est destinée au dépôt

After careful analysis of the GEMC results for phase composition presented in the paper, we noticed that the data at 353.02 K and 5.623 MPa seemed out of trend when compared to the data of the same system at other conditions. Our hint was reinforced by fitting power laws with asymptotic behavior at critical point [START_REF] Rainwater | Vapor-liquid equilibrium of near-critical binary alkane mixtures[END_REF] [2] [3] simultaneously on molecular simulation phase densities and compositions. The critical phase behavior data calculated by power laws with asymptotic behavior at critical point (Eq. ( 1)) are presented in Table 1 and plotted on Figures 1 and2.

(

) 1
The molecular simulation was restarted with a slightly different global composition of the CO 2 (1) + R-1234ze(E) (2) mixture ( instead of ). By monitoring properties such as pressure, density and composition of each simulation box, plotted in Figure 3, we can conclude that the system with a global composition of has reached a thermodynamic equilibrium at around 50 million attempted Monte Carlo moves (MC steps). The corrected GEMC results for this system are shown in Table 2. This correction induces a slight modification of mean absolute deviations between GEMC simulation results and experimental data ( , and ), but that doesn't influence the conclusion presented in the paper.

Comment

Phase compositions and densities, at a given pressure, temperature and global composition, can be extracted from a single GEMC simulation. Hence, the critical coordinates can be predicted, using power laws with asymptotic behavior at critical point, taking into account both phase compositions and densities. The static-analytic method used in this work to perform the isothermal VLE data measurement can only deliver phase compositions, and another method is required to get experimental phase densities.

Comparison of phase compositions predictions between GEMC simulations and REFPROP version 9.0 and 10.0 is already presented in the paper. The critical points predicted by power laws with asymptotic behavior at critical point fitted on GEMC results are plotted on Figures 1 and2. On Figure 2, predicted phase densities are compared to calculated phase densities, provided by the Peng-Robinson equation of state (PR EoS), despite lack of experimental phase densities. The critical composition and density are both overestimated by GEMC and REFPROP predictions, but GEMC predictions are more accurate. Near critical point, GEMC and REFPROP version 10.0 predictions are both more accurate for the liquid phase rather than the vapor phase.

In conclusion, GEMC simulations, with molecular models only fitted with pure compound data, provide better vapor-liquid data predictions of the CO 2 + R-1234ze(E) mixture near critical point than REFPROP version 10.0, which highly overestimates the critical composition, despite using binary interaction parameters fitted over Raabe's molecular simulation data [START_REF] Raabe | Molecular simulation studies on the vapor--liquid phase equilibria of binary mixtures of R-1234yf and R-1234ze (E) with R-32 and CO2[END_REF] for the mixture CO 2 + R-1234ze(E), at lower temperatures. Experimental phase densities for the CO 2 (1) + R-1234ze(E) mixture are needed to do quantitative comparison with the presented predictions. 6.6 a u(P), u(x 1 ), u(y 1 ), u(ρ L ) and u(ρ V ) are the statistical uncertainties for a 99% confidence interval (i.e.

)

  Corrigendum to "Vapor-liquid equilibrium and molecular simulation data for carbon dioxide (CO 2 ) + trans-1,3,3,3-tetrafluoroprop-1-ene (R-1234ze(E)) mixture at temperatures from 283.32 to 353.02 K and pressures up to 7.6MPa" Rémi Fauve, Christophe Coquelet, Houriez Céline, Pierre-Arnaud Artola, Elise El Ahmar, Bernard Rousseau To cite this version: Rémi Fauve, Christophe Coquelet, Houriez Céline, Pierre-Arnaud Artola, Elise El Ahmar, et al.. Corrigendum to "Vapor-liquid equilibrium and molecular simulation data for carbon dioxide (CO 2 ) + trans-1,3,3,3-tetrafluoroprop-1-ene (R-1234ze(E)) mixture at temperatures from 283.32 to 353.02 K and pressures up to 7.6MPa". International Journal of Refrigeration, 2018, 98, 10.1016/j.ijrefrig.2019.05.039. hal-02184056 Corrigendum to "Vapor-liquid equilibrium and molecular simulation data for carbon dioxide (CO 2 ) + trans-1

Figure 1 :

 1 Figure 1: Pressure as a function of CO 2 mole fraction in the CO 2 (1) + R-1234ze(E) (2) mixture at 353.02 K. : GEMC simulations (: previous data, ■: corrected data). Solid line: power laws with asymptotic behavior at critical point on GEMC results. ×: critical point predicted by power laws on GEMC results. : experimental isothermal VLE data. : critical point predicted by power laws on experimental data. Dashed line: calculated with PR EoS, Wong-Sandler mixing rules and NRTL activity coefficient model. +: critical point calculated using our model. Dotted line: calculated using REFPROP 10.0 [5].

Figure 2 :

 2 Figure 2: Pressure as a function of density in the CO 2 (1) + R-1234ze(E) (2) mixture at 353.02 K. : GEMC simulations (: previous data, ■: corrected data). Solid line: power laws with asymptotic behavior at critical point on GEMC results. ×: critical point predicted by power laws on GEMC results. Dashed line: calculated with PR EoS, Wong-Sandler mixing rules and NRTL activity coefficient model. +: critical point calculated using our model. Dotted line: calculated using REFPROP 10.0 [5].

Figure 3 :

 3 Figure 3: Evolution of pressures, densities and compositions of CO 2 in the CO 2 (1) + R-1234ze(E) (2) mixture at 353.02 K during GEMC simulations, with a global composition of . Blue solid line: simulation box A. Orange solid line: simulation box B.

Table 1 : Predictions using power laws with asymptotic behavior at critical point of critical composition (x c1 ), pressure (P c ) and density (ρ c ) of the CO 2 (1) + R-1234ze(E) (2) binary system at 353.02 K, with GEMC results.

 1

		P c	x c1	ρ c	λ	γ	λ 1	λ 2	μ
	GEMC	6.770	0.5896	469.9	-21.29	482.1 -0.01688 -0.09103	0.1797

Table 2 : GEMC simulation results for the Vapor Pressure P, Liquid-Phase Molar Fraction x, Gas-Phase Mole Fraction y, Saturated Liquid Density ρ L and Saturated Vapor Density ρ V of the VLE for the CO 2 (1) + R-1234ze(E) (2) mixture system a

 2

	P /MPa	u(P) /MPa	x 1	ux  	y 1	uy  	ρ L /kg.m -3	u(ρ L ) /kg.m -3	ρ V /kg.m -3	u(ρ V ) /kg.m -3
					T: 353.02 K
	5.637	0.070	0.4097	0.0063	0.5769	0.0089	749	13	245.5


Frederic A Banville

Julien Moreau

Kevin Chabot

Andrea Cattoni

Ulrike Fröhlich

Jean-François Bryche

Stéphane Collin

Paul G Charette

Michel Grandbois

Michael Canva
email: michael.canva@usherbrooke.ca

Nanoplasmonics-enhanced label-free imaging of endothelial cell monolayer integrity

Keywords: Label-free microscopy, cell-based biosensing, cell signaling, endothelium, nanostructured surface, plasmonics, spatial resolution

Introduction

Investigating the interactions and responses of eukaryotic cells to external stimuli such as toxins, pathogens and hormones is instrumental in cell biology and for the development of new drugs. Cells have a large number of membrane-bound receptors that are essential to their metabolisms and functional activities. In particular, G-protein-coupled receptors (GPCRs), such as the PAR1 receptor of thrombin, compose the largest family of such receptors. The screening and monitoring of GPCRs signaling in cell-based assays have shown tremendous potential in the drug discovery process [START_REF] Fang | Label-Free Cell-Based Assays for GPCR Screening[END_REF][START_REF] Scott | Label-free whole-cell assays: expanding the scope of GPCR screening[END_REF].

In recent years, the development and validation of biosensors modalities exploiting whole live cells as sensing elements have proven to be efficient methods for the analysis of receptor-dependent signaling activity. Most commonly used techniques employ fluorescent probes to specifically mark structures and molecular targets, as it is the case for total internal reflection fluorescence (TIRF) and surface plasmon enhanced fluorescence (SPEF) [START_REF] Chabot | Identification of the molecular mechanisms in cellular processes that elicit a surface plasmon resonance (SPR) response using simultaneous surface plasmon-enhanced fluorescence (SPEF) microscopy[END_REF][START_REF] Goedhart | Fluorescence resonance energy transfer imaging of PKC signalling in living cells using genetically encoded fluorescent probes[END_REF]He et al., 2009;[START_REF] Liebermann | Surface-plasmon field-enhanced fluorescence spectroscopy[END_REF][START_REF] Ni | Live-cell imaging of cell signaling using genetically encoded fluorescent reporters[END_REF]. However, while being highly specific techniques, the use of labelling may impact the cells [START_REF] Progatzky | From seeing to believing: labelling strategies for in vivo cell-tracking experiments[END_REF], which is not the case for non-invasive labelfree methods [START_REF] Bourassa | Surface Plasmon Resonance to Study Cell Signaling and GPCR Functional Selectivity in Live Cells[END_REF][START_REF] Bousse | Whole cell biosensors[END_REF][START_REF] Chabot | Biosensing based on surface plasmon resonance and living cells[END_REF][START_REF] Fang | Label-Free Biosensors for Cell Biology[END_REF][START_REF] Pancrazio | Development and application of cell-based biosensors[END_REF]. Cell activity has been successfully monitored using several label-free biosensing methods, in particular by electrochemical impedance spectroscopy [START_REF] Giaever | A morphological biosensor for mammalian cells[END_REF][START_REF] Mcguiness | Impedance-based cellular assay technologies: recent advances, future promise[END_REF], resonant Waveguide Grating [START_REF] Fang | Resonant Waveguide Grating Biosensor for Living Cell Sensing[END_REF][START_REF] Ferrie | Resonant waveguide grating imager for live cell sensing[END_REF], surface plasmon resonance [START_REF] Chabot | Identification of the molecular mechanisms in cellular processes that elicit a surface plasmon resonance (SPR) response using simultaneous surface plasmon-enhanced fluorescence (SPEF) microscopy[END_REF][START_REF] Chabot | Long range surface plasmon resonance for increased sensitivity in living cell biosensing through greater probing depth[END_REF][START_REF] Chabot | Biosensing based on surface plasmon resonance and living cells[END_REF][START_REF] Giebel | Imaging of Cell/Substrate Contacts of Living Cells with Surface Plasmon Resonance Microscopy[END_REF][START_REF] Maltais | Label-free monitoring of apoptosis by surface plasmon resonance detection of morphological changes[END_REF][START_REF] Peterson | Surface plasmon resonance imaging of cells and surface-associated fibronectin[END_REF][START_REF] Wang | Mapping Single-Cell-Substrate Interactions by Surface Plasmon Resonance Microscopy[END_REF] and metal-clad waveguides (Söllradl et al., 2018b(Söllradl et al., , 2018a[START_REF] Söllradl | Metal clad waveguide (MCWG) based imaging using a high numerical aperture microscope objective[END_REF]. Evanescent-field based methods such as surface plasmon resonance (SPR) and metal-clad waveguides (MCWG) are highly sensitive to small variations in refractive index at the sensor surface (hundred of nanometers). This selectivity in volume can be especially useful when studying cell-substrate interactions such as cell adhesion to the surface [START_REF] Howe | Surface plasmon resonance imaging of excitable cells[END_REF][START_REF] Kreysing | Nanometer-Resolved Mapping of Cell-Substrate Distances of Contracting Cardiomyocytes Using Surface Plasmon Resonance Microscopy[END_REF][START_REF] Peterson | Mass measurements of focal adhesions in single cells using high resolution surface plasmon resonance microscopy[END_REF][START_REF] Son | Label-free quantification of cell-to-substrate separation by surface plasmon resonance microscopy[END_REF][START_REF] Toma | Label-Free Measurement of Cell-Electrode Cleft Gap Distance with High Spatial Resolution Surface Plasmon Microscopy[END_REF][START_REF] Watanabe | Scanning and non-scanning surface plasmon microscopy to observe cell adhesion sites[END_REF] and changes in a cell monolayer integrity [START_REF] Cuerrier | Surface Plasmon Resonance Monitoring of Cell Monolayer Integrity: Implication of Signaling Pathways Involved in Actin-Driven Morphological Remodeling[END_REF]Söllradl et al., 2018a).

SPR imaging (SPRI) has been used to spatially resolve individual cells and subcellular structures [START_REF] Peterson | High resolution surface plasmon resonance imaging for single cells[END_REF]. However, SPRI is limited in spatial resolution due to the mode attenuation distance, which results in a directional blur in acquired images that was described by analytical models [START_REF] Berger | Resolution in surface plasmon microscopy[END_REF][START_REF] Yeatman | Resolution and sensitivity in surface plasmon microscopy and sensing[END_REF]. Various approaches have been proposed to address this issue, but carry losses in sensitivity, temporal resolution or image contrast [START_REF] Banville | Improved resolution in SPR and MCWG microscopy by combining images acquired with distinct mode propagation directions[END_REF][START_REF] Berguiga | Time-lapse scanning surface plasmon microscopy of living adherent cells with a radially polarized beam[END_REF][START_REF] De Bruijn | Surface plasmon resonance microscopy: improvement of the resolution by rotation of the object[END_REF][START_REF] Giebel | Imaging of Cell/Substrate Contacts of Living Cells with Surface Plasmon Resonance Microscopy[END_REF][START_REF] Huang | Surface Plasmon Resonance Imaging Using a High Numerical Aperture Microscope Objective[END_REF][START_REF] Somekh | High-resolution scanning surface-plasmon microscopy[END_REF][START_REF] Wei | Characteristic investigation of scanning surface plasmon microscopy for nucleotide functionalized nanoarray[END_REF]. In previous works, we showed that nanostructured metal films are well suited for high resolution SPRI as they support a "hybrid" plasmonic mode, which is the result of a strong coupling between propagating (SPP) and localized (LSP) modes [START_REF] Sarkar | Hybrid Plasmonic Mode by Resonant Coupling of Localized Plasmons to Propagating Plasmons in a Kretschmann Configuration[END_REF]. This hybrid mode benefits from a reduction in mode attenuation distance due to the localized mode properties. In a previous manuscript by our group [START_REF] Banville | Spatial resolution versus contrast tradeoff enhancement in high-resolution surface plasmon resonance imaging (SPRI) by metal surface nanostructure design[END_REF], we demonstrated that the nanostructured film geometry can be designed in such a way as to improve spatial resolution while mitigating imaging performance losses. We showed that a trade-off between the attenuation length and the sensitivity is required, while maintaining a high image contrast. The numerical analysis of the nanostructured film geometry confirmed that the improvements in spatial resolution derives from the decrease in the uniform gold film thickness, while the high contrast is rather associated to the presence of the nanostructures. In this previous work, we have identified an optimal nanostructured film geometry (grating of 25 nm thick, 200 nm width and 400 nm period on a 25 nm Au film) that was applied to the design of our nanostructured chips proposed in the current manuscript. The performances were compared to those of "conventional" SPR sensor chips (50 nm continuous Au film), and experimental results showed a significant decrease in attenuation length while maintaining high image contrast in SPR imaging, where features as small as 1 micron wide could be resolved in all dimensions.

Quantitative high spatial resolution imaging is of particular interest in label-free studies of cell monolayer permeability. Indeed, most commonly used techniques employ fluorescent markers to spatially track intercellular gap formation and associated changes in cell monolayer integrity. Earlier published work from our group based on MCWG imaging showed that intercellular gaps in endothelial cell monolayers can be resolved in a label-free manner (Söllradl et al., 2018a). However, only "large" gaps (radius > 4 µm) were resolved as the spatial resolution limit of the MCWG system was 5 µm and smaller gaps could not be resolved.

In this study, we used nanostructured metal surfaces as a method to increase spatial resolution in SPRI and thus to improve the performance of this imaging modality to quantify morphological changes in an endothelial cell layer exposed to permeability enhancing agents. We fabricated the nanostructured metal surfaces by low-cost and large surface area Soft Nanoimprint lithography and we showed multiple reuse of the same substrate without degradation of its functionality. We demonstrated that surface nanostructuring, by enhancing spatial resolution in SPRI, also enhances measurement sensitivity of cell activity by improving detection of reversible intercellular gap formation in the cell layer.

Materials and methods

Sensor chip design

The imaging system setup presented in Fig. 1A is based on a high numerical aperture microscope objective and has previously been described [START_REF] Söllradl | Metal clad waveguide (MCWG) based imaging using a high numerical aperture microscope objective[END_REF]. The excitation light (LED filtered at 830 nm, FWHM of 10 nm) is focused on the back focal plane of the objective (NA = 1.46), reflected by the sensor chip placed atop the objective, and imaged by a CMOS camera. The light incident on the sensor chip is collimated and the incidence angle is chosen for maximum mode coupling by moving the focal point in the back focal plane of the objective with the motorized stage along y-z axes.

The sensor chip metal surface nanostructure geometry (Fig. 1B) was designed numerically as explained elsewhere [START_REF] Banville | Spatial resolution versus contrast tradeoff enhancement in high-resolution surface plasmon resonance imaging (SPRI) by metal surface nanostructure design[END_REF] to achieve the optimal compromise between spatial resolution limited by the mode attenuation length, image contrast and sensitivity. The chips are composed of a BK7 glass cover slip (170 μm thick, 22×22 mm, Fisher Scientific), a 3 nm Cr adhesion layer, a 25 nm continuous Au film (h1), 25 nm thick (h2) square Au nanostructures (width w of 200 nm, grating period Λ of 400 nm). They were fabricated by lift-off of gold using soft UV nanoimprint lithography (soft UV-NIL) over a surface area of 1 cm 2 . This technique uses cheap and flexible polydimethylsiloxane (PDMS) based stamps (hard-PDMS/PDMS) replicated from a Si master fabricated by electron beam lithography and dry etching. Since the PDMS based stamp can be used tens of time, this technology offers fast large-scale patterning at low cost compared to electron beam lithography (Cattoni et al., 2011a(Cattoni et al., , 2011b)). Fig. 1C shows SEM images of a nanostructured chip fabricated with high resolution on a large scale (grating surface of 1 cm 2 ). The unstructured sensor chips used for reference had a 50 nm continuous Au layer (BK7 glass cover slip, 3 nm Cr adhesion layer) as most commonly used in SPR biosensing. A layer of cells was grown on the sensor surfaces in a biological medium. As cells are much larger (>10 µm) than the grating dimensions, they completely cover nanostructures. Optical coupling conditions were chosen to obtain a reflectance minimum (maximum coupling to the SPR mode) in areas without cells. This results in cells appearing as bright objects in the SPRI images and areas devoid of cells appearing as dark regions as can be seen in Fig. 2. The round oscillatory patterns visible in SPRI images do not correspond to biological structures at the metal surface, but rather to coherence artifacts due to dust particles in the optical path. All SPRI images in this work are presented with the same grayscale range. Fig. 2 shows glioblastoma cells (U-373 GM) imaged with brightfield along with unstructured and nanostructured surface SPRI chips. Cells imaged on the structured film (B) are better resolved than on the unstructured film (A), while maintaining a high image contrast as shown in (C). In a previous work [START_REF] Banville | Spatial resolution versus contrast tradeoff enhancement in high-resolution surface plasmon resonance imaging (SPRI) by metal surface nanostructure design[END_REF], we showed by numerical modeling and confirmed experimentally that the attenuation length for a 50 nm unstructured Au film is ~11 µm and ~1.7 µm for the proposed nanostructured film. This reduction in attenuation length improves the spatial resolution, which results in gaps between cells and cell edges being more defined on the nanostructured sensor chip, the plasmonic mode allowing to isolate the structural parts in the immediate vicinity (~100 nm) of the surface.

Cell culture and reagents

Two cell types were used: glioblastoma cell line U-373 GM (ATCC, Manassas, USA) and an immortalized endothelial cell line EA.hy 926 (kindly provided by Dr. CJ Edgell, University of North Carolina, USA). The two cell types served respectively to qualitatively and to quantitatively investigate the impact of surface nanostructuring on cell activity studies through improved spatial resolution. Cells were maintained in EME (U-373 GM) or DMEM (EA.hy 926) supplemented with 10% temperature inactivated FBS, 2 mM L-glutamine, 50 IU/ml penicillin and 50 g/ml streptomycin at 37 °C in a 5% CO2 incubator on 100 mm diameter tissue culture Petri dishes (Corning) and were sub-cultured upon reaching 70-80% confluence using Trypsin/EDTA (0.25%), within approximately three days.

Both types of sensor chips (unstructured and nanostructured surfaces) were seeded with ~1.5 x 10 5 cells (U-373 GM) or ~2.5 x 10 5 cells (EA.hy 926) directly atop the uncoated ethanol-sterilized Au film. U-373 GM were grown to ~50 % confluence in two days, while EA.hy 926 were grown to full confluence within seven days. The presence of a nanoscale surface grating was not found to affect the growth rate nor the morphology of the cells.

Prior to the experiments, the sensor chips were washed twice in HEPES-Buffered Salt Solution (HBSS) heated at 37 °C, then mounted in our custom sample holder, maintained at 37 °C (TC1-100, Bioscience Tools). Cells were kept in the HBSS buffer solution for an hour to stabilize on the imaging system prior to measurements. An optimal plasmon coupling angle was identified by scanning angularly the incident light for maximal image contrast between the cell layer and the surrounding areas devoid of cells. This coupling angle was kept fixed throughout the image sequence acquisition.

LPS stimulation (Lipopolysaccharide, Sigma-Aldrich, 0.5 mg/ml solution) was used for the estimation of the threshold for intercellular gap detection, described in the following section, while thrombin was used to disrupt reversibly the monolayer (Sigma-Aldrich, 5 and 10 nM solutions), thus mimicking biological conditions of modulation in cell monolayer permeability. The sensor chips were cleaned with soap, distilled water, ethanol and dried with N2 at the end of each experiments. The nanostructured chips were reused multiple times (over 50 times each) in the course of our studies, for static observation of individual cells and dynamic monitoring of cell activity in confluent cell populations, without significant performance deterioration. To qualify this stability, we have checked that the incident light angle for maximum plasmonic coupling did not significantly drift during the several months of experiments, which would have been evident if any degradation of the nanostructured metal surface had occurred. Their low-cost and reusability validate their reliability and usefulness in practical use (see Fig. 1 in Appendix A for SEM images of the nanostructured sensor chip prior to initial use and after multiple reuses).

Threshold estimation for intercellular gap detection

The SPRI instrument image acquisition parameters were chosen to minimize background noise while maximizing pixel intensity dynamic range. SPRI images were generated at 10-second intervals by averaging three successive CMOS camera images, low-pass filtered for high-frequency noise reduction and normalized by subtracting a dark reference acquired at the beginning. The endothelial cell monolayer integrity disruption following stimulation was characterized by monitoring and analyzing the formation of intercellular gaps in the monolayer. To do so, the image pixels were segmented into two populations corresponding to the presence or absence of cells using an intensity-based thresholding algorithm [START_REF] Otsu | A Threshold Selection Method from Gray-Level Histograms[END_REF]. The thresholding process was found to be robust owing to the high contrast in the SPRI images between cellular regions and intercellular gaps, allowing the characterizing of intercellular gap size distribution in any given image. The segmentation threshold pixel intensity values were determined from calibration experiments by stimulating a confluent layer of EAh926 cells with the cytotoxin LPS. In those experiments, the LPS stimulation lead to irreversible loss in the monolayer integrity, as can be seen in Fig. 3A (0 min and 100 min). These experiments allowed the acquisition of sequences of intensity distribution histograms spanning the range from full cell confluence to complete cell monolayer detachment. This shift between both populations was necessary to identify the intensity threshold defining them, which could not be accurately obtained in the thrombin experiment, which exhibit a partial and reversible cell layer response. Those threshold values were applied to all experiments (one for each sensor chip type, unstructured and nanostructured) by conserving the acquisition parameters and sensor chip geometries.

Three images from a sequence acquired during a typical calibration experiment on nanostructured film are shown in Fig. 3A (T = 0 min, 57 min, 100 min) with their associated histograms (Fig. 3B). The two threshold values were calculated from the image in each of the two corresponding calibration sequences where the cell surface coverage was approximately 50%.

Pixel intensity histograms for such images had a wide distribution (large full-width at half maximum, FWHM) with a relatively low pixel count at maximum (dotted curve in Fig. 3B), compared to the histograms for images of full confluence (plain curve in Fig. 3B) and full detachment (dashed curve in Fig. 3B). Fig. 3C presents the evolution over time of the image histogram maximum values (blue curve) and of the image histogram FWHM (red curve). The two 50% confluence images were selected automatically from the calibration data shown in Fig. 3C at the point where the histogram FWHM was largest and pixel count at maximum simultaneously lowest (dashed black line). The intensity-based thresholding algorithm was then applied to those images resulting in pixel intensity thresholds (dashed green vertical line in Fig. 3B) for the nanostructured and unstructured films respectively of 29568 and 30208 (see Fig. 2 in Appendix A for the unstructured film pixel intensity threshold evaluation). Thereafter, the sizes of the gaps in the endothelium cell layer were quantified by using connected-component labeling with the following sequence:

1) Gaussian-filtering to remove high-frequency noise (3×3 kernel) by using the OpenCV Python function GaussianBlur(). 2) Image binarization by automatic clustering-based thresholding using the OpenCV Python function threshold().

3) Connected-component labeling using the scikit-image Python package functions: label() to connect regions in the binary images, regionprops() to measure the properties of the labeled image regions. 4) Statistical analysis of regions area.

Gap areas under 1 µm 2 were found to be highly dependent on image noise and were not considered in the statistical analyses. For visualization purposes, as shown for example in Fig. 4 below, gap contours were highlighted (yellow) in the images using the morphological transformation "gradient" with the Python function morphologyEx().

Results and discussion

Monitoring of cell response in confluent endothelial cell monolayers

Cell responses following protease-activated receptor 1 (PAR1) activation by thrombin have been studied for its role in haemostasis and inflammation mechanisms [START_REF] Coughlin | Thrombin signalling and protease-activated receptors[END_REF]. Thrombin has been extensively used to destabilize endothelial cell layers for instance to investigate its effect on barrier function permeability [START_REF] Canva | A functional siRNA screen identifies RhoGTPase-associated genes involved in thrombin-induced endothelial permeability[END_REF][START_REF] Bae | Concentration dependent dual effect of thrombin in endothelial cells via Par-1 and Pi3 Kinase[END_REF][START_REF] Minami | Thrombin and Phenotypic Modulation of the Endothelium[END_REF][START_REF] O'brien | Thrombin Responses in Human Endothelial Cells: CONTRIBUTIONS FROM RECEPTORS OTHER THAN PAR1 INCLUDE THE TRANSACTIVATION OF PAR2 BY THROMBIN-CLEAVED PAR1[END_REF]Söllradl et al., 2018a;[START_REF] Troyanovsky | Thrombin enhances the barrier function of rat microvascular endothelium in a PAR-1-dependent manner[END_REF][START_REF] Van Der Heijden | Opposing Effects of the Angiopoietins on the Thrombin-Induced Permeability of Human Pulmonary Microvascular Endothelial Cells[END_REF]. Thrombin binds with the cell protease-activated receptor 1 (PAR1), activates intracellular signaling cascades which induces endothelial cell contraction, cell-cell molecular junction disassembly and the modulation of cell attachment onto the substrate [START_REF] Opal | Endothelial barrier dysfunction in septic shock[END_REF]. These events are associated to transient and reversible gap formation in the endothelial cell layer and have previously been reported [START_REF] Rabiet | Thrombin-Induced Increase in Endothelial Permeability Is Associated With Changes in Cell-to-Cell Junction Organization[END_REF][START_REF] Vouret-Craviari | Regulation of the Actin Cytoskeleton by Thrombin in Human Endothelial Cells: Role of Rho Proteins in Endothelial Barrier Function[END_REF]. In these studies, the cytoskeleton was stained, in particular the F-actin with fluorescent-labelled phalloidin, and fluorescence images show that thrombin induces cytoskeletal reorganization resulting in the rounding of the endothelial cells (HUVECs and Ea.hy 926 cell lines), which forms intercellular gaps significantly increasing monolayer permeability.

To demonstrate the potential of nanostructured SPRI chips in label-free cell studies, EA.hy 926 endothelial cells were grown to confluence and stimulated with thrombin. Figs. 4A-B present the time sequence of SPRI images acquired during endothelial cell layer integrity disruption following thrombin stimulation (10 nM concentration). The intercellular gaps (dark zones selected as described in previous section) are outlined in yellow to facilitate visualization. Negative controls were performed prior to stimulation by injecting HBBS buffer solution and did not result in observable cell response. As shown in the image sequence, intercellular gaps become apparent at 8 min. Afterwards, the endothelial cells gradually recover to return to a quasi-confluent monolayer. In the images, a larger number of small gaps are visible on the nanostructured film, while mainly fewer large gaps are seen on the unstructured film. This phenomenon is most likely a consequence of the lower spatial resolution when using an unstructured film where small gaps are either unobservable or combined with other gaps due to the blur in SPRI images in the direction of mode propagation (red arrows, from left to right). In addition, loss of information in gap morphology with the unstructured film is also noticeable by the smoothness of the edges and the preferential orientation of the gaps along the direction of mode propagation.

Two thrombin concentrations (red: 5 nM, black: 10 nM) were used to induce endothelial monolayer response in order to compare the effects on gap formation. The image data were analyzed in terms of the coverage percentage by the cell layer as a function of time as shown in Fig. 5. As expected, monolayer integrity is rapidly lost after stimulation and gradually recovers over time to return to a quasi-confluent layer, after 25-30 min (5 nM) and 40-45 min (10 nM). It has been shown that nanostructures can have a significant impact on cellular phenotype, such as on cell adhesion, morphology, proliferation, migration, alignment, etc. [START_REF] Skoog | Biological responses to immobilized microscale and nanoscale surface topographies[END_REF]. However, studies have determined that nanostructures with heights inferior to 35 nm have little to no effect on cells and no observable phenotype changes have been recorded in such cases [START_REF] Dalby | Rapid fibroblast adhesion to 27nm high polymer demixed nano-topography[END_REF][START_REF] Loesberg | The threshold at which substrate nanogroove dimensions may influence fibroblast alignment and adhesion[END_REF]. This is indeed the case for the proposed nanostructured surface, as shown in the brightfield images in Fig. 2 which shows no obvious impact of surface nanostructuring on the general morphology of cells, or in the time course of the cell response presented in Fig. 5, which shows similar responses profile for both sensor chip types following thrombin stimulation.

Quantification of intercellular gap morphology dynamics in endothelial cell monolayers

The complete sequence of images shown partially in Fig. 4 was analyzed to quantify gap concentration dynamics as a function of size upon thrombin stimulation for both unstructured and nanostructured sensor chips. The statistics were extracted from the data collected and quantified via the connected-component labeling process previously described. Figs. 6A-D show the total number of intercellular gaps in the images for both sensor chip types in four size ranges over time: 1 to 2 µm 2 , 2 to 5 µm 2 , 5 to 10 µm 2 and over 10 µm 2 . For example, gap areas in the 1 to 2 µm 2 category correspond to zones having between 69 and 139 pixels, when considering an individual pixel surface area of 0.0144 µm 2 (0.120 µm x 0.120 µm). Gap areas under 1 µm 2 were not considered in the analyses as their labeling was assumed to be sensitive to image noise. As shown by the black line plots for the nanostructured film in Figs. 6A-D, intercellular gap formation is triggered by thrombin stimulation at 2.5 min and gaps rapidly develop until approximately 10 min, after which the endothelial cells start to recover. The number of gaps then decreases as the layer gradually returns to a quasi-confluent monolayer (light grey region) and there is a significant drop in larger gaps over 5 µm 2 after 40-45 min. The number of smaller gaps (1 to 5 µm 2 ) is high until around 80 min and then returns to the pre-stimulation baseline levels.

Fig. 6. Quantification of intercellular gap morphology dynamics and effect of thrombin concentration. Total number of gaps detected over time following 10 nM thrombin injection at 2.5 min with areas between 1 and 2 µm 2 (A), 2 and 5 µm 2 (B), 5 and 10 µm 2 (C) and over 10 µm 2 (D). Gaps with areas under 10 µm 2 are detected in greater numbers for the structured film (black) compared to the unstructured film (red); E) Intercellular gaps detected at 8 min for both sensor chip types at two thrombin concentrations (5 nM and 10 nM) and for no thrombin stimulation (0 nM).

As shown in Fig. 6, the number of detected intercellular gaps under 10 µm 2 is significantly greater for the nanostructured film and the data shows much more variation over time owing to the higher spatial SPRI resolution. Conversely, in the data for the unstructured film, the total number of gaps under 10 µm 2 varies little over time. Interestingly, the nanostructured film data after 40-45 min for gaps below 5 µm 2 shows that the endothelial monolayer integrity takes much longer to recover than would otherwise be determined from the unstructured film data or from Fig. 5, which may be significant in terms of membrane permeability to small molecules. Indeed, the overall cell coverage dynamics presented in Fig. 5 are mainly governed by large gaps in the endothelial layer. For example, at the 8 min mark (Fig. 6E), the total surface of small intercellular gaps for the structured and unstructured films is respectively ~500 µm 2 and ~130 µm 2 , while the total surface imaged is ~31000 µm 2 . This corresponds to a respective drop in cell coverage of 1.6% and 0.4%, which is significantly smaller than the one registered in Fig. 5 (12% and 13%). Both analyses should then be viewed as complementary rather than conflicting. Fig. 6E quantifies the impact of thrombin concentrations (0 nM, 5 nM and 10 nM) on gap dynamics for the two sensor chip types at the 8 min mark. For the unstructured film, the number of small gaps detected (< 10 µm 2 ) does not vary with thrombin concentration, and it is only for areas over 10 µm 2 that a significant number of gaps are quantified. This difference in detection is the result of smaller gaps combining into larger gaps due to the blurring effect associated with a longer attenuation length. This further supports the fact that gaps with sizes under that of the mode attenuation length cannot be detected accurately. Conversely, in the case of the nanostructured film, the cell layer response is quantifiable at lower concentration of thrombin stimulation (5 nM), where significant numbers of gaps between 1 and 5 µm 2 are detected. Indeed, to the exception of gaps areas between 5 and 10 µm 2 , the number of detected gaps is well above the baseline of 0 nM. Clearly, the ability of the nanostructured sensor chips to detect small gaps (1 to 5 µm 2 ) confers a strong ability to study cellular dynamics with SPRI such as endothelial cell layer activation by thrombin.

Conclusion

In this work, we demonstrated a novel surface imaging method for monitoring and quantifying cell surface activity by using the case of confluent endothelial cell monolayers. This was achieved by significantly improving the spatial resolution in SPRI systems by way of metal surface nanostructuring while preserving sensitivity and image contrast. Although still dependant on the mode attenuation length, micrographs of live cells obtained with the proposed nanostructured chips were found to have sharper and more defined edges. These nanostructured sensor chips were fabricated by low-cost Soft Nanoimprint lithography over a large surface area (1 cm 2 ), and were demonstrated to be reusable.

The performances of the proposed sensor chips for cell applications were evaluated by conducting experiments in which endothelial cell monolayers were stimulated with agents known to disrupt cell-cell junctions and cell-substrate adhesions. Specifically, thrombin was used to transiently and reversibly generate gaps in the endothelial layer while monitoring and quantifying their evolution over time. We demonstrated that the detection of intercellular gaps, ranging from 1 to 10 µm 2 , is increased several folds throughout experiments when using a nanostructured metal film. This high sensitivity to low micrometer sized gaps detection provided by nanostructured biochip makes them well suited for label-free endothelial cell monolayer studies and should prove very useful for the monitoring of other subcellular features such as cell focal adhesions. Further cell monolayer studies would benefit from exploiting our nanostructured sensor chips in labeled experiments, such as combining SPR and fluorescence imaging, where complementary data could be acquired simultaneously. national nanofabrication network RENATECH and is also financially supported by FRQNT. The C2N work was partially supported by the French RENATECH network.

Fig. 1 .

 1 Fig. 1. Nanostructured chips used in live-cell SPR imaging. A) Schematic of the optical system based on a high numerical aperture microscope objective; B) Schematic of the SPRI chip: BK7 glass substrate -Cr adhesion film (3 nm) -flat or nanostructured Au films. The flat film is composed of a uniform film (height of h1), while the nanostructured film is composed of a uniform film (height of h1) and a grating (height of h2, width of w and period of Λ) periodic along x and y axes; C) SEM image of nanostructured sample fabricated by soft UV-NIL with inset showing high fabrication resolution in terms of structure shape and grating dimensions (h1 = h2 = 25 nm, w = 200 nm, Λ = 400 nm).

Fig. 2 .

 2 Fig. 2. Surface nanostructuring impact on SPR cell imaging. A) Brightfield and reflectance images (λ = 830 nm) of glioblastoma cells (U-373 GM) atop an unstructured gold film (50 nm); B) Brightfield and reflectance images of glioblastoma cells atop the nanostructured gold film (25 nm continuous film, 25 nm thick nanostructures, width of 200 nm, grating period of 400 nm); C) Line profiles of SPRI images showing better resolved cell edges for the structured film.

Fig. 3 .

 3 Fig. 3. Threshold identification for intercellular gap detection: nanostructured metal films. A) SPRI images (λ = 830 nm) of EA.hy 926 endothelial cells on a nanostructured Au film where the monolayer integrity is disrupted following LPS stimulation. At 0 min, the cell monolayer confluence is close to 100%. At 100 min, virtually no cells are visible on the sensor surface; B) Histograms of pixel intensity of three SPRI images taken at different times (0, 57 and 100 min). The green dashed line corresponds to the pixel intensity threshold calculated by automatic clustering-based thresholding from the image at 57 min; C) Evolution over time of histogram maximum value (blue) and FWHM (red). The image selected for the automatic clustering-based thresholding corresponds to the image where the FWHM is highest and the histogram maximum is lowest (black dashed line). The markers in (B) and (C) correspond to the maximum values in the pixel histograms at T = 0 min (circle), T = 57 min (square) and T = 100 min (triangle).

Fig. 4 .

 4 Fig. 4. Endothelial cell layer activation by thrombin: gap formation monitoring. Reflectance images (λ = 830 nm) of EA.hy 926 endothelial cells on unstructured (A) and structured (B) Au films before and after cell layer activation by thrombin stimulation (concentration of 10 nM). The images show the evolution of intercellular gaps outlined in yellow that appear after stimulation and gradually disappear until cell layer integrity is recovered. The direction of plasmon mode propagation is from left to right (red arrows). The nanostructured film images show a higher spatial resolution resulting in improved gap morphology measurement throughout the experiment. The circles in the legend represent equivalent gap pixel areas of 1, 2, 5 and 10 µm 2 , respectively.

Fig. 5 .

 5 Fig. 5. Endothelial cell layer activation by thrombin: monolayer confluence tracking. Percent coverage of the endothelial cell layer as a function of time following stimulation with thrombin (black: 10 nM, red: 5 nM). The minimum in monolayer integrity occurs at 2.5 minutes after injection followed by full recovery at 40-45 min (light grey). The curves obtained from unstructured and nanostructured surface show similar response profiles and no observable phenotype changes due to the surface nanostructure are observed.

Acknowledgements

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) with Discovery Grants for M.G. (2014-05930), M.C. and P.G.C (2015-05188), and by the Fonds de Recherche du Québec -Nature et Technologies (FRQNT) for M.G. and P.G.C (2013PR168908). F.A.B. is supported by a doctoral scholarship from the FRQNT and by a French doctoral contract (MESRI) with the Université Paris-Saclay. LN2 is an international laboratory (Unité Mixte Internationale UMI 3463) jointly managed by French CNRS and the Université de Sherbrooke as well as Université de Lyon (ECL, INSA de Lyon, CPE) and the Université Grenoble Alpes (UGA). LN2 is associated to the French