Biosensors and Bioelectronics 194 (2021) 113589

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Biosensors and Bioelectronics
journal homepage: www.elsevier.com/locate/bios

Short communication

Molecular profiling of extracellular vesicles via charge-based capture usingoxide nanowire microfluidics
Takao Yasui a, b, c, *, Piyawan Paisrisarn a, Takeshi Yanagida d, e, f, **, Yuki Konakade a,
Yuta Nakamura a, Kazuki Nagashima b, d, Marina Musa a, Ivan Adiyasa Thiodorus a,
Hiromi Takahashi a, Tsuyoshi Naganawa a, Taisuke Shimada a, Noritada Kaji c, g,
Takahiro Ochiya h, Tomoji Kawai e, Yoshinobu Baba a, c, i, ***a

Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
Japan Science and Technology Agency (JST), PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japanc
Institute of Nano-Life-Systems, Institutes of Innovation for Future Society, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japand
Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japane
The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka-cho, Ibaraki, Osaka, 567-0047, Japanf
Institute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka, 816-8580, Japang
Department of Chemistry and Biochemistry, Graduate School of Engineering, Kyushu University, Moto-oka 744, Nishi-ku, Fukuoka, 819-0395, Japanh
Department of Molecular and Cellular Medicine, Tokyo Medical University, 6-7-1 Nishishinjyuku, Shinjuku-ku, Tokyo, 160-0023, Japani
Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Anagawa 4-9-1, Inage-ku, Chiba, 263-8555, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Extracellular vesicle
Nanowire
Microfluidics
Extracellular vesicle membrane protein
Nanowire-induced charge-based capture
In-situ profiling of extracellular vesicle
membrane protein

Extracellular vesicles (EVs) have shown promising features as biomarkers for early cancer diagnoses. The outerlayer of cancer cell-derived EVs consists of organotropic metastasis-induced membrane proteins and specificallyenriched proteoglycans, and these molecular compositions determine EV surface charge. Although many efforts
have been devoted to investigating the correlation between EV subsets obtained through density-, size-, andimmunoaffinity-based captures and expressed membrane proteins, understanding the correlation between EV
subsets obtained through surface charge-based capture and expressed membrane proteins is lacking. Here, we
propose a methodology to profile membrane proteins of EV subsets obtained through surface charge-based
capture. Nanowire-induced charge-based capture of EVs and in-situ profiling of EV membrane proteins are thetwo key methodology points. The oxide nanowires allowed EVs to be obtained through surface charge-based
capture due to the diverse isoelectric points of the oxides and the large surface-to-volume ratios of the nano­wire structures. And, with the ZnO nanowire device, whose use does not require any purification and concen­
tration processes, we demonstrated the correlation between negatively-charged EV subsets and expressed
membrane proteins derived from each cell. Furthermore, we determined that a colon cancer related membraneprotein was overexpressed on negatively charged surface EVs derived from colon cancer cells.

1. Introduction
Extracellular vesicles (EVs; diameters of 30–2000 nm) are membrane
vesicles, such as exosomes, microvesicles, and apoptotic bodies (Evan­s-Osses et al., 2015; Raposo and Stoorvogel, 2013; Szatanek et al., 2015),

and the EVs contain nucleic acids, proteins, and metabolites. The EVs are
secreted into the body’s blood circulation system by most types of cells.
Furthermore, EVs maintain biological functions after their internaliza­tion into recipient cells, and therefore, they can regulate cellular infor­
mation (Bobrie et al., 2011; Schorey and Bhatnagar, 2008; Thery et al.,* Corresponding author. Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603,
Japan.
** Corresponding author. Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656,
Japan.
*** Corresponding author. Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603,
Japan.
E-mail addresses: yasui@chembio.nagoya-u.ac.jp (T. Yasui), yanagida@g.ecc.u-tokyo.ac.jp (T. Yanagida), babaymtt@chembio.nagoya-u.ac.jp (Y. Baba).
https://doi.org/10.1016/j.bios.2021.113589
Received 30 November 2020; Received in revised form 9 July 2021; Accepted 24 August 2021
Available online 27 August 2021
0956-5663/© 2021 The Authors.
Published by Elsevier B.V. This is an open(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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licenseT. Yasui et al.

Biosensors and Bioelectronics 194 (2021) 113589

2009; van Niel et al., 2006; Willms et al., 2016). Since EVs can preserve
cancer- and disease-related genetic and proteomic information (Ieroet al., 2008; Properzi et al., 2013; Taylor and Gercel-Taylor, 2008), they
have been used as a source of cancer and disease biomarkers. To takeadvantage of their full potential as biomarkers, methodologies that not
only capture special subsets of EVs but also profile membrane proteins of
EVs are required to identify the correlation between the captured EV
subsets and profiled EV membrane proteins. Current methodologies for
EV capture are mainly divided into three groups (Szatanek et al., 2015):
density-based capture using ultracentrifugation or differential centrifu­
gation (Jeppesen et al., 2019), size-based capture using size exclusion
chromatography, and immunoaffinity-based capture (for specificmembrane proteins, e.g., CD9, CD63, and CD81). In addition, polymer
precipitation (for example, ExoQuick™, a size-based capture according
to the kit manufacturer’s instruction manual) (Peterson et al., 2015) andmicrofluidic-based platforms (density-, immunoaffinity-, and size-based
captures) (Contreras-Naranjo et al., 2017) have been reported as
emerging alternatives. The EV subsets obtained through density-, size-,
and immunoaffinity-based captures have EV information corresponding
to EV contents or specific membrane proteins (CD9, CD63, or CD81), but
not molecular compositions of the EV surface that reflect donor cell
information.
Since the outer layer of cancer cell-derived EVs can consist of lipid
bilayers, organotropic metastasis-induced membrane proteins (Hoshino
et al., 2015), and specifically enriched proteoglycans (Melo et al., 2015),
the idea that EVs have a correlation between surface molecules andcharge is rational. And therefore, understanding the correlation between
EV subsets obtained through surface charge-based capture andexpressed membrane proteins should also be important, as is the casewith density-, immunoaffinity-, and size-based captures. Fewer efforts
have been devoted to investigating the correlation between EV subsets
obtained through surface charge-based capture and expressed mem­brane proteins. Among the developed methodologies, the charge-based
capture has advantages of rapid and simple operation with high EV yieldand high RNA throughput (Table S1). We previously introduced ananowire-based concept that could collect over 99% of the EVs present;
the analysis targets were urinary microRNAs (Kitano et al., 2021; Yasui
et al., 2017). The correlation between EV subsets obtained throughsurface charge-based capture and expressed membrane proteins has notbeen addressed as yet, however. Here we propose a methodology usingoxide nanowires with different surface potentials to subdivide EVs basedon surface charge and we investigate a correlation between surface
charge and expressed proteins on the EVs.

medium (EMEM, American Type Culture Collection Co., Ltd.), andHybri-Care Medium (American Type Culture Collection Co., Ltd.),
respectively. EV zeta potential was measured using commercial appa­
ratuses (Malvern Panalytical Ltd.).
2.2. Nanowire fabrication and characterization
We fabricated ZnO nanowires on Si substrate using the previously
reported method (Yasui et al., 2017), and deposited a TiO2 or a SiO2
layer on the ZnO nanowires by atomic layer deposition (Figs. S1–S3).
The morphology and the composition of ZnO (bare), ZnO/TiO2 (core/­shell), and ZnO/SiO2 (core/shell) nanowires were characterized by fieldemission scanning electron microscopy (FESEM), scanning transmission
electron microscopy (STEM) and energy dispersive x-ray spectroscopy(EDS). For the cross-sectional SEM-EDS analysis, we utilized an accel­
erating voltage of 15 kV. For the single-nanowire STEM-EDS analysis,
we used an accelerating voltage of 30 kV. The EDS mapping images were
512 x 384 pixels and the delay time for each pixel was 0.1 ms. The
images were integrated for 100 cycles. The peaks of Zn Kα (8.630 keV),O Kα (0.525 keV), Si Kα (1.739 keV), and Ti Kα (4.508 keV) were chosen
to construct the elemental mapping images. Nanowire zeta potential wasmeasured using commercial apparatuses (Otsuka Electronics Co., Ltd.).
2.3. EV capture on nanowires
We used a custom-made Teflon holder in the experimental capturingof EVs on each nanowire. The nanowires on a substrate were set into one
part of the unassembled holder (Fig. S4a), and the holder parts werethen fasted together with screws (Fig. S4b). 1 mL of EV-suspended PBS
was supplied to the holder and the EVs were captured onto each nano­wire. EV concentration and size were analyzed using a nanoparticle
tracking analysis (NTA) instrument (Malvern Panalytical, Ltd.). Afterappropriate dilutions, video data were collected 5 times for a 60-s time
period for each video. Camera level and detection threshold were set to
13 and 5, respectively. Data analysis was performed automatically by
NanoSight NTA 3.2 software (Malvern Panalytical, Ltd.). After drying
the EVs on the ZnO nanowires, we coated the EVs with gold film using aplasma exposure system (Vacuum Device Inc.) to a thickness around 25
nm for imaging by FESEM (Carl Zeiss AG).
2.4. Nanowire microfluidic device fabrication
A glass substrate (26 × 38 mm, Matsunami Glass Ind., Co., Ltd.) was
cleaned and then heated at 300 ◦ C. A solution mixture of 75 mM zincacetate dihydrate and 75 mM ethanolamine was prepared by dissolving
them in 2-methoxyethanol. This solution was used to spin-coat a ZnOlayer on the glass substrate (7000 rpm, 30 s), followed by heating of thecoated substrate at 300 ◦ C for 3 min; this ZnO layer deposition step was
repeated 6 times. Next, positive photoresist (OFPR8600, Tokyo Ohka
Kogyo Co., Ltd.) was coated on the substrate (1500 rpm, 120 s) and then
the nanowire pattern was formed by photolithography. The ZnO nano­wires were grown by immersing the substrate in a solution mixture of 20
mM hexamethylenetetramine (HMTA, Wako Pure Chemical Industries,Ltd.) and 20 mM zinc nitrate hexahydrate (Thermo Fisher Scientific Inc.)at 95 ◦ C for 8 h. After removal of the resist, a sheet of poly (dime­
thylsiloxane) (PDMS) (Silpot 184, Dow Corning Corp.) patterned withmicrochannels was bonded to the substrate (Fig. S5).

2. Materials and methods
2.1. Cell culture and EV purification by ultracentrifugation
Human breast adenocarcinoma cells (MDA-MB-231, American Type
Culture Collection Co., Ltd.) were cultured in Dulbecco’s modified Ea­
gle’s medium (DMEM, Thermo Fisher Scientific Inc.) with 10%
exosome-depleted fetal bovine serum (FBS, System Biosciences, LLC.)
without any addition of antibiotics. In each passage, 2 × 106 cells wereseeded into 15 mL of cell medium in a culturing flask and cultured in an
incubator (Panasonic Corp.) at 37 ◦ C and in 5% CO2. After 48 h, the cell
medium was taken from the culturing flask and filtered through a 0.22μm filter (Merck Millipore Ltd.) to remove cellular debris. Next, 20 mL of
the filtered medium was ultracentrifuged (80 min, 4 ◦ C, 110,000g).
After discarding the supernatant, we added 10 mL of 0.22-μm filteredphosphate-buffered saline (PBS; Thermo Fisher Scientific Inc.) to washthe collected EVs, and this was ultracentrifuged again (80 min, 4 ◦ C,
110,000g). After discarding the supernatant, 1 mL of 0.22-μm filtered
PBS was added to collect EVs, and the collected EVs were stored at 4 ◦ C.
For HepG2, HTC116 (American Type Culture Collection Co., Ltd.), CCD18Co, and Hs578Bst, the media we used were DMEM, McCoy’s 5A(American Type Culture Collection Co., Ltd.), Eagle’s minimal essential

2.5. In-situ profiling of EV membrane proteins
When introducing EVs into the nanowire device, we allowed the EVs
to be captured onto the ZnO nanowires for 1 h at room temperature; then
PBS was introduced into the device to remove the uncaptured EVs. Next,
we introduced 1% bovine serum albumin (BSA) solution (Kirkegaard &Perry Laboratories, Inc.) into the device and let it stand for 15 min. Afterwashing out the device using PBS, we introduced a mouse monoclonal
2

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Biosensors and Bioelectronics 194 (2021) 113589

anti-human AlexaFluor488-labeled CD63 antibody (10 μg/mL, Santa
Cruz Biotechnology, Inc.) or a mouse monoclonal anti-human CD9,
CD81, and CD147 antibody (10 μg/mL, Abcam, Plc.) into the device, andlet the antibody solution stand for 15 min. Lastly, we washed out thedevice using PBS. For CD9, CD81, and CD147 detections, additionally,
we introduced a goat polyclonal anti-mouse AlexaFluor488-labeled IgGsecondary antibody (5 μg/mL, Abcam, Plc.) into the device, and let thesecondary antibody solution stand for 15 min. Finally, we washed out
the device using PBS and followed that by fluorescence observation withan EM-CCD camera (C9100-13, Hamamatsu Photonics K.K.; measuredarea, 212 μm2) through the fluorescence microscope (Olympus, Co.,
Ltd.), and then the fluorescence intensity was analyzed by imageprocessing software (AquaCosmos, Hamamatsu Photonics K.K.;
measured area, 212 μm2) (Fig. S6, Supplementary Movie 1).

fluorescently labeled antibodies demonstrate in-situ profiling of EV
membrane proteins corresponding to the antibody types. Fig. S7 is aschematic of our proposed concept to perform surface charge-based EV
capture followed by in-situ profiling of EV membrane protein via thenanowire device (Supplementary Movie 1). Oxide nanowires may be agood candidate for EV surface charge-based capture due to the diverse
isoelectric points of oxides (Parks, 1965) and the large
surface-to-volume ratios of nanowire structures (Rahong et al., 2016;
Yasui et al., 2019). The microchannels play an important role in short­ening distances and increasing contact frequencies between nanowires
and EVs (Sackmann et al., 2014). Since the herringbone structure in ourprevious design (Kitano et al., 2021; Yasui et al., 2017) generated con­vection flow, which may have the risk of causing non-charge-based
capture of EVs, we fabricated the microchannels without mixing struc­tures (straight channel design). Thus, the present nanowire device al­lows us to analyze the relationship between surface charge and proteinson EVs.
To confirm the feasibility of charge-based capture on nanowires, we
captured EVs onto charged surfaces of the nanowires (Fig. 1a, Supple­
mentary Movie 2). The cell-derived EVs in PBS, which were purified byultracentrifugation, had a mean zeta potential of − 21.0 mV (Fig. 1b) anda mean diameter of about 94.6 nm (Fig. 1c). We deposited two differentoxide materials, TiO2 and SiO2, on ZnO nanowires to form different
surface potentials of the nanowires in the same pH range (Figs. S1–S3).
Around pH = 7, the ZnO (bare) nanowires had a positively charged

3. Results and discussion
3.1. Surface charge-based EV capture followed by in-situ profiling of EV
membrane protein via the oxide nanowires in microchannels
For charge-based EV capture followed by in-situ profiling of EV
membrane proteins, we introduce biological samples and then fluo­
rescently labeled antibodies into an array of the oxide nanowires in
microchannels; we call this a nanowire device. First, the nanowires
harvest EVs from the introduced biological samples, and then, theFig. 1. Nanowire-induced charge-based capture of EVs. (a) Schematic illustrations showing steps in calculating capture efficiency for surface charge-based
capture of EVs. (b) Zeta potential of the cell-derived EVs in PBS, and ZnO (bare), ZnO/TiO2 (core/shell) and ZnO/SiO2 (core/shell) nanowires in water. (c) A
cryo-TEM image of the cell-derived EVs; scale bar, 100 nm. The white arrow indicates the EVs. (d) Capture efficiency of EVs using different material nanowires (pink,
ZnO (bare); orange, ZnO/TiO2 (core/shell); cyan, ZnO/SiO2 (core/shell)). Nanowires of each material on Si substrate were set into a Teflon holder (Fig. S4) to avoid
any unintended adsorption onto the sample holder. Initial concentration of EVs was 1.0 × 109 (/mL). Error bars show the standard deviation for a series of mea­
surements (N = 3). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)3

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Biosensors and Bioelectronics 194 (2021) 113589

surface, and the ZnO/TiO2 (core/shell) and the ZnO/SiO2 (core/shell)nanowires had a negatively charged one (Fig. 1b). The ZnO (bare), ZnO/
TiO2 (core/shell), and the ZnO/SiO2 (core/shell) nanowires could cap­
ture EVs in response to the surface potential (Fig. 1d and S4). Although
the positively charged surface of the ZnO (bare) nanowires showed thehighest capture efficiency among the three types of nanowires, thenegatively charged surface of the ZnO/TiO2 and the ZnO/SiO2 nano­wires could also capture EVs. This result implied that the captured
subsets of EVs using each nanowire type were different; the ZnO (bare)nanowires could capture EVs in a charge-based manner, while the ZnO/
SiO2 nanowires could capture EVs in a mechanically entangled manner.
The ZnO (bare) nanowires were our first choice in this paper from thestandpoint of capture efficiency; however, the ZnO/SiO2 nanowires also
would be good candidates to further investigate EV characteristics of themechanically entangled EVs. The EV subsets obtained through chargebased capture would provide a unique insight into EV information
corresponding to EV contents or specific membrane proteins.
We thought it reasonable that surface potential, density and aspectratio of nanowires could further improve the capture efficiency of EVs.
Considering the relationship between zeta potential of nanowires andcapture efficiency, we assumed more positive surface materials, such as
NiO, MgO, and PbO (Parks, 1965), would be better suited to enhance thecapture efficiency. Since the ZnO surface area relates to the capture
efficiency, increasing the nanowire density in the array with enoughspace for EVs to enter and increasing the aspect ratio of the nanowires bymaking them thinner and longer, would also offer ways to enhance thecapture efficiency. Using a precursor of NiO, MgO, and PbO for atomic
layer deposition and adding PEI and ammonia into the growth solution
for making nanowires thinner and longer such as done in the literature(Liu et al. 2020a, 2020b; Sakai et al., 2019) would allow researchers to
capture more EVs in the nanowire device.
For in-situ profiling of the membrane protein of the EVs, here, weused the ZnO (bare) nanowires to capture EVs. We fabricated ZnO
nanowires on a glass substrate (Fig. 2a and S5; 700 μm × 15 mm; height,
2 μm) and then bonded a sheet of poly (dimethylsiloxane) (PDMS) withmicrochannels to the substrate (Fig. 2b; 700 μm × 15 mm; height, 20μm) to form the nanowire device (Fig. 2c). Analysis of an FESEM image
showed that the nanowires had an average diameter of 100 nm and thenanowire density was 20 nanowires/μm2 (Fig. 2d). We could estimate
2.0 × 108 nanowires were present in the fabricated area, resulting inaround a 12-fold larger surface-to-volume ratio compared to the nonanowire surface. To introduce biological samples, we supplied 1 μL
of sample through an inlet, and aspirated the sample using a vacuum
pump (Fig. 2e). Considering the spatial volume of one microchannel, we
could introduce 0.21 μL amounts of biological samples into the micro­
channel. Generally, EV concentrations in biological samples (Dragovic
et al., 2011) are 108-1010/mL and the number of EVs in the micro­
channel are 2.0 × 105–2.0 × 107. Therefore, we could say that thepresent nanowire device has a large enough number of nanowires (2.0 ×
108 nanowires) to capture EVs, as charge-based subsets, in biologicalsamples.
We verified whether the nanowire device demonstrated in-situ
profiling of the membrane protein of the charge-based captured EVs
onto the ZnO (bare) nanowires. We introduced EVs and fluorescencelabeled antibodies into the nanowire device, and followed that byobserving the fluorescence intensities (Fig. S6). Compared with in-situ
profiling of the membrane protein of EVs in a microchannel without
nanowires and no EVs in the nanowire device, we confirmed chargebased EV capture followed by in-situ profiling of EV membrane pro­teins using the ZnO nanowires (Fig. 2f). The FESEM images also allowed
us to confirm the EV capture onto the ZnO nanowires (Fig. 2g). To show
the superiority of our nanowire-based methodology to the conventional
methodology, we used a 96-well plate, protein adsorption platform forcomparison (Fig. 2h). Comparison of p-values between the cases of EVs
captured onto nanowires and EVs captured onto plate wells of the 96well plate showed that nanowires could capture EVs and profile

membrane proteins efficiently (Fig. 2f and h). We concluded thatcharge-based EV capture by the nanowire device outperformed protein
adsorption-based EV capture by the 96-well plate platform, and ournanowire device was suitable for membrane protein profiling of EVs.
3.2. Correlation between surface charge and membrane proteins
To elucidate a correlation between surface charge and membraneproteins on EVs, we profiled three membrane proteins, CD9, CD63, andCD81, on EVs in the nanowire device. We evaluated the effect of thepurification method before introduction into the nanowire device on insitu profiling of the membrane protein of the charge-based captured EVs.
Cell-derived EVs in cell medium, without any purification and concen­
tration processes, showed larger fluorescence intensity than those pu­
rified and concentrated by ExoQuick and ultracentrifugation (Fig. 2i)with similar CD9/CD81 ratios, though CD63 on EVs using charge-based
capture had a little bit higher expression level than for levels obtainedusing conventional techniques (ExoQuick and ultracentrifugation). The
charge-based capture on nanowires could purify and concentrate EVs
onto them from cell supernatant. Then, we profiled the expression levelof membrane proteins derived from each cell, liver cancer cells (HepG2),
breast cancer cells (MDA-MB-231), breast cells (HS578Bst), colon cancer
cells (HCT116), and colon cells (CCD-18Co), and determined that eachof the cell-derived EVs had unique expression profiles of CD9, CD63, andCD81 (Fig. 2j). As reported using ultracentrifugation, the concentrationsof collected EVs differed among different cells and the ratio of expressed
membrane protein on EVs also differed among different cells (Borzi
et al., 2019; Charoenviriyakul et al., 2017; Dash et al., 2021; Hikita
et al., 2018). Reconstruction of the data points of fluorescence intensity
clarified that CD9 and CD81 would have an inherent expression level
related to each cell type, and CD63 would have an inherent expression
level related to EV concentrations (Figs. S8a–S8c). A correlation of
expression levels between CD9 and CD81 might be worth considering
further; a high expression level of CD9 had a low expression level ofCD81 and vice versa. With the ZnO nanowire device, whose use does not
require any purification and concentration processes, we demonstrated
the correlation between negatively-charged EV subsets obtained
through surface charge-based capture and expressed membrane proteinsderived from each cell.
Some reports have shown the use of EV membrane proteins in cancer
and other disease diagnoses has performance superior to that of standard
clinical biomarkers (Im et al., 2014; Logozzi et al., 2009; Yoshioka et al.,2014). Therefore, we profiled CD9 and CD147 membrane proteins,which have both been found to be highly expressed on EVs and on coloncancer related EVs, respectively (Boye et al., 2012; Choi et al., 2012;
Edgar, 2016; Evans-Osses et al., 2015; Xiao et al., 2012; Yoshioka et al.,2014). We confirmed that EVs derived from each cell type were captured
on the ZnO nanowires and CD9 and CD147 membrane proteins were
profiled on the EV surface (Fig. S9), leading to the negatively charged
EVs derived from cells being CD9 and CD147 double-positive. Specificmembrane proteins on cancer cell-derived EVs seemed to be more highly
enriched than those on non-cancer cell-derived EVs (Minciacchi et al.,2015), and the fluorescence intensity of the membrane proteins forcancer cell-derived EVs was higher than that for non-cancer cell-derived
EVs even though the EV concentrations were comparable. Profilingmembrane proteins of negatively charged EVs demonstrated that coloncancer cell-derived EVs had a significant difference in expression level
ratio between CD9 and CD147, compared to other cancer cell-derived
EVs and non-cancer cell-derived EVs (Fig. 3a). The breast cancercell-derived EVs and the breast cell-derived EVs expressed both CD9 andCD147, but there was no significance difference in their expression level
ratios (CD147/CD9). Since a correlation between high expression of
CD147 and poor prognosis has previously been shown in colorectal
cancer (Boye et al., 2012), it makes sense that the present methodology
has a potential for ex-vivo analysis of tumor-derived EVs.
Finally, we introduced a urine sample from a healthy volunteer into4

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compared to colon cancer cell-derived EVs and colon cell-derived EVs;
however, the expression level ratio (CD147/CD9) of urinary EVs was
identical to that of breast cell-derived EVs (Fig. 3c). Considering the factthat the urinary EVs from the healthy volunteer include all EVs derived
from all types of cells, it is plausible that the expression level ratio(CD147/CD9) of urinary EVs is identical to that of breast cell-derived

our microfluidics device and confirmed that EVs in the urine were
captured on the ZnO nanowires and CD9 and CD147 membrane proteins
were profiled on the EV surface. The present nanowire device satisfac­
torily profiled membrane proteins of urinary EVs from the healthy
volunteer (Fig. 3b). The expression level ratio (CD147/CD9) of urinary
EVs from the healthy volunteer showed a significance difference,(caption on next page)5

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Fig. 2. In-situ profiling of charge-based captured EV membrane proteins. (a) Photo showing the patterned area of ZnO (bare) nanowires on a glass substrate;
scale bar, 10 mm. (b) A photo of a PDMS sheet with fabricated microchannels; scale bar, 10 mm. (c) A schematic cross-sectional illustration of the nanowire device.
(d) An FESEM image of ZnO nanowires; scale bar, 1 μm. (e) Photos taken during experimental procedures. (f) Box and whiskers plot of CD63 membrane protein
profiling of captured EVs using the microfluidic devices (pink, with ZnO nanowires; cyan, without ZnO nanowires; purple, no EVs). A measurement series was N =16. Initial concentration of EVs was 1.0 × 1010 (/mL). The horizontal black dotted line shows the signal level at 3 standard deviations above the background (3SD).
(g) An FESEM image of captured EVs (colored in pink) on ZnO nanowires (colored in blue); scale bar, 100 nm. (h) Box and whiskers plot of CD63 membrane protein
profiling of captured EVs using the 96 well plate (moss green, with EVs; yellow without EVs). A measurement series was N = 6. In (f) and (h), colored box lengths
represent the interquartile range (first to third quartiles), the line in the center of each box represents the median value, the bars show the data range (maximum tominimum), and the p-value was calculated by a non-parametric test, the Mann–Whitney U test (***p < 0.0001). (i) Box and whiskers plot of membrane protein
profiling of captured EVs (pink, CD9; orange, CD63; cyan, CD81). EVs were purified from HepG2 cell-cultured medium by different protocols: no purification,
ExoQuick and ultracentrifugation. A measurement series was N = 20. (j) Box and whiskers plot of membrane protein profiling of captured EVs (pink, CD9; orange,
CD63; cyan, CD81) in different cell-cultured medium. A measurement series was N = 20. In (i) and (j), colored box lengths represent the interquartile range (first to
third quartiles), the line in the center of each box represents the median value, and the bars show the data range (maximum to minimum). (For interpretation of thereferences to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3. Membrane protein, CD9 and CD147, profiling of EVs. (a) Box and whiskers plot of the ratio of CD147 fluorescence intensity to CD9 fluorescence intensity.
The p-value was calculated by a non-parametric test, the Mann–Whitney U test (n.s.: not significant, ***p < 0.0001). (b) Box and whiskers plot of membrane protein
profiling of captured EVs (pink, CD9; light green, CD147) in urine. A measurement series was N = 20. (c) Box and whiskers plot of the ratio of CD147 fluorescenceintensity to CD9 fluorescence intensity. The p-value was calculated by a non-parametric test, the Mann–Whitney U test (n.s.: not significant, ***p < 0.0001). In (a–c),colored box lengths represent the interquartile range (first to third quartiles), the line in the center of each box represents the median value, and the bars show thedata range (maximum to minimum). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

EVs and has a significance difference to that of colon cell-derived EVs.
These results and previously reported data (Kitano et al., 2021; Yasui
et al., 2017) imply that our methodology holds promise for application
to urine-based cancer diagnosis.

EV membrane proteins, CD9, CD63, CD81, and CD147. The ZnO (bare)nanowires allowed us to obtain a higher expression level ratio (CD147/
CD9) from colon cancer cell-derived EVs and to detect an expression
level ratio (CD147/CD9) from the EVs of a healthy volunteer urine
sample. Our methodology showed the potential to provide a novel in­
dicator of the EV subset role (surface charge) for cancer diagnosis,
although we need to perform further trials for the confirmation of theindicator. Since we have already identified cancer-related microRNAs
from urine samples using ZnO nanowires (Kitano et al., 2021; Yasui
et al., 2017), we expect it would also be possible to identify the mem­brane protein-based markers from urine samples after making a corre­
lation between in-situ EV profiling and EV-encapsulated microRNAs. At

4. Conclusions
To summarize, we have demonstrated our nanowire device could
achieve membrane protein profiling of EV subsets obtained throughsurface charge-based capture from biological samples. The positively
charged surface of the ZnO (bare) nanowires had an important role inthe charge-based capture of EVs that we then used for in-situ profiling of6

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T. Yasui et al.

this point, a startup company (Craif Inc.) has already introduced a ZnO
nanowire-based device to the market based on microRNA profiling, andin the future, in-situ EV profiling should come to be commercialized
widely. Since pH of the solution can affect surface charge of EVs (Mid­ekessa et al., 2020), for future practical use, we need a protocol to adjust
this pH by adding pH adjuster solution before supplying sample into thenanowire device. Although we need to perform more trials for cancer
diagnosis, the present results have led us to believe our developed
approach will be a powerful tool that offers a new strategy for re­searchers to perform cancer diagnosis using urine samples.

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CRediT authorship contribution statement
Takao Yasui: Conceptualization, Methodology, Formal analysis,
Investigation, Writing – original draft, Writing – review & editing,Visualization, Supervision, Project administration, Funding acquisition.
Piyawan Paisrisarn: Methodology, Investigation, Writing – review &editing, Visualization. Takeshi Yanagida: Conceptualization, Method­
ology, Investigation, Supervision, Project administration, Funding
acquisition. Yuki Konakade: Methodology, Validation, Investigation.
Yuta Nakamura: Methodology, Investigation. Kazuki Nagashima:
Methodology, Investigation, Writing – original draft. Marina Musa:
Methodology, Investigation. Ivan Adiyasa Thiodorus: Methodology,
Investigation. Hiromi Takahashi: Methodology. Tsuyoshi Naganawa:
Methodology. Taisuke Shimada: Methodology. Noritada Kaji:Conceptualization. Takahiro Ochiya: Conceptualization, Supervision.
Tomoji Kawai: Conceptualization, Supervision. Yoshinobu Baba:
Conceptualization, Supervision, Project administration, Funding
acquisition.
Declaration of competing interest
The authors declare no competing financial interests.
Acknowledgements
This research was supported by Japan Science and Technology
Agency (JST) PRESTO (JPMJPR19H9), JST SICORP (JPMJSC19E3),
AMED (Grant Number JP21he2302007 and JP21zf0127004), the JSPS
Grant-in-Aid for Scientific Research (S) 18H05243, the JSPS Grant-inAid for Scientific Research (B) 21H01960, the JSPS Grant-in-Aid for
Exploratory Research 20K21124, the JSPS Grant-in-Aid for Scientific
Research on Innovative Areas “Chemistry for Multimolecular Crowding
Biosystems”, and research grants from each of the following: the Murata
Science Foundation, Advanced Technology Institute Research Grants
2019, Foundation of Public Interest of Tatematsu, the Nitto Foundation,
the G-7 Scholarship Foundation, the Nanotechnology Platform Program(Molecule and Material Synthesis) of the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), and the Cooperative Research
Program of the “Network Joint Research Center for Materials andDevices".
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.bios.2021.113589.
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