PLGA-PEI and PLGA-Cy5.5 materials
Poly (D,L-lactide-co-glycolide) (PLGA, 50:50, MW 38000-54,000; Sigma-Aldrich, St. Louis, MO, USA) and polyethylenimine (MW ~ 25,000, Mn ~ 10,000 (PEI, Sigma-Aldrich) were used to synthesize the PLGA-PEI copolymer. N,N’-Dicyclohexylcarbodiimide (DCC) and N-hydroxysuccinimide (NHS) were prepared as a linker between the PLGA and PEI. PLGA (330 mg) and 113.67 mg of PEI, 18.33 mg of DCC and 11 mg of NHS were dissolved in 100 mL of dimethylsulfoxide (DMSO) solution and incubated for 24 h. After the completion of this reaction, the reaction mixture was dialyzed (MW cutoff = 10,000) with deionized water to remove excess DCC, NHS, and PEI. After dialysis, the polymer solution was freeze dried to obtain the powdered sample.
To synthesize the PLGA-Cy5.5 fluorescence polymer, 330 mg of PLGA was dissolved in DMSO along with 60 mg of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC, Thermo Fisher Scientific, Waltham, MA, USA), 132 mg of NHS, and 9.9 mg of Cy5.5 (Cy5.5, red; Lumiprobe Co., Hannover, Germany), and incubated for 24 h. After the completion of this reaction, the reaction mixture was extensively dialyzed (MW cutoff = 10,000) with deionized water to remove excess EDC, NHS, and Cy5.5. The resulting PLGA-Cy5.5 polymer was freeze dried to obtain the powdered sample [15].
Synthesis of polymeric clustered SPIO (PCS) NPs
To manufacture the positively charged surface-modified NPs (PLGA-PEI PCS NPs), 0.1 mg of the PLGA-PEI copolymer and 0.4 mg of PLGA-Cy5.5 were dissolved in 0.1 mL DMSO. Iron oxide (II, III) magnetic NPs solution (0.1 mL containing 5 mg/mL) was added drop-wise to 3 mL of deionized water. The vial was vortexed for 5 min followed by 3 min of sonication. The solution mixture was stirred at room temperature for 6 h. At the end of the process, the obtained NPs were further purified by ultra-centrifugation and stored at 4°C until further use.
PLGA and SPIO were used to manufacture the negatively charged, surface-modified NPs. EDC and NHS were prepared as a linker between the Cy5.5 fluorescence molecule that was synthesized for the detection of NPs in the cells. Specifically, these PCS NPs, otherwise known as PLGA-Cy5.5, were synthesized by EDC-NHS coupling. For this, 330 mg of PLGA was dissolved in DMSO along with 60 mg of EDC, 132 mg of NHS, and 9.9 mg of Cy5.5, and incubated for 24 h. After the completion of this reaction, the reaction mixture was extensively dialyzed (MW cutoff = 10,000) with deionized water to remove excess EDC, NHS and Cy5.5. The resulting PLGA-Cy5.5 particles were freeze dried to obtain the powdered samples. We used 100% PEI to create positively charged NPs, and 100% PLGA to make negatively charged NPs. In addition, it has been made positively charged NPs with the composition of PLGA:PEI in an 8:2 ratio.
Characterization of NPs
The PCS NPs with a polymeric shell consisting of 10 nm diameter SPIOs in the core were prepared using an oil-in-water emulsion method. In a typical experiment, 1 mg of PLGA-Cy5.5 was dissolved in acetonitrile at 25°C. SPIO (0.1 mL containing 5 mg/mL) was added drop-wise to 3 mL of deionized water. Thereafter, the vial was vortexed for 5 min followed by 3 min of sonication. The solution mixture was stirred at room temperature for 6 h. At the end of the process, the obtained NPs were further purified by ultra-centrifugation and stored at 4°C until further use. For comparison, we purchased positively charged (FluidMAG-Chitosan, cat. 4118-1; chemicell GmbH, Berlin, Germany) and negatively charged (FluidMAG-CMX, cat. 4106-1; chemicell GmbH) NPs 100 nm in size. The surface charge was reported to be + 50 mV for FluidMAG-Chitosan [14] and -14 mV for FluidMAG-CMX [30].
The size and surface zeta potential of the NPs were obtained by dynamic light scattering using a Zetasizer-ZS90 apparatus (Malvern Instruments, Malvern, UK). The morphology of the particles was characterized by scanning electron microscopy (SEM) using a JSM-7100F instrument (JEOL, Tokyo, Japan). The samples for SEM were prepared by adding a droplet of the NP suspension (2 μL) to a polished silicon wafer. After drying the droplet at room temperature for 4 h, the sample was coated with platinum and imaged using SEM. To further understand the internal SPIOs core, TEM measurements were performed. The samples for TEM were prepared by the drop casting method [15, 25] over a carbon grid.
Cell culture
MSCs (cat. MUBMX-01001) and green fluorescent protein (GFP)-labeled MSCs (cat. MUBMX-01101) were purchased from Cyagen (Santa Clara, CA, USA). The cells were cultured in MEM-alpha containing 10% fetal bovine serum and 1× antibiotics. The medium was changed every 2 days after washing with Dulbecco's phosphate-buffered saline in a humidified atmosphere of 5% CO2.
Flow cytometry analysis
The GFP-labeled MSCs were prepared in 60 mm culture dishes (1×105 cells/well). After PCS NPs were added to the cells for 24 h, the cells were washed twice with PBS. Thereafter, samples were analyzed by flow cytometry using a FACS Aria3 flow cytometer (Becton Dickinson, San Jose, CA, USA). The data were analyzed using FACS Diva software.
Viability assay
Cell viability was measured by counting viable cells using by the EZ-3000 CCK kit (Dogen, Seoul, Korea) (CCK-8). The MSCs were seeded (5×102 cells/well) into 96-well plates and incubated at 37°C in 5% CO2 for 24 h. The next day, the culture medium was replaced with 200 μL of fresh medium containing different concentrations of PCS NPs. After incubation, 20 μL of CCK-8 was added and the cells were protected from light. The absorbance values at 450 nm were measured with an enzyme-linked immunosorbent assay reader after 4 h incubation.
Fluorescence analysis
The MSCs and GFP-labeled MSCs were seeded (5×103 cells/well) into 4-well cell culture plates. The culture medium was replaced with fresh medium after 24 h of incubation and NPs were added. The MSCs were washed twice with PBS after incubation with NPs, fixed in 10% paraformaldehyde, and stained with 4′,6-diamidino-2-phenylindole (DAPI). Even though the NPs labeled with Cy5.5 (red) were easy to detect in cells, the cellular organelles were stained with specific antibodies in the cells. The primary antibodies used were anti-EEA1 (ab2900; Abcam, Cambridge, MA, USA) for early endosomes, anti-Rab7 (Aab126712; Abcam) for late-endosomes, and anti-GM130 (ab169276; Abcam) for the Golgi apparatus. To localize the PCS NPs in cellular organelles, goat anti-rabbit IgG H&L (Alexa fluor 488, green) secondary antibody (ab150077; Abcam) was used. In addition, Lyso-Tracker green DND-26 (cat. L7526; Life Technology, Carlsbad, CA, USA) was used to track the final location of the NPs in the cells. All samples were observed by confocal microscopy (Carl Zeiss Microscopy GmbH, Jena, Germany) and images were analyzed using ZEN lite ver. 2.3 software.
Inhibitors
Two inhibitors for the internalization of NPs were used: Dynasore (ab120192, Abcam) and Pitstop2 (SML-1169, Sigma-Aldrich). The concentration of both was 40 μM/dish and they remained active within 3 h prior to NP treatment. Dynasore inhibits dynamin, which is an important factor in receptor-mediated endocytosis 51. Pitstop2 inhibits clathrin-mediated endocytosis in the receptor-mediated endocytosis pathway. Both inhibitors were added to MSCs for 30 min before treatment with positively charged PCS NPs. CID 1067700 (cat. 2184, Axon Medchem, Groningen, Netherlands) was purchased to inhibit Rab7 by competitive inhibition.
TEM
Cells were fixed in 2.5% glutaraldehyde for 2 h at 4ºC and the specimens were solidified on 2% agar. Samples were post fixed in 1% osmium tetroxide after being washed with 0.1 M cacodylate buffer. Dehydration was performed in a step-wise manner using 50% to 100% ethanol. The samples were embedded in Epon resin. Ultrathin sections were cut using an ultra-microtome and stained using uranyl acetate and lead citrate. TEM was performed at 63 kV.
Preparation of exosomes
The medium of cultured MSCs was obtained to isolate exosomes using Total Exosome Isolation reagent (4478359; Invitrogen, Carlsbad, CA, USA), strictly according to the manufacturer’s instructions. PBS was used to re-suspend the purified exosomes. Exosomes were kept either at -80°C for long-term preservation or at -20°C for short-term preservation. Exosomal proteins were isolated using lysis buffer containing 0.1% Triton-X100 in PBS with a protease inhibitor cocktail.
Real-time RT-PCR
To prepare mRNA samples a total of 10 µL, including 2 µL of mRNA and 8 µL of reverse transcriptase reagents, was used. The mixture included 1 µL of 10× enzyme mix, 2 µL of 5× enzyme reaction buffer, and 5 µL of nuclease-free water. According to the manufacturer’s protocol on cDNA synthesis kit (TOYOBO, Osaka, Japan), were incubated at 42°C for 60 min and deactivated at 95°C for 5 min. cDNA was diluted 1:10 with 90 µL nuclease-free water for miRNA real-time RT-PCR.
Real-time RT-PCR was performed using the sequence detection system 7900 (Applied Biosystems, Foster City, CA, USA). Samples were subjected to reverse transcription using the SYBR Select master mix (Applied Biosystems) following the manufacturer’s protocol. The following primers were used for sequencing: 18S rRNA, forward: 5’- CTA ACC CGT TGA ACC CCA TT - 3’ and reverse: 5’- CCA TCC AAT CGG TAC TAG CG - 3’; Beclin-1, forward: 5’ - GGA GCT GGA AGA TGT GGA AA - 3’ and reverse: 5’ - ACT CCA GCT GCC TTT TA - 3’; LC3-α, 5’- GCC TGT CCT GGA TAA GAC CA - 3’ and reverse: 5’- GGT TGA CCA GCA GGA AGA AG - 3’; LC3-β, 5’- CCG AGA CCT TCA AGC AG - 3’ and reverse: 5’- CTC CCC CTT GTA TCG CTC TAT - 3’; p62, 5’- GTC TTG GGG AAG GGT TCA AT - 3’ and reverse: 5’-GGG GGT CCA AAG ACT TCA AT - 3’; ATG5, 5’- ATA TGA AGG CAC ACC CCT GA - 3’ and reverse: 5’ - CAT CCT TGG ATG GAC AGT GTA G - 3’; STX17, 5’- GAT CCC AAC AGA CCT GGA GA- 3’ and reverse: 5’- GAT ATT GGA GCG CAG TTG CT - 3’. The total reaction volume of 10 µL included 1 µL cDNA, 5 µL of premix, 1 µL of 10 pmol forward and reverse primers, and 3 µL of nuclease-free water. Real-time PCR cycle conditions were pre-amplification at 95°C for 3 min, 40 cycles of 95°C for 10 sec, 60°C for 60 sec, and melting curve analysis. All real-time PCR assays were performed in duplicate. Normalization of mRNA expression level was calculated using 18S rRNA.
miRNA analysis in exosomes
RNA samples were obtained from the medium treated with 5 or 20 μg/mL PCS NP for 24 h. The concentration of the sample was confirmed through Quality Control analysis and was confirmed to be between 0.17 and 0.26 ng/μL. To check the purity and quantity of RNA, a nanodrop was used to measure the absorbance at 260 nm and 280 nm. All raw data were extracted automatically in the Affymetrix data extraction protocol using the provided Affymetrix GeneChip® Command Console® Software (AGCC). The CEL files were imported, miRNA levels were normalized using the RMA algorithm, the detection above background p-values were calculated for all data, and the results were exported using the Affymetrix® Power Tools (APT) Software.
Array data were filtered by species-specific annotated probes. The comparative analysis between test and control samples was carried out using fold change. All statistical testing and visualization of differentially expressed genes was conducted using R statistical language 3.3.3 (https://www.r-project.org/).
Magnetic attraction
MSCs were sub-cultured (2×10 5 cells) and incubated in a 60 mm dish. The next day, the NPs were added and a magnet the same size at the plate was immediately placed under the floor of the plate. The reaction time was changed to 1, 6, and 24 h, and then the cells were washed with PBS. Cells were detached with trypsin and the mean intensity value was measured using FACS in a round vial. In addition, electron microscopy images were analyzed in accordance with the TEM test conditions.
Imaging analysis quantification of exosome amounts
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Statistical analyses
Real-time PCR results were analyzed to compare autophagy mRNA levels, Mann-Whitney U test and receiver operating characteristic curve analysis were performed using SPSS version 24.0 (IBM, Armonk, NY, USA). All graphs were plotted with GraphPad PRISM version 5.0 (GraphPad Inc., La Jolla, CA, USA). The sensitivity and specificity of the statistically significant mRNAs were analyzed with GraphPad PRISM version 5.0. p-values < 0.05 were considered to be statistically significant.