D Barbet

J R Franck

M Jourdain

Bardiès

S Chiavassa

I Aubineau-Lanièce

A Bitar

A Lisbona

J Barbet

D Franck

J R Jourdain

M Bardiès

Validation of a personalised dosimetric evaluation tool (Oedipe) for targeted radiotherapy based on the Monte Carlo MCNPX code

Validation of a personalized dosimetric evaluation tool (Oedipe) for targeted radiotherapy based on the Monte

Carlo MCNPX code

Introduction

When using radionuclides in nuclear medicine for therapy, especially for radioimmunotherapy (RIT), dosimetric calculations should be made for each patient [START_REF] Bardiès | Dosimetry and microdosimetry of targeted radiotherapy[END_REF]. This principle has been adopted by the European directive Euratom 97/43 and put into practice in various European countries including France (implementing decree 2003-270 of the 24th March 2003 and the UK (in the Ionising Radiation (Medical Exposure) regulations 2000). Dosimetry of patients in nuclear medicine is based on the MIRD formalism [START_REF] Loevinger | MIRD primer for absorbed dose calculations[END_REF].

€ D (r k ) = ˜ A h ⋅ S(r k ← r h ) h ∑ (1)
where € D (r k ) is the mean absorbed dose (Gy) in the target region r k , € ˜ A h is the cumulated activity (Bq.s) in the different source regions r h and € S(r k ← r h ) is the S factor (Gy.Bq -1 .s -1 ) i.e. the mean absorbed dose in the target region r k per unit of activity accumulated in the source r h . The spatial distribution of the activity accumulated in the various source regions is determined by quantitative scintigraphic imaging.

S factors are dependent on the geometry of the source and target regions. This geometry can be defined mathematically or sampled using digital images, using a voxel-based approach. Moreover, depending the aim of the study, calculations can be performed for a patient model or specifically for a given patient.

In a diagnostic context, the usual dosimetric model consists of using tabulated S factors calculated for mathematical models [START_REF] Snyder | absorbed dose per unit cumulated activity for selected radionuclides and organs MIRD Pamphlet n° 11[END_REF][START_REF] Clairand | Improvement of internal dose calculation using mathematical models of different adult heights[END_REF]. This approach, when applied for radioprotection, [START_REF] Weber | Stylized versus tomographic: an experience on anatomical modelling at RPI Monte Carlo[END_REF], makes it possible to evaluate the effective dose delivered, or to compare the doses delivered by different radiopharmaceuticals [START_REF] Liu | A radionuclide therapy treatment planning and dose estimation system[END_REF]. Numerous mathematical reference models exist (ICRP 23 1975[START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF][START_REF] Stabin | Mathematical Models and Specific Absorbed Fractions of Photon Energyin the Nonpregnant Adult Female and the End of Each Trimester of Pregnancy[END_REF], but voxelbased models that can be used for dosimetric means have also been proposed [START_REF] Zubal | Computerized Threedimensional Segmented Human Anatomy[END_REF], Caon 2004).

In order to reach the precision required for therapeutic applications, S factors specific to each patient must be taken into consideration i.e. the anatomy, composition and density of tissues, including tumours, all have to be taken into account. The currently used methods for anatomical investigation (MRI, CT scan, etc.) generate digital images composed of voxels. Compared to MRI images, CT images have the advantage of providing information concerning the electronic density of different tissues. Such information can be used for dosimetry calculations.

To our knowledge, no dosimetry approaches exist that enable mathematic modelling of the anatomy of an individual patient. The generation of mathematical models of different sizes and weights [START_REF] Clairand | Improvement of internal dose calculation using mathematical models of different adult heights[END_REF] would be close to a patient-specific approach although dosimetry would still be performed for a model rather than for a given patient.

The S factors calculated from anatomical images are different for each patient. The heterogeneity of the medium considered makes the calculation of S factors difficult. The commonly used method to resolve this type of problem is the use of Monte Carlo codes, which make it possible to simulate the transport of radiations and to record energy deposits in complex geometries and for heterogeneous media (that cannot be modelled analytically). There are, however, two limits to the use of Monte Carlo codes:

Firstly, Monte Carlo techniques require considerable computing resources, especially for extended geometries (whole body). Despite increases in computer capacity, the calculation time limits the application of the Monte Carlo method to dosimetry in the clinical context.

Secondly, the results obtained using Monte Carlo simulations must be validated. There are multiple sources of variation in the results:

-Different calculation approaches (i.e. different Monte Carlo codes, different methods of dealing with the interactions, different cross-sections, etc.), -Different geometries (i.e. patient-specific vs. model) -Different geometry definitions (voxel-based vs. mathematical definitions of organs)

An extreme example would consist of comparing S factors obtained for a patient (i.e. geometry obtained by imaging), calculated on the voxel scale by Monte Carlo simulation with S factors obtained for a mathematical model, calculated on the organ scale using a hybrid method (point kernel (Ryman et al 1987b[START_REF] Berger | MIRD Pamphlet N°2: Energy deposition in water by photons from point isotropic sources[END_REF]) + Monte Carlo): in this case, the differences encountered cannot be analysed in detail.

The aim of this paper is to validate our method of patient-specific dosimetry based on a Monte Carlo-type approach. Oedipe, an acronym of Tool for the Evaluation of Personalised Internal Dose, is a user-friendly graphical interface developed in IDL ® language. Using this tool, patient anatomical data defined on the voxel scale from CT or MRI images can be associated with a Monte Carlo code, in this case, the MCNPX code [START_REF] Hughes | MCNPX the LAHET/MCNP Code Merger XTM-RN (U) 97-012 ICRP Publication 38[END_REF]. Oedipe also allows for the treatment of anatomical data and the definition of source regions on the organ or voxel scale. The simulation results are treated and displayed in the interface in the form of a list of absorbed doses by target organs, and/or isodose curves superimposed on anatomical structures when the calculation is performed on the voxel scale.

We initially compared our results with those published by [START_REF] Yoriyaz | Absorbed fractions in a voxel-based phantom calculated with MCNP-4B code[END_REF] considering the same voxel-based model [START_REF] Zubal | Computerized Threedimensional Segmented Human Anatomy[END_REF]. Yoriyaz's results were obtained using the Monte Carlo MCNP4B code (Briesmeister et al 1997), which is related to the MCNPX code used by ourselves.

In a previous publication, we compared the MCNPX and EGS4 codes during a dosimetric study for the treatment of medullary thyroid cancer [START_REF] Chiavassa | Dosimetric Comparison of Monte Carlo codes (EGS4, MCNP, MCNPX) Considering External And Internal Exposures Of The Zubal Phantom To Electron And Photon Sources[END_REF]. Here we present a comparison with the data published by Petoussi-Henß for a Monte Carlo code developed by the GSF laboratory [START_REF] Petoussi-Henß | Voxel Anthropomorphic Models as a Tool for Internal Dosimetry[END_REF]. In both cases, the geometries considered are similar [START_REF] Zubal | Computerized Threedimensional Segmented Human Anatomy[END_REF].

The effect of the calculation method was evaluated by comparing the S factors used by the MIRDOSE3 software [START_REF] Stabin | MIRDOSE/ Personal Computer Software for Internal Dose Assessment in Nuclear Medicine[END_REF] for the adult mathematical model [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF] with S factors calculated directly using the Monte Carlo MCNPX code for the same mathematical model. Finally, we compared the results obtained for the same geometry (adult model by Cristy) defined differently (mathematical vs. voxel-based definition of organs). This comparison also enabled us to evaluate the effect of the voxel sampling size on the results obtained.

Materials and Methods

Oedipe

Oedipe is a graphical user interface that associates patient-specific anatomical data (tissue morphology, composition and density) derived from CT or MRI images (in DICOM format), with the Monte Carlo MCNPX code [START_REF] Hughes | MCNPX the LAHET/MCNP Code Merger XTM-RN (U) 97-012 ICRP Publication 38[END_REF]. Image processing tools have been implemented in the interface. CT images can be automatically segmented according to the 4 main densities of the human body: air [-1000 ; -800], lungs ]-800 ; -500], soft tissue ]-500 ; 400] and bone ]400 ; 1000]. Regions of interest can also be defined by manual outlining (tumours or organs of interest, MRI images). An electronic density is thus attributed to each segmented region.

The user can define the source regions at an organ or voxel scale: a cumulated activity can be attributed to each defined source region by outlining or an entry file for cumulated activity can be defined in each case for each voxel making up the anatomical image.

The entry file for the MCNPX code is created automatically from entry data. The general MCNPX Monte Carlo calculation code [START_REF] Hughes | MCNPX the LAHET/MCNP Code Merger XTM-RN (U) 97-012 ICRP Publication 38[END_REF] on which Oedipe is based is an extension of the MCNP code and allows for the simulation of all particle types. The photons and the electrons are simulated between 1 keV and 1 GeV. The photon cross-sections are derived from Evaluated Nuclear Data Files [START_REF] Hubbell | Atomic Form Factors, Incoherent Scattering Functions, and Photon Scattering Cross Sections[END_REF]. The transport of electrons is derived from the ITS3.0 code [START_REF] Halbleib | ITS Version 3.0 : Intergrated TIGER Series of Coupled Electron/Photon Monte Carlo Transport Codes SAND91-1634[END_REF]. Statistical uncertainty (1 sigma), proportional to € 1/ N (N being the number of simulated particles) is automatically estimated for each result obtained.

The MCNPX code proposes a specific format for geometry definition called 'repeated structures'. This format is particularly well suited for voxel-based geometries as the voxel is an elementary structure that is repeated many times in the geometry. Oedipe uses the repeated structure format but MCNPX also allows for a mathematical definition of the geometry.

Once the simulation is finished, Oedipe automatically reads, processes and displays the results. The dose calculation can be carried out on the organ or voxel scale. In the case of a calculation of the absorbed dose on the voxel scale, the results are displayed in the form of isodose curves superimposed on anatomical images.

Validation a. Validation

In a previous publication [START_REF] Yoriyaz | Absorbed fractions in a voxel-based phantom calculated with MCNP-4B code[END_REF], Yoriyaz provided Specific Absorbed Fractions Φ (SAFs, kg -1 ) for monoenergetic photons of 0.01, 0.05, 0.1, 0.5, 1, 2 and 4 MeV and S factors (mGy.MBq -1 .s -1 ) for monoenergetic electrons of 0.935 MeV obtained with the Monte Carlo MCNP4B code. The geometry considered was the Zubal model [START_REF] Zubal | Computerized Threedimensional Segmented Human Anatomy[END_REF]. This is a head-torso reference model comprising 68 tissues and organs, which consists of a 3-dimensional array of 128 x 128 x 246 cubic voxels, of 4 mm on each side (Figure 1a). Yoriyaz considered bone and lungs with respective densities of 1.4 and 0.296 g.cm -3 . The density of soft tissue (1.04 g.cm -3 ) was considered for the rest of the model. These calculations were performed by the intermediary of the SCMS interface. Equivalent calculations were performed for the same geometry with the MCNPX code, by the intermediary of the Oedipe interface. Comparison of the results constitutes a validation of the Oedipe interface (Figure 2). In order to obtain a statistical incertitude < 5%, we simulated 100000-10 million photons and 1-5 million electrons. The corresponding calculation times (CPU) are between 30 min and 4 days for photons and between 2 and 10 hours for electrons (using a 2x2 GHz G5 bi processor Power Mac). In this publication, Yoriyaz does not provide statistical uncertainty associated with the calculation, although 2-10 million simulations were carried out for photons and electrons.

SCMS (Yoriyaz

b. Effect of the specificity of Monte Carlo calculation codes

Petoussi-Henß proposed Specific Absorbed Fractions (SAFs, kg -1 ) for monoenergetic photons of 0.03, 0.1 and 1 MeV, considering 8 source/target organs of the Zubal model (Petoussi-Henß et al 1998). The bones and lungs were considered with respective densities of 1.4 and 0.26 g.cm -3 . The density of soft tissue (1.05 g.cm -3 ) was attributed to the rest of the model. These calculations were performed using the Monte Carlo code developed by the German National Institute for Radiation Protection (GSF). This code generates photons and monitors them individually [START_REF] Veit | Tomographic anthropomorphic models, Part I: Construction technique and description of models of an 8 week old baby and a 7 year old child GSF[END_REF]. The interactions under consideration in the human body are the photoelectric effect, the Compton effect and pair production. The cross-sections corresponding to these interactions are derived from the ORNL library [START_REF] Roussin | Description of the DLC-99/HUGO Package of Photon Interaction Data in ENDF/B-V Format ORNL/RSIC-46[END_REF]) for simple elements. The cross-sections for the different human tissues are calculated from element data, according to the composition and density of the tissue. The energy transferred to the point of interaction is considered to be deposited locally and the secondary electron is not monitored ('kerma' approximation). The energy cut-off is 4 keV.

We have performed equivalent calculations for the same geometry using the MCNPX code (Figure 3). The MCNPX code monitors the secondary electrons by default and thus does not consider a local energy deposition at the point of photon interaction. We modified the MCNPX code (p and phys mode: p j 1 j) in order to apply the same approximation as the GSF code.

The statistical uncertainty associated with SAFs published by Petoussi-Henß is < 5%. Our calculations have an uncertainty of < 2% (CPU time between 30 sec. and 1,30 hours using a 2x2 GHz G5 bi-processor Power Mac).

Petoussi-Henß

c. Effect of the calculation method

The MIRDOSE3 software developed by Stabin [START_REF] Stabin | MIRDOSE/ Personal Computer Software for Internal Dose Assessment in Nuclear Medicine[END_REF] uses S factors calculated for the standard mathematical models developed by the Oak Ridge National Laboratory (ORNL). These models are representative of the entire population: newborn babies, children of 1, 5 and 10 years of age, teenagers of 15 years of age, adult males [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF] as well as adult females and females at 3, 6 and 9 months of pregnancy [START_REF] Stabin | Mathematical Models and Specific Absorbed Fractions of Photon Energyin the Nonpregnant Adult Female and the End of Each Trimester of Pregnancy[END_REF]. These S factors are obtained for a given radionuclide based on Specific Absorbed Fractions (SAFs) Φi and mean energies Δi emitted by nuclear transition for each radiation of i type according to equation 2:

€ S(r k ← r h ) = Δ i Φ i (r k ← r h ) i ∑ (2)
The mean energies Δi emitted by nuclear transition were derived from the publication by Weber et al [START_REF] Weber | Stylized versus tomographic: an experience on anatomical modelling at RPI Monte Carlo[END_REF]. The SAFs were calculated using different methods:

-For photons [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF], the Monte Carlo ALGAMP code (Ryman et al 1987a) was used with 60000 simulated particles. When the statistical uncertainty exceeded 50%, a point kernel method was used (Ryman et al 1987b), based on data published by Berger [START_REF] Berger | MIRD Pamphlet N°2: Energy deposition in water by photons from point isotropic sources[END_REF]). These calculations were carried out for an infinite homogeneous medium (water). However, the density of bone and lung was partly taken into account using correction factors.

-For non-penetrating radiation (electrons and beta), SAFs are usually calculated according to the MIRD formalism [START_REF] Loevinger | MIRD primer for absorbed dose calculations[END_REF]:

€ Φ(r k ← r h ) = 1/m k for self-irradiation, and € Φ(r k ← r h ) = 0 for cross-irradiation.
In order to evaluate the effect of the calculation method on the S factors, we compared the S factors tabulated by MIRDOSE3 for the mathematical adult ORNL model [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF] (Figure 1b) and iodine 131 with those calculated directly by MCNPX for the same model and the same radioelement (Figure 4). We used a mathematical geometric definition of this model in MCNPX format (Oedipe was therefore not used for geometry definition). We took all of the iodine 131 emissions (photons, electrons and beta) into consideration according to the ICRP 38 data (ICRP 1983). The sources were uniformly distributed in the 5 source organs. The maximum statistical uncertainty associated with the calculations was fixed at 5%. Ten to fifteen million particles were simulated for this purpose (CPU time between 15 and 26 hours with a 2x2 GHz G5 bi-processor Power Mac). Patient anatomy is taken into account based on digital CT or MRI images. The format of these images leads to the geometry being defined by a voxel approach. In a previous publication, Peter [START_REF] Peter | Analitical versus voxelized phantom representation for Monte Carlo simulation in radiological imaging IEEE trans[END_REF] compared the use of mathematical and voxel-based models for the Monte Carlo simulation of SPECT imaging. According to Peter, The voxel sampling of the geometry results in structural alterations, especially for thin or small structures. For example, closed regions can become disconnected because of voxel-sampling. The effect of such alterations becomes critical when they involve source regions. Furthermore, Peter noted errors when taking into account the course of particles arriving tangentially manner to voxel-based surfaces.

MIRDOSE3

To assess the impact of using voxel-based geometry on S factor calculations, we have sampled the mathematical adult ORNL model [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF] with a spatial resolution of 256 x 256 x 348 voxels of 1.58 x 1.58 x 5 mm 3 . Based on the voxel-based model, we created a second model by decreasing the spatial resolution to obtain a matrix of 128 x 128 x 348 voxels of 3.16 x 3.16 x 5 mm 3 . We next eliminated the slices containing only air in the 3 dimensions, so as to obtain two smaller matrices of 254 x 145 x 348 voxels (Figure 6a) and 127 x 73 x 348 voxels (Figure 6b).

Using the Monte Carlo MCNPX code, we calculated S factors (mGy.MBq -1 .s -1 ) for the mathematical model and the two voxel-based models (Figure 5). We simulated all iodine 131 emissions (photons, electrons and beta) according to the ICRP 38 data (ICRP 1983). The sources were distributed uniformly in 5 organs, and the calculations were carried out in 13 target organs. The maximum statistical uncertainty associated with our calculations was fixed at 5%. Ten to fifteen million particles were simulated for this purpose.

Geometry

Adult 5: Parameters to study the effect of voxel sampling and voxel size.

Calculation Times

The Monte Carlo method requires long calculation times and significant memory capacity, thus limiting the use of patient-specific dosimetric studies in the clinical context. Furthermore, according to Peter and Xu [START_REF] Peter | Analitical versus voxelized phantom representation for Monte Carlo simulation in radiological imaging IEEE trans[END_REF][START_REF] Weber | Stylized versus tomographic: an experience on anatomical modelling at RPI Monte Carlo[END_REF], the use of a voxel-based geometry rather than a mathematically defined approach results in a considerable increase in the memory required as well as in calculation times. However the number of simple structures (and hence interfaces) that constitute a voxel-based geometry is greater than in the case of mathematical models.

The Oedipe software is based on the Monte Carlo MCNPX code. The version 2.5e of this code was optimised in order to reduce calculation times [START_REF] Hendricks | [END_REF]). This optimisation is associated with the use of a repeated structure format. Preliminary studies show that the use of the MCNPX2.5e code with repeated structures decreases the CPU time by a factor of at least 100 compared to the previous version MCNPX2.4 (data not shown). This decrease in calculation time is the result of an improvement in the calculation algorithms and is not due to variance reduction techniques.

In order to determine the impact of voxel sampling and voxel size on the amount of memory and the calculation time, we compared these parameters by considering an identical geometry (Adult ORNL model) that was either mathematical or voxel-based with 2 voxel sizes (Figure 5), following the procedure described in 2.2.d.

Results and Discussion

Validation

Tables 1 to 7 show the SAFs (kg -1 ) calculated by Yoriyaz using MCNP4B and our results using MCNPX for photons with an energy of between 10 keV and 4 MeV. The ratio between these values was generally close to 1: [0.94; 1.05] for 10 keV, [0.98; 1.12] for 50 keV, [0.97; 1.11] for 100 keV, [0.97; 1.07] for 500 keV, [0.94; 1.06] for 1 MeV, [0.94; 1.04] for 2 MeV and [0.96; 1.09] for 4 MeV. Occasionally, however, some values gave higher ratios:

-The SAF (Liver←Kidneys) values for 10 keV and the SAF (Spleen←Adrenals) values for 2 MeV gave a ratio of approximately 20 and 0.1 respectively.

-The ratios calculated for the SAFs (Target←Lungs) at 500 keV were between 0.87 and 1.22.

-Finally, the SAFs (Pancreas←Pancreas) at 2 and 4 MeV gave respective ratios of 1.18 and 1.51.

The S factors calculated using MCNP4B and MCNPX for electrons of 0.935 MeV (table 8) gave ratios between 0.98 and 1.02 except for two very small values, S (Lungs←Kidneys) and S (Kidneys←Lungs) .

On the whole, we achieved good concordance between the results obtained using MCNP4B and MCNPX with only a few values giving large differences. Unlike the MCNPX2.5e code, the MCNP4B code is not optimised for the decrease in calculation times, certain values published by Yoriyaz are associated with considerable statistical uncertainty (Yoriyaz, personal communication). This probably explains the occasional differences noted.

Effect of Monte Carlo calculation code specificities

In a previous study [START_REF] Chiavassa | Dosimetric Comparison of Monte Carlo codes (EGS4, MCNP, MCNPX) Considering External And Internal Exposures Of The Zubal Phantom To Electron And Photon Sources[END_REF], Oedipe was compared to the Monte Carlo EGS4 code in a dosimetric study for the treatment of medullary thyroid cancer. The results obtained using the same voxel-based model (Zubal) showed variations below 10%. In this example, the EGS4 and MCNPX codes gave similar results.

In this paper, we compared MCNPX (Oedipe) to a Monte Carlo code developed by the GSF laboratory (table 9). The ratio between the SAFs (kg -1 ) calculated using the two codes increased when the photon energy decreased. These ratios were less than 1.18, 1.09 and 0.96 for 30 keV, 100 keV and 1 MeV respectively. These variations can be explained by the different specificities of the two codes: cross-sections and energy cut-off (1 keV for MCNPX versus 4 keV for the GSF code). The difference between the results obtained with the GSF and MCNPX codes was thus greater than in the previous example, but can be considered as acceptable, particularly in the case of photons of 100 keV and 1 MeV.

Effect of the calculation method

The S factors integrated into the MIRDOSE3 software and those calculated by MCNPX were similar (table 10). The ratios between the values were between 0.93 and 1.06 for the organs considered, except for the skin (with a ratio of 1.10 when the source was distributed in the lungs). Moreover, the ratio between the S factors (Brain←Kidneys) was equal to 0.62. The values were nevertheless small.

The values shown in MIRDOSE3 were obtained from Monte Carlo simulations accepting a high statistical uncertainty or a point kernel method [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF], according to the calculation methods available at the time. All of the values calculated using MCNPX had an associated statistical error below 5%. It is also worth noting that the values noted for the Iodine 131 emissions come from different sources: ICRP 38 for our calculations and Weber et al. [START_REF] Weber | Stylized versus tomographic: an experience on anatomical modelling at RPI Monte Carlo[END_REF]) for the values present in MIRDOSE3. Taking into account these large differences in the calculation methods used, the differences observed were remarkably small.

Effect of voxel sampling and voxel size

Voxel sampling of the adult ORNL mathematical model led to variations in organ volume (table 11). The ratio between the organ volumes given by the mathematical model and the voxel-based model was close to 1 (between 0.99 and 1.03) for organs that are relatively large and compact. This was more marked for small sized organs such as adrenals (up to 1.07), or elongated organs such as the pancreas (up to 1.08). As expected, the greatest variation was noted for the skin (up to 1.29) which is a thin organ. The thickness of the skin in the mathematical model is 2 mm [START_REF] Cristy | Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources ORNL[END_REF].

The S factors calculated using MCNPX for the two voxel-based models are given in table 12. These values were compared to S factors calculated using MCNPX for the adult mathematical ORNL model (table 10). With the exception of the skin, the ratios between the S factors of the mathematical model and the most accurately sampled model (voxels of 1.58 x 1.58 x 5 mm 3 ) were between 0.93 and 1.08. As expected, for the voxel-based model where the spatial resolution was decreased (voxels of 3.16 x 3.16 x 5 mm 3 ) the ratios were generally higher (0.93 to 1.12). The ratios between the S factors were more pronounced for the skin: up to 0.80 for the higher resolution sampled model and 0.81 for the lower resolution sampled model. This difference is in concordance with the differences in volume noted for the skin between the 3 models (table 11).

The effect of voxel sampling of the adult mathematical ORNL model was less critical in the results published by Peter [START_REF] Peter | Analitical versus voxelized phantom representation for Monte Carlo simulation in radiological imaging IEEE trans[END_REF] where an accurately detailed cardiac mathematical model was used, which was more distorted by voxel sampling. However, it is obvious that certain thin or small organs cannot be realistically represented by a voxel-based geometry. Nevertheless, representation of individualised patient anatomy based on CT or MRI images is vital in targeted radiotherapy. Furthermore, the use of voxel-based geometries implies that a heterogeneous distribution of cumulated activity can be taken into account in source organs and absorbed doses can be calculated at the tissue level.

Calculation times

The definition of geometry in the form of voxels leads to an increase in the amount of memory necessary for Monte Carlo calculations. We performed simulations for the adult mathematical ORNL model and 2 voxel-based representations of this model. The simulations carried out for the voxelbased model with the higher resolution (1.58 x 1.58 x 5 mm 3 ) and the lower resolution (3.16 x 3.16 x 5 mm 3 ) necessitated 4 and 2 fold more memory respectively than the simulations with the mathematical model. Compared to the mathematical model, the size of the entry files was increased approximately 50-fold for the higher resolution voxel-based model and about 25-fold for the lower resolution model (table 13).

Contrary to the studies published by Peter and Xu [START_REF] Peter | Analitical versus voxelized phantom representation for Monte Carlo simulation in radiological imaging IEEE trans[END_REF][START_REF] Weber | Stylized versus tomographic: an experience on anatomical modelling at RPI Monte Carlo[END_REF], in our study we found the calculation times to be approximately 2-fold lower for the voxel-based model than for the mathematical model (table 13). As expected, a mathematical definition of the geometry does not enable use of the repeated structures format related to optimisation of the MCNPX2.5e code. This example illustrates the capacity of the improvements in calculation algorithms made for this code.

Conclusion

Oedipe is a patient-specific dosimetric tool based on the Monte Carlo MCNPX code. Comparison of the data calculated by Yoriyaz using the same method with the MCNP4B codes constitutes the validation of this tool. The comparisons discussed in this paper validate the Oedipe software by independently taking into account the various parameters influencing dosimetric calculations.

The current Monte Carlo codes have different specificities. The two examples discussed in this paper (MCNPX/EGS4 and MCNPX/code GSF) show that the use of different codes (MCNPX/code GSF) has an impact on dosimetric calculations, but that in general the results obtained are similar. The MCNPX code is well known and widely used by the scientific community. In addition, the statistical relevance of the results obtained can be evaluated and the number of simulated particles subsequently adjusted according to the precision required.

The comparison of the results obtained by MCNPX with those integrated in MIRDOSE3 shows a striking similarity when the geometries under consideration are identical.

Oedipe enables a patient's individual anatomy to be taken into consideration instead of having to use a standard model, with the aim of achieving the precision required for targeted radiotherapy. Taking the anatomy of each patient into account using CT or MRI images logically results in defining the geometry with the help of voxels. This format has numerous advantages. Firstly, it enables the heterogeneous distribution of the cumulated activity at the heart of the organs to be considered and the distribution of the absorbed dose in the tissue to be calculated at the tissue scale. Moreover, using this format it is possible to take advantage of the optimisation of the MCNPX2.5e code. Our comparisons show that for this code, the calculation time is 2-fold less for the voxel-based model than for the same model defined mathematically. The results obtained for the two geometrical definitions are similar except for small size organs or thin organs such as the skin. One drawback of voxel-based geometries is the size of the voxels, which limits the definition of this type of organ.

A major limitation of patient-specific dosimetric studies using Monte Carlo calculations is the excessive calculation times often necessary for this method. Oedipe uses the optimised MCNPX2.5e code. The calculation times necessary are compatible with clinical use, thus leaving no doubt that studies of patient-specific dosimetry can be undertaken at the organ scale in the context of clinical applications of targeted radiotherapy. Dosimetric studies on the voxel scale could also be envisaged but would require longer calculation times. This limitation should be easily overcome by the use of computer networks working in parallel.

1.42e-1 3.00e-2 1.75e-2 3.80e-2 4.47e-3 6.09e-2 Liver MCNPX 1.37e-1 2.92e-2 1.61e-2 3.75e-2 4.32e-3 5.98e-2 MCNP4B 3.00e-2 2.71e-1 6.19e-3 6.04e-2 5.19e-2 1.15e-1 Kidneys MCNPX 2.87e-2 2.65e-1 5.75e-3 5.87e-2 4.99e-2 1.13e-1 MCNP4B 1.84e-2 6.50e-3 8.03e-2 1.22e-2 2.30e-2 1.37e-2

M3

MCNPX Adrenals 1.20e-6 1.19e-6 2.03e-6 2.05e-6 1.24e-6 1.27e-6 2.58e-6 2.88e-6 6.66e-7 6.29e-7 Brain 5.50e-9 5.70e-9 1.47e-9 2.39e-9 5.71e-9 6.00e-9 4.16e-9 4.34e-9 3.33e-8 3.47e-8 Breast 2.17e-7 2.15e-7 7.49e-8 7.30e-8 1.50e-7 1.46e-7 1.96e-7 1.90e-7 6.68e-7 6.77e-7 Small Intestine 3.26e-7 3.32e-7 5.89e-7 6.01e-7 2.91e-7 2.91e-7 3.95e-7 3.83e-7 5.02e-8 5.35e-8

Stomach 4.21e-7 4.11e-7 6.93e-7 6.96e-7 2.02e-6 2.01e-6 3.42e-6 3.29e-6 3.24e-7 3.24e-7 Kidneys 8.13e-7 8.07e-7 1.17e-4 1.15e-4 1.85e-6 1.83e-6 1.40e-6 1.35e-6 1.95e-7 2.04e-7 Liver 2.12e-5 2.13e-5 8.13e-7 8.05e-7 2.14e-7 2.17e-7 1.02e-6 1.01e-6 5.44e-7 5.52e-7 Lungs 5.45e-7 5.54e-7 1.95e-7 2.07e-7 4.51e-7 4.57e-7 4.72e-7 4.77e-7 3.35e-5 3.30e-5 Pancreas 1.02e-6 1.01e-6 1.40e-6 1.36e-6 3.58e-6 3.55e-6 3.56e-4 3.51e-4 4.72e-7 4.74e-7 Skin 1.17e-7 1.14e-7 1.33e-7 1.24e-7 1.21e-7 1.13e-7 9.87e-8 9.40e-8 1.29e-7 1.17e-7 Spleen 2.14e-7 2.17e-7 1.85e-6 1.83e-6 1.93e-4 1.91e-4 3.58e-6 3.52e-6 4.51e-7 4.51e-7 Thymus 1.76e-7 1.85e-7 6.79e-8 6.88e-8 1.04e-7 1.12e-7 1.74e-7 1.84e-7 7.89e-7 7.88e-7 Uterus 1.03e-7 1.10e-7 1.98e-7 1.98e-7 9.27e-8 9.50e-8 1.23e-7 1.24e-7 2.02e-8 2.08e-8 Table 10: S factors (mGy.Mbq -1 .s -1 ) used by MIRDOSE3 (M3) and calculated using MCNPX for the adult ORNL mathematical model. The source of 131 I is uniformly distributed amongst the source organs. 11: Organ volumes (cm 3 ) given by the adult ORNL mathematical model and the same model sampled considering two voxel sampling sizes: 254 x 145 x 348 voxels of 1.58 x 1.58 x 5 mm 3 and 127 x 73 x 348 voxels of 3.16 x 3.16 x 5 mm 3 . The ratios of the organ volumes given by the mathematical model and the two voxel-based models are given in parentheses. VBM 2 Adrenals 1.27e-6 1.20e-6 1.99e-6 2.06e-6 1.27e-6 1.32e-6 2.94e-6 2,91e-6 6.69e-7 6.22e-7 Brain 5.58e-9 5.56e-9 2.22e-9 2.34e-9 5.86e-9 5.69e-9 4.06e-9 4.13e-9 3.56e-8 3.53e-8 Breast 2.21e-7 2.20e-7 7.32e-8 7.27e-8 1.43e-7 1.41e-7 1.88e-7 1.93e-7 6.85e-7 6.83e-7 Small Intestine 3.45e-7 3.44e-7 6.38e-7 6.44e-7 3.07e-7 3.09e-7 4.05e-7 4.08e-7 5.52e-8 5.53e-8

Organ volumes (cm

Stomach 4.18e-7 4.18e-7 7.11e-7 7.11e-7 2.05e-6 2.04e-6 3.27e-6 3.24e-6 3.16e-7 3.14e-7 Kidneys 8.09e-7 8.06e-7 1.18e-4 1.18e-4 1.83e-6 1.84e-6 1.37e-6 1.35e-6 2.01e-7 2.00e-7 Liver 2.11e-5 2.12e-5 8.10e-7 8.07e-7 2.22e-7 2.21e-7 1.05e-6 1.06e-6 5.58e-7 5.61e-7 Lungs 5.63e-7 5.62e-7 2.05e-7 2.02e-7 4.41e-7 4.40e-7 4.71e-7 4.67e-7 3.40e-5 3.39e-5 Pancreas 1.07e-6 1.07e-6 1.38e-6 1.36e-6 3.47e-6 3.43e-6 3.79e-4 3.77e-4 4.70e-7 4.66e-7 Skin 1.43e-7 1.41e-7 1.42e-7 1.36e-7 1.38e-7 1.40e-7 1.14e-7 1.09e-7 1.47e-7 1.42e-7 Spleen 2.21e-7 2.19e-7 1.84e-6 1.84e-6 1.91e-4 1.91e-4 3.46e-6 3.39e-6 4,43e-7 4.36e-7 Thymus 1.80e-7 1.97e-7 6.82e-8 6.66e-8 1.13e-7 1.19e-7 1.78e-7 1.64e-7 8.00e-7 7.81e-7 Uterus 1.10e-7 1.05e-7 2.02e-7 1.97e-7 9.22e-8 9.33e-8 1.26e-7 1.22e-7 2.03e-8 2.04e-8 Table 12: S factors (mGy.MBq -1 .s -1 ) calculated using MCNPX for 131 I and for 2 voxel-based representations of the adult ORNL mathematical model. Two samplings are considered: 254 x 145 x 348 (VBM 1) and 127 x 73 x 348 voxels (VBM 2). 15.2 7.9 7 Table 13: Amount of memory (Mbytes), simulation entry file size (Mo) and calculation time (hours) necessary to calculate the S factors using MCNPX in 13 target organs of the adult ORNL model with a statistical incertitude less than 5% (using a G5 bi-processor Power Mac). The iodine 131 is uniformly distributed in the lungs.

  Study parameters for the calculation of Monte Carlo codes specificities.

Figure 1 :

 1 Figure 1: Voxel-based Zubal model (a) and the standard adult ORNL mathematical model (b).(b) (a)

Table 1 :

 1 SAF values (kg -1 ). Photon energy of 10 keV.

	Source organ

Table 2 :

 2 SAF values (kg -1 ). Photon energy of 50 keV.

	Lungs	MCNPX	1.75e-2	6.10e-3	7.79e-2	1.19e-2	2.15e-2	1.34e-2
	Pancreas	MCNP4B MCNPX	3.79e-2 3.77e-2	5.86e-2 5.87e-2	1.16e-2 1.14e-2	1.38 1.35	4.56e-2 4.40e-2	2.59e-1 2.53e-1
	Spleen	MCNP4B MCNPX	4.38e-3 4.28e-3	5.18e-2 4.96e-2	2.16e-2 1.97e-2	4.56e-2 4.41e-2	4.30e-1 4.15e-1	2.99e-2 2.90e-2
	Adrenals	MCNP4B MCNPX	5.97e-2 6.08e-2	1.18e-1 1.20e-1	1.41e-2 1.26e-2	2.59e-1 2.49e-1	3.02e-2 2.78e-2	5.35 5.29
					Source organ
	Target organ	Method	Liver	Kidneys	Lungs	Pancreas	Spleen	Adrenals
	Liver	MCNP4B MCNPX	9,01e-2 8,67e-2	2,32e-2 2,19e-2	1,42e-2 1,29e-2	2,89e-2 2,79e-2	5,73e-3 5,28e-3	4,35e-2 4,21e-2
	Kidneys	MCNP4B MCNPX	2,31e-2 2,16e-2	1,65e-1 1,62e-1	6,82e-3 6,16e-3	4,36e-2 4,23e-2	3,65e-2 3,43e-2	7,65e-2 7,48e-2
	Lungs	MCNP4B MCNPX	1,42e-2 1,30e-2	6,81e-3 6,22e-3	5,02e-2 4,88e-2	1,14e-2 1,07e-2	1,70e-2 1,56e-2	1,27e-2 1,19e-2
	Pancreas	MCNP4B MCNPX	2,88e-2 2,85e-2	4,35e-2 4,30e-2	1,12e-2 1,10e-2	8,20e-1 8,15e-1	3,54e-2 3,34e-2	1,60e-1 1,57e-1
	Spleen	MCNP4B MCNPX	5,75e-3 5,25e-3	3,62e-2 3,39e-2	1,67e-2 1,54e-2	3,51e-2 3,28e-2	2,59e-1 2,51e-1	2,47e-2 2,34e-2
	Adrenals	MCNP4B MCNPX	4,31e-2 4,05e-2	7,55e-2 7,57e-2	1,30e-2 1,21e-2	1,63e-1 1,59e-1	2,33e-2 2,39e-2	3,34 3,31

Table 3 :

 3 SAF values (kg -1 ). Photon energy of 100 keV.

	Source organ

Table 4 :

 4 SAF values (kg -1 ). Photon energy of 500 keV.

	Source organ

Table 5 :

 5 SAF values (kg -1 ). Photon energy of 1 MeV.

	Source organ

Table 6 :

 6 SAF values (kg -1 ). Photon energy of 2 MeV.

	Source organ

Table 7 :

 7 SAF values (kg -1 ). Photon energy of 4 MeV.

	Source organ

Table 9 :

 9 SAF (kg -1 ) calculated using 2 different Monte Carlo codes (code GSF and MCNPX) in 8 sourcetarget organs of the voxel-based Zubal model, considering monoenergetic photons of 30 keV, 100 keV and 1 MeV.

	Target Organs	M3	Liver MCNPX	Kidneys M3 MCNPX	Source Organs Spleen M3 MCNPX	Pancreas M3 MCNPX	Lungs

Acknowledgements

The comparisons published in this paper were made possible thanks to the invaluable data of H. Yoriyaz and N. Petoussi-Henß on their calculation methods. The mathematical definition of the adult ORNL model in MCNPX format was provided by S. Ménard and C. Furstoss of the External Dosimetry Laboratory (SDE) at the French Institute of Radiation Protection and Nuclear Safety (IRSN). The authors wish to thanks Dr. Glenn Flux for checking the manuscript.


Victor Prévost

Karine Anselme

Olivier Gallet

Mathilde Hindié

Tatiana Petithory

Jules Valentin

Mathieu Veuillet

Lydie Ploux
email: ploux@unistra.fr

Real-time imaging of bacteria/osteoblast dynamic co-culture on bone implant material in an in vitro post-operative contamination model

Keywords: Real-time imaging, bacteria/eukaryotic cells co-culture, biomaterials, confocal fluorescence microscopy, fibronectin

Biomedical implants are an important part of evolving modern medicine but have a potential drawback in form of post-operative pathogenic infection. Accordingly, the "race for surface" combat between invasive bacteria and host cells determines the fate of implants. Hence, proper in vitro systems are required to assess effective strategies to avoid infection. In this study we developed a real time observation model, mimicking post-operative contamination, designed to follow E. coli proliferation on titanium surface occupied by human osteoblastic progenitor cells (STRO). This model allowed us to monitor E. coli invasion of human cells and titanium surface coated and uncoated with fibronectin. We showed that the surface colonization of bacteria is significantly enhanced on fibronectin coated surfaces irrespective of if areas were uncovered or covered with human cells. We further revealed that bacterial colonization of the titanium surfaces is enhanced in co-culture with STRO cells. Finally, this co-culture system provides a comprehensive system to describe in vitro and in situ bacterial and human cells and their localization but also to target biological mechanisms involved in adhesion as well as in interactions with surfaces, thanks to fluorescent labelling. This system is thus an efficient method for studies related to design and function of new biomaterials.

INTRODUCTION

A major challenge in bone implant integration is the prevention of biomaterials associated infection (BAI). The consequences of BAI for patients are severe as their hospitalization time is increased and may sometimes lead to complementary surgery. [START_REF] Grainger | Critical factors in the translation of improved antimicrobial strategies for medical implants and devices[END_REF][START_REF] Engelsman | The risk of biomaterial-associated infection after revision surgery due to an experimental primary implant infection[END_REF] BAI are commonly studied in vitro with either eukaryotic or prokaryotic cells. [START_REF] Wu | Differential response of Staphylococci and osteoblasts to varying titanium surface roughness[END_REF][START_REF] Wang | Effect of adsorbed fibronectin on the differential adhesion of osteoblast-like cells and Staphylococcus aureus with and without fibronectin-binding proteins[END_REF][START_REF] Vasilev | Tunable antibacterial coatings that support mammalian cell growth[END_REF][START_REF] Ploux | Opposite Responses of Cells and Bacteria to Micro/Nanopatterned Surfaces Prepared by Pulsed Plasma Polymerization and UV-Irradiation[END_REF][START_REF] Burton | Molecular Gradients of Bioinertness Reveal a Mechanistic Difference between Mammalian Cell Adhesion and Bacterial Biofilm Formation[END_REF][START_REF] Anselme | The interaction of cells and bacteria with surfaces structured at the nanometer scale[END_REF][START_REF] Franková | Comparing biocompatibility of gingival fibroblasts and bacterial strains on a different modified titanium discs[END_REF][START_REF] Anagnostou | Osteoblast functions on functionalized PMMA-based polymers exhibiting Staphylococcus aureus adhesion inhibition[END_REF] The "race for the surface" [START_REF] Gristina | Biomaterial-centered infection: microbial adhesion versus tissue integration[END_REF] between bacteria and cells during BAI, has been more recently reproduced in vitro using a co-culture model with host cells and bacteria. The simplest in vitro BAI models on various biomaterials are based on static co-cultures of human eukaryotic cells (gingival fibroblasts or osteoblasts) with different strains of Streptococci, [START_REF] Zhao | Soft tissue integration versus early biofilm formation on different dental implant materials[END_REF][START_REF] Zara | Anti-adhesive and pro-apoptotic effects of 2-hydroxyethyl methacrylate on human gingival fibroblasts co-cultured with Streptococcus mitis strains[END_REF][START_REF] Gallorini | Cell-protection mechanism through autophagy in HGFs/S. mitis co-culture treated with Chitlac-nAg[END_REF][START_REF] Di Giulio | Streptococcus mitis/human gingival fibroblasts co-culture: the best natural association in answer to the 2-hydroxyethyl methacrylate release[END_REF][START_REF] Cataldi | Adhesion of human gingival fibroblasts/Streptococcus mitis co-culture on the nanocomposite system Chitlac-nAg[END_REF] Pseudomonas [START_REF] Zhao | Osteoblast integration of dental implant materials after challenge by sub-gingival pathogens: a co-culture study in vitro[END_REF][START_REF] Hindie | Effects of Fibronectin Coating on Bacterial and Osteoblast Progenitor Cells Adherence in a Co-culture Assay[END_REF] or Staphylococci [START_REF] Zaatreh | Co-Culture of S. epidermidis and Human Osteoblasts on Implant Surfaces: An Advanced In Vitro Model for Implant-Associated Infections[END_REF][START_REF] Zaatreh | Thin magnesium layer confirmed as an antibacterial and biocompatible implant coating in a coculture model[END_REF][START_REF] Foss | Bacteria and osteoblast adhesion to chitosan immobilized titanium surface: A race for the surface[END_REF] in multi-well plates. Even though these models provide a wealth of knowledge their preparatory procedures along with chemical fixation to quantify the amount and viability of cells or bacteria are done long after the co-culture is initiated. Due to this the initial events of interaction between material-cell/bacteria and cell-bacteria remains largely unexplored.

Thus, there is a need to develop the existing methods in order to explore the preliminary interactions of cell and bacteria on implants. In situ optical microscopies can allow continuous observation of real-time interactions in co-cultures. When equipped with culture set-ups to provide continuous nutrient medium exchange, these systems, furthermore, provide adequate conditions for live cells and bacteria. In 2000, Heydorn et al. used such a hydrodynamic system to evaluate in vitro reproducibility of biofilms. [START_REF] Heydorn | Quantification of biofilm structures by the novel computer program COMSTAT[END_REF] Later on, microfluidic systems were applied to cellular or bacterial culture separately. [START_REF] Velve-Casquillas | Microfluidic tools for cell biological research[END_REF][START_REF] Lee | Microfluidic Approach to Create Three-Dimensional Tissue Models for Biofilm-Related Infection of Orthopaedic Implants[END_REF] However, while they enabled real-time imaging under flow, these devices were inadequate for investigating large pieces of biomaterial. Considering this, in 2006 Busscher and Van der Mei proposed a new type of flow chamber, which allowed working with a bigger volume of culture medium and larger culture surfaces. [START_REF] Busscher | Microbial adhesion in flow displacement systems[END_REF] In 2009, the same group proposed for the first time a prokaryote and eukaryote co-culture model based on previously developed flow chamber. [START_REF] Subbiahdoss | Microbial biofilm growth vs. tissue integration: "The race for the surface" experimentally studied[END_REF] Later, this co-culture model was used to evaluate the simultaneous response of Staphylococci and osteoblasts on different materials. [START_REF] Subbiahdoss | Bacterial biofilm formation versus mammalian cell growth on titanium-based mono-and bi-functional coating[END_REF][START_REF] Subbiahdoss | Mammalian cell growth versus biofilm formation on biomaterial surfaces in an in vitro post-operative contamination model[END_REF][START_REF] Subbiahdoss | Microbial biofilm growth versus tissue integration on biomaterials with different wettabilities and a polymer-brush coating[END_REF][START_REF] Rochford | An in vitro investigation of bacteria-osteoblast competition on oxygen plasma-modified PEEK[END_REF][START_REF] Saldarriaga Fernandez | Competitive time-and density-dependent adhesion of staphylococci and osteoblasts on crosslinked poly(ethylene glycol)-based polymer coatings in co-culture flow chambers[END_REF] In 2011, they improved their co-culture model by associating three different bacterial strains, macrophages and osteoblasts to study tissue integration in BAI context. [START_REF] Subbiahdoss | In Vitro Interactions between Bacteria, Osteoblast-Like Cells and Macrophages in the Pathogenesis of Biomaterial-Associated Infections[END_REF] However, chemical fixation of cells was still needed in this model before their observation. Lee et al. have provided significant advances to the field by an attempt to simulate 3D tissue with osteoblastic cells in an in vitro co-culture system, designed to simulate BAI with S. epidermidis in actual organs. [START_REF] Lee | Microfluidic Approach to Create Three-Dimensional Tissue Models for Biofilm-Related Infection of Orthopaedic Implants[END_REF] Nevertheless, most of these approaches focused on specific biological behaviors resulting from the bacteria/cell co-culture, without any consideration for biological interactions with the underlying material. Aiming at mimicking clinically relevant cells/bacteria/material complex environments, in 2014 Yue et al. focused on the interaction of bacteria and cells with active titanium surfaces. [START_REF] Yue | Simultaneous interaction of bacteria and tissue cells with photocatalytically activated, anodized titanium surfaces[END_REF] However, bacteria-cell interaction was not evaluated and the system did not allow dynamic fluid circulation or real-time imaging of the co-culture. The most comprehensive co-culture model developed until now was reported by Lee et al. while studying interactions between mouse osteoblasts (MC3T3-E1) and S. epidermidis on a titanium surface by real-time imaging and recording live cells and bacteria under an upright fluorescent microscope. [START_REF] Lee | Effects of Staphylococcus epidermidis on Osteoblast Cell Adhesion and Viability on Ti Alloy Surface in Microfluidic Co-Culture Environment[END_REF] However, images of both cells and bacteria could not be obtained at the same time.

Thus ultimately, there is still a lack of a co-culture system that includes all the methodologies and experimental approaches needed to provide a thorough and dynamic description of interactions between mammalian cells, bacteria and materials. Such a dynamic microfluidic flow system would allow renewing culture medium of fluorescently stained cells and bacteria and real-time florescence imaging at high resolution of material-cell/bacteria and cellbacteria interactions. With this aim we developed a versatile and reliable system based on a flow chamber designed for investigation of any biomaterial. [START_REF] Cottenye | Vesicular structures self-assembled from oligonucleotide-polymer hybrids: Mechanical prevention of bacterial colonization upon their surface tethering through hybridization[END_REF][START_REF] Böhmler | Impact of chemical heterogeneities of surfaces on colonization by bacteria[END_REF] Previous studies postulated bacterial adhesion prior to cell arrival on the implant, in order to simulate an infection occurring during the operation and before tissue integration. [START_REF] Subbiahdoss | Microbial biofilm growth vs. tissue integration: "The race for the surface" experimentally studied[END_REF] Herein, we explore another aspect of implant contamination by focusing on the infection by bacteria of cells previously adhered to the biomaterial. This situation can occur during early stage of tissue integration or when integration is already achieved i.e. a post-operative contamination model. [START_REF] Subbiahdoss | Mammalian cell growth versus biofilm formation on biomaterial surfaces in an in vitro post-operative contamination model[END_REF] With this objective, we attempted to develop a dynamic system to control cell viability and mimic arrival of bacteria via bloodstream by renewing culture medium through use of a peristaltic pump. Moreover, our aim was to deal with orthopedic materials such as ceramic or titanium with variable surface chemistry and topography. With the help of an upright confocal laser microscope equipped with a 63X magnification objective of high optical aperture, well-adapted for imaging bacteria, we imaged in situ bacteria/cell growth on such opaque materials. Furthermore, we optimized the set-up previously designed by our lab. [START_REF] Böhmler | Impact of chemical heterogeneities of surfaces on colonization by bacteria[END_REF] Notably the distance between the material sample and the microscope objective was adapted to the short focal-length of the objective and for the reduction of the culture medium height crossed by light. To be able to follow the behavior of both bacteria and cells, their staining became a key factor. Thus, for bacteria, we took advantage of GFP-expressing bacteria technology. [START_REF] March | Biotechnological applications of green fluorescent protein[END_REF] For cells, labeling required adjustments to compromise on sufficient fluorescence emission versus oxidative toxicity resulting from photo-bleaching. [START_REF] Waters | Accuracy and precision in quantitative fluorescence microscopy[END_REF][START_REF] Wang | Fluorescence proteins, live-cell imaging, and mechanobiology: seeing is believing[END_REF][START_REF] Stephens | Light microscopy techniques for live cell imaging[END_REF] Thus, the real-time and high magnification imaging of the initial contamination of pre-adhered osteoblastic progenitor cells by E. coli on clinically non-transparent relevant titanium surfaces became possible.

MATERIALS AND METHODS

Flow chamber design and circuit construction

Flow chamber and procedures were designed on the basis of set-ups and procedures developed for investigation of bacteria/material interface and described previously. [START_REF] Cottenye | Vesicular structures self-assembled from oligonucleotide-polymer hybrids: Mechanical prevention of bacterial colonization upon their surface tethering through hybridization[END_REF][START_REF] Böhmler | Impact of chemical heterogeneities of surfaces on colonization by bacteria[END_REF] The flow chamber was fabricated out of polycarbonate Makrolon™ (NEOTEC Plastique SAS, France). It exhibits a co-culture chamber with a 20x40 mm flat bottom and a height of 1.8 mm. These dimensions allow the incorporation of large surface material, e.g. 16 mm diameter titanium disks in a centered circular cavity. The total volume of the chamber (1.44 ml) contains enough culture medium to ensure cell viability during non-dynamic phases (cell adhesion to surface, bacteria adhesion to surface, cell fluorescent staining). The chamber was sealed by a 0.17 mm thick glass cover slip (Menzel-Gläser, Fisher Scientific, France) with the help of a thin line of biocompatible glue (Aqua SiliconeTM, Den Braven, Netherlands). The use of this wide glass cover slip gives a large field of view and allows scanning the entire surface of the material with minimum adjustments. The input and the output sides of the chamber were connected to the circuit by 0.5 mm diameter cylindrical holes on which were plugged flexible tubes (TYGON R-3603, Fisher Scientific, France) (Figure 1). A peristaltic pump (ISMATEC ISM945D, Fisher Scientific, France) provided fresh culture medium through the circuit at the constant flow of 0.028 ml/min (1.68 ml/h) except during inoculation with bacteria (0.28 ml/min) to mimic short inoculation phase duration (<10 min). The medium is stored in a sterile bottle connected to the circuit across a 0.22 µm Millipore filter, which prevents undesired chamber contamination. Indeed, this medium does not contain antibiotics, and therefore is vulnerable to bacterial proliferation. A manual valve is used to switch between medium and bacterial solution for starting and stopping the infection step. A tube leads from the valve to the plugged 2 ml syringe that contains the bacterial solution. A passive bubble trap extracts gas bubbles out of the incoming medium. [START_REF] Sternberg | Methods for Dynamic Investigations of Surface-Attached In Vitro Bacterial and Fungal Biofilms[END_REF] Then the chamber volume is gently renewed by a laminar flow as evaluated elsewhere. [START_REF] Böhmler | Impact of chemical heterogeneities of surfaces on colonization by bacteria[END_REF] The observation phase flow is 1.68 ml/h and the total chamber volume is changed every 51 min. Outcoming medium is driven to the waste bottle.

The entire circuit fits under an upright confocal microscope (Carl Zeiss LSM700) within an environmental chamber. During experiments, temperature is maintained at 37°C and the atmosphere inside the environmental chamber is a mixture of air and 5% CO2. The air mixture reaches the culture medium through gas exchange across porous circuit wires.

Microscopy techniques: Confocal objective and automation

Observation was performed with an upright confocal microscope (Carl Zeiss LSM700). Upright configuration permits to solve part of the biological versus material concern, as opaque implantable material such as titanium will be observed from the top when cells is growing upon it. A W Plan-Apochromat 63x/1.0 from Zeiss with a 2.1 mm focal distance was used. This objective is designed for immersion in water and does not possess an optical correction for glass coverslip. However, it was shown to be efficient for bacteria and cell imaging on biomaterials through the liquid (distillated water drop) / transparent solid (glass coverslip) / liquid (culture medium) optical interfaces of the sealed flow chamber (Figure 2). Compared to 50x long distance air objective micrographs, on the same flow chamber, the acquired information is greatly improved, both quantitatively and qualitatively (Figure S1).

The confocal microscope is equipped with a motorized X-Y stage that permits an automated acquisition on various surface locations. Eight positions were fixed at the beginning of experiment. Evolution of the surface colonization was then automatically followed on these locations throughout the duration of the experiment (Figure 2b).

Biomaterial and fibronectin surface functionalization

The material used was a mirror polished titanium disk of 16 mm diameter and 0. and then heat-sterilized (180°C, 1h). The titanium surface was functionalized with 50 µg/ml human plasma-derived fibronectin (Fn) during 30 min at room temperature, then rinsed three times with sterile PBS and stored in sterile PBS. [START_REF] Poulouin | Purification and stability of human plasma fibronectin[END_REF] To allow the simultaneous study with and

Cell culture and medium

The eukaryotic cells F/STRO-1+A (STRO) i.e. immortalized osteoblast progenitors from human fetal bone marrow stroma were a kind gift from Dr Pierre J. Marie (Inserm U1132, University Paris Diderot). Cells used were between 20 th and 35 th passage to maintain the homogeneity of the observed population. STRO cells were cultured in ISCOVE's modified Dulbecco's Medium (IMDM, Gibco) supplemented with 10% of Fetal Bovine Serum (FBS) (v/v), 2mM of L-glutamine and 100 U/ml penicillin-streptomycin (100µg/ml) solutions (further on referred to as complete medium). They were maintained at 37°C in 5% CO2 humidified atmosphere and were passaged at 80-90% confluence using 5% (v/v) Trypsin-EDTA. Cell density used was 7.5 10 4 cell/ml, and injected in the sealed flow chamber before each experiment and incubated overnight to promote cell adhesion.

Bacterial culture and medium

Prokaryote cells were modified Escherichia coli (K12 SCC1) which express the eGFP fluorescent molecule. They were obtained from Prof. Chun Chau (Division of Genomics & Genetics, Nanyang Technological University). [START_REF] Miao | Dual fluorescence system for flow cytometric analysis of Escherichia coli transcriptional response in multi-species context[END_REF] Bacteria were thawed from -80°C storage solution, and grown overnight at 30°C on Luria Broth (LB) agar medium in Petri dish. One colony was used to inoculate 15 ml LB solution before overnight incubation at 30°C. Lag-phase of bacterial growth was obtained by inoculating 30 ml of LB solution with the previously incubated bacterial solution. After 4 hours of growth, the Lag-phase solution was centrifuged (3500 rpm during 20 min at 4°C) and the medium was replaced by the co-culture medium.

Bacterial concentration of this solution was estimated by measurement of absorbance at 600 nm wavelength. This solution was diluted in co-culture medium to obtain a final 2 ml solution at an optical absorbance of 0.01 (~5 10 6 bacteria mL -1 ), stored in a 5 ml syringe which was plugged into the flow chamber circuit.

Co-culture medium

Co-culture experiments were carried out in a cell culture medium adequate for mammalian cells, in order to describe in a close manner in situ cell behavior in context of implant contamination. Phenol red-free ISCOVE medium was used to minimize interactions with fluorochromes and to optimize microscope laser signal in red wavelengths. Phenol red-free ISCOVE medium was supplemented with 5% of FBS and 1% of L-glutamine (referred as coculture medium) and was verified to allow normal growth of bacteria (Figure 4). The coculture medium was maintained at 37°C before and during the experiments.

Bacterial inoculation in the co-culture chamber

Bacterial inoculation phase followed three steps: 1) the syringe content was pumped through the circuit to the co-culture chamber with a flow of 0.28 ml/min; 2) Once the total volume of the bacterial solution had replaced the flow chamber volume, the pump was stopped and the circuit closed before and after the flow chamber to avoid undesired flow movements during the sedimentation of bacteria. The sedimentation phase was maintained for 10 minutes, which is the estimated time needed by bacteria to reach and adhere to the titanium surface;

3) The volume of co-culture chamber was then replaced with fresh medium at a 0.28 ml/min flow, in order to rinse out planktonic bacteria without displacing the adhered ones. When 2 ml of fresh medium was pumped through the circuit, the flow was set to 0.028 ml/min for the rest of the experiment.

Labelling of cells

To assess STRO cell behavior in real-time during bacterial contamination, their membrane was stained prior to the beginning of the experiment. Staining of live STRO cells was performed with Cell Tracker Red dye (CellTracker Red CMTPX Dye, Invitrogen, USA) by injection into the flow chamber through the junction that is also used to connect the syringe for injection of the bacterial solution (Figure 1). This was done after overnight adhesion of cells onto the titanium surface. 2 ml of 10 µM Cell Tracker Red dye solution (diluted in red phenol free ISCOVE medium) was gently injected in the flow chamber after removing culture medium. Cells were incubated with the stain solution during 30 minutes at 37°C with 5% CO2, and then incubated during 30 minutes with co-culture medium. Afterwards, medium was renewed with fresh coculture medium, and the flow chamber was left for 30 minutes at 37°C with 5% CO2

atmosphere until the start of experiment.

Confocal microscope settings

Acquisition parameters were set on ZEN® software for Zeiss confocal microscope. For each of the 8 positions on the titanium surface, the microscope automated plate was configured to capture 10 images following the z axis, which resulted in 10.49 µm thick 3D stacks. The microscope pinhole was maintained below 1 Airy Unit (52 µm). 555 nm and 488 nm lasers were used to excite Cell Tracker Red (with 4.0 % of the maximal laser power), and eGFP fluorochromes (with 4.6% of the maximal laser power) respectively. The W Plan -Apochromat 63 x objective was used at 0.5X zoom and final images resulted of 4x4 tile scan merged with 10% overlap. Gain levels of the photomultipliers were maintained in the range of linearity.

Image analysis

Bacterial counting, cell area calculation and comparison of the surface areas occupied by cells versus bacteria were processed using ImageJ® software (NIH, version 2.0.0-RC-61/1.51n, USA),

with "Analyze Particles" and "Image calculator" functions. 44 Each acquisition was composed of two tracks i.e. cell and bacterial track corresponding to the red and green channel respectively. Z-projection function was applied to the cell track to reduce the acquired 3D stacks into a 2D image that contains all the information about cell shape and cell area. Zprojection was also applied to bacterial stack to measure bacterial density. 3D observations were done with unreduced stacks (see Results). Measurements were done after setting the upper grayscale threshold in order to get higher signal/noise ratio. Objects were then identified and saved as binary images from which noise was removed. Final data such as cell number, shape (circularity, perimeter), area and bacterial size and number were extracted from these binary images. Images of minimum 30 cells were treated with ImageJ® software.

Circularity was calculated by using the formula 4π(area/perimeter 2 ) while Area and Perimeter correspond respectively to the area of the selection and the length of the outside boundary of the selection. The total population of adhered bacteria was measured as the number of bacteria adhered on each micrograph. Besides, two sub-populations were determined: The population of bacteria adhered on the titanium (uncoated or coated with fibronectin) are called as "on substrate"; The population of bacteria adhered on the cells are called as "on cell". The sub-populations are distinguished from each other by superposing the mask of the cells as determined on the Z-projection of the cell track on the Z-projection of the bacterial track. Experiments were repeated at least three times.

Statistical analysis

Results are depicted as the average and standard deviation (error bars) of the experimental values. The statistical significance of differences was assessed by bilateral Student's t tests and ANOVA with significance threshold of 0.05. The alternative hypothesis (H1: μ1 ≠ μ2) was assumed to be true when the main hypothesis (H0: μ1 = μ2) was rejected. [START_REF] Scherrer | Gaëtan Morin éditeur -Les Editions de la Chenelière inc[END_REF]

RESULTS

Live imaging

With the help of our experimental set up, we were able to visualize both live eukaryotic and prokaryotic cells during the initial time period of infection by bacteria of titanium surfaces already colonized by eukaryotic cells. As shown in Figure 5, the quality of images was high enough to allow further image treatment and analysis for a precise quantification of cell area, cell shape and bacterial number (Figure S3). In addition, 3D visualization provided information about the organization of bacterial consortium and their localization relative to mammalian cells (Figure S4), as illustrated by Figures 6A.1 and 6A.2. This further allowed us to study potential correlation between bacteria and cell localizations. On the uncoated and fibronectin coated titanium substrates, the density of bacteria appeared not to be affected by cell contact.

coli (in green) adhered on titanium surface without (-Fn) (A,C,E,G) and with (+Fn) fibronectin coating (B,D,F,H).

Images were obtained at Δt=0 (A,B), 1 (C,D), 2 (E,F) and 3 (G,H) hours after infection and removal with fresh medium of the planktonic bacteria (step 3, see Experimental Section above). Figure 6: 3D visualization of bacteria (green) on STRO cells (red) adhering on a fibronectin (+Fn) coated sample after 2 hours of co-culture with (A) top view (1) and transversal view (2 and 3) and (B) projected

top view with high magnification.

Cell adhesion and shape

Three different cell shape descriptors were compared after 3 hours of co-culture and single cell culture conditions: cell area, circularity and perimeter. These parameters are descriptors for cell adhesion, morphology and spreading onto the surface respectively. As depicted in Figure 7, average cell area, circularity and perimeter did not significantly vary with culture condition when cells were cultured on titanium substrates. In contrast average cell area and circularity decreased in presence of bacteria compared to cells alone on Fn coated titanium.

For cells alone, cellular circularity value had decreased on fibronectin while cellular perimeter logically increased, indicating that cells were not as round as on titanium. Interestingly, this effect of fibronectin coating on cell shape descriptors was not visible anymore when cells were co-cultured with bacteria.

Bacteria proliferation and localization

From inoculation (t0) to 3 hours afterwards, bacterial density was higher on titanium coated with fibronectin compared to uncoated titanium irrespective of culture conditions as shown on Figures 8.A and 8.B. Bacterial proliferation was also significantly enhanced on fibronectin coating compared to uncoated titanium, leading to a time offset in the kinetics of biofilm formation between both surfaces. This was particularly evident for bacteria in co-culture with the mammalian cells (Figure 8.B). Small colonies formed by sessile bacteria and in contact with each other were observed on the entire surface coated with fibronectin (Figures 5.F and 5.H, see zoom in Figure S3), which is characteristic of early stage of biofilm formation. [START_REF] Reisner | Development and maturation of Escherichia coli K-12 biofilms[END_REF] In contrast, colonies were less present on uncoated titanium (Figures 5.E and 5.G). This revealed a biofilm growth delay on uncoated titanium especially if mammalian cells were present. The quantity and the distribution of bacteria adhered after 3 hours in co-culture on fibronectincoated titanium (Figure 5.H, see zoom in Figure S3) can be interpreted as early stage biofilm, while the sessile bacteria and the sparse colonies observed on uncoated titanium (Figure 5.G)

are not yet representative of a biofilm. Figure 9 shows the comparison of bacterial density on top of cells or on the substrate on coated and uncoated titanium substrates. The difference between the density of bacteria upon and beside cells was not significant on titanium substrate. In contrast, bacterial colonization of coated titanium substrate was less on cells than on the substrate. This demonstrates that bacteria were not uniformly distributed over the sample in presence of adsorbed fibronectin. Their localization appeared to be affected by the presence of mammalian cells, resulting in a denser bacterial population on fibronectin-coated titanium than on the cell surface fraction. Interestingly, bacterial proliferation was promoted by the presence of fibronectin both upon and beside cells. A significantly higher density of bacteria

DISCUSSION

It has been shown in previous studies that E. coli bacteria can easily grow on untreated Ti surfaces. [START_REF] Lorenzetti | The influence of surface modification on bacterial adhesion to titanium-based substrates[END_REF] E. coli has also been found on failing dental titanium implants, confirming their affinity for orthopedic titanium substrates. [START_REF] Leonhardt | Microbial findings at failing implants[END_REF] These observations were confirmed by our results that showed a high bacterial proliferation on uncoated titanium surfaces. Surface roughness, wettability and substrate composition (such as RGD motifs) have been shown to have significant impact on bacterial adhesion and proliferation. [START_REF] Yeo | Implant surface factors and bacterial adhesion: a review of the literature[END_REF] Moreover, the affinity of E.

coli for fibronectin has been demonstrated for several strains. [START_REF] Zulfakar | Bacterial attachment to immobilized extracellular matrix proteins in vitro[END_REF] Even, fibronectin is to be a receptor for the adherence of several E. coli strains onto eukaryotic cells, which further allows bacteria's internalization by the eukaryotic cells. [START_REF] Gophna | Role of fibronectin in curli-mediated internalization[END_REF][START_REF] Yáñez | The Role of Fibronectin in the Adherence and Inflammatory Response Induced by Enteroaggregative Escherichia coli on Epithelial Cells[END_REF] In our experiments, fibronectin coating was done to introduce chemical modification to the titanium surface and favor cell adhesion. As expected, the growth of E. coli on fibronectin-coated titanium surface was higher compared to uncoated.

The behavior of osteoblastic cells on titanium is known for being modulated by the surface chemical and topographical properties [START_REF] Anselme | The interaction of cells and bacteria with surfaces structured at the nanometer scale[END_REF][START_REF] Anselme | Relative influence of surface topography and surface chemistry on cell response to bone implant materials. Part 2: biological aspects[END_REF] but also by the shear stress provided by flow. [START_REF] Fritton | Fluid and Solute Transport in Bone: Flow-Induced Mechanotransduction[END_REF] However, only insignificant shear stress (0.3 mPa) was here induced by the flow of fresh medium compared to those provided by interstitial fluid transport in bone tissue (35-3000 mPa. [START_REF] Fritton | Fluid and Solute Transport in Bone: Flow-Induced Mechanotransduction[END_REF][START_REF] Ren | Biomechanical and biophysical environment of bone from the macroscopic to the pericellular and molecular level[END_REF][START_REF] Riquelme | Mitogen-activated Protein Kinase (MAPK) Activated by Prostaglandin E2 Phosphorylates Connexin 43 and Closes Osteocytic Hemichannels in Response to Continuous Flow Shear Stress[END_REF] On the other hand, it has been shown that surface modification, e.g. increasing of roughness and wettability, can stimulate cell adhesion and differentiation. [START_REF] Guehennec | Surface treatments of titanium dental implants for rapid osseointegration[END_REF] For example, mouse osteoblastic cells have been shown to spread and form more cytoplasmic extensions and filopodia on mirror polished titanium than on rough titanium. We confirmed these observations. In addition, similar to bacteria's growth, the spreading of cells was enhanced on substrates with adsorbed fibronectin. Moreover, cells were not as round as on the uncoated titanium substrate. This behavior has also been observed with human fibroblasts adhering on surfaces with adsorbed fibronectin. [START_REF] Rico | Substrate-Induced Assembly of Fibronectin into Networks: Influence of Surface Chemistry and Effect on Osteoblast Adhesion[END_REF] Finally, we confirm here that fibronectin promotes both STRO cell spreading and E. coli K12 adhesion and growth on titanium.

The ability of E coli to adhere cell membrane has been shown to involve specific interactions between fibronectin and specific receptors on E. coli. [START_REF] Martino | Isolation of an Escherichia coli strain mutant unable to form biofilm on polystyrene and to adhere to human pneumocyte cells: involvement of tryptophanase[END_REF][START_REF] Hoepelman | Consequences of microbial attachment: directing host cell functions with adhesins[END_REF] Therefore, bacteria adhesion is expected to be favored on cells and on a fibronectin coating in contrast to titanium surface.

We confirmed the enhancement of bacterial adhesion on fibronectin coating compared to titanium surface. However, we observed that bacteria were more prone to adhere and grow on the fibronectin-coated titanium surface compared to on cells while they adhered similarly on cell membrane and on the uncoated material. Nevertheless the ability of STRO cells to reduce bacterial ability to adhere to cell membrane has already be observed. [START_REF] Hindie | Effects of Fibronectin Coating on Bacterial and Osteoblast Progenitor Cells Adherence in a Co-culture Assay[END_REF] Furthermore, this may be the result of cell defense mechanisms such as the controlled release of antimicrobial peptide through bacteria-host cells binding, [START_REF] Josse | Staphylococcus aureus vs. Osteoblast: Relationship and Consequences in Osteomyelitis[END_REF] which may have adversely and specifically affected the colonization by E. coli of the cell membrane.

Nevertheless, the colonization of cells by bacteria in the presence of adsorbed fibronectin on the substrate was slightly yet significantly higher than on cells cultured on uncoated titanium substrate. Vroman effect due to culture medium, especially FBS, may lead to the increase in fibronectin quantity in culture medium by the release of pre-adsorbed fibronectin. [START_REF] Hirsh | The Vroman effect: Competitive protein exchange with dynamic multilayer protein aggregates[END_REF] Released fibronectin may then react with cell membrane and finally result in the enhancement of bacterial adhesion on cells compared to with uncoated substrate. However, according to initial protein content of FBS (~40 mg/mL) [START_REF] Zheng | Proteomic Analysis for the Assessment of Different Lots of Fetal Bovine Serum as a Raw Material for Cell Culture. Part IV. Application of Proteomics to the Manufacture of Biological Drugs[END_REF] and rate of release expected due to Vroman effect (15-20% of the adsorbed quantity), [START_REF] Hirsh | The Vroman effect: Competitive protein exchange with dynamic multilayer protein aggregates[END_REF] protein content of FBS is not expected to be increased of more than 0.01% of the initial content if fibronectin is adsorbed at about 1 µg/cm 2 . [START_REF] Ba | Protein covalent immobilization via its scarce thiol versus abundant amine groups: Effect on orientation, cell binding domain exposure and conformational lability[END_REF] Therefore, Vroman effect is not expected to be high enough to significantly change the quantity of proteins adsorbed on the cell and consequently change cell invasion by bacteria. The most probable hypothesis is thus based on the capacity of sessile bacteria to spread on bacteria-free part of the surface to colonize it: [START_REF] Harshey | Bacterial motility on a surface: many ways to a common goal[END_REF] Cell invasion was probably the result of spreading from colonies already developed on the fibronectin-coated surface aside from sedimentation and further proliferation of pioneer bacteria directly adhered on the cells.

Since bacteria were adhered in higher amount on fibronectin-coated substrate than on uncoated substrate, cell invasion by spreading is thus probably higher on the fibronectincoated substrate.

We also observed that bacterial proliferation is significantly promoted in the presence of human cells, which seems contradictory to the unfavorable invasion of the cell membrane discussed above. Promotion of bacterial proliferation in coculture could be related to the capacity of bacteria to perceive mammalian cells signal proteins as a trigger for growth and invasion. [START_REF] Kendall | What a Dinner Party! Mechanisms and Functions of Interkingdom Signaling in Host-Pathogen Associations[END_REF] For example, cytokines such as interleukins are likely to interact with E. coli receptor and to stimulate bacterial growth. TNF factor was shown to stimulate virulence and invasive behavior in E. coli. Also, adrenaline and noradrenaline enhance growth, motility, virulence and biofilm formation of enterohemorrhagic E. coli. [START_REF] Kendall | What a Dinner Party! Mechanisms and Functions of Interkingdom Signaling in Host-Pathogen Associations[END_REF] Since interleukins such as IL-1, hormones, and growth factors such as TNF are secreted in the supernatant of mesenchymal stromal cells in significant amount, [START_REF] Amable | Protein synthesis and secretion in human mesenchymal cells derived from bone marrow, adipose tissue and Wharton's jelly[END_REF] these cellular secretions could be a key factor in increasing bacterial growth we observed under co-culture conditions. However, the positive effect of these factors released in the environment may be locally counteracted by cell defense mechanisms occurring at the cell membrane. [START_REF] Josse | Staphylococcus aureus vs. Osteoblast: Relationship and Consequences in Osteomyelitis[END_REF] Bacteria proliferation may be thus favored on the coated or uncoated titanium surfaces while specifically prevented on the cell membranes.

Cell number was not affected by the co-culture, which demonstrates the non-lethal effect of E. coli K12 on cells in our co-culture model. This was expected since E. coli K12 is considered as a non-pathogenic strain [START_REF] Blattner | The complete genome sequence of Escherichia coli K-12[END_REF][START_REF] Boysen | SILAC-based comparative analysis of pathogenic Escherichia coli secretomes[END_REF] as the level of toxicity of compounds secreted by K12 (MG1655) strains has been shown to be significantly lower compared to pathogenic E. coli.

Molecules such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and chaperone Hsp70 that are crucial for bacteria development in host tissues by helping both bacterial adhesion and fighting against host defense mechanisms, are missing in this strain. [START_REF] Egea | Role of secreted glyceraldehyde-3-phosphate dehydrogenase in the infection mechanism of enterohemorrhagic and enteropathogenic Escherichia coli: interaction of the extracellular enzyme with human plasminogen and fibrinogen[END_REF][START_REF] Henderson | Bacterial moonlighting proteins and bacterial virulence[END_REF] This absence of GAPDH and Hsp70 in the K12 bacteria secretome can explain the non-lethal effect.

Nevertheless, an adverse effect of the co-culture was observed on the cell spreading as shown by the decrease in cell area in co-culture with bacteria compared to cell culture alone. This illustrates that cells are less comfortable in presence of bacteria than alone as previously observed also by other authors (4-8). The reasons are probably that occupation of the surface by bacteria proliferating faster than cells as well as nutrients consumption by bacteria reduce surface area and nutrient availability for cells, [START_REF] Lee | Effects of Staphylococcus epidermidis on Osteoblast Cell Adhesion and Viability on Ti Alloy Surface in Microfluidic Co-Culture Environment[END_REF] thus inhibiting cell spreading.

Finally, these results show that our system is suitable for studying complex interactions between bacteria, cells and biomaterial surface. As far as we know our system is one of the rare published systems allowing in situ imaging of live cells and bacteria with the possibility to tune the biomaterial substrate and to follow evolution of the co-culture. Even microtextured or porous surfaces can be considered without compromising quality of the images in contrast to what may be suspected from reflectance and dispersion of incident light on topographically complex surfaces (Figure S5). On fibronectin-coated titanium biomaterials typically, STRO cells were here shown to be not only directly affected by the biomaterial and by the presence of K12 strain E. coli bacteria, probably due to competition for space, also indirectly by the effect of the biomaterial on bacteria (i.e. more bacteria on cells on the coated versus uncoated surfaces). Bacterial colonization was also shown to be favored by both the presence of cells and the biomaterial. Biological and biochemical processes involved in BAI and infection of cells can be also targeted thanks this co-culture system. Aside from description of the bacterial and human cells and their localization, our method enables harvesting of medium samples throughout the co-culture and their further biochemical analysis e.g. of the secretome. Bacterial species and strains e.g. pathologic strains can be also varied, which will allow the study of specific infection situations such as toxin-related pathogenicity, intracellular infection of cells by bacteria or host defense mechanisms. [START_REF] Kendall | What a Dinner Party! Mechanisms and Functions of Interkingdom Signaling in Host-Pathogen Associations[END_REF][START_REF] Dapunt | The osteoblast as an inflammatory cell: production of cytokines in response to bacteria and components of bacterial biofilms[END_REF][START_REF] Ribet | How bacterial pathogens colonize their hosts and invade deeper tissues[END_REF][START_REF] Stones | Against the tide: the role of bacterial adhesion in host colonization[END_REF] Finally, our co-culture system could be also used as a pre-contamination model by inoculating the bacteria before the cells.

CONCLUSION

This new dynamic flow chamber system mounted under an upright confocal microscope enables the real-time observation of contamination of pre-adhered STRO cells by E. coli on clinically relevant titanium surface. High quality of the images associated to simultaneous acquisition of fluorescent micrographs of cells and bacteria allows not only the quantification of variations in prokaryote's and eukaryote's behavior during the early stage of a simulated infection on an implanted surface, but also collection of accurate information regarding their localizations and interactions. Moreover, our results suggest that bacteria take advantage of the presence of eukaryotic cells and proliferate faster in co-culture context. These characteristics are shown to be the determining factors in very early steps of infection for the "race for the surface", that decide the fate of implant's integration with human preosteoblastic cells. The resulting protocol and analysis prepare the way to a visual approach of the interactions between cells and bacteria, with the opportunity to test various eukaryotic and prokaryotic strains, and use different materials and surface treatments, in a reliable in vitro simulation of BAI. This system is thus an efficient method for studies related to design and function of new biomaterials and represents a real innovation compared to the existing co-culture set-ups.

TABLE OF CONTENTS GRAPHIC

Figure 1 :

 1 Figure 1 : Scheme of the nutrient and inoculation circuit for co-culture in flow chamber.

Figure 2 :

 2 Figure 2 : Setting of the flow chamber under upright confocal microscope (A) and positions of image acquisition on the titanium surface uncoated or coated by human plasma-derived fibronectin (Fn) (B).

  5 mm height provided by Prof. H. Pelletier (INSA, Strasbourg). The disk was cleaned by successive ultrasonic baths in PBS-SDS 1%, Acetone, Chloroform, Ethanol (15 min each), dried with nitrogen gasA B

  without fibronectin culture conditions, only half of the surface was coated with fibronectin by immersing half of the titanium surface in fibronectin solution (Figure 3.D). The coating was visualized by immunostaining with F3648 anti-Fn antibody (Sigma) and the corresponding secondary antibody labelled to TRITC. (Figure 3.A, B, C).

Figure 3 :

 3 Figure 3: Immunostaining (red) of human plasma fibronectin coated on half titanium surface. (A) A scratch on the surface after staining enables to visualize the fibronectin layer revealed by fluorescent labeling. Boundary between coated and uncoated surface is observed in (B) and (C) surfaces. Size of (A) and (B) micrographs is 256 µm x 256 μm. (D) Image showing immersion of half surface of titanium substrate in solution of fibronectin.

Figure 4 :

 4 Figure 4: E. coli SCC1 growth in different media with time: Luria Broth (LB), ISCOVE, ISCOVE + 1% Lglutamine, ISCOVE + 1% L-glutamine + 5% FBS.

Figure 5 :

 5 Figure 5: Fluorescent real-time images of STRO cells (in red) in co-culture with bacteria E. coli (in green)

Figure 7 :

 7 Figure 7: Shape descriptors of cells after 3 hours in co-culture and single cell culture conditions onto titanium surface without (-Fn) or with (+Fn) fibronectin: cellular average area (a), circularity (b) and perimeter (c). *: Significant difference with single cell culture on fibronectin (pFN) coated titanium, assessed with Student t-test and ANOVA (p-value < 0.05).

Figure 8 :

 8 Figure 8: Influence of fibronectin (Fn) coating on bacterial density in single E. coli cultures (A) or in cocultures (B), and influence of co-culture on bacteria density on titanium uncoated (-Fn) (C) or coated

Figure 9 :

 9 Figure 9: Bacteria density after 3 hours on top of cells or on the substrate with (+Fn) and without (-Fn) fibronectin coating. Significant difference between cell and substrate (*) and between +Fn and -Fn ( §) (p-value < 0.05).

  was observed after 3 hours of culture on fibronectin-coated titanium surfaces as well on the substrate as on cells.

ACKNOWLEDGEMENTS

The authors thank CNRS (the French National Center for Scientific Research) and ANR (the French National Research Agency) for their financial support. The authors acknowledge N.T.

Wakhloo for English revision of the manuscript.

SUPPORTING INFORMATION

STRO cells visualized with air and water objectives (Figure S1).

Example of image treatment and analysis (Figure S2). 5 to facilitate visualization of the sessile bacteria (Figure S3).

Detail of Figure

Example of cell masks used for colocalization analysis (Figure S4).

Example of micrographs obtained with a micro-structured surface (Figure S5).