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. 2020 Aug 5;12(8):734.
doi: 10.3390/pharmaceutics12080734.

In Vitro Study of Extracellular Vesicles Migration in Cartilage-Derived Osteoarthritis Samples Using Real-Time Quantitative Multimodal Nonlinear Optics Imaging

Affiliations

In Vitro Study of Extracellular Vesicles Migration in Cartilage-Derived Osteoarthritis Samples Using Real-Time Quantitative Multimodal Nonlinear Optics Imaging

Leonardo Mortati et al. Pharmaceutics. .

Abstract

Mesenchymal stromal cells (MSCs)-derived extracellular vesicles (EVs) are promising therapeutic nano-carriers for the treatment of osteoarthritis (OA). The assessment of their uptake in tissues is mandatory but, to date, available technology does not allow to track and quantify incorporation in real-time. To fill this knowledge gap, the present study was intended to develop an innovative technology to determine kinetics of fluorescent MSC-EV uptake by means of time-lapse quantitative microscopy techniques. Adipose-derived mesenchymal stromal cells (ASCs)-EVs were fluorescently labeled and tracked during their uptake into chondrocytes micromasses or cartilage explants, both derived from OA patients. Immunofluorescence and time-lapse coherent anti-Stokes Raman scattering, second harmonic generation and two-photon excited fluorescence were used to follow and quantify incorporation. EVs penetration appeared quickly after few minutes and reached 30-40 μm depth after 5 h in both explants and micromasses. In explants, uptake was slightly faster, with EVs signal overlapping both extracellular matrix and chondrocytes, whereas in micromasses a more homogenous diffusion was observed. The finding of this study demonstrates that this innovative technology is a powerful tool to monitor EVs migration in tissues characterized by a complex extracellular network, and to obtain data resembling in vivo conditions.

Keywords: cartilage; coherent anti-stokes raman scattering; extracellular vesicles; mesenchymal stem cells; microscopy; osteoarthritis; second harmonic generation; time-lapse; two-photon excitation fluorescence.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Characterization of ASCs and ASC-EVs. (a) Flow cytometry analysis of MSC (CD73, CD90 and CD44) and hemato-endothelial (CD31, CD34 and CD45) markers, presence and absence, respectively, confirming ASCs identity. Representative plots are shown. (b) Representative NTA plot of ASC-EVs; (c) Transmission electron micrographs of ASC-EVs showing particles with characteristic cup-shaped morphology. (d) Flow cytometry of FITC-labeled calibrating nanobeads (framed in red, from 160 to 500 nm) assuring calibration of flow cytometer and comparison with CFSE-labeled ASC-EVs (framed in green); (e) Presence of MSC-markers CD44, CD73 and CD90 on CFSE-labeled ASC-EVs. Representative plot is shown under the FITC + gate of EVs + CFSE; (f) Presence of EV-markers CD9 (weak), CD63 and CD81 on CFSE-labeled ASC-EVs. Representative plots are shown under the FITC + gate of EVs + CFSE.
Figure 2
Figure 2
ASC-EVs endpoint incorporation in chondrocyte micromasses. (a) Transversal section of a representative chondrocyte micromass not treated with CFSE-EVs showing absence of background fluorescence. A representative picture is shown. (b through d) Increasing magnification of a representative chondrocyte micromass after incubation with CFSE-EVs, with the original region of panel c depicted in panel b by the white square as well as the original region of panel d being squared in panel c. It is possible to observe a homogenous signal all over the micromass section, including the center of the pellet (b) and, with a higher magnification (c and d), fluorescence results associated with both chondrocytes and intercellular matrix. Representative pictures are shown.
Figure 3
Figure 3
Three-dimensional (3D) reconstructions of the pellet micromass during the time-lapse. In column (a) the CARS signal at 2848 cm−1 originating mainly by the cells lipid structures is shown in red, in column (b) the TPEF signal related to the EVs fluorescence is shown in green, while in column (c) the two channels are merged. In the top right corner of each image is depicted the related timeframe and close to the boundary box are shown the dimensions in μm starting from the origin vertex.
Figure 4
Figure 4
3D reconstructions of the cartilage during the time-lapse. In column (a) the CARS signal is shown in red at 2848 cm−1 originating mainly in the cells lipid structures; in column (b) the SHG signal generated by the collagen is shown in blue; in column (c) the TPEF signal related to the EVs fluorescence is shown in green. In column (d) all the three signals are visualized together, while in column (e) only the CARS and the TPEF are visualized, and in column (f) only the SHG and the TPEF are visualized. In the top right corner of each image the related timeframe is depicted and close to the boundary box the dimensions in μm are shown starting from the origin vertex.
Figure 5
Figure 5
Variation of the co-localization ratios of the EVs with respect to the SHG signal and to the CARS signal. SHG is related to the collagen and thus the extracellular matrix, shown in blue. CARS is related to the lipid cell structures, shown in red. The curves indicate the maximum co-localization value and the average co-localization value computed from the set of Z slices composing the Z-stack for each timeframe.
Figure 6
Figure 6
3D surfaces of the thicknesses extracted from the acquired and processed data. (a) The 3D surface on the left is related to the thickness of the cartilage cell structure computed from the CARS signal, in the center, the 3D surface is related to thickness of the collagen and thus the ECM from the SHG signal, while on the right the 3D surface is related to the EV penetration depths in the cartilage after 5 h from the TPEF signal; (b) the 3D surfaces show the dynamic of the EV penetration in the cartilage during the time-lapse; in the top right corner of each image the related timeframe is depicted; (c) the 3D surfaces show the dynamic of the EV penetration in the chondrocytes micromass during the time-lapse, in the top right corner of each image the related timeframe is depicted.
Figure 6
Figure 6
3D surfaces of the thicknesses extracted from the acquired and processed data. (a) The 3D surface on the left is related to the thickness of the cartilage cell structure computed from the CARS signal, in the center, the 3D surface is related to thickness of the collagen and thus the ECM from the SHG signal, while on the right the 3D surface is related to the EV penetration depths in the cartilage after 5 h from the TPEF signal; (b) the 3D surfaces show the dynamic of the EV penetration in the cartilage during the time-lapse; in the top right corner of each image the related timeframe is depicted; (c) the 3D surfaces show the dynamic of the EV penetration in the chondrocytes micromass during the time-lapse, in the top right corner of each image the related timeframe is depicted.
Figure 7
Figure 7
Dynamics of the EV uptake in the samples during the time-lapse experiment in terms of the average thickness of penetration, the average occupied area and the average occupied volume. (a) the dotted curves show the average EV penetration depth in the cartilage (blue) and the chondrocytes pellet (red), while the continue curves plot the related exponential fit for the cartilage (blue) and the chondrocytes pellet (red); (b) the dotted curves show the average occupied area of the EVs in the cartilage (blue) and the chondrocytes pellet (red); (c) the dotted curves show the average occupied volume of the EVs in the cartilage (blue) and the chondrocytes pellet (red).

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