Protein Profiling and Sizing of Extracellular Vesicles from Colorectal

Jan 4, 2018 - Representative cryo-TEM micrographs of EVs isolated from the supernatant of human colorectal cancer HCT15 cell cultures. (d) Schematic...
0 downloads 0 Views 3MB Size
Download PDF
www.acsnano.org

Protein Profiling and Sizing of Extracellular Vesicles from Colorectal Cancer Patients via Flow Cytometry Ye Tian,† Ling Ma,† Manfei Gong,† Guoqiang Su,*,‡ Shaobin Zhu,† Wenqiang Zhang,† Shuo Wang,† Zhibin Li,§ Chaoxiang Chen,† Lihong Li,† Lina Wu,† and Xiaomei Yan*,† Downloaded via TUFTS UNIV on July 16, 2018 at 03:47:36 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

†

MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, People’s Republic of China ‡ Department of General Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian 361003, People’s Republic of China § Epidemiology Research Unit, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian 361003, People’s Republic of China S Supporting Information *

ABSTRACT: Extracellular vesicles (EVs) have stimulated considerable scientific and clinical interest, yet protein profiling and sizing of individual EVs remains challenging due to their small particle size, low abundance of proteins, and overall heterogeneity. Building upon a laboratory-built high-sensitivity flow cytometer (HSFCM), we report here a rapid approach for quantitative multiparameter analysis of single EVs down to 40 nm with an analysis rate up to 10 000 particles per minute. Statistically robust particle size distribution was acquired in minutes with a resolution and profile well matched with those of cryo-TEM measurements. Subpopulations of EVs expressing CD9, CD63, and/ or CD81 were quantified upon immunofluorescent staining. When HSFCM was used to analyze blood samples, a significantly elevated level of CD147-positive EVs was identified in colorectal cancer patients compared to healthy controls (P < 0.001). HSFCM provides a sensitive and rapid platform for surface protein profiling and sizing of individual EVs, which could greatly aid the understanding of EV-mediated intercellular communication and the development of advanced diagnostic and therapeutic strategies. KEYWORDS: extracellular vesicles, exosomes, flow cytometry, protein profiling, sizing, subpopulation identification, cancer diagnosis

E

a single cell type) are highly heterogeneous in size and in surface proteins, and a specific vesicle subtype could be solely responsible for a particular function.16,19−21 This distinct subset could be easily masked by other abundant EVs if ensembleaveraged techniques, such as Western blotting and enzymelinked immunesorbent assay (ELISA) are used for analysis.16 Therefore, a sensitive, specific, and rapid methodology is urgently needed to measure the abundance of these protein markers on EVs at the single-particle level. EVs have been mainly classified as exosomes (30−100 nm) and microvesicles (100−1000 nm) with regard to their biogenesis pathways, being secreted from late endosomal

xtracellular vesicles (EVs) are nanoscale membrane vesicles naturally secreted by almost all cell types to mediate intercellular communication via transfer of macromolecules such as RNA and proteins.1−3 These vesicles have stimulated considerable scientific and clinical interest owing to their increasingly recognized pathophysiological roles and potential utility as diagnostic and therapeutic tools.4−13 Surface proteins of EVs play important roles in reflecting their subcellular origin and the donor cell type,14,15 directing their targeting and capture by recipient cells,8,12,14,16 and protecting them from phagocytosis to diminish clearance from the circulation.12 Thus, protein profiling of EVs is indispensable in understanding EV functions, distinguishing disease-related subtypes for cancer diagnosis,7,9,17 predicting metastatic propensity and determining organ sites of future metastasis,8,18 and assessing the quality of EV-based drug delivery systems.4,12 However, EVs released from different cell types (and even from © 2018 American Chemical Society

Received: November 2, 2017 Accepted: January 4, 2018 Published: January 4, 2018 671

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

Cite This: ACS Nano 2018, 12, 671−680

Article

ACS Nano

Figure 1. HSFCM analysis of EVs at the single-particle level. (a) Two secretory pathways for EVs derived from cells. (b) Schematic depiction of EV isolation by differential ultracentrifugation from conditioned medium of human cell cultures and human blood samples. (c) Representative cryo-TEM micrographs of EVs isolated from the supernatant of human colorectal cancer HCT15 cell cultures. (d) Schematic diagram of the laboratory-built HSFCM for the simultaneous detection of side scattering (SSC) and two-color fluorescence of single EVs. (e) Representative SSC burst traces of PBS (i) and EVs (ii) with a bin width of 100 μs. ▼ denotes peak that saturates the APD detector. (f) SSC distribution histograms of PBS (red) and EVs (black), derived from data collected over 2 min each. (g) Representative SSC burst traces of EVs isolated from HCT15 cells before (i) and after (ii) 1% Triton X-100 treatment for 1 h. (h) Purity assessment of EVs isolated from the supernatants of different cell line cultures and from PFP of a healthy donor.

particle size and morphology was identified. To enable multiparameter analysis of single EVs, the original laboratorybuilt two-channel HSFCM was upgraded to equip three singlephoton-counting avalanche photodiode (APD) detectors for the simultaneous detection of side scattering (SSC) and twocolor fluorescence (Figure 1d and Figure S1). Compared to conventional FCM, the reduced probe volume (∼25 fL) for background reduction, extended dwell time of the nanoparticles in the laser beam for enhanced photon generation, and the high quantum yield of single-photon-counting APD are the key characteristics of HSFCM to achieve significantly enhanced sensitivity.29,30 The sample stream flowing out of a tapered capillary tubing (40 μm i.d. and 240 μm o.d.) was hydrodynamically focused by the sheath fluid to a very fine stream (∼1.4 μm) that traverses the central region of the focused laser beam (∼16 μm diameter). Figure 1e (i) shows the representative SSC burst trace of PBS filtered through a 220 nm filter, and the event rate for impurity particles was found to be 2−4 events/s. For EVs isolated from the colorectal cancer cell line HCT15, a large variation in the scattered light intensity was observed (Figure 1e (ii)). Occasionally, the scattered signal of large size nanoparticle saturated the detector (denoted in the burst trace by the peak marked by a solid inverted triangle, ▼). Figure 1f depicts the SSC burst area distribution histograms derived from data collected over 2 min each for PBS (red line) and EVs (black line). The very broad SSC distribution of EVs reflects their large intrinsic heterogeneity in particle size. High-quality isolation of EVs is essential to the accuracy of downstream biochemical analysis, such as proteomic profiling and EV-mediated transfer of functional mRNAs and miRNAs.1,15 Because lipoproteins and some other contaminants unavoidably coexist in the isolates,33 it is therefore important to assess the purity of EVs. We used Triton X-100 to lyse the phospholipid membranes of EVs and enumerated the particles before and after the treatment.34 Figure 1g (i) and (ii) show the representative SSC burst traces of EVs prior to and after 1% Triton X-100 treatment for 1 h (Figure S2). A

compartments named multivesicular bodies or budded from the cells’ plasma membrane, respectively.3,6,22,23 The current approach to confirm and quantify specific protein expression on individual EVs has been immuno-electron microscopy (IEM) with gold-labeled antibody,1,9,24,25 yet its routine application is prohibited due to the tedious procedures and limited statistical power. Although flow cytometry (FCM) has been applied to analyze surface proteins of individual EVs using fluorescence threshold triggering,26,27 the minimum detectable vesicle sizes are 150−190 nm for dedicated FCM and 270−600 nm for conventional FCM.28 Employing strategies for singlemolecule fluorescence detection in a sheathed flow, we have recently developed high-sensitivity flow cytometry (HSFCM) that enables light scattering detection of single silica nanoparticles and viruses as small as 24 and 27 nm in diameter, respectively.29,30 Here we report an ultrasensitive flow cytometer-based approach for protein profiling and sizing of individual EVs down to 40 nm. Subpopulations of EVs expressing specific proteins were quantified upon immunofluorescent staining and single-particle enumeration. The potential of using distinct EV subsets for disease diagnosis and treatment monitoring was examined by analyzing the particle concentration of CD147-positive EVs in platelet-free plasma (PFP) of colorectal cancer patients.

RESULTS AND DISCUSSION Highly Sensitive Flow Cytometric Technology for Single EV Analysis and Purity Assessment. The two secretory pathways for EVs derived from cells are schematically shown in Figure 1a. Because protocols classically used to isolate exosomes or microvesicles normally coisolate a mixed population of EVs, in the present study we isolated EVs from conditioned medium of human cell cultures and human blood samples by high-speed ultracentrifugation (Figure 1b).31,32 Figure 1c shows the representative cryo-TEM micrographs of EVs isolated from the culture supernatant of the human colorectal cancer cell line HCT15, and a large heterogeneity in 672

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano

Figure 2. Comparison of HSFCM, cryo-TEM, and NTA for particle size distribution analysis of EVs. (a) Histogram of particle size with a bin width of 10 nm for EVs isolated from colorectal cancer HCT15 cells by cryo-TEM (n = 1034). (b) SSC distribution histogram of a mixture of monodisperse SiNPs of five different diameters ranging between 47 and 123 nm, and fit to a sum of Gaussian peaks. (c) Plot of the Gaussianfitted SSC intensity (after refractive index correction) as a function of EV particle size. (d) Histogram of particle size with a bin width of 1 nm for the EV sample by HSFCM (n = 10 720). (e) Compiled particle size distribution histograms for individual samples of five sizes of SiNPs measured separately by NTA. (f) Particle size distribution histogram of the EV sample measured by NTA.

distribution histogram of the EV sample (Figure 1f) can be converted to particle size distribution, and the bin width was set at 1 nm (Figure 2d). Clearly individual EVs as small as 40 nm can be measured by the HSFCM. Note that EVs larger than 175 nm saturated the detector at the present instrument setting. Nonetheless, among 10 720 events detected in 2 min for the EV sample, the number of peaks that saturated the detector was determined to be 98. Thus, more than 99% of the EV population can be accurately sized by HSFCM. The measured peak position and median size of EVs were 50 and 64.5 nm, respectively. The individual suspensions of these five different sizes of SiNPs and EV sample were also analyzed by NTA in parallel, and the particle size distribution histograms are shown in Figure 2e and Figure 2f, respectively. The comparison of size distribution histograms of monodisperse SiNPs among TEM, HSFCM, and NTA is provided in Figure S4. In contrast to the baseline separation of the five SiNPs populations by HSFCM, significant overlap was observed in the compiled size distribution profiles obtained on NTA (Figure 2e) due to severe peak broadening. With regard to the EV sample, the measured peak position and median size were 105 and 107.6 nm, respectively, which deviated largely from the corresponding values by cryo-TEM and HSFCM measurements. Protein Profiling of Individual EVs for Subpopulation Identification. Because there has been no purification method available to separate EVs based on their biogenesis, the unclear ratios of each subpopulation of EVs often lead to inconsistent results.16,37 EVs isolated from HCT15 cell culture supernatant were stained with phycoerythrin (PE) conjugated monoclonal antibodies (MAbs) specific to CD9, CD63, or CD81 (Figure S5), the classically used markers of exosomes.3,15,22 As HSFCM can detect a single antibody conjugated with PE (Figure S6), we reasoned that immunostained individual EVs should be readily detected. The bivariate dot-plots of PE fluorescence versus SSC are shown in Figure 3a. It was determined that for

significant reduction in event rate was observed for EVs upon detergent treatment. It was determined that EVs isolated from cell culture supernatants of different cell lines contain 85−90% of membrane vesicles, whereas this percentage dropped to 70% for EVs isolated from PFP due to the abundant presence of lipoprotein particles in plasma (Figure 1h). High-Resolution Size Distribution Analysis of EVs. Particle size and the distribution of EVs are important physical properties affecting cellular uptake, including intercellular communication and therapeutic efficiency.35,36 Here, we compare HSFCM with cryo-TEM and nanoparticle tracking analysis (NTA) on particle size distribution analysis of EVs. To acquire high quality size distribution of EVs, an isolate from HCT15 cells was characterized by analyzing more than 1000 individual vesicles on 80 cryo-TEM micrographs. The size distribution profile is depicted in Figure 2a with a bin width of 10 nm. Although a very wide size distribution ranging from 30 to 600 nm was identified (inset), approximately 65% of the vesicles fell in the size range of 30−100 nm and 86% in the size range of 30−200 nm. The peak position and median size of EVs were determined to be 55 and 75 nm, respectively. To examine the performance of HSFCM in the absolute size distribution analysis of EVs, a series of monodisperse silica nanoparticles (SiNPs) were synthesized and used as the size reference standards (Figure S3). Figure 2b shows the SSC distribution histogram of a mixture of 47, 59, 74, 94, and 123 nm SiNPs. To correct the deviation induced by the slight refractive index mismatch between SiNPs (1.461) and EVs (1.400),28 a correction factor for SSC intensity was derived for every size of SiNPs based on the Mie theory calculation (Table S1). When the centroids of the SSC intensity obtained from the fitted Gaussian curves were corrected for each size population of the SiNPs, a calibration curve of the scattered light intensity and the particle size of EVs was obtained with a size dependence of 5.01th order (Figure 2c). Therefore, the SSC 673

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano

Figure 3. HSFCM analysis of specific proteins on EVs isolated from HCT15 cells upon immunofluorescent staining. (a) Bivariate dot-plots of PE orange fluorescence versus SSC for an EV isolate fluorescently labeled with PE-conjugated MAbs specific to CD9 (i), CD63 (ii), or CD81 (iii). (b) Distribution profiles of the protein copy number on single EVs for CD9 (i), CD63 (ii), and CD81 (iii). Bar length represents the interquartile range (first to third quartiles). (c) Bivariate dot-plots of PerCP-Cy5.5 red fluorescence versus PE orange fluorescence for the EV isolate upon dual immunofluorescent staining with antibodies against CD9/CD81 (i), CD63/CD81 (ii), or CD9/CD63 (iii).

copy number within that EV subset. It is worth noting that the large size of IgG-PE conjugates relative to EVs could have limited the number of probes that can bind to a given EV. Additionally, antigen clustering may render the steric hindrance of IgG-antigen binding even worse. If the probes can be replaced with organic dye-labeled small ligands, such as antigenbinding fragment (Fab), single-chain variable fragment (scFv), affibody, or aptamer, the measured copy number of antigen expression may be more accurate. Nevertheless, to investigate the coexpression of CD9, CD63, and CD81 on the surface of a single vesicle, we analyzed the same EV sample by dual immunofluorescent staining (Figure S8). Figure 3c displays the bivariate dot-plots of PerCP-Cy5.5 red fluorescence versus PE orange fluorescence. Subpopulations of EVs coexpressing CD9/CD81, CD63/CD81, and CD9/ CD63 were measured to be 31.8%, 31.1%, and 22.3%, respectively. In addition to the natural decrease of EV populations coexpressing two proteins, the inefficient immunofluorescent labeling of two proteins together due to the steric hindrance could also play an important role in the reduced percentages of positive populations. Analysis of CD147-Positive EVs Derived from Colorectal Cancer Cell Lines. Although the potential of using EVs as biomarkers for cancer diagnosis has been promising, the

EVs recovered by 100 000g ultracentrifugation, the percentages of CD9, CD63, or CD81 positive EVs were 55.2%, 46.0%, and 55.0%, respectively. The number of MAbs bound on each individual EV can be derived by calibrating the PE fluorescence intensity against the median fluorescence of single PEconjugated MAbs. On the basis of the 1:1 binding ratio between the MAb and antigen, the copy number distribution of CD9, CD63, and CD81 expressed on single EVs can be obtained as depicted in Figure 3b, i−iii. It was determined that for EVs with specific protein expression on the surface, the median copy numbers for CD9, CD63, and CD81 on each individual EV were only 5, 6, and 5, respectively. The interquartile range (first to third quartiles) is also indicated for each distribution. These results agreed well with the representative TEM images reported in the literature upon immunogold labeling,1,9,24 though the classical TEM approach lacks statistical rigor. Meanwhile, the bulk analysis of protein levels of CD9, CD63, and CD81 in EVs was carried out on a conventional flow cytometer (BD FACSAria III) by incubating EVs with 4-μm aldehyde/sulfate latex beads and staining with specific MAbs conjugated with PE (Figure S7).1,9,12 Compared with the bead-based assay, single-vesicle analysis by HSFCM can reveal not only the population percentage of an EV subset expressing specific proteins but also the distribution of protein 674

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano

Figure 4. Analysis of CD147-positive EVs in cell culture supernatant of two colorectal cancer cell lines and a normal colon fibroblast cell line. (a−c) Bivariate dot-plots of CD147 orange fluorescence versus SSC for EVs isolated from CCD-18Co (a), HCT15 (b), and HCT116 (c) cells upon immunofluorescent labeling with PE-conjugated MAb against CD147. (d) Comparison of the ratios of CD147-positive EVs (n = 3 for each sample) measured by HSFCM with immunoblotting data (10 μg protein was loaded per lane).

quantification of specific EV subtypes in clinical samples remains challenging. CD147, a transmembrane protein important for lactate transportation,38 is overexpressed in some human tumor types.39 Recently, a high level of CD147 expression was confirmed for EVs isolated from colorectal cancer cell lines, and circulating CD147/CD9 double-positive EVs derived from colorectal cancer were detected using photosensitizer beads.17 Here, we apply the HSFCM to analyze CD147 expression on EVs isolated from colorectal cancer cell lines (HCT15 and HCT116) and from a normal colon fibroblast cell line (CCD-18Co) (Figure 4). The bivariate dotplots of CD147 PE fluorescence versus SSC for EVs isolated from different cell lines indicate that whereas less than 10% of the EV population was CD147 positive for CCD-18Co cells (Figure 4a), this percentage increased to nearly 50% for colorectal cancer cell lines (Figure 4b,c). The elevated CD147 expression level for EVs derived from cancer cell lines suggests its potential utility in cancer diagnosis. HSFCM offers several advantages over traditional analytic methods such as Western blot: while Western blot analysis shows the relative protein amounts in samples (Figure 4d), HSFCM also reveals the percentage of EVs that express CD147 and allows correlating the protein abundance with the vesicle size at the single-particle level. For example, CD147-positive EVs exhibit a range of sizes depending on their cell origin, e.g., small size for HCT 15 and large size for HCT 116 cells (Figure 4). Analysis of CD147-Positive EVs in Plasma of Colorectal Cancer Patients and Healthy Donors. Colorectal cancer has a 40−50% mortality rate, and early diagnosis is critical for appropriate early intervention.40 Because CD147 levels differed among normal and cancerous cell lines (Figure 4d), we sought to determine if CD147 levels could be used to differentiate between EVs isolated from blood samples of colorectal cancer patients (n = 37) and healthy donors (n = 32) (Table 1). The isolation procedure is described in Figure 1b, and as little as 50 μL of PFP is adequate for HSFCM analysis. We first measured the particle concentration of EVs in plasma using rhodamine B isothiocyanate (RBITC)-encapsulated 80 nm diameter fluorescent SiNPs of known particle concentration as an internal standard (Figure 5a).29 We found that the EV concentration in plasma of both cancer patients and healthy donors can vary 40- to 50-fold for different individuals, and there was no significant difference in the mean concentration of EVs between cancer patients [(0.9 ± 1.2) × 109/mL] and healthy controls [(1.2 ± 1.2) × 109/mL] (Figure 5b and Table 2). We then analyzed the particle concentration of CD147positive EVs upon fluorescent staining with PE-conjugated

Table 1. Clinical Information of Healthy Donors and Patients characteristic Healthy donors Age Median Range Sex Male Female Colorectal cancer patients Age Median Range Sex Male Female Stage I II III IV

diagnosis (Figure 5b,d,e and Figure 6)

longitudinal study (Figure 5f)

n = 32

n = 17 n = 15 n = 37

− − − − − − − n = 16

62 45−74

60 45−71

n = 22 n = 15

n = 11 n=5

n n n n

n n n n

26 22−60

= = = =

7 11 12 7

= = = =

3 9 3 1

antibody. Figure 5c shows the bivariate dot-plot of PE fluorescence versus SSC for EVs isolated from a patient sample, of which CD147-positive EVs was found to be 15.4%. Employing 100 nm orange FluoSpheres of known particle concentration to calibrate the sample flow rate, the plasma concentrations of CD147-positive EVs was measured.29 Figure 5d shows that the mean concentration of CD147-positive EVs were (4.1 ± 2.3) × 107 particles/mL for healthy donors and (2.9 ± 2.9) × 108 particles/mL for colorectal cancer patients, and the difference is significant (P < 0.001). The ROC curve shown in Figure 6 indicates that the concentration of CD147positive EVs (from colorectal cancer patients and healthy donors) reveals an excellent classifier with an AUC of 0.932. Meanwhile, an elevated level of CD147-positive EVs was identified for patients at all the cancer stages, even stage I (Figure 5e). These results indicate that the particle concentration of CD147-positive EVs might be useful in the early diagnosis of colorectal cancer. When the blood samples were analyzed pre- and postsurgery (postoperative day 7−10), 13 out of 16 colorectal cancer patients showed a significant decrease (P < 0.05) in the particle concentration of CD147positive EVs after surgical resection (Figure 5f). Though more 675

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano

Figure 5. Analysis of CD147-positive EVs in plasma samples of colorectal cancer patients and heathy donors by HSFCM. (a) Bivariate dotplot of the BRITC orange fluorescence versus SSC for a mixture of 80 nm-diameter fluorescent SiNPs with EVs isolated from PFP of a healthy donor. (b) Plasma particle concentrations of EVs in healthy donors (n = 32) and colorectal cancer patients (n = 37) (mean ± s.d.). P-value was calculated by ANOVA test. (c) Bivariate dot-plot of the PE orange fluorescence versus SSC for EVs isolated from a patient sample upon immunofluorescent staining with PE-conjugated MAb against CD147. (d) Plasma particle concentrations of CD147-positive EVs in healthy donors (n = 32) and colorectal cancer patients (n = 37) (mean ± s.d.). P-value was calculated by ANOVA test. (e) Plasma concentration of CD147-positive EVs in healthy donors and colorectal cancer patients at different cancer stages. The box length represents the interquartile range (first to third quartiles). The line in the center of the boxes represents the median value. P-value was calculated by ANOVA test. (f) Changes in plasma concentrations of CD147-positive EVs in colorectal cancer patients (n = 16) before (preoperation) and after (7−10 days postoperation) surgical removal of the tumor (mean ± s.d.). P-value was calculated by paired two-tailed Student’s t test.

potential of using particle concentration of disease-specific EV subsets in diagnosis and prognosis.

Table 2. Particle Concentration Measurement of Total EVs and CD147-Positive EVs in Platelet-Free Plasma of Healthy Donors and Colorectal Cancer Patients Total EV (H)b Total EV (C)c CD147+ EV (H) CD147+ EV (C)

n

lowest

highest

32

1.3 × 108 /mL 1.2 × 108 /mL 0.6 × 107 /mL 3.2 × 107 /mL

5.3 × 109 /mL 6.2 × 109 /mL 1.0 × 108 /mL 12 × 108 /mL

37 32 37

ratioa 41 52 17 38

CONCLUSIONS Protein profiling and sizing of individual EVs are fundamental to the understanding of their functions. Through the development of HSFCM, we expanded the robust statistical power, high throughput, and multiparameter capability of flow cytometry for single-cell analysis to single EVs as small as 40 nm. In particular, we demonstrated that based on light scattering measurement of individual vesicles, accurate and statistically representative particle size distribution of EVs can be acquired in just minutes with a resolution comparable to that of cryo-TEM. Although the size of cell-derived vesicles has been reported ranging from 30 to 1000 nm, the vast majority (97%) of EVs isolated from HCT15 cell line were identified to be smaller than 150 nm. More importantly, the capacity to fluorescently stain and detect proteins of interest facilitates the

mean ± s.d. (0.9 ± /mL (1.2 ± /mL (4.1 ± /mL (2.9 ± /mL

1.2) × 109 1.2) × 109 2.3) × 107 2.9) × 108

a

Ratio between the highest and the lowest particle concentrations bH denotes healthy donors. cC denotes cancer patients.

clinic samples need to be tested to further validate the feasibility of using CD147-positive EVs for colorectal cancer diagnosis and treatment monitoring, this result suggests the great 676

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano

width (0.2 ms) were used as the criteria for burst (or peak) identification. For each burst that satisfied the criteria, the integrated number of photons (background subtracted) was stored as the burst area for the histogram or dot-plot construction. Unless stated otherwise, each distribution histogram or dot-plot was derived from data collected over 1 min. Ultrapure water supplied by an ultrapure water system (PURELAB Ultra FLC00006307, ELGA) served as the sheath fluid via a gravity feed. The flow rate was regulated by adjusting the relative height between the sheath supply bottle and the waste container. On the basis of the overlap of the focused laser spot and the sample stream, the calculated detection volume was ∼25 fL. According to Poisson statistics, for a nanoparticle concentration of approximately 5 × 109/mL, the probability that two nanoparticles will pass through the detection volume simultaneously is 0.7%. In the HSFCM setup, the sample stream is completely illuminated within the central region of the focused laser beam, and the detection efficiency is approximately 100%.43,44 Cell Culture. Human colorectal cancer cell lines (HCT15 and HCT116) and a normal colon fibroblast cell line (CCD-18Co) were purchased from American Type Culture Collection (ATCC). HCT15 cells were cultured in RPMI-1640 medium (Gibco), HCT116 cells in McCoy’s 5A medium (Gibco), and CCD-18Co cells in minimal essential medium (MEM) (Gibco). All media were supplemented with 10% FBS and penicillin-streptomycin (Invitrogen). The FBS used above was depleted of EVs by ultracentrifugation at 100 000g for 18 h at 4 °C. Blood Samples. Fasting venous blood samples were collected at the First Affiliated Hospital of Xiamen University. A total of 69 individuals (37 colorectal cancer patients and 32 healthy donors) were enrolled. Informed written consent was obtained from all the patients and the collection of human blood samples was approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University. On the day of surgery, 2.7 mL blood was collected into a tube containing buffered sodium citrate (109 mM, nine parts blood to one part sodium citrate solution) (13 × 75 mm, BD Vacutainer) from each cancer patient before surgical incision. The blood sample was not agitated before the first centrifugation and was centrifuged twice at 2500g for 15 min at room temperature to extract platelet-free plasma (PFP) within 2 h after blood collection. PFP was aliquoted and kept at −80 °C until used, and freeze−thawing was avoided as much as possible after that. Likewise, blood samples were collected on day 7−10 after surgery for 16 patients with colorectal cancer. EV Isolation from Cell Culture Medium and PFP. Cells were grown in EV-depleted medium until they reached a confluency of ∼90% (after approximately 24 h). The conditioned medium was collected and centrifuged at 800g for 5 min at 4 °C to pellet the cells. The supernatant was centrifuged at 2000g for 10 min at 4 °C to remove cellular debris. To prepare EVs, the conditioned medium supernatant upon 2000g centrifugation or the PFP from colorectal cancer patients and healthy donors were centrifuged at 100 000g for 2 h at 4 °C unless otherwise specified. The pellet was washed with 12.5 mL of PBS and followed by a second ultracentrifugation at 100 000g for 2 h at 4 °C. Afterward, the supernatant was discarded and EVs were resuspended in PBS unless specified otherwise (Figure 1b). For the purity measurement, to 45 μL of isolated EVs, 5 μL of 10% Triton X-100 (Sigma-Aldrich) was added and incubated for 1 h on ice. A Beckman Coulter XE-90K Ultracentrifuge equipped with an SW 41 Ti rotor was used for the ultracentrifugation throughout present study. Reagents. PE-conjugated mouse antihuman CD9 antibody (clone M-L13), PE-conjugated mouse antihuman CD63 antibody (clone H5C6), PE-conjugated mouse antihuman CD81 antibody (clone JS81), PE-conjugated mouse antihuman CD147 antibody (clone HIM6), PE-conjugated mouse IgG1, κ (clone MOCP-21), PerCP-Cy5.5conjugated mouse antihuman CD9 antibody, PerCP-Cy5.5-conjugated mouse antihuman CD63 antibody, and mouse antihuman CD147 antibody (clone HIM6) were all purchased from BD Biosciences. Horseradish peroxidase-labeled goat antimouse was purchased from Sigma-Aldrich. Immunofluorescent Staining. For cell cultures, EVs were isolated from 5 mL of conditioned medium upon differential

Figure 6. ROC curves between healthy donors and colorectal cancer patients assessed by the concentration of CD147-positive EVs (AUC: 0.932; 95% Cl: 0.873−0.992) or total concentration of EVs (AUC: 0.631; 95% Cl: 0.498−0.764) in PFP samples. AUC, area under the curve; Cl, confidence interval.

identification of distinct EV subtypes, including disease-related EVs. With only 2% of the sample amount required for immunoblotting, HSFCM enables protein profiling of individual EVs along with quantitative enumeration of a distinct EV subset. The high predictive value of plasma CD147-positive EV concentration for the diagnosis and treatment monitoring of colorectal cancer is also demonstrated in the present study. Although only one protein marker (CD147) is used here for colorectal cancer diagnosis, the multicolor analysis capability of HSFCM offers distinct advantages in multiparameter analysis of single EVs. So that multiple EV populations could be distinguished to enhance the specificity, sensitivity, and accuracy of diagnostics.41,42 Moreover, HSFCM can be readily applied to the detection and analysis of EVs in other body fluids (e.g., urine, ascites, saliva, semen, and breast milk) and for the quality control analysis of engineered EVs used as therapeutic agents. In summary, HSFCM enables us to create an objective benchmark to insight into heterogeneous EV populations, which is highly desirable to decipher the biology of EVs and promote the development of EV-based liquid biopsy and therapeutics.

MATERIALS AND METHODS HSFCM Instrumentation. The laboratory-built two-channel HSFCM was upgraded to enable simultaneous three-channel detection.29 A 200 mW 532 nm continuous-wave solid-state Nd:YAG laser was attenuated to 16 mW and used as the excitation source. The light emitted by individual nanoparticles or EVs was collected perpendicularly to both the laser beam and the sample stream by an infinity-corrected microscope objective (Olympus ULWD MSPlan 50×, 0.55 N.A.). The light was then directed by two dichroic beam splitters (FF555-Di03 and FF649-Di01, Semrock) into three light paths for the simultaneous detection of side scattering (FF01−524/24), orange fluorescence (FF01−579/34), and red fluorescence (FF01−710/40) upon focusing by an aspheric lens onto a single-photon counting APD for each channel. The output signal from the APD detector was counted using a National Instruments DAQ card (PCIe-6321, National Instruments). A custom program written with LabVIEW 2012 software was used for the data acquisition and processing, and the bin width was set to 100 μs. The threshold levels for both the peak height (a digital discriminator level set to 3 times the standard deviation of the background) and the peak 677

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano centrifugation, and the pellet was resuspended in 50 μL PBS. A volume of 20 μL of PE-conjugated antibody against CD9, CD63, or CD81 or 5 μL of PE-conjugated mouse antihuman CD147 antibody and PerCPCy5.5-conjugated antibody (for CD9 and CD63) was added. For EVs isolated from human PFP, 50 μL of PFP were ultracentrifuged at 100 000g for 2 h at 4 °C. The pellet was resuspended in 50 μL of PBS and 10 μL of PE-conjugated mouse antihuman CD147 antibody was added. The mixture was incubated at 37 °C for 30 min and washed twice with PBS by ultracentrifugation at 100 000g for 2 h at 4 °C. The pellet was resuspended in 100 μL of PBS for HSFCM analysis. Western Blot Analysis. EVs were lysed in RIPA buffer and the protein concentration was measured using Micro-BCA (Thermo Scientific) for EV preparations. Equivalent micrograms of proteins were loaded for each sample onto 12% polyacrylamide gels. Following electrophoresis, the proteins were transferred from gel onto a polyvinylidene fluoride membrane (PVDF, Millipore). The membrane was blocked with 5% nonfat dry milk in TBST for 30 min at room temperature and incubated with primary antibodies overnight at 4 °C. Following incubation with horseradish peroxidase-conjugated secondary antibody, the blot was developed with chemiluminescent reagents from Advansta. Flow Cytometry Analysis of Protein Expression on EVs via Bead-Based Assay. EVs were attached to 4-μm aldehyde/sulfate latex beads (Invitrogen) by mixing 10 μg of EVs in a 3 μL volume of beads for 15 min at room temperature with continuous rotation. This suspension was diluted to 0.3 mL with PBS and left rotating for 30 min at room temperature. The reaction was stopped with 100 mM glycine and 2% BSA in PBS and left rotating for 30 min at room temperature. EVs-bound beads were washed once in 2% BSA, centrifuged for 2 min at 15 000g, and resuspended in 200 μL 2% BSA. To every 10 μL of the EVs-bound beads, 10 μL of PE-conjugated antibody (CD9, CD63, CD81, or IgG1, κ isotype) and 30 μL of BSA in PBS were added to make a 50 μL volume with 2% BSA. The mixture was incubated at 37 °C for 30 min, diluted to 1 mL with 2% BSA, and centrifuged for 2 min at 15 000g. The supernatant was discarded and the beads were resuspended in 1 mL of PBS for flow cytometry analysis on a BD FACSAria III instrument. Cryo-Transmission Electron Microscopy. Cryo-TEM measurement was performed using a Tecnai F20 transmission electron microscope (FEI) operating at 200 kV. A 3-μL aliquot of the EV solution was pipetted onto the copper grid in the FEI Vitrobot sample plunger. For the EV sample isolated from HCT15 cell culture, 80 TEM micrographs were collected, and 1034 individual vesicles were examined to determine the particle size distribution histogram and the median size. Nanoparticle Tracking Analysis. NTA was carried out using a ZetaView system (Particle Metrix). Before measurements, accuracy of the ZetaView was evaluated by measuring polystyrene beads with a diameter of 100 nm. SiNPs and EVs isolated from HCT15 cell culture were measured after PBS dilution to achieve a particle count in the range of 3−6 × 107/mL. All settings for the camera were fixed for all measurements during the session [shutter: 70, camera gain: 854.40]. NTA measurement was recorded and analyzed at 11 positions. For each sample, at least three videos of 30 s with more than 600 detected tracks per video were taken and analyzed using the ZetaView 8.04.02 software with default settings. The Brownian motion of each particle was tracked between frames to calculate its size using the Stokes− Einstein equation. SSC Correction Based on Mie Theory Calculation. The intensity of light scattered by a single spherical particle was calculated by the Mie theory. The numerical calculation incorporates all the light scattering parameters, including particle diameter, refractive indices of the particle and the surrounding medium, solid collection angle, polarization and wavelength of the incident light.45 MiePlot, a computer program for scattering of light from a sphere using Mie theory and the Debye series was used to calculate the scattering intensity for spheres of different diameters in a fixed scattering angle and known refractive index.46 The ratio between the intensity of light scattered by a SiNP and that of an EV of the same particle size is taken as the correction factor for that particle size.

Statistical Analysis. GraphPad Prism version 6.0 (GraphPad Software) and Origin 9.0 software (OriginLab) were used for all the calculations. ANOVA tests were performed to calculate differences of the concentration of total EVs or CD147-positive EVs in human PFP samples. Paired two-tailed Student’s t test was applied to test differences in the concentration of CD147-positive EVs in the longitudinal cohort between preoperative and postoperative blood samples. ROC curves were constructed for CD147-positive EVs or total EVs in PFP to describe the accuracy of detecting cancer. Differences with P < 0.05 were considered statistically significant. Origin 9.0 was used for calculation of the mean and the standard deviation (s.d.). Figures were prepared using GraphPad Prism and Origin softwares.

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.7b07782. Figures S1−S8 and Table S1 (PDF)

AUTHOR INFORMATION Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Xiaomei Yan: 0000-0002-7482-6863 Notes

The authors declare the following competing financial interest(s): S.Z. and X.Y. declare competing financial interests as cofounders of NanoFCM Inc., a company committed to commercializing the HSFCM technology.

ACKNOWLEDGMENTS This research was supported by the National Natural Science Foundation of China (21225523, 91313302, 21027010, 21475112, 21627811, and 21521004), and the National Key Basic Research Program of China (2013CB933703). REFERENCES (1) Valadi, H.; Ekstrom, K.; Bossios, A.; Sjostrand, M.; Lee, J. J.; Lotvall, J. O. Exosome-Mediated Transfer of mRNAs and microRNAs is a Novel Mechanism of Genetic Exchange between Cells. Nat. Cell Biol. 2007, 9, 654−659. (2) Skog, J.; Wurdinger, T.; van Rijn, S.; Meijer, D. H.; Gainche, L.; Sena-Esteves, M.; Curry, W. T., Jr.; Carter, B. S.; Krichevsky, A. M.; Breakefield, X. O. Glioblastoma Microvesicles Transport RNA and Proteins That Promote Tumour Growth and Rrovide Diagnostic Biomarkers. Nat. Cell Biol. 2008, 10, 1470−1476. (3) Thery, C.; Ostrowski, M.; Segura, E. Membrane Vesicles as Conveyors of Immune Responses. Nat. Rev. Immunol. 2009, 9, 581− 593. (4) Alvarez-Erviti, L.; Seow, Y.; Yin, H.; Betts, C.; Lakhal, S.; Wood, M. J. Delivery of siRNA to the Mouse Brain by Systemic Injection of Targeted Exosomes. Nat. Biotechnol. 2011, 29, 341−345. (5) Peinado, H.; Aleckovic, M.; Lavotshkin, S.; Matei, I.; Costa-Silva, B.; Moreno-Bueno, G.; Hergueta-Redondo, M.; Williams, C.; GarciaSantos, G.; Ghajar, C.; Nitadori-Hoshino, A.; Hoffman, C.; Badal, K.; Garcia, B. A.; Callahan, M. K.; Yuan, J.; Martins, V. R.; Skog, J.; Kaplan, R. N.; Brady, M. S.; et al. Melanoma Exosomes Educate Bone Marrow Progenitor Cells Toward a Pro-Metastatic Phenotype through MET. Nat. Med. 2012, 18, 883−891. (6) Andaloussi, S. E.; Mager, I.; Breakefield, X. O.; Wood, M. J. Extracellular Vesicles: Biology and Emerging Therapeutic Opportunities. Nat. Rev. Drug Discovery 2013, 12, 347−357. 678

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano (7) Im, H.; Shao, H.; Park, Y. I.; Peterson, V. M.; Castro, C. M.; Weissleder, R.; Lee, H. Label-Free Detection and Molecular Profiling of Exosomes with a Nano-Plasmonic Sensor. Nat. Biotechnol. 2014, 32, 490−495. (8) Hoshino, A.; Costa-Silva, B.; Shen, T. L.; Rodrigues, G.; Hashimoto, A.; Tesic Mark, M.; Molina, H.; Kohsaka, S.; Di Giannatale, A.; Ceder, S.; Singh, S.; Williams, C.; Soplop, N.; Uryu, K.; Pharmer, L.; King, T.; Bojmar, L.; Davies, A. E.; Ararso, Y.; Zhang, T.; et al. Tumour Exosome Integrins Determine Organotropic Metastasis. Nature 2015, 527, 329−335. (9) Melo, S. A.; Luecke, L. B.; Kahlert, C.; Fernandez, A. F.; Gammon, S. T.; Kaye, J.; LeBleu, V. S.; Mittendorf, E. A.; Weitz, J.; Rahbari, N.; Reissfelder, C.; Pilarsky, C.; Fraga, M. F.; Piwnica-Worms, D.; Kalluri, R. Glypican-1 Identifies Cancer Exosomes and Detects Early Pancreatic Cancer. Nature 2015, 523, 177−182. (10) Zomer, A.; Maynard, C.; Verweij, F. J.; Kamermans, A.; Schafer, R.; Beerling, E.; Schiffelers, R. M.; de Wit, E.; Berenguer, J.; Ellenbroek, S. I.; Wurdinger, T.; Pegtel, D. M.; van Rheenen, J. In Vivo Imaging Reveals Extracellular Vesicle-Mediated Phenocopying of Metastatic Behavior. Cell 2015, 161, 1046−1057. (11) Sheridan, C. Exosome Cancer Diagnostic Reaches Market. Nat. Biotechnol. 2016, 34, 359−360. (12) Kamerkar, S.; LeBleu, V. S.; Sugimoto, H.; Yang, S.; Ruivo, C. F.; Melo, S. A.; Lee, J. J.; Kalluri, R. Exosomes Facilitate Therapeutic Targeting of Oncogenic KRAS in Pancreatic Cancer. Nature 2017, 546, 498−503. (13) Liang, K.; Liu, F.; Fan, J.; Sun, D.; Liu, C.; Lyon, C. J.; Bernard, D. W.; Li, Y.; Yokoi, K.; Katz, M. H.; Koay, E. J.; Zhao, Z.; Hu, Y. Nanoplasmonic Quantification of Tumor-Derived Extracellular Vesicles in Plasma Microsamples for Diagnosis and Treatment Monitoring. Nat. Biomed. Eng. 2017, 1, 0021. (14) Colombo, M.; Raposo, G.; Thery, C. Biogenesis, Secretion, and Intercellular Interactions of Exosomes and Other Extracellular Vesicles. Annu. Rev. Cell Dev. Biol. 2014, 30, 255−289. (15) Kowal, J.; Arras, G.; Colombo, M.; Jouve, M.; Morath, J. P.; Primdal-Bengtson, B.; Dingli, F.; Loew, D.; Tkach, M.; Thery, C. Proteomic Comparison Defines Novel Markers to Characterize Heterogeneous Populations of Extracellular Vesicle Subtypes. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, E968−E977. (16) Tkach, M.; Thery, C. Communication by Extracellular Vesicles: Where We Are and Where We Need to Go. Cell 2016, 164, 1226− 1232. (17) Yoshioka, Y.; Kosaka, N.; Konishi, Y.; Ohta, H.; Okamoto, H.; Sonoda, H.; Nonaka, R.; Yamamoto, H.; Ishii, H.; Mori, M.; Furuta, K.; Nakajima, T.; Hayashi, H.; Sugisaki, H.; Higashimoto, H.; Kato, T.; Takeshita, F.; Ochiya, T. Ultra-Sensitive Liquid Biopsy of Circulating Extracellular Vesicles Using ExoScreen. Nat. Commun. 2014, 5, 3591. (18) Costa-Silva, B.; Aiello, N. M.; Ocean, A. J.; Singh, S.; Zhang, H.; Thakur, B. K.; Becker, A.; Hoshino, A.; Mark, M. T.; Molina, H.; Xiang, J.; Zhang, T.; Theilen, T. M.; Garcia-Santos, G.; Williams, C.; Ararso, Y.; Huang, Y.; Rodrigues, G.; Shen, T. L.; Labori, K. J.; et al. Pancreatic Cancer Exosomes Initiate Pre-Metastatic Niche Formation in the Liver. Nat. Cell Biol. 2015, 17, 816−826. (19) Chevillet, J. R.; Kang, Q.; Ruf, I. K.; Briggs, H. A.; Vojtech, L. N.; Hughes, S. M.; Cheng, H. H.; Arroyo, J. D.; Meredith, E. K.; Gallichotte, E. N.; Pogosova-Agadjanyan, E. L.; Morrissey, C.; Stirewalt, D. L.; Hladik, F.; Yu, E. Y.; Higano, C. S.; Tewari, M. Quantitative and Stoichiometric Analysis of the microRNA Content of Exosomes. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 14888−14893. (20) Consortium, E.-T.; Van Deun, J.; Mestdagh, P.; Agostinis, P.; Akay, O.; Anand, S.; Anckaert, J.; Martinez, Z. A.; Baetens, T.; Beghein, E.; Bertier, L.; Berx, G.; Boere, J.; Boukouris, S.; Bremer, M.; Buschmann, D.; Byrd, J. B.; Casert, C.; Cheng, L.; Cmoch, A.; et al. EV-TRACK: Transparent Reporting and Centralizing Knowledge in Extracellular Vesicle Research. Nat. Methods 2017, 14, 228−232. (21) Zabeo, D.; Cvjetkovic, A.; Lässer, C.; Schorb, M.; Lötvall, J.; Höög, L. L. Exosomes Purified from a Single Cell Type Have Diverse Morphology. J. Extracell. Vesicles 2017, 6, 1329476.

(22) Thery, C.; Zitvogel, L.; Amigorena, S. Exosomes: Composition, Biogenesis and Function. Nat. Rev. Immunol. 2002, 2, 569−579. (23) Baietti, M. F.; Zhang, Z.; Mortier, E.; Melchior, A.; Degeest, G.; Geeraerts, A.; Ivarsson, Y.; Depoortere, F.; Coomans, C.; Vermeiren, E.; Zimmermann, P.; David, G. Syndecan-Syntenin-ALIX Regulates the Biogenesis of Exosomes. Nat. Cell Biol. 2012, 14, 677−685. (24) Colombo, M.; Moita, C.; van Niel, G.; Kowal, J.; Vigneron, J.; Benaroch, P.; Manel, N.; Moita, L. F.; Thery, C.; Raposo, G. Analysis of ESCRT Functions in Exosome Biogenesis, Composition and Secretion Highlights the Heterogeneity of Extracellular Vesicles. J. Cell Sci. 2013, 126, 5553−5565. (25) Brisson, A. R.; Tan, S.; Linares, R.; Gounou, C.; Arraud, N. Extracellular Vesicles from Activated Platelets: a Semiquantitative Cryo-Electron Microscopy and Immuno-Gold Labeling Study. Platelets 2017, 28, 263−271. (26) van der Vlist, E. J.; Nolte-’t Hoen, E. N.; Stoorvogel, W.; Arkesteijn, G. J.; Wauben, M. H. Fluorescent Labeling of Nano-Sized Vesicles Released by Cells and Subsequent Quantitative and Qualitative Analysis by High-Resolution Flow Cytometry. Nat. Protoc. 2012, 7, 1311−1326. (27) Stoner, S. A.; Duggan, E.; Condello, D.; Guerrero, A.; Turk, J. R.; Narayanan, P. K.; Nolan, J. P. High Sensitivity Flow Cytometry of Membrane Vesicles. Cytometry, Part A 2016, 89, 196−206. (28) van der Pol, E.; Coumans, F. A.; Grootemaat, A. E.; Gardiner, C.; Sargent, I. L.; Harrison, P.; Sturk, A.; van Leeuwen, T. G.; Nieuwland, R. Particle Size Distribution of Exosomes and Microvesicles Determined by Transmission Electron Microscopy, Flow Cytometry, Nanoparticle Tracking Analysis, and Resistive Pulse Sensing. J. Thromb. Haemostasis 2014, 12, 1182−1192. (29) Zhu, S.; Ma, L.; Wang, S.; Chen, C.; Zhang, W.; Yang, L.; Hang, W.; Nolan, J. P.; Wu, L.; Yan, X. Light-Scattering Detection below the Level of Single Fluorescent Molecules for High-Resolution Characterization of Functional Nanoparticles. ACS Nano 2014, 8, 10998− 11006. (30) Ma, L.; Zhu, S.; Tian, Y.; Zhang, W.; Wang, S.; Chen, C.; Wu, L.; Yan, X. Label-Free Analysis of Single Viruses with a Resolution Comparable to That of Electron Microscopy and the Throughput of Flow Cytometry. Angew. Chem., Int. Ed. 2016, 55, 10239−10243. (31) Thery, C.; Amigorena, S.; Raposo, G.; Clayton, A. Isolation and Characterization of Exosomes from Cell Culture Supernatants and Biological Fluids. Curr. Protoc. Cell Biol. 2006, DOI: 10.1002/ 0471143030.cb0322s30. Chapter 3, Unit 3.22. (32) Lacroix, R.; Judicone, C.; Poncelet, P.; Robert, S.; Arnaud, L.; Sampol, J.; Dignat-George, F. Impact of Pre-Analytical Parameters on the Measurement of Circulating Microparticles: Towards Standardization of Protocol. J. Thromb. Haemostasis 2012, 10, 437−446. (33) Sodar, B. W.; Kittel, A.; Paloczi, K.; Vukman, K. V.; Osteikoetxea, X.; Szabo-Taylor, K.; Nemeth, A.; Sperlagh, B.; Baranyai, T.; Giricz, Z.; Wiener, Z.; Turiak, L.; Drahos, L.; Pallinger, E.; Vekey, K.; Ferdinandy, P.; Falus, A.; Buzas, E. I. Low-Density Lipoprotein Mimics Blood Plasma-Derived Exosomes and Microvesicles During Isolation and Detection. Sci. Rep. 2016, 6, 24316. (34) Hergenreider, E.; Heydt, S.; Treguer, K.; Boettger, T.; Horrevoets, A. J.; Zeiher, A. M.; Scheffer, M. P.; Frangakis, A. S.; Yin, X.; Mayr, M.; Braun, T.; Urbich, C.; Boon, R. A.; Dimmeler, S. Atheroprotective Communication between Endothelial Cells and Smooth Muscle Cells through miRNAs. Nat. Cell Biol. 2012, 14, 249−256. (35) Kanada, M.; Bachmann, M. H.; Hardy, J. W.; Frimannson, D. O.; Bronsart, L.; Wang, A.; Sylvester, M. D.; Schmidt, T. L.; Kaspar, R. L.; Butte, M. J.; Matin, A. C.; Contag, C. H. Differential Fates of Biomolecules Delivered to Target Cells via Extracellular Vesicles. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, E1433−E1442. (36) Caponnetto, F.; Manini, I.; Skrap, M.; Palmai-Pallag, T.; Di Loreto, C.; Beltrami, A. P.; Cesselli, D.; Ferrari, E. Size-Dependent Cellular Uptake of Exosomes. Nanomedicine 2016, 13, 1011−1020. (37) Cocucci, E.; Racchetti, G.; Meldolesi, J. Shedding Microvesicles: Artefacts No More. Trends Cell Biol. 2009, 19, 43−51. 679

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680

Article

ACS Nano (38) Weidle, U. H.; Scheuer, W.; Eggle, D.; Klostermann, S.; Stockinger, H. Cancer-Related Issues of CD147. Cancer Genome Proteome 2010, 7, 157−169. (39) Li, Y.; Xu, J.; Chen, L.; Zhong, W. D.; Zhang, Z.; Mi, L.; Zhang, Y.; Liao, C. G.; Bian, H. J.; Jiang, J. L.; Yang, X. M.; Li, X. Y.; Fan, C. M.; Zhu, P.; Fu, L.; Chen, Z. N. HAb18G (CD147), a CancerAssociated Biomarker and its Role in Cancer Detection. Histopathology 2009, 54, 677−687. (40) Kuipers, E. J.; Grady, W. M.; Lieberman, D.; Seufferlein, T.; Sung, J. J.; Boelens, P. G.; van de Velde, C. J.; Watanabe, T. Colorectal Cancer. Nat. Rev. Dis. Primers 2015, 1, 15065. (41) Ko, J.; Bhagwat, N.; Yee, S. S.; Ortiz, N.; Sahmoud, A.; Black, T.; Aiello, N. M.; McKenzie, L.; O’Hara, M.; Redlinger, C.; Romeo, J.; Carpenter, E. L.; Stanger, B. Z.; Issadore, D. Combining Machine Learning and Nanofluidic Technology to Diagnose Pancreatic Cancer Using Exosomes. ACS Nano 2017, 11, 11182−11193. (42) Yang, K. S.; Im, H.; Hong, S.; Pergolini, I.; Del Castillo, A. F.; Wang, R.; Clardy, S.; Huang, C. H.; Pille, C.; Ferrone, S.; Yang, R.; Castro, C. M.; Lee, H.; Del Castillo, C. F.; Weissleder, R. Multiparametric Plasma EV Profiling Facilitates Diagnosis of Pancreatic Malignancy. Sci. Transl. Med. 2017, 9, eaal3226. (43) Yang, L.; Zhu, S.; Hang, W.; Wu, L.; Yan, X. Development of an Ultrasensitive Dual-Channel Flow Cytometer for the Individual Analysis of Nanosized Particles and Biomolecules. Anal. Chem. 2009, 81, 2555−2563. (44) Zhu, S. B.; Yang, L. L.; Long, Y.; Gao, M.; Huang, T. X.; Hang, W.; Yan, X. M. Size Differentiation and Absolute Quantification of Gold Nanoparticles via Single Particle Detection with a LaboratoryBuilt High-Sensitivity Flow Cytometer. J. Am. Chem. Soc. 2010, 132, 12176−12178. (45) Bohren, C. F.; Huffman, D. R. Absorption and Scattering of Light by Small Particles; Wiley-VCH: New York, 1983. (46) The Optics of a Water Drop: Mie Scattering and the Debye Series; http://www.philiplaven.com (accessed October 19, 2016).

680

DOI: 10.1021/acsnano.7b07782 ACS Nano 2018, 12, 671−680