEP Buffer Preparation and Conductivity Measurement
DEP buffer with low electrical conductivity was prepared to keep cells viable during the processes of dielectrophoresis. As it has been previously reported [31
], the low conductive DEP buffer [48
] was composed of 8.6% sucrose (product no: LC-4469.1, NeoFroxx, Hesse, Germany), 0.3% glucose (CAS number 59-99-7, Sigma-Aldrich, Darmstadt, Germany) and 0.1% bovine serum albumin in distilled water (BSA, product code: P06-1391050, PAN-Biotech, Aidenbach, Germany).
The conductivity of the DEP buffer was 0.002 S/m, as measured by a Corning Model 311 Portable conductivity meter at room temperature (Cambridge Scientific Products, Watertown, MA, USA).
2.2. Cell Culture
In this study, U937 human monocyte cells (ATCC number: CRL1593.2) provided from ATCC (American Type Culture Collection, Manassas Virginia) and U937-differentiated macrophages were obtained by phorbol 12-myristate 13-acetate (PMA, Sigma Aldrich) treatment of U937 monocytes.
U937 cells were maintained in RPMI 1640 medium (Product Number: P04-18047, PAN-Biotech, Aidenbach, Germany) with 10% fetal bovine serum (FBS) (PAN Biotech, catalogue number: P40-37500, Aidenbach, Germany) using a T75 tissue culture flask (TPP® Sigma, catalogue number: Z707554) at 37 °C with 5% CO2 in humidified air. U937 cells were grown until 80–90% confluency. Cells were centrifuged at 3000 rpm (Z601039, Hettich® EBA 20 centrifuge, Merck, Darmstadt, Germany) for 5 min. The number of cells was determined using a hemocytometer (Marienfeld, Germany). The final cell concentration was adjusted to 3 × 106 cells/mL.
The macrophage differentiation was performed using the 10 ng/mL concentration of the PMA treatment of 3 × 106 U937 cells in 22.1 cm2 plates (TPP® Product No:93060, Trasadingen, Switzerland) for 72 h. Next, the cells were maintained in medium without PMA for 48 h. Then, the cells were collected by treating with the 0.25% (v/v) Trypsin-EDTA (PAN Biotech, catalogue number: P10-019100, Aidenbach, Germany) solution. The cells were centrifuged at 1800 rpm (Z601039, Hettich® EBA 20 centrifuge, Merck, Darmstadt, Germany) for 10 minutes to remove the remaining culture medium and washed twice using the DEP buffer.
2.3. 3D Carbon DEP Device
The fabrication process and features of the 3D carbon DEP devices were previously reported [48
]. The carbon electrode array, a 1.8 mm wide, 3.2 cm long channel, was featured 218 intercalated rows with 14 or 15 electrodes each [50
]. Individual electrodes had a height of 100 μm and a diameter of 50 μm (Figure 1
). The numerical analysis to estimate the induced fluidic, electromagnetic and dielectrophoretic forces in the 3D carbon electrode array was earlier studied using both finite element analysis and numerical models [48
2.4. Experimental Setup
The experimental setup consisted of a signal generator (Model: GFG-8216A, GW Instek, New Taipei City, Taiwan) with an oscilloscope (Part Number: 54622D, Agilent Technologies, Santa Clara, CA, USA) to create and observe the electric field, a desktop-acquired upright microscope (Model: Nikon ME600 Eclipse, Nikon Instruments Inc., Melville, NY, USA) to monitor cells and acquire images and a programmable syringe pump (Model: NE-1000, New Era Pump Systems Inc., Farmingdale, NY, USA) to flow the cells into the 3D carbon DEP device. We used 20–200 μL pipette tips (Manufacturer ID: 3120000917, Eppendorf, Hamburg, Germany) to connect microperforated Tygon tubing (Manufacturer ID: AAQ02103-CP S-54-HL, Cole-Parmer, Vernon Hills, IL, USA) into the inlet and outlet ports of the 3D carbon-DEP chip (Figure 1
The experiment started with the sterilization of the electrode array using 70% ethanol and rinsing with deionized (DI) water using a syringe pump with a 20 µL/min flow rate. Next, the microfluidic chip was filled with the DEP buffer and the bubbles were removed. Then, 40 µL of the cell suspension was injected into the chip using a syringe pump with 10 µL/min flow rate. When the cells reached the electrode area, the flow was stopped, and the cells were released for 30 s. The experiments were started when the electric field was applied using the signal with 10–20 Vpp
frequencies ranging from 50 kHz–1 MHz [30
2.5. Image Acquisition and Data Analysis
The image sequences of cells were recorded using the Nikon ME600 Eclipse upright microscope (Nikon Instruments Inc., Melville, NY, USA) with 10× magnification in tiff sequence format. The VideoLAN Client (VLC, VideoLAN version 1.8, Paris, France) program was used to convert image sequences into the movies.
The acquired images were manually analyzed using open-access ImageJ software (Version 2.0 National Institutes of Health, Rockville, MD, USA). The crossover frequencies of single cells were determined by computing the movement of the cells according to their initial positions, as described in references [30
]. In total, 50 monocyte cells and 30 macrophage cells were followed, and their positions were recorded. Using GraphPad Prism (Version 5.0) software, Student’s t-test was performed to compare dielectric mobilities of monocyte and macrophage populations. * implies that data are significantly different with p
The deformation index was calculated by manually measuring the height and width of 45 single monocyte and macrophage cells, and these single cells were continuously monitored, in each frequency. One-way analysis of variance and Tukey’s multiple comparison test were carried out using GraphPad Prism (Version 5.0) software to determine the significance. * and ** indicate that data are significantly different with p < 0.5 and p < 0.05, respectively. All measurements were provided in detail in the figure legends.
Monocytes and macrophage cells, sharing the same cell origin, have been compared according to their dielectrophoretic mobility and deformation. Both monocyte and macrophage populations exhibited inter-individual difference due to their intrinsic properties such as size, shape, and changes in membrane surface organization that may result in heterogeneity in their DEP responses.
Here, the crossover frequency of U937 monocytes was around 150 kHz. The U937-differentiated macrophage cells exhibited weak crossover frequency around 50 kHz (Figure 2
). We used the computational tool for dielectric modeling published by Cottet, J. et al. and obtained the CM factor
values for the monocytes (
as 0.976 and 0.979, respectively [67
] (Table 1
). Since the
was slightly higher than the (
, macrophages were exhibited pDEP behavior earlier than monocytes (see Figure 2
b,c and Figure 3
). The uniformity of pDEP responses of the monocytes was improved with increasing frequencies (see Supplementary Video 1
), the macrophages displayed both nDEP and pDEP fractions for the whole frequencies ranging from 50 kHz–1 MHz (see Supplementary Video 2
). Although there was no significant difference between the trapping regions of the cells (Student’s t-test: p
value was 0.892, where * p
< 0.5 was significant), the DEP movement of macrophages were more heterogeneous than monocytes (Figure 3
). We previously reported dielectrophoretic characterization and separation of U937 monocytes and U937-differentiated macrophages according to their crossover frequencies in [30
This study, contrary to our previous work, reported that the translational DEP forces were not only moved cells according to their polarizability differences inside the electrode array, they also created irreversible deformation on the cells. Monocyte and macrophage cells display high plasticity among immune cells [9
]. When DEP forces were introduced, the deformation index of monocytes first increased (0–400 kHz), then decreased with increasing pDEP forces (400 kHz–1 MHz), as shown in Figure 4
. On the other hand, the deformation index of the macrophage cells did not exhibit significant difference for the frequencies ranging from 50 kHz to 1 MHz (Figure 4
). When the dielectrophoretic deformation indexes of the monocyte and macrophage cell populations were compared, according to Tukey’s multiple comparison test, the increase in the deformation index of monocytes was significantly higher than the deformation index of macrophages at 300 kHz (p
< 0.5) and 400 kHz (p
< 0.05), as shown in Figure 5
. Here, we calculated the DEP deformation indexes of the cells (Figure 4
a: location of the cells according to electrodes) as we measured their translational mobility due to applied FDEP
a: position of the cells according to electrodes). Therefore, it relied on the spatial distribution of the cells within the electrode array since the DEP forces depend on polarizability of the cells according to their intrinsic properties. The applied DEP forces synchronized the cells spatiotemporally within the electrode array and we measured the deformation of single cells at their specific locations when the specific frequencies and voltages were applied, therefore, we achieved to obtain consistent results for the dielectric deformation indexes of the cells (Figure 4
In addition to experimental results, the dielectrophoretic mobility and membrane relaxation time values were predicted using the physical and electrical properties of the monocyte and macrophage cells, and the low conductive DEP buffer (Table 1
]. The calculated values were quite similar both for monocytes and macrophage cells. However, the values in Table 1
should be carefully interpreted since there are still unknown dielectric parameters for the U937 monocytes and U937-differentiated macrophages.
Our results marshalled considerable evidence for the feasibility of using dielectric mobility and dielectric deformation index as a dielectric biomarker that presents biophysical differences between the cell lines which shares the same origin. To the best of our knowledge, this is the first study that presents dielectric deformation indexes of cells and may become a practical method for achieving a specific, high-throughput, continuous, label-free, sensitive electromechanical characterization and classification technique for U937 monocytes and U937-differentiated macrophages.
Our further studies will focus on separation and recovery of cells with different deformation indexes from the 3D carbon DEP platform for downstream analysis using immunostaining and quantitative reverse transcription-polymerase chain reaction (RT-qPCR) techniques. Hence, we can promptly explain the dielectrophoretic mobility and deformation differences in terms of transcription and protein expression levels in the membrane surface and cytoskeletal components. Moreover, we can employ this method for further characterization of macrophage subpopulations, and it may provide value in increasing our understanding of the nature of tumor associated macrophages (TAMs).
This study presents heterogeneity of monocytes and macrophages according to their intrinsic dielectrophoretic properties in terms of dielectrophoretic deformation indexes. We performed dielectric deformation measurements of the U937 monocytes and U937-differentiated macrophages with similar radius and dielectric characteristics using 3D carbon electrode microfluidic platform both at population- and single-cell level. We calculated deformation indexes of the cells when 10–20 Vpp voltage with frequencies ranging from 50 kHz to 1 MHz have been applied.
Our results showed that the crossover frequency for the monocytes was around 150 kHz [30
]. Monocytes presented maximum deformation at 400 kHz and minimum deformation around 1 MHz frequencies at 20 Vpp
. On the other hand, the crossover frequency for the macrophages, which differentiated from monocytes, was lower than 50 kHz, 10 Vpp
]. Moreover, the dielectrophoretic deformation index for the macrophages was not significantly varied from 50 kHz to 1 MHz frequency range. We conclude that the change of the deformation index for macrophages was less in comparison to monocytes. Both dielectric mobility and deformation spectra revealed significant differences between the dielectric responses of U937 monocytes and U937-differentiated macrophages, which share the same origin.
Our method can be advanced for the development of label-free, specific, and sensitive single-cell characterization tools. This technique eliminates the possibility of damaging the cells by aggressive shear forces while allowing these cells to be used for further downstream analysis. To advance this work, we focus on development of automated image analysis tools to obtain directly deformation indexes and mobility data of cells from the acquired DEP videos.
Here, we particularly underlined FDEP-generated deformation index of monocytes and macrophages, since these cells are among the white blood cells which are capable of infiltrating different types of tissues. Further DEP studies might interrogate to quantify other immune cells or their subsets (TAMs), and whether their intrinsic cellular heterogeneity can be quantified according to their dielectrophoretic deformation indexes.