Combinatorial in Vitro and in Silico Approach To Describe Shear

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Letter

A Combinatorial in vitro & in silico Approach to Describe Shear-force Dependent Uptake of Nanoparticles in Microfluidic Vascular Models Verena Charwat, Isabel Olmos Calvo, Mario Rothbauer, Sebastian Rudi Adam Kratz, Christian Jungreuthmayer, Jürgen Zanghellini, Johannes Grillari, and Peter Ertl Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04788 • Publication Date (Web): 26 Feb 2018 Downloaded from http://pubs.acs.org on February 26, 2018

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Analytical Chemistry

A Combinatorial in vitro & in silico Approach to Describe Shear-force Dependent Uptake of Nanoparticles in Microfluidic Vascular Models Verena Charwat,£,‡ Isabel Olmos Calvo,†,‡ Mario Rothbauer,¥ ,‡ Sebastian Rudi Adam Kratz, ¥ Christian Jungreuthmayer, §,˥ Jürgen Zanghellini,§,£ Johannes Grillari£, and Peter Ertl¥* †

Department of Medicine III, Medical University Vienna, Austria Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria § ACIB - Austrian Centre for Industrial Biotechnology, Vienna, Austria ¥ Faculty of Technical Chemistry, Vienna University of Technology, Vienna, Austria £

KEYWORDS. microfluidics, in vitro, in silico, nanoparticle uptake, critical shear stress, endothelial cells ABSTRACT: In the present work, we combine experimental and computational methods to define the critical shear stress as an alternative parameter for nanotoxicological and nanomedical evaluations using an in vitro microfluidic vascular model. We demonstrate that our complementary in vitro & in silico approach is well suited to assess the fluid flow velocity above which clathrinmediated (active) nanoparticle uptake per cell decreases drastically although higher numbers of nanoparticles per cell are introduced. Results of our study revealed a critical shear stress of 1.8 dyn/cm2, where maximum active cellular nanoparticle uptake took place, followed by a 70% decrease in uptake up 249 nm nanoparticles at 10 dyn/cm2, respectively. The observed non-linear relationship between flow velocity and nanoparticle uptake strongly suggests that fluid mechanical forces also need to be considered in order to predict potential in vivo distribution, bioaccumulation and clearance of nanomaterials and novel nanodrugs.

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The vascular system enables blood circulation via a complex 28 vessel network to transport oxygen, nutrients, signaling mole- 29 cules, carbon dioxide, waste products and many more through 30 the entire body. The continuous motion of the blood also cre- 31 ates fluid mechanical forces, which play important roles in 32 many physiological and pathological events.1 Endothelial 33 cells, which form the inner lining of blood vessels, are highly 34 sensitive to hemodynamic shear stresses,2,3 which magnitude 35 varies greatly in different locations throughout the human 36 body depending on apparent flow velocities and local vascu- 37 lar geometries defined by vessel diameter and bifurcations 38 (e.g. branching).3 Although deviating numbers are found in 39 literature, the range of physiological shear forces starts at 40 about 1-5 dyn/cm-2 in the post-capillary venules,4,5 reaches 41 about 20-30 dyn/cm-2 in arteries and values up to 90 dyn/cm-2 42 are reported for smallest capillaries.6,7 43 Due to the importance of fluid mechanical forces, vascular 44 researchers have already implemented a variety of perfused 45 cell culture systems to study hypertension,8 platelet 46 adhesion,9,10 angiogenesis,11 and cancer extravasation.12 Addi- 47 tionally, microfluidic devices containing vascular endothelial 48 cells are increasingly being employed in biocompatibility 49 studies, drug testing and cytotoxicity tests.13-19 For instance, 50 cytotoxicity testing under physiological relevant shear force 51 conditions is of particular importance in drug development, 52 since the vasculature plays a key role in uptake and distribu- 53 tion of drugs, toxins and nanomaterials. Among these nanopar- 54

ticle - cell interactions are of particular interest due to the increased production and widespread application of engineered nanomaterials in various industries including food packaging, cosmetics, functional clothing, building industry and the medical field.20-23 In recent years, substantial progress has also been made in the field of nanomedicine to deliver nanodrugs and contrast agents with tailor-made features such as size, shape, material and functionality including stability, magnetic and electric properties, surface functionalization, responsive materials and encapsulation.24,25 Independent of the nanomaterial used, known features that influence cellular uptake, distribution and cytotoxicity include size, size variation, agglomeration state, number of particles, surface roughness and reactivity.26,27 Although microfluidic cell culture systems have already been employed to assess dose-response relationships and nanomaterial-cell interactions,22,28-34 little is still known about the influence of flow velocities and elevated shear force conditions on nanoparticle uptake rates of mammalian cells.35,36 This so far overlooked aspect is however key in understanding distribution, bioaccumulation and clearance of nanomaterials in different parts of the human circulatory system. Here, we present a combinatorial strategy based on experimental in vitro data that are used in a two-step in silico approach to estimate the critical shear stress where maximum active cellular nanoparticle uptake takes place. Knowledge on the interaction of nanoparticles with endothelial cells in the

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presence of increasing flow velocities provides for the first time a better understanding of nanoparticle resident times in vascular networks. Results shown in Figure 1 demonstrate the influence of fluid flow and nanoparticle size on the uptake behavior of endothelial cells using 49 nm and 249 nm polystyrene nanoparticles. The overall workflow, experimental setup and microfluidic design can be seen in Figure 1A and S1. Based on initial cell culture optimization results shown in Figure S2, a 16h preincubation period in the presence of 3 dyn/cm2 was employed to render endothelial cultures more in vivo-like prior the 6h nanoparticle exposure under varying shear force conditions. Follow the removal of unbound nanoparticles (1h washing step) fluorescent images were taken and analyzed. While endothelial cells displayed cobble-stone morphology at low shear, first in vivo-like morphology (e.g. cell elongation) appeared at shear forces above 1.4 dyn/cm2 followed by a pronounced morphological orientation and cell elongation along the fluid trajectory after an exposure period of 12h at shear force conditions above 3 dyn/cm2. To further exclude adverse material effects that may influence our uptake study, a preliminary biocompatibility study in the presence of increasing concentrations of fluorescent polystyrene nanoparticles were conducted. Results of the study revealed no negative effects on metabolic activity of endothelial cells in the pres- 63 ence of 49 nm (83% ± 8 vs control) and 249 nm (88% ± 11 vs 64 control) polystyrene nanoparticles at a concentration of 4% 65 (v:v), respectively. An additional quantification of dose de- 66 pendent uptake behavior pointed at a time dependent relation- 67 ship between nanoparticle size and uptake, since a signal de- 68 cline and saturation in nanoparticle uptake was observed a 12h 69 exposure period (Figure 1BC). Consequently, a nanoparticle 70 exposure period of 6 h were chosen for following microfluidic 71 experiments to ensure constant (linear) cellular uptake rates. 72 To further highlight and visualize the difference in nanoparti- 73 cle - cell interactions between static and perfused cell cultures, 74 fluorescent images of endothelial cells were taken after over- 75 night nanoparticle exposure. As shown in Figure 1D low fluo76 rescence intensity (top panel, white arrow) and depleted areas were found around individual cells indicating that nanoparticle 77 uptake occurred also from the vicinity of the cells (limited by 78 diffusion and cell movement) and not only from the apical cell 79 moiety. Interestingly, intracellular nanoparticle aggregation 80 occurred predominantly around the nucleus. In turn, under 81 microfluidic laminar flow conditions, which are known to 82 prevent nanoparticle sedimentation (bottom panel), more 83 homogeneous distribution of nanoparticles along the cell or- 84 ganelles can be observed (Figure 1D; bottom panel). To dis- 85 tinguish whether nanoparticle adhesion at the outer cell mem- 86 brane or intracellular particle uptake took place, confocal 87 microscopy was performed in subsequent experiments. Figure 88 2AB shows confocal images of endothelial cells incubated 89 with 249 nm nanoparticles displaying intracellular localization 90 of nanoparticles in the cytoplasm and around the nucleus. 91 Since various nanoparticle uptake pathways in confluent cells 92 exist, a series flow cytometric analysis were performed to 93 determine (a) the main cellular uptake route and (b) to identify 94 the ratio between active and passive uptake pathways.37 In 95 following experiments, endothelial cell cultures were incubated with 4% nanoparticle suspensions for a period of 6h in the absence and presence of increasing concentrations of saccharose.

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Figure 1. (A) Workflow of the experimental microfluidic protocol for assessment of critical shear force on endothelial microvascular models. (B, C) Quantification (plate reader and cytometry) of time and dose dependent (0.01, 0.1, 0.4 and 1 mg mL-1) uptake of 49 nm (B) and 249 nm (C) polystyrene nanoparticles using flow cytometry. Complementary data analysis derived from image analysis in situ is shown for 4% 249 nm polystyrene nanoparticles (red trace). (D) Fluorescence images of HUVEC incubated with 4% 249 nm nanoparticles overnight under static and dynamic conditions. In the static fluorescence image cell outlines are marked in white and arrows indicate the NP-depleted area around the cells. Scale bars, 50 µm.

As shown in Figure 2C (right) a concentration increase of saccharose from 0.12 M to 0.45 M reduced cellular uptake of 249 nm nanoparticles from about 90% to 5%, thus indicating that 249 nm polystyrene nanoparticles are almost exclusively transferred actively into the cell via clathrin-mediated pathway. This is in line with previously reported studies on the impact of nanoparticle size on uptake routes.38 In comparison, approx. 25% of the 49 nm nanoparticles still crossed the cell membrane in the presence of saccharose as shown in Figure 2C (left). While prolonged exposure to saccharose was used to specifically inhibit clathrin-mediated uptake pathways, the applied temperature decrease from 37 °C to 4 °C was implemented to effectively block any active uptake routes.39 Overall, results in Figure 2C show that 249 nm polystyrene nanoparticles mainly undergo active uptake mechanisms. However, since passive transfer of nanoparticles across the cell membrane may obscure the outcome of our shear-dependent uptake study, only the bigger 249 nm nanoparticles were chosen for subsequent microfluidic measurements.


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48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Figure 2. (A) Interference and fluorescence microscopy overlay of 65 blue (nucleus), red (f-actin) and green (249 nm nanoparticles) 66 show localization in the cytoplasm around the nuclei. Scale bar, 67 50 µm. (B) Top view and cross sections of a z-stack (plane resolu- 68 tion 2 µm) showing cell cytoplasm (red) and 249 nm nanoparti- 69 cles (green). (C) Identification of passive uptake and clathrin- 70 dependent endocytosis as the uptake mechanism for 49 nm and 71 249 nm nanoparticles. To inhibit active uptake samples were 72 exposed to low temperature (4ºC) and saccharose. 73 74 Initial experiments (data not shown) using labelled polysty- 75 rene nanoparticles showed a markedly decrease in fluorescent 76 intensity when using 2 and 6 µL min-1 flow rates, thus indicat- 77 ing that increased fluid velocities negatively impact cellular 78 capability to actively take up nanoparticles. To identify the 79 critical shear force parameter, where maximum active cellular 80 nanoparticle uptake of vascular cells takes place, a combinato- 81 rial in vitro & in silico analysis approach was investigated. 82 Figure 3 shows experimental and fitted data of seven microflu- 83 idic uptake experiments using confluent endothelial cell popu- 84 lations, which were exposed to 249 nm nanoparticles (4%) for 85 a period of 6h in the presence of 0, 0.045, 0.225, 2.25, 3.6, 4.5 86 and 9 µL min-1 flow rates covering 0 – 20 dyn cm-2 (see exper- 87 imental set up in Figure 1A). Experimental results in Figure 88 3A (top panel; green squares) show gradual increase of nano- 89 particle uptake up to a flow rate of approx. 2.25 µL min-1 or 4 90 dyn cm-2 due to continuous supply of nanoparticles delivered 91 to the cell surface. Interestingly, above a flow rate of 2.25 µL 92 min-1 nanoparticle uptake gradually decreased, despite the 93 linear increase in the number of nanoparticles entering the 94 microfluidic system. For example, a flow increase from 2.25 95 µL min-1 or 4.5 dyn cm-2 to 4.5 µL min-1 or 9 dyn cm-2 resulted 96 in a 70% decrease of nanoparticles taken up by endothelial 97 cells. To verify that the gradual increase of clathrin-mediated 98 nanoparticle uptake below the 4.5 dyn cm-2 threshold is caused 99 by shear-dependent increase of clathrins at the cell surface,100 endothelial cells were subjected to increasing flow rates, thus101 shear stress, for 6 h. Results shown in Figure S-5A demon-102 strate a shear-dependent increase of clathrin receptors at the103 cell surface by 1,27-fold and 1,74-fold for endothelial cells104 subjected to a shear of 1.69 dyn cm-2 and 3.5 dyn cm-2, respec-105 tively. Additionally, Figure S-5B shows that for the same106 number of nanoparticles transported to the cell surface during107 a 6 h exposure at low shear conditions a 4%the nanoparticle108 solution yielded a 2,8-fold higher uptake of 249 nm polysty-109

(bottom panel) shows two in silico approaches suited to assess wall shear forces by either using a simplified ‘parallel plate model’ (analytical, red curve; see also Figure S3A) and the more complex CFD model (green curve; see also Figure S3BC). The observed decrease of particle uptake above 2.25 µL min-1 suggests that above a critical shear stress value the interaction time between nanoparticles and the cell surface becomes limited for active particle uptake via clathrindependent endocytosis despite of the higher clathrin density. Since ligand-receptor interactions at the cell surface require sufficient interaction time to ensure receptor-ligand bond formation under fluid shear force,40,41 we hypothesize that similar effects take place during nanoparticle adhesion at the cell membrane and cellular uptake. It is, however, important to note that the local nanoparticle concentration and not the total number of particles transported over time is in addition to shear a determining factor that influences nanoparticle uptake in vascularized microfluidic systems. In a next set of computational simulations (Figure 3) the total mass of particles touching the cell surface and number of particles escaping the simulation volume via the cell membrane at different flow rates were estimated using equations given in supplementary information S2 to S4. The analytical approach was initially used to calculate the critical shear stress of τcrit = 1.7 dyn cm-2, which corresponds to a rate of 2429 escaped particles per second (Gcrit). Although robust and simple, the analytical approach cannot account for complex biological parameters such as cell shape, surface charge, cell deformability as well as particle trajectories. To overcome these limitations, the more complex CFD approach was used to accurately estimate nanoparticle movement in the microchannel, nanoparticle adhesion at the cell surface and their escape/uptake via the cell membrane in the presence of varying biophysical parameters including apparent pressure, fluid velocity, wall shear stress and kinetic energy. The physical parameters were individually calculated at each mesh point of the geometric model, while nanoparticle movement was simulated using the Starccm+'s Lagrangian particle tracking model, where trajectories of 57 000 particles moving through the microchannel were computed (see Figure S4A). Results of the simulations are shown Figure 3B revealing, for instance, an escape rate of 1871 particles per second for a simulated input flow rate of 0.045 µL min-1 (blue curve). It is evident from the simulation that the theoretical adhered particle mass (e.g. particles in contact with the cell) increases linearly (green curve), while lower numbers of escaped particles (blue curve) are obtained in the presence of increasing input flow rates. In an attempt to allow the prediction of shear-force dependent nanoparticle uptake of vascular endothelial cells, our experimental in vitro data were fitted into a flow-dependent nanoparticle uptake function provided in equation S7. Results of our combinatorial in vitro & in silico approach are shown in Figure 3C underscoring the similarity between both in silico models and experimental in vitro data. Estimated critical shear stress parameters, where maximum active uptake of 249 nm nanoparticles takes place, are τcrit of 1.79 dyn cm-2 (Gcrit = 2557 s-1) and τcrit = 1.70 dyn cm-2 (Gcrit = 2429 s-1) when using analytical and CFD simulations, respectively. In other words, 249 nm spherical polymeric polystyrene nanomaterials may be predominantly taken up in postcapillary venules where shear forces range from 1-5 dyn/cm-2.4,5 Postcapillary venules represent the microvasculature where extravasation of blood cells

rene nanoparticles compared to a 2% solution. Figure 3A



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induced by inflammatory mediators (e.g. hyperpermeability) 30 and blood-interstitial fluid exchanges takes place. 31 In conclusion, the presented combinatorial in vitro & in silico 32 approach offers, for the first time, the possibility to estimate 33 flow-dependent nanomaterial uptake of dynamic biological systems based on the determination of the critical shear force 34 parameter. It is envisioned that the critical shear force parame- 35 ter will complement known key nanomaterial factors influenc36 ing cell-nanomaterial interactions such as size, surface chemistry, roughness, density, thus facilitating the design of novel 37 nanomaterials. 38

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Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Additional figures as noted in the manuscript text, simulation details, experiment details (.pdf).

AUTHOR INFORMATION Corresponding Author * [email protected]

Present Addresses ˥ current address:

39 Department of Biomedical and Health Technologies, Vienna, 40 Austria 41 TGM – Technologisches Gewerbemuseum, HTBLuVa 42 Wien XX, Vienna, Austria 43 44 45 46

Author Contributions The manuscript was written through contributions of all authors. / All authors have given approval to the final version of the manuscript. / ‡These authors contributed equally.

47 ACKNOWLEDGMENT 48 This work has been supported by Federal Ministry of Science, 49 Research and Economy (BMWFW), the Federal Ministry of 50 Traffic, Innovation and Technology (bmvit), the Vienna Science 51 and Technology Fund (WWTF), the Styrian Business Promotion 52 Agency SFG, the Standortagentur Tirol, the Government of Low53 er Austria and ZIT—Technology Agency of the City of Vienna 54 through the COMET-Funding Program managed by the Austrian 55 Research Promotion Agency FFG, and the European Union’s 56 Horizon 2020 research and innovation programme under grant 57 agreement No. 685817. The authors thank Carina Huber-Gries 58 (FH Technikum, Vienna), the BioSensor Technologies unit of 59 AIT Austrian Institute of Technology GmbH (Vienna) and Medi60 cal University Vienna for supporting the project.

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61 62 63 64 65 66 67 68 Figure 3. (A) Flow rate dependent uptake of 249 nm nanoparticles 69 by endothelial cells using the combinatorial method based on 70 experimental results (green squares, top panel) and a mathemati71 cal fitting function (blue curve, top panel), as well as the positionindependent wall shear stress (bottom panel, red curve) and the 72 critical wall shear stress (bottom panel, blue curve) as a function 73 of input flow rate. (B) Rate of particles escaping the simulation 74 volume via cell surfaces as function of the input flow rate (blue line, left y-axis) with mass of adhered nanoparticles on the cell 75 surface. (red and green lines, right y-axis). (C) Combinatorial 76 approach to identify critical shear stress parameters obtained by 77 experimental in vitro (fitted blue line) and in silico CFD ap78 proaches (red and green symbols). 79 80 ASSOCIATED CONTENT 81

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Combinatorial in Vitro and in Silico Approach To Describe Shear

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