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#-DNA and Aptamer Mediated Sorting and Analysis of Extracellular Vesicles Chao Liu, Junxiang Zhao, Fei Tian, Jianqiao Chang, Wei Zhang, and Jiashu Sun J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/jacs.9b00007 • Publication Date (Web): 21 Feb 2019 Downloaded from http://pubs.acs.org on February 21, 2019

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λ-DNA and Aptamer Mediated Sorting and Analysis of Extracellular Vesicles Chao Liu,†, ‡ Junxiang Zhao,†, ‡ Fei Tian,† Jianqiao Chang,† Wei Zhang,†, ‡ and Jiashu Sun*,†, ‡ † CAS

Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China. ‡ University of Chinese Academy of Sciences, Beijing 100049, China. Supporting Information Placeholder ABSTRACT:

Extracellular vesicles (EVs) are heavily implicated in diverse pathological processes. Due to their small size, distinct biogenesis, and heterogeneous marker expression, isolation and detection of single EV subpopulations are difficult. Here we develop a λ-DNA and aptamer mediated approach allowing for simultaneous size-selective separation and surface protein analysis of individual EVs. Using a machine learning algorithm to EV signature based on their size and marker expression, we demonstrate that the isolated microvesicles are more efficient than exosomes and apoptotic bodies in discriminating breast cell lines, and Stage II breast cancer patients with varied immunohistochemical expression of HER2. Our method provides an important tool to assess the EV heterogeneity at the single EV level with potential value in clinical diagnostics.

Extracellular vesicles (EVs) are secreted membrane-enclosed vesicles, playing important roles in intercellular communication and participating in different pathological processes.1 Tumorderived EVs contain surface protein and genetic information reflective of their parent cells and circulate abundantly in the blood, serving as potential biomarkers for early diagnosis and monitoring of tumors.2-7 EVs are mainly classified into three subpopulations, i.e. exosomes (EXOs, 30 to 200 nm in size), microvesicles (MVs, 200 – 1000 nm), and apoptotic bodies (ABs, larger than 1000 nm) depending on their biogenesis and size.8-9 To investigate the heterogeneity of EVs and their diagnostic value, high-resolution separation and sensitive detection of individual EV subpopulations are critically required. Current techniques to isolate and purify EV subpopulations rely on time-consuming and labor intensive ultracentrifugation and ultrafiltration using large volumes of cell culture medium or serum samples.10 Commercially viable methods for profiling and analysis of EV subpopulations, such as enzyme-linked immunosorbent assays, western blotting, and flow cytometry, may not be sensitive enough to assess EV-to-EV variability.11-13 Recent single-EV studies by advanced analytical tools, such as Laser tweezer Raman spectroscopy, microfluidic immunoassay, and high-sensitivity flow cytometry unexpectedly revealed that individual EVs from the same cell type displayed substantial heterogeneity.12, 14-18 However, the requirement of isolation of EV subpopulations prior to detection limits the wide acceptance of these single EV analysis tools. Therefore, new and effective methods for simultaneous separation of EV subpopulations and

detection of individual EVs in small samples become a realistic goal. DNA molecules provide the potential means to sort and detect EVs in raw samples. The solution of double-stranded λ-DNA molecules has a strong elastic effect, allowing for hydrodynamic focusing or sorting of particles within a microchannel.19 Aptamers are short single-stranded oligonucleotides exhibiting high affinity and selectivity for a given target, showing great promising for detection of EV surface proteins.20-21 Here we report a λ-DNA and

Figure 1. Schematic of λ-DNA mediated sorting of EV subpopulations and aptamer based analysis of individual EVs. (a) Labeling of cell-originating EVs including exosomes (EXOs, red), microvesicles (MVs, green), and apoptotic bodies (ABs, blue) with fluorescent HER2 and EpCAM aptamers. (b) Size-selective separation of EV subpopulations by λ-DNA mediated viscoelastic microfluidics. Fluorescence microscopy images showed HER2 (red) and EpCAM (green) expression of isolated individual EVs. Scale bar, 5 μm.

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Figure 2. Size-selective separation of nano/microparticles mediated by λ-DNA. Scale bars from top to bottom, 150 μm, 10 μm, and 250 μm.

the sheath fluid was 1× TBE. The sample and sheath flow rates were 50 μL h-1 and 5000 μL h-1, respectively. An efficient separation of EV subpopulations was observed as indicated by predominant amounts of red (DiD-labeled EXOs), green (DiIlabeled MVs), and blue dots (DiO-labeled ABs) at Outlets 1, 2, and 3, respectively (Figure S9a). NTA characterization of isolated EVs showed 96 % separation efficiency and 91 % recovery for EXOs with a cut-off size of 200 nm at Outlet 1, and 94 % separation efficiency and 92 % recovery for MVs at Outlet 2 (Figure S9b). The separation efficiency and recovery for ABs were 93 % and 89 % determined by microscopic counting of ABs at Outlet 3. TEM imaging of EVs confirmed that all EV subpopulations remained intact after size-selective microfluidic separation (Figure S9c). Immuno-staining of exosomal marker TSG101 of isolated EVs showed a much higher expression level of TSG101 of EVs at Outlet 1 than those at Outlet 2, verifying a nearly absolute separation of EXOs and MVs by microfluidic sorting (Figures S9d and S10). In comparison, conventional ultracentrifugation combined with ultrafiltration was inefficient in separating EXOs and MVs as indicated by similar expression levels of TSG101 of bulk isolated EXOs and MVs. Together, our data indicated an improved separation of EV subpopulations by λDNA mediated viscoelastic microfluidics.

aptamer mediated approach for isolation and detection of EV subpopulations at the single-EV level. DNA aptamers (specific to HER2 and EpCAM) are first labeled onto EV surfaces followed by size-selective separation of EV subpopulations by λ-DNA mediated viscoelastic microfluidics, allowing for two-dimensional analysis of single EVs by size and marker expression (Figure 1). A machine learning algorithm is applied to analyze EV subpopulation signature for discrimination of different breast cell lines and breast cancer patients with varied HER2 expression. A microfluidic co-flow system of viscoelastic sample fluid and Newtonian sheath fluid was designed for size-selective separation of EXOs, MVs, and ABs within 30 min (Figure S1). The sample flow containing three EV subpopulations and viscoelastic λ-DNA (48.5 kbp, 150 ppm) was initially aligned along the microchannel centerline by sheath flow (1× TBE solution) (Figure S2). EXOs with sizes smaller than 200 nm were immersed in the viscoelastic sample fluid under the centerline-directed elastic lift force Fe.22 In contrary, MVs and ABs with sizes similar or larger than 200 nm were repelled by the Fe directing towards the Newtonian sheath stream without elasticity (Figure 1b).23 The competition between Fe ( Fe  a 3 in which a is the size of EV), inertial lift force Fi ( Fi  a 4 ), and viscous drag force Fd ( Fd  a ) resulted in distinct lateral positions for EXOs, MVs, and ABs at the downstream (Figure 1b and Figure S3).24-26 To visualize the separation process in the microfluidic co-flow system, we used fluorescent polystyrene particles of different sizes (100, 200, 300, 500, and 2000 nm in diameter) to represent EXOs, MVs, and ABs. As expected, different sized particles with initial position around the centerline migrated laterally toward the sidewalls in a sizedependent manner under the optimized flow conditions. 100 and 200 nm particles, 300 and 500 nm particles, and 2000 nm particles were collected at Outlets 1, 2, and 3, respectively (Figure 2 and Figures S4-7). We also recorded the particle trajectories without the use of λ-DNA by the same microfluidic device, and no separation was observed as a result of the lack of Fe (Figure S8). The separation of EV subpopulations by the microfluidic coflow system was characterized by fluorescence microscopy, nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and immuno-staining. The sample fluid was the mixture of EVs with 150 ppm λ-DNA in 1× TBE buffer and

Figure 3. Heterogeneous expression of HER2 and EpCAM on isolated individual EVs from different breast cell lines. (a) Fluorescence images of isolated EXOs, MVs, and ABs from BT474 cell line. Scale bar, 20 μm. (b) Heatmap of the percentage of HER2- or EpCAM-positive EVs (EXOs, MVs, and ABs) from five cell lines. (c) The expression levels of HER2/EpCAM on individual EVs from BT-474 cell line by fluorescence intensity measurement.

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Figure 4. Discrimination of breast cell lines by EV signature. (a) t-SNE visualization of discrimination of cell lines by different EV subpopulations. (b) MV signature showing the best performance in discriminating cell lines. For detection of EV subpopulations, we incubated DiI-labeled EVs with Cy5-conjugated HER2 (human epidermal growth factor receptor 2) aptamer and FAM-conjugated EpCAM (epithelial cell adhesion molecule) aptamer before sorting (Table S1). Target proteins on EV surface could be recognized efficiently by small aptamers (2 – 3 nm in size), allowing for aptamer-based single EV analysis with high signal-to-noise ratios (Figures S11-13).27-28 To evaluate whether the experimental conditions affected the aptamer affinity to targets, we counted the number of HER2/EpCAMpositive EVs from SK-BR-3 cell line by fluorescence microscopy with varied parameters. We observed that the presence of λ-DNA during separation or the binding of two aptamers to EVs did not affect EV surface protein detection (Table S2). The ratio of red (Cy5)/green (FAM) to blue (DiI) fluorescent dots indicated the percentage of HER2/EpCAM-positive EV subpopulations. The fluorescence intensity of red/green dots represented the expression levels of HER2/EpCAM on individual EVs. In this manner, a DNA-mediated microfluidic platform for isolation and profiling of EV subpopulations was implemented. We next analyzed the HER2 and EpCAM profiles on isolated EV subpopulations (EXOs, MVs, and ABs) from breast cancer cell lines (HER2-positive BT-474 and SK-BR-3, and HER2negative MDA-MB-231 and MCF-7) and human mammary epithelial cell line (MCF-10A) (Figure S14).29-30 As shown in Figure 3a, HER2 and EpCAM expression on isolated individual EVs from BT-474 cell line was clearly indicated by red and green fluorescence, and the presence of both HER2 and EpCAM on the same EV was also observed. The percentage of HER2-positive EV subpopulations (EXOs, MVs, and ABs) from BT-474 and SKBR-3 was much higher than those from MDA-MB-231, MCF-7, and MCF-10A, indicating that HER2 overexpression occurred in EV subpopulations derived from HER2-positive cell lines (Figure 3b). EpCAM-positive cell lines (BT-474, SK-BR-3, and MCF-7) produced more EpCAM-positive EXOs, MVs, and ABs than EpCAM-negative cell line (MDA-MB-231 and MCF-10A).31 The expression levels of HER2/EpCAM on individual EXOs, MVs, and ABs were obtained by fluorescence intensity measurement, which varied significantly among individual EVs (Figure 3c and Figure S15). The mean expression levels of HER2 and EpCAM on EV subpopulations from the same cell line were also different, which was verified by dot blotting (Figure S16). These analyses revealed the heterogeneous marker expression on single EV subpopulations. To discriminate five different breast cell lines (BT-474, SKBR-3, MDA-MB-231, MCF-7, and MCF-10A), t-distributed stochastic neighbor embedding (t-SNE) and linear discriminant analysis (LDA) were applied to the EV subpopulation signature (a

Figure 5. Discrimination of the Stage II breast cancer patients with varied expression patterns of HER2 and healthy controls. (a) The concentrations of HER2-positive EXOs, MVs, and ABs in the clinical cohort. (b) Comparison of expression levels of HER2 and EpCAM on individual EXOs, MVs, and ABs between a cancer patient and a control. (c) t-SNE visualization of discrimination among cancer patients with varied HER2 expressions and controls by different EV subpopulations. combination of the percentage of HER2- and EpCAM-positive EVs and the mean expression level of HER2 and EpCAM on individual EVs) (Figure S17). The t-SNE plot and the sum of LDA scores (Ψ) indicated that MVs attained the best performance to classify cell lines than EXOs, ABs, and EVs (Figure 4 and Figures S18-19). The variation of performance among EV subpopulations may arise from their distinct biogenesis. EXOs are formed and released into extracellular environment upon fusion of multivesicular body (MVB) and plasma membrane, MVs are produced by outward budding of plasma membrane, and ABs are released as plasma membrane blebs during apoptosis. The resemblance of the cell membrane and MV membrane is relatively high. As conventional approaches were ineffective in isolating EV subpopulations and investigating biomarker profiles of single EVs, the molecular information of individual EV subpopulations was neglected in most studies. This single EV analysis platform was subsequently applied to discriminate the Stage II breast cancer patients with varied immunohistochemical expression of HER2 (n = 7: 5 for HER2 (3+/2+) and 2 for HER2 (1+/0) expressions) and healthy controls (n = 4) (Table S3). EV subpopulations in diluted serum samples could be sorted by size efficiently through λ-DNA mediated approach, as indicated by a much higher expression level of TSG101 for EVs collected at the Outlet 1 than those at the Outlets 2 and 3 (Figures S20). The concentrations of HER2-positive EXOs, MVs, and ABs for HER2 (3+/2+) patients were significantly higher than those for HER2 (1+/0) patients and controls, while the concentrations of EpCAM-positive EXOs, MVs, and ABs for breast cancer patients were higher than those for controls (Figure 5a and Figures S21-22). A given cancer patient (No. 1) showed higher mean expression levels of HER2 and EpCAM on individual EV subpopulations than a healthy control (No. 8) (Figure 5b), whereas no significance was observed

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between two cohorts (Figure S23). Using the concentrations of HER2- and EpCAM-positive EVs as the input for the LDA algorithm, a clear discrimination by MVs among cancer patients with varied HER2 expressions and controls was found (Figure 5c). The scatter plot of the sum of LDA scores (Ψ) also verified that MVs provided the highest discrimination ability among clinical samples (Figure S24). In conclusion, this work provided the first DNA-mediated approach for simultaneously sorting and detecting individual EV subpopulations. This platform enabled deciphering of the heterogeneity of single EVs, and discriminating breast cell lines and breast cancer patients through analysis of EV signatures. Further work will be attempted to expand the panel of aptamers for the multiple detection of EV markers, and to establish the diagnostic value of EV subpopulations in other types of cancers.

ASSOCIATED CONTENT Supporting Information Experimental details and data. The Supporting Information is available free of charge on the ACS Publications website.

AUTHOR INFORMATION Corresponding Author [email protected]

ORCID Jiashu Sun: 0000-0003-4255-6202

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT This work was supported financially by NSFC (21622503 and 21475028), and Youth Innovation Promotion Association CAS (2016035).

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