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Journal of Extracellular Biology logoLink to Journal of Extracellular Biology
. 2024 Nov 29;3(11):e70018. doi: 10.1002/jex2.70018

Effects of electric fields on the release and content of extracellular vesicles

Yihua Wang 1, Gregory A Worrell 1,2, Hai‐Long Wang 1,2,✉
PMCID: PMC11605478  PMID: 39619686

Abstract

Extracellular vesicles (EVs) are small membrane‐bound structures that originate from various cell types and carry molecular cargos to influence the behaviour of recipient cells. The use of EVs as biomarkers for diagnosis and as delivery vehicles for treatment in a wide range of human disease is a rapidly growing field in research and clinical practice. We hypothesized that electric fields (EFs) could influence the release and content of EVs. To examine this hypothesis, we developed a specialized bioreactor enabling cells to thrive in a three‐dimensional setting, replicating in‐vivo conditions amidst programmable EF environments. We established a three‐step EV purification protocol to achieve high‐density production of EVs. We also performed mass spectrometry‐based proteomics analysis on EV‐carrying proteins and used high‐resolution nanoparticle flowcytometry for single‐vesicle analysis. Findings from this report suggest that electrical stimulation, employing physiologically relevant amplitudes typical in therapeutic deep brain stimulation, influences the release of EVs and their cargo content in a frequency‐dependent fashion. This conclusion could carry significant implications for both fundamental biological understanding and medical advancements. First, it raises an intriguing question about how the endogenous electrical activity of neuronal and other cellular assemblies influence the production and composition of EVs. Second, it reveals a novel underlying mechanism of how therapeutic electrical stimulations can modulate EVs and treat human brain disorders. Third, it provides a novel approach to utilize electrical stimulation for generating desired EV cargos in a programmable setting.

Keywords: electric fields, extracellular vesicles, frequency

1. INTRODUCTION

Electric fields (EFs) are produced by stationary and moving electrical charges (Feynman, 1970) and have a wide frequency spectrum. The EF has a vast range of everyday applications, from wireless cellular communications, electrical brain stimulation, to medical imaging, that rely on the fundamental aspects of the EF frequency, which plays a critical role in applications from the behaviour of cells to the functions of complex biological systems. Extracellular vesicles (EVs) are small membrane‐bound bubbles released by living cells into the extracellular space and function as critical mediators for intercellular communications. EVs contain a wide range of bioactive molecules, including proteins, lipids, and nucleic acids, that can be transferred from one cell to another (Tetta et al., 2013). Using EVs as biomarkers for diagnosis or as delivery vehicles for treatment is a rapidly growing field of research and clinical practice.

The notion of modulating the release, size distributions, and cargo contents of EVs through EFs has many potential implications to both fundamental biological understanding and therapeutic applications. One of the most intriguing questions is how endogenous electrical activity emerging from neuronal assemblies, such as the local field potential, could affect the dynamics of cells releasing EVs and subsequently change biological functions. Notably, recent investigations show that endogenously generated EFs from collective neuronal activity directly affect the brain glymphatic system (Jiang‐Xie et al., 2024), possibly through direct effects on extracellular volume changes with awake‐sleep behavioural states (Mivalt et al., 2023). Secondly, this finding reveals a previously unappreciated mechanism of action for therapeutic electrical brain stimulation used to treat human brain disorders. Thirdly, it offers a novel method of producing pharmaceutical EVs, which is advantageous over other methods using chemical reagents. Electrical stimulation is a clean, controllable, and versatile method that can vary in frequency, field strength, and waveform morphology.

Previously, we demonstrated that low‐frequency EFs altered the size distribution of EVs released from rat primary astrocytes (Wang et al., 2019). We also revealed the frequency‐dependent effects of EF on the mobility of intracellular vesicles in astrocytes and demonstrated that low frequency (2 Hz) EF increased intracellular vesicle mobility (Wang et al., 2021). Thus, we hypothesized that external EFs at physiological currents would affect the release, size distributions and contents of EVs. One of the major technical difficulties in study the EF effects on EVs was the lack of an ideal electrical stimulation device that could create a spatially uniform electric field. In the last serval years, we have made several technical improvements. First, we cultivated cells in a hollow‐fibre bioreactor (Fibercell System) that provides a three‐dimensional environment mimicking in‐vivo conditions and allows continuous EV productions over a longer period; Second, we designed a novel EF stimulation device that encompasses the bioreactor and delivers spatially uniform distributed EFs over the bioreactor geometry, so that all collected EVs are released from cells under an analogous EFs (see Figure 1a,b); Third, we developed a unique three‐step EV‐purification method that not only avoids the use of ultracentrifugation that cause damages to EVs (Lobb et al., 2015; Mol et al., 2017) but also greatly improves the purity and quantity of EV output. All these improvements will facilitate mass productions of EV cargos in uniform populations.

FIGURE 1.

FIGURE 1

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The custom‐made EF stimulation device and experimental procedure. (a) a Fibercell cartridge in‐situ inside the electrical stimulation device; (b) a map showing the simulated electric field; (c) the timeline of experimental procedure. The dash line indicates time for electrical stimulations. Low G. means low glucose, high G. means high glucose. The green checkmarks indicate those three samples were used for downstream analyses; (d) a flowchart of purifications and detections for EVs. EF, electric field; EVs, extracellular vesicles.

The EF effect on EVs is likely to be a general phenomenon to all living cells, with different cell types responding to their distinctive spectrum of frequencies. To evaluate the stimulation device and examine our improved purification protocol, we choose a human cell line (HT‐1080) over primary glial cells used previously (Wang et al., 2019) due to the requirement of the large seeding‐cell number (>100 million) for the medium‐size Fibercell cartridge. The HT‐1080 is a human fibrosarcoma cell line commonly used in cancer research and has been characterized genetically and phenotypically. These cells are known to be highly invasive and have been used as a model system to investigate the molecular mechanisms underlying cancer progression and test for potential cancer therapies. In this proof‐of‐concept study, we selected three EF frequencies that are currently used in therapeutic deep‐brain stimulation. The findings outlined here regarding the impact of EF on HT‐1080 are solely intended to illustrate the frequency‐dependent influence of EF on EVs, rather than being directly applicable to cancer treatment.

2. METHODS AND MATERIALS

2.1. Cell cultivation

Human fibrosarcoma cell line (HT1080) was cultivated following the standard protocol with Dulbecco's Modified Eagle Medium (DMEM) at 37°C with 5% CO2. The low glucose culture media included DMEM (1 g/L glucose, Gibco 11885‐084, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% (v/v) Foetal Bovine Serum (FBS, Gibco 10437‐028, Thermo Fisher Scientific) and 1% (v/v) Penicillin Streptomycin (Gibco 15140122, Thermo Fisher Scientific). The high glucose culture media included DMEM (4.5 g/L glucose, Gibco 11965‐092, Thermo Fisher Scientific), 10% (v/v) CDM‐HD serum replacement (FiberCell Systems Inc., New Market, MD) and 1% (v/v) Penicillin Streptomycin (Gibco 15140122).

2.2. Generation of cell conditioned media

We used the hollow‐fibre cell culture cartridge for all experiments. Each fresh cell cartridge (C2011, FiberCell Systems Inc.) was prepared according to the manufactory guideline through sequentially wash with PBS for 72 h, DMEM + 1% Penicillin‐Streptomycin for 28 h and DMEM + 10% FBS + 1% Penicillin‐Streptomycin for 44 h. Normally, each cell cartridge allows up to 100 days for collections of conditioned media. We limited our collection duration to 2 weeks. To maximize the efficiency of each cartridge, we doubled the required cell seeding number and started with 2 × 108 cells on day 1 for each experiment. Five sequential cell conditioned media was then collected on day 3, 5, 8, 10 and 12, respectively. Electrical stimulations were started on day 3 right after the first conditional media was collected and stopped till day 12 after the fifth sample collection. The low glucose culture media was replaced by the high glucose culture media after the second sample was collected on day 5. The glucose level of the cell culture media was monitored daily using blood glucose meters (AimStrip Plus, Germaine laboratories, INC, San Antonio, TX, USA; ACCU‐CHEK Guide Me, Roche Diabetes Care, Inc., Indianapolis, IN, USA). We monitored temperature on the surface of cartridge in all experiment using a thermometer (TENMA 72–7715, Newark, Chicago, IL, USA). Measured temperatures were maintained at 37.0 ± 0.2°C.

2.3. Electrical stimulation

The electrical stimulation was applied through a custom‐made device that delivers a uniformly distributed electric field with adjustable frequency and amplitude (Figures 1a,b and S1). The EFs were created with a function generator (4055B, B&K Precision, Yorba Linda, CA, USA). We applied a square wave with 0.4 ms pulse width and 4 V of amplitude that produces an electric field strength of 5 mV/mm within the cartridge space according to a simulation performed with the QuickField 6.4 software (Tera Analysis Ltd, Columbia SC, USA). All stimulation protocols followed a repeated 2‐min pattern that includes 120 pulses. For electrical stimulation (ES) at 2 Hz, all 120 pulses were evenly delivered within the first minute, leaving the remaining minute as a quiet period; For ES at 20 Hz, 120 pulses were delivered within the first 6 s, leaving the remaining 114 s as a quiet period. Similarly, for ES at 200 Hz, 120 pulses were delivered within the first 0.6 s, leaving the remaining 119.4 s as a quiet period. So that, the total number of delivered pulses and charge delivered for each ES conditions were equal. Quiet periods were necessary to avoid the overheating caused by repeatedly applied electric field. Electrical stimulation was started immediately after collecting the first batch of EVs and continued until the last batch of EV collection, with only brief interruptions during each collection of cell culture medium (less than an hour).

2.4. EV purifications

For EV purification, 20 mL of cell conditioned media was first subjected to a low‐speed centrifugation at 3000 × g (20 min, 4°C) to remove cell debris, with the supernatant filtered through a 0.22 µm syringe filter (Millex‐GS, SLGS033SS, MilliporeSigma, Burlington, MA, USA). Then we followed the three‐step purification protocol: (1) reducing sample volume using 10 KD Amicon Ultra‐15 centrifugal filter units (UFC 901024, MilliporeSigma); (2) removing mostly soluble proteins with a self‐prepared solution of polyethylene glycol precipitation (PEG). In details, a working PEG solution was prepared with PEG8000 (Sigma Aldrich) at 24% with PBS and 75 mM NaCl. Concentrated cell conditioned media from step 1 was thoroughly mixed with the working PEG solution at 2:1 volume ratio (final concentration of 8% PEG), mixed and stored at 4°C overnight and finally centrifuged at 1500 × g for 30 min at 4°C. The obtained pellet was dissolved in 500 µL of calcium/magnesium‐free PBS; (3) removing free polyethylene glycol and other remaining soluble proteins using size‐exclusion chromatograph (SEC). In details, 500 µL sample from step 2 was overlaid to a self‐prepared SEC column (Bio‐Rad Glass Econo‐Column Chromatography Columns, 7371512, packed with 10 mL Sepharose CL‐6B, 17016001, from Cytiva, Muskegon, MI). A low‐pressure liquid chromatograph system (Biologic LP, Bio‐Rad) was used to deliver a constant flowrate at 500 µL/min with the collecting fraction volume of 200 µL. Purified EVs were recovered from the first elution peak that includes five fractions with most protein concentrations measured by the UV absorbance at 280 nm. We took 40 µL from each of the five fractions, pooled together, and stored at −80°C till proteomics analysis. Remaining purified EV samples were stored at 4°C till nanoparticle flowcytometry analysis.

2.5. Nanoparticle flowcytometry analysis

High‐resolution flowcytometry for nanoparticle analyses were performed using the Flow Nanoanalyzer (N30, NanoFCM, Xiamen, China), which simultaneously detects signals of light scattering and multi‐wavelength fluorescence from single particle. Concentration and size distribution of EVs were determined by comparing measured values with standard calibration beads of known concentration and sizes (Zhu et al., 2014). In detail, 100 µL of isolated EV sample was mixed with 4 µL of Alexa488 anti‐human TNFRSF10B antibody (FAB6311G, R&D Systems, Minneapolis, MN, USA) and 2 µL of PE anti‐human CD63 antibody (561925, BD Biosciences, Franklin Lakes, NJ, USA) and incubated at 37°C for 30 min. Unlabelled antibodies were removed using a second application of SEC with a fresh qEV single column (70 nm Gen 2, IZON, Medford, MA). Again, labelled EVs were recovered in the first elution peak and then subsequently subjected to nanoparticle data collections. All data were converted to Flow Cytometry Standard 3.0 using NF Profession (Version 1.0, NanoFCM) and analysed with FlowJo (Version 10.8.1, BD, Franklin Lakes, NJ, USA). Subgroups of EVs were selected from the NanoFCM raw data based on fluorescence signals larger than the corresponding baseline thresholds. Average size distributions were then determined from the light scattering signals of each EV subgroup.

2.6. Mass spectrometry data acquisition and processing

Mass spectrometry (MS) based proteomics of EV‐related proteins were performed at the Mayo Clinic Proteomics Core. In details, 200 µL of each isolated EV sample were first precipitated with cold acetone, then digested with trypsin and extracted using the S‐Trap micro kit (ProtiFi, Fairport, NY, USA). The concentration of peptide was determined using Pierce quantitative fluorometric peptide assay (Cat# 23290, Thermo Scientific, Rockford, IL, USA). 11–18 µL of the peptides were analysed through nano‐ESI‐LC/MS/MS using the Orbitrap Exploris mass spectrometer coupled to a Dionex nano‐LC system (Thermo Scientific, Waltham, MA, USA). Liquid chromatography (LC) was performed using multistep linear gradients with solvent A (2% acetonitrile and 0.2% formic acid in H2O) and solvent B (80% acetonitrile, 10% isopropyl alcohol, and 0.2% formic acid in H2O) under a constant flow rate of 300 nL/min. The mass spectrometer was set at a resolution of 60,000 (at 200 m/z) in data dependent acquisition, with a full MS1 scan ranging from 340 to 1600 m/z. Dynamic exclusion was set to 25 s with cycle time in 3 s.

All raw MS files were analysed using MaxQuant (Version 1.6.17.0) (Cox & Mann, 2008). MS/MS spectra was set up to search against the SwissProt (Consortium, 2022) Human Database (2022 ver. 1), assuming trypsin digestion with up to two missed cleavages with the fragment ion tolerance of 20 PPM and parent ion tolerance of 4.5 ppm. Cysteine carbamidomethylation was set as a fixed modification and methionine oxidation was set as a variable modification. The false discovery rate was set to 0.01 for protein level and peptide spectrum match. Protein identifications required at least one unique or razor peptide per protein group. Only proteins with unique peptide were used in further data analysis. Contaminants, and reverse identification were excluded from further data analysis.

2.7. Bioinformatics analyses

We used relative intensity based absolute quantification (riBAQ) to determine the relative molar abundances of proteins, which normalized each protein's iBAQ value to the sum of all iBAQ values from that sample (Shin et al., 2013). The heatmap was generated using MATLAB (The MathWorks Inc (2020). MATLAB Version: 9.8.0 (R2020a)). We used the relative difference to show effects of EF on the abundance of EV‐related proteins. Calculations of relative differences (δ) for each identified protein (k) were performed using the following equation.

δk=log10riBAQ3−log10riBAQ2log10riBAQ3+log10riBAQ2+log10riBAQ4−log10riBAQ3log10riBAQ4+log10riBAQ3k

For each experimental condition, we discarded the first and the last samples and only used the middle three collected samples, namely, 2, 3, and 4 representing samples collected on day 5, day 8, and day 10, respectively. Colours of each horizontal lines in the heatmap represent δ values. To determine whether the protein has been identified as a membrane protein or existing in extracellular exosome, we used a custom code in MATLAB to search against the SwissProt human database (downloaded in March 2023, 69670 entries; 2118 entries). The biological processes were obtained using DAVID bioinformatics resource (Huang da et al., 2009a, 2009b). Knowledge graphs (KG) were generated by the Python language package of NetworkX (Aric A. Hagberg, 2008).

2.8. Statistical evaluations

To determine the significance of differences in the percentage of dual‐labelling positive EVs between different experimental conditions, we performed a two‐sample weighted Kolmogorov–Smirnov test (KS‐test) using the Python package of SciPy(Van Rossum & Drake Jr, 1995; Virtanen et al., 2020). The weight was determined using exponential smoothing methods (Hyndman & Athanasopoulos, 2018). Other codes were all written in MATLAB.

3. RESULTS

In total, we performed three rounds of experiments, with each round comprised of four different conditions that include the control condition without electrical stimulation (No‐ES) and three other conditions with electrical stimulation (ES) at different frequencies, including 2 Hz (low), 20 Hz (intermedia), and 200 Hz (high), with the same current density that are currently used in therapeutic deep brain stimulation. For each condition, we cultivated HT1080 cells in a fresh Fibercell cartridge for a 2‐week period and harvested conditioned media five times (see Figure 1c). The EVs collected from the first round were used for protocol optimization, in which we added a membrane‐permeable live‐cell labelling dye (Calcein‐AM) to distinguish intact EVs from other nano particles or soluble proteins (Gray et al., 2015). We used fluorescence‐detection size‐exclusion chromatography to simultaneously monitor both fluorescent and ultraviolet (UV) signals and confirm that the first elution peak of UV absorbance at 280 nm superimposes with the first peak of the fluorescent signal for the EV population (Kitka et al., 2019) (see Figure S2). Thus, the first peak of UV signal can be used to ensure that the EV elution is free of other unknown soluble proteins. Because the Calcein fluorescent dye interferes with MS and nanoparticle flowcytometry analyses, we did not add Calcein‐AM in the 2nd and 3rd rounds experiments and only subjected them to downstream analysis following the flowchart illustrated in Figure 1d.

We submitted 24 samples of purified EVs (including two rounds, four conditions and three consecutively collected samples per condition) to the Mayo Clinic Proteomics Centre for MS data analysis (see Section 2 for details). The overall summary is presented by six Venn diagrams (see Figure 2), in which EVs samples collected at the same timeframe were composited into one diagram consisting of four experimental conditions, including No‐ES, and ES at 2, 20 and 200 Hz. The percentage value of each region represents the proportional number of identified proteins for that region. Individual rounds of data were listed in columns with the 2nd round on the left and the 3rd round on the right. In each Venn diagram, the mostly overlapped region at the centre jointed by four sets of data had the largest percentage value, which represent common proteins identified in all four experimental conditions. In the 2nd round, the percentage of common protein had a range from 41% to 71%, especially in the 3rd and 4th collections. In the 3rd round, however, it had a narrower range from 55% to 60% for common proteins. For this reason, we assumed that the last batch of samples was more consistence than the previous batch.

FIGURE 2.

FIGURE 2

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Venn diagrams display numbers of identified proteins of extracellular vesicles (EVs) in samples collected from four different experimental conditions including experiment without electrical stimulation (No‐ES), and with electrical stimulation (ES) at 2, 20 and 200 Hz. Each Venn diagram represents date collected at the same time frame. The 2nd round is on the left and 3rd round on the right. The percentage value of each region represents the proportional number of identified proteins from that region.

The common way of studying differentially expressed proteins is to compare protein abundances from two experiments of different conditions and use ‘volcano’ plots to visualize their significance changes. Multiple rounds of experiments are usually needed to achieve the statistical significance. So, we must include all our available data from 2nd and 3rd round experiments for the p‐value calculations. We compared protein abundances of EVs collection under ES to that of No‐ES but treated those calculations separately for EVs collected at different time points (2nd, 3rd and 4th collections). As the result, nine volcano plots were generated (see details in Figure S3). We listed only 43 proteins obtained from the 2nd EV collections that have more than 2‐fold of changes with statistical significance (p < 0.05) (see Figure S4), considering that the 3rd and 4th collections of the 2nd round had anomalous percentages for common proteins. The weakness of using this method was due to our limited data set.

Alternatively, we adopted the relative‐difference (δ) algorithm (Tornqvist et al., 1985) to illustrate how different EFs affect EV protein expression (see Section 2 for details). Unlike the common way of studying differentially expressed protein that compare data obtained from different experimental conditions, the δ‐analysis calculates changes on consecutively collected samples from the same experimental condition, so that potential variations caused by different cell‐growing cartridges or by different batches of seeding cells can be avoid. This way, a positive δ value means an increased protein expression, and a negative δ value means a decreased protein expression, during the 2‐week period when cells were cultivated inside a Fibercell cartridge. We subjected the middle three consecutively collected samples from all four conditions in the last batch for δ‐analysis. There was a total of 2078 proteins identified from the last batch of samples, and we focused on 732 selected proteins that were present in all samples (see Table S1). Other candidates that were not present in all samples were not included in this δ‐analysis.

Within the selected 732 protein candidates, 176 (24%) of them had increased expression under the control condition of No‐ES. At the first glimpse, this was at odd to the traditional assumption that cells in a control condition are supposed to be stable. But all living cells are constantly growing, and their dynamics can vary depending on the context and the intervention given by the short cultivation period after cell seeding.

Following the same protocol, we found that EFs with different frequencies affects expressions of EV‐related proteins differently. Figure 3 is a heatmap summarizing 732 proteins, with the red colour indicating the most positive δ value and the blue colour showing the most negative δ value. At the bottom are four pie diagrams showing percentages for increased/decreased protein expression under four different experimental conditions. We found that for ES at 2 Hz, 214 (29%) of 732 identified proteins had increased expressions; 158 (22%) for ES at 20 Hz and a sizable 422 (58%) for ES at 200 Hz. It is worth noting the significantly large percentage of increased protein expression under ES at 200 Hz. We will further dive into this later.

FIGURE 3.

FIGURE 3

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A heatmap summarizing mass spectrometry analysis of 732 proteins. Each line in the heatmap represents the δ value of a unique protein, with its colour reflecting the intensity labelled in the scalebar on the right that is proportional to the δ value. At the bottom are pie diagrams showing percentages of increased protein expression (in red) under four different experimental conditions.

To gain an in‐depth understanding of the biological meaning of these proteins, we performed enrichment analysis based on previously published results using DAVID (the database for annotation, visualization, and integrated discovery) (Dennis et al., 2003) to extract biological features associated with the name list of 732 proteins. We found 115 types of biological processes associated with 450 proteins (Table S2).

To bridge the connection between EF effects and biological features, we used Knowledge Graphs (KG) that connects extracted biological features to associated proteins along with their calculated δ values. Because a graph illustrating the entire network of all 115 types of biological features is overwhelming for human eyes, we therefore created a compact list that only includes the top 10 candidates with the most positive δ values and 10 others with the most negative δ values for each experimental condition. After taking out 21 duplicates, the final concise list contains 59 proteins (see Table S1), which was used to illustrate how EF affects biological features of EVs. There were 37 types of biological processes associated with 34 proteins (Table S2). For a better visualization, we only included those biological processes connected with two or more associated proteins. The resulting KG consisted of 17 biological processes and 29 proteins.

In Figure 4, we combined four KGs representing data from their corresponding experimental conditions, in which biological processes were illustrated as filled grey octagons and associate proteins were indicated by filled circles, with their radii proportional to corresponding absolute δ values. Blue/red colour indicated genes for decreased/increased protein expression. Grey lines represented previously reported connections between biological processes and associated proteins.

FIGURE 4.

FIGURE 4

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Knowledge graphs showing effects of EF (2, 20, 200 Hz) on biological processes and associated proteins. Grey octagons represent identified biological processes; red/blue cycles indicate proteins with positive/negative δ values. The radius of each cycle is proportional to the absolute δ value of corresponding protein. Grey lines represent previously reported connections between biological processes and associated proteins. Abbreviations of biological processes: Host‐v. I., Host‐virus interaction; Prot.Trans., Protein transport; Angioge., Angiogenesis; Cell Ad., Cell adhesion; ER‐Go.T., ER‐Golgi transport; Antiv.D., Antiviral defense; Innate I., Innate immunity; Inflam.R., Inflammatory response; Neuroge., Neurogenesis; mRNA Sp., mRNA splicing; mRNA Pr., mRNA processing; U.C.P., Ubl conjugation pathway; Trans.R., Transcription regulation; Trans., Transcription.

KG is a class of message passing neural networks commonly used for processing data that connects objects with edges. It has been used for deep learning in computing science that is beyond the focus for this work (Yue et al., 2020). But, with a glimpse of these graphs we can still get some interesting biological features that were altered under ES at a specific frequency. For example, at the lower‐right corner of each graph is a subgroup network for mRNA processing/splicing associated with two proteins (PRPF8 and HNRNPC). Under ES at 200 Hz, these two proteins were significantly increased, suggesting that mRNA activities can be enhanced by ES at a high frequency, corresponding to the overall significantly increased protein expression under ES at 200 Hz. Another interesting spot is at the identified protein of S100A9 that has associations with four biological features including immunity, innate immunity, inflammatory response, and apoptosis. We observed significant increases of S100A9 under ES at a low frequency of 2 Hz. The third interesting spot is at the protein of TNFRSF10B that is associated with apoptosis. We detected its biggest increase under ES at 2 Hz, suggesting that immunity/apoptosis could be enhanced by ES at a low frequency (we have detailed analysis on TNFRSF10B later in the manuscript). Under ES at 20 Hz, however, we observed some significantly decreased protein expression, such as EXOC4, CSEL1 and SLC26A2, that have associations with protein transport.

The MS data represents total EV‐related proteins, regardless of whether the protein is from the cytosolic origin or on EV surface. Identifying EV surface proteins is more relevant because they not only carry information on their tissues of origin but also play important roles as critical mediators for intercellular communications. Next, we performed high‐resolution nanoparticle flowcytometry by detecting EVs at the single‐vesicle level to identify whether the increased protein detection was due to the increased number of EVs or the increased surface protein expression.

Using flowcytometry, we first measured the total concentration of nano particles from each sample of purified EVs and did not observe significant changes caused by ES at all three frequencies (see Figure S6). Then, we used a fluorescent‐tagged antibody targeting specifically to the extramembrane domain of a unique surface protein. Screening through all EV surface proteins would be valuable, but the total cost is also prohibitively expensive. Based on prior studies in the literature we focused on selected targets following these criteria (1) it must be an EV‐surface protein; (2) it has a higher fraction among all detected proteins; (3) it is a well‐studied protein with established important biological relevance and (4) it has a commercially available antibody suitable for flow cytometry. As the result, we choose the TNFRSF10B (Tumour Necrosis Factor receptor superfamily member 10b, also known as the death receptor 5, DR5, or the TRAIL receptor 2, TRAIL‐R2) as the evaluation target. TNFRSF10B contains an intracellular death domain, which can be activated by TNF‐related apoptosis inducing ligands and transduces an apoptosis signal (Chaudhary et al., 1997). Monoclonal antibodies targeting TNFRSF10B have been developed and are currently under clinical trials (such as Contumumab, Lexatumumab, Tigatuzumab and Drozitumab) for patients suffering from a variety of cancer types (Lemke et al., 2014; Snajdauf et al., 2021; Thapa et al., 2020). The question here is whether EVs bearing TNFRSF10B are modulated by external EF. In addition, we included CD63, one of the commonly used EV markers (Andreu & Yanez‐Mo, 2014) as the secondary targeting protein for dual labelling single‐vesicle characterization. Samples of EVs were co‐incubated with the TNFRSF10B antibody tagged with Alexa488‐ and CD63 antibody tagged with Phycoerythrin (PE) at 37°C for 30 min (see Section 2 for details) before final measurements on the Flow Nanoanalyzer (N30, NanoFCM, XiaMen, China).

Figure 5a includes scatter plots showing populations of EVs detected from flowcytometry with the excitation wavelength at 488 nm. The Alexa488 signal was represented by the vertical axis, whereas the PE signal was indicated by the horizontal axis. For each scatter plot, the distribution was convened into four quarters based on baseline cutoffs on the green or red fluorescent signals. On the top‐left corner (Q1) was the population of EVs only bearing TNFRSF10B. Following clockwise on the top‐right corner (Q2) is the population of EVs bearing both TNFRSF10B and CD63, and on the lower‐right (Q3) is the population for EVs only bearing CD63. Q4 on the lower‐left is the distribution of non‐specific EVs or other nano particles that are not considered further. Detailed scatter plots showing populations of EVs collected from three subsequent collections were included in Figure S5.

FIGURE 5.

FIGURE 5

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Nanoparticle flowcytometry analysis of TNFRSF10B/CD63 dual labelling extracellular vesicles (EVs). (a) Scatter plots display populations of EVs collected at four different experimental conditions. Each scatter plot is convened into four quarters based on baseline cutoffs on the green or red fluorescent signals. Q1 for TNFRSF10B+ only, Q2 for both TNFRSF10B+ and CD63+, Q3 for CD63+ only, and Q4 for non‐specific nano particles, with the numbers show corresponding percentages of each quarter. (b) The bar graph compares EV populations, with the height of each bar indicating the percentage for individual EV population (PQ1, PQ2 and PQ3). Samples from the 2nd, 3rd and 4th collections were indicated in blue, orange and grey colours, accordingly (p < 0.05). PQ1, PQ2 and PQ3 were the average of the corresponding collections from the 2nd and the 3rd round. Error bars show standard deviation.

To illustrate changes of EVs, we used a bar graph (Figure 5b), in which the height of each bar indicates the percentage (P) for three populations (PQ1, PQ2 and PQ3). Samples from consecutive collections (2nd, 3rd and 4th) were labelled with blue, orange and grey colours, accordingly. Four different experimental conditions were marked as No‐ES, ES at 2, 20 and 200 Hz, respectively.

Starting from Q1 (TNFRSF10B+ only), the PQ1 from 2nd collections of all four conditions were around ∼1.5–2%. However, in the 3rd and 4th collections PQ1 under ES at 2 Hz were at ∼7%–8% (about 4‐fold increase) when compared to that of No‐ES or ES at 200 Hz (p < 0.05). PQ2 under ES at 2 Hz had a similar increase from 2nd to 3rd or 4th collections. Interestingly, PQ3 under ES at 20 Hz was lower than any other three conditions (p < 0.05). In conclusion, ES at 2 Hz frequency selectively increased the percentage of EV bearing TNFRSF10B, but ES at 20 Hz frequency reduced percentage of EVs bearing CD63. We did not observe significant changes for EVs bearing TNFRSF10B or CD63 under ES at 200 Hz.

We also measured the average size (Ø) for each EV population. Q2 bearing both TNFRSF10B and CD63 had the largest size (>90 nm), followed by Q1 bearing TNFRSF10B and Q3 bearing CD63. But no detectable difference was found for the average size of individual population when comparing that from No‐ES to ES at any three frequencies (see Table 1), suggesting that EF may not affect EVs‐releasing mechanism but could modulate specific protein synthesis and packing along the EV‐forming pathway.

TABLE 1.

Average EV size (Ø) for each subgroup of population.

ØQ1(nm) ØQ2(nm) ØQ3(nm) ØQ4(nm)
No‐ES 84.2 ± 1.6 92.3 ± 3.6 76.0 ± 3.7 76.2 ± 4.0
2 Hz 87.7 ± 10.4 93.6 ± 8.0 72.8 ± 2.5 74.0 ± 2.9
20 Hz 84.4 ± 6.2 98.5 ± 6.8 75.6 ± 3.6 76.2 ± 2.7
200 Hz 81.9 ± 0.9 93.4 ± 3.5 73.3 ± 2.5 74.2 ± 2.0
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Note: Q1: EVs only bearing TNFRSF10B; Q2: EVs bearing both TNFRSF10B and CD63; Q3: EVs bearing CD63 only; Q4: Non‐specific nano particles. ØQ2 has the largest size (>90 nm), followed by ØQ1 and then ØQ3. Four different experimental conditions including experiment without electrical stimulation (No‐ES), and with electrical stimulation (ES) at 2, 20 and 200 Hz. No detectable difference was found for the average size of individual population when comparing that from No‐ES to ES at any three frequencies.

In addition, we tested another exosomal surface protein, the glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) that has been reported as contributing to EV assembly and secretion (Dar et al., 2021). However, we did not observe significant changes for EVs bearing GAPDH under EF stimulations at any of the three frequencies (p > 0.05) (see Figure S7). Such a result was expected because GAPDH, as one of the housekeeping genes, is commonly used as expression controls due to its constant expression in the cells (Barber et al., 2005).

In summary, we performed MS‐based proteomics and nanoflow cytometry‐based single‐vesicle detections to provide strong evidence that EF affects EVs in a frequency‐dependent manner. Although this study is in its preliminary stages, our gathered results indicated that low frequency (2 Hz) EF increases the percentage of EVs carrying specific proteins linked to the group of biological processes including immunity, inflammation, and apoptosis. But EF at the intermediate frequency (20 Hz) decreases expression of proteins linked to protein transport. Furthermore, enhanced protein expressions were found in association with mRNA processing/splicing under EF at a higher frequency (200 Hz). All these findings warrant further investigations.

4. DISCUSSIONS

This is a continuation and expansion of work we started 5 years ago (Wang et al., 2019). One of the major problems at that time was the lack of a controllable stimulation device for producing uniform EF. In the past several years, we successfully created a specialized electrical stimulation device that can deliver uniformly distributed EF to a three‐dimensional space where cells grow in a near‐vivo environment. We also developed our unique three‐step EV‐purification protocol (see discussion in the supplementary file). Numerous topics merit mention. But in the discussion here we will mainly focus on two aspects: profile of exosomal protein; and the potential benefit of using EF in cancer treatments. Again, findings outlined in this report regarding the impact of EF on HT‐1080 are solely intended to illustrate the frequency‐dependent influence of EF on EVs, rather than the applicability to cancer treatment.

We selected the hollow‐fiber cell cultivation system (Fibercell System) as a platform to investigate how EF modulates EVs. Previous reports have shown that EVs generated with this bioreactor technology have a low immunogenicity and immuno‐regulatory antigenic signature (Gobin et al., 2021). EVs can be produced in a large‐scale system over a long production period that allows frequent harvesting with the same quality and quantity (Gobin et al., 2021). Our methods revealed here can be readily adopted to future large‐scale production of EVs.

The choice of the 2‐week period for cell cultivation was based on a careful consideration that it was long enough for multiple harvestings needed for downstream analysis, but not too long to overstretch the entire experimental schedule. Instead of comparing data between different experimental conditions, we used the relative‐difference algorithm computing data from consecutively collected samples under the same experimental condition. Therefore, any unknown issues caused by differences in seeded cells or by different Fibercell cartridges can be kept at the minimum, and the calculated relative difference of identified proteins reflects how EF affects EVs over time. However, the timing phenomenon could be critical, for example, as previously reported that the timing of applying electrical stimulation is an important factor deciding the success rate and maturity of regenerating rat sciatic nerves (Bojovic et al., 2015; Yeh et al., 2010). In this report, we did not specifically study the time‐course of the EF effects.

We used KG to illustrate how EF affects biological features in associations with EV‐carrying proteins and indicated that different biological features respond differently to EF frequencies. For example, a significantly elevated percentage of proteins with increased expression was found at a higher frequency. Specifically, two proteins (PRPF8 and HNRNPC) associated with mRNA processing/splicing were found with elevated expressions at 200 Hz, whereas the expression of S1009A associated with immunity/apoptosis was increased only at 2 Hz.

There is a significant body of literature about interactions between EF and biological entities dating back to the late 18th century of Luigi Galvani (Mauro, 1969). Interestingly, several recently published reports coincided with our findings. For example, several studies report that stimulation with 5, 50 and 60 Hz electromagnetic fields can exert a certain action on autoimmunity and immune cells (Guerriero & Ricevuti, 2016; Rosado et al., 2018), and cause apoptosis (Barati et al., 2021). Others reported that electrical stimulation (100 Hz to 1K Hz) increased protein synthesis in articular cartilage explants (Macginitie et al., 1994), which is in line with our findings of increased mRNA processing at 200 Hz, considering that mRNA translation is a key focal point of gene expression regulation. It is worthy to note that these published literatures focused only on interactions between EF and tissues or cells. Our report is the first, to the best of our knowledge, that extends the frequency‐dependent EF effects to EVs.

EVs also carry different nucleic acids, including microRNAs that regulate cell growth and metabolism by post‐transcriptional inhibition of gene expression. Examining microRNA profile will not only allow the determination of the origin of the cells and their status, but also allow analyses to their targeted cell groups and possible functions. Exploring microRNA profile is another important aspect in the future direction of high‐throughput genomics study of EVs. Previously, we demonstrated that ES at different frequency could modulate the microRNA (miRNA) of astrocyte‐derived EVs. (Wang et al., 2019). Two Hertz ES was found to cause changes in the small RNAs cargo in the EVs derived from cultured hypertrophic cardiomyopathy cardiomyocytes (James et al., 2021).

Results of MS studies are not directly comparable to flowcytometry analysis, because the plasma membrane of EVs was kept intact during nanoflow cytometry rather than being disrupted from the sample preparation step for MS studies. Without a membrane‐permeation reagent, exogenic fluorescence‐tagged antibodies were limited on accessing EV surface proteins. Proteins from the cytosolic origin cannot be detected. In flowcytometry analysis, we used antibodies targeting at the extramembrane domain of TNFRSF10B and CD63 for dual labelling experiments and found three distinguished EV populations, including EVs only bearing TNFRSF10B, EVs only bearing CD63 and EVs bearing both TNFRSF10B/CD63 subgroups.

Several membrane‐spanning tetraspanin proteins, including CD9, CD63 and CD81 have been regarded as typical EV markers for the last two decades (Escola et al., 1998; Thery et al., 1999). EVs bearing CD63 may correspond to the endosome‐derived exosomes, whereas those bearing only CD9 or CD81 come from plasma membrane and are called ectosomes (Kowal et al., 2016). Previous study revealed that CD63 and CD9 are present on two distinct and one common populations of EVs (Mathieu et al., 2021), like what we had observed in TNFRSF10B‐CD63 dual labelling experiments. Thus, we assumed that EVs bearing TNFRSF10B are likely ectosomes. It would be interesting to use a dual‐labelling experiment with TNFRSF10B and CD9 to test whether these two proteins present in the same population of EVs.

Clearly, the subgroup of EVs bearing only TNFRSF10B is very different from the subgroup of EVs bearing CD63 only, as indicated by their size measurements through side scattered light. Among three EV subgroups, the TNFRSF10B+/CD63+ dual‐labelled subgroup has the largest average size, followed by the TNFRSF10B+ only labelled subgroup and then the CD63+ only subgroup (Table 1). There is a possibility that our measured average size could be artificially enlarged due to attached antibodies, considering the size of a vesicle is on the same scale to that of the antibody‐tag complex. Single vesicle with more attached antibodies would appear to be in a greater dimension owning to the side scattered light detection methodology. Alternatively, the Cryo‐EM technology would be an optimal tool for size determinations of EVs, which we also plan to use in future investigations. Nevertheless, we did not observe any EF induced change in the average size of any individual subgroup.

Interestingly, we found 3–4‐fold increased percentage of EVs bearing TNFRSF10B at a low frequency of 2 Hz. TNFRSF10B is mainly located on plasma membrane. As an important mediator of the extrinsic pathways of apoptosis, TNFRSF10B has quickly emerged as the target of therapies for cancer treatments that utilize two types of pharmaceutical agents, including recombinant human TRAIL proteins (such as Dulanermin) and TRAIL‐R2 agonist antibodies (such as Conatumumab, Lexatumumab, Tigatuzumab and Drozitumab) (Lemke et al., 2014; Snajdauf et al., 2021; Thapa et al., 2020). Although the preclinical results for TRAIL‐R2 agonist antibodies were promising, the response/recovery rate was low when they were tested in patients (Snajdauf et al., 2021; von Karstedt et al., 2015). A recent study suggested that the abundance of TNFRSF10B on cell surface could be regulated by vesicle transport and the higher expression of TNFRSF10B was correlated with higher first/post‐progression survival in chemotherapy‐treated lung cancer patients (Wang et al., 2020). The finding of increased population for EVs bearing TNFRSF10B at a lower EF frequency suggests a potential therapeutic treatment through the combination of chemotherapy with EF enhancement that would be beneficial to patients who have decreased response to initial TRAIL‐R2 treatment.

Frequencies of endogenous EF in certain body regions are varying through regular activities and associated with physiological conditions during wakefulness and sleep. For example, a recent study showed that patients with Parkinson's disease have different EEG frequency patterns in the supplementary motor area, accompanied by reduced walking speed and step length. When patients walked with enhanced arm swing, their EEG frequency pattern, walking speed and step length became normal (Weersink et al., 2021). On the other hand, the frequency of exogenous EF stimulation is also important. For example, 20 Hz accelerates axonal regeneration in rat (Al‐Majed et al., 2000) and 2 Hz promote proliferation of satellite cell in muscles disuse atrophy (Wan et al., 2016).

Most previously reported studies of EF effects on biological functions have focused on the field intensity rather than the frequency (Love et al., 2018; McCaig et al., 2009), and commonly the applied intensity is higher than endogenous electric field. Some reports also described EF effects on EVs release (Fukuta et al., 2020; Zhang et al., 2023), but to our knowledge the frequency‐dependent effects of the EF have not been investigated. Our findings support the importance of the EF frequency and highlight the potential importance of even endogenous electrical activities. For example, sleep‐wakefulness behavioural states show markedly different EEG frequency and waveform amplitudes that may be important for their underlying physiological functions. So far, we know little about the mechanistic explanation of how endogenous electrical activities affect biological functions. It was only recently demonstrated that the brain glymphatic clearance strongly depends on local field potential activity (Jiang‐Xie et al., 2024).

We all inhabit a universe charged with EF originating from the cosmos, earth, man‐made, and endogenous sources. Like a symphony orchestra, endogenous and exogenous electrical activities interact in complex ways that may have important biological relevance. To conclude this discussion, we would like to use a famous quote: ‘If you want to find the secrets of the universe, think in terms of energy, frequency and vibration’. Although there is no verifiable source of when and where Nicola Tesla said this, it is the frequency that matters.

AUTHOR CONTRIBUTIONS

Yihua Wang: Formal analysis (equal) Gregory A. Worrell: Writing—review and editing (equal) Hai‐Long Wang: Initial scientific concept; formal analysis (equal); writing and editing (equal).

CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest to declare. All co‐authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication.

Supporting information

Supplemental Materials

JEX2-3-e70018-s005.docx (2.1MB, docx)

Supporting Information

JEX2-3-e70018-s001.xlsx (62.5KB, xlsx)

Supporting Information

JEX2-3-e70018-s004.xlsx (13KB, xlsx)

Supporting Information

JEX2-3-e70018-s003.xlsx (33.1KB, xlsx)

Supporting Information

JEX2-3-e70018-s002.xlsx (12.6KB, xlsx)

ACKNOWLEDGEMENTS

This work was supported by a NIH BRAIN initiative R01 grant to HLW and GAW (NS112144 from NINDS and NIMH) and the Minnesota Partnership for Biotechnology and Medical Genomics (MNP #17.16). We acknowledge the assistance of the Mayo Clinic Proteomics Core, which is a shared resource of the Mayo Clinic Cancer Centre (NCI P30 CA15083). The authors also wish to thank Drs. Vanda Lennon, YongJie Yang and Artūrs Abols for reading the manuscript and providing valuable inputs.

Wang, Y. , Worrell, G. A. , & Wang, H.‐L. (2024). Effects of Electric Fields on the Release and Content of Extracellular Vesicles. Journal of Extracellular Biology, 3, e70018. 10.1002/jex2.70018

DATA AVAILABILITY STATEMENT

The data set to support the findings of this study are available on Zenodo with the fingerprint ‘https://doi.org/10.5281/zenodo.8305453 ′.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Materials

JEX2-3-e70018-s005.docx (2.1MB, docx)

Supporting Information

JEX2-3-e70018-s001.xlsx (62.5KB, xlsx)

Supporting Information

JEX2-3-e70018-s004.xlsx (13KB, xlsx)

Supporting Information

JEX2-3-e70018-s003.xlsx (33.1KB, xlsx)

Supporting Information

JEX2-3-e70018-s002.xlsx (12.6KB, xlsx)

Data Availability Statement

The data set to support the findings of this study are available on Zenodo with the fingerprint ‘https://doi.org/10.5281/zenodo.8305453 ′.

Articles from Journal of Extracellular Biology are provided here courtesy of Wiley on behalf of the International Society for Extracellular Vesicles (ISEV)

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