Jérémy Pépin

Lucie De Longprez

Fabrice Trovero

Emmanuel Brouillet

Julien Valette

Dr Julien Flament
email: julien.flament@cea.fr

Affiliations

Complementarity of gluCEST and 1 H-MRS for the study of mouse models of Huntington's disease

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Neurodegenerative diseases are characterized by a slow degeneration of the neuronal structure, leading to cognitive, psychiatric and motor symptoms like in Huntington's disease (HD). Because of population aging and increase of life expectancy, their prevalence is increasing constantly and will become a major public health issue in the coming years.

These disorders are very complex and can exhibit various hallmarks at molecular or cellular levels such as oxidative stress, neuroinflammation, transcriptional and epigenetic deregulation, mitochondrial impairments or defects in Ca 2+ homeostasis [START_REF] Jellinger | Recent advances in our understanding of neurodegeneration[END_REF][START_REF] Vk | Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer's disease, Parkinson's disease, and related disorders[END_REF]. In most cases, neurodegenerative diseases are of unknown origin.

However, some diseases such as HD are mostly inherited and can therefore be easily diagnosed.

Neuroimaging of these genetic diseases can provide relevant biomarkers, for instance atrophy of the striatum measured in HD patients [START_REF] Aylward | Onset and rate of striatal atrophy in preclinical Huntington disease[END_REF][START_REF] Tabrizi | Predictors of phenotypic progression and disease onset in premanifest and early-stage Huntington's disease in the TRACK-HD study: analysis of 36-month observational data[END_REF], which are of interest to characterize disease progression. However, little is known about biological mechanisms underlying degenerative processes. In spite of tremendous efforts made, there is currently no treatment to cure neurodegenerative diseases or to slow down their progression. A key point towards development of innovative therapeutic approaches is the identification of relevant biomarkers to allow early detection and accurate monitoring of disease progression. Moreover, such biomarkers would be extremely informative to evaluate therapeutic response of potential treatments.

In addition to cognitive and/or motor tests, anatomical imaging is one of the most robust criteria to characterize disease progression. For instance, MRI can be used to accurately measure macroscopic alterations of brain structures. Although very informative as they can be reliable markers of disease progression [START_REF] Aylward | Onset and rate of striatal atrophy in preclinical Huntington disease[END_REF][START_REF] Tabrizi | Predictors of phenotypic progression and disease onset in premanifest and early-stage Huntington's disease in the TRACK-HD study: analysis of 36-month observational data[END_REF][START_REF] Paulsen | Detection of Huntington's disease decades before diagnosis: the Predict-HD study[END_REF], morphological changes are likely a long-term reflect of biological events that occurred several years before brain cell degeneration. Besides, structure alterations provide poor information about cellular and biochemical events occurring in the disease [START_REF] Dekosky | Looking backward to move forward: early detection of neurodegenerative disorders[END_REF].

Thanks to its intrinsic ability to distinguish specific proton resonances, in vivo MRS can provide rich and valuable insights into biological processes in healthy or pathological conditions. Among all metabolites, glutamate (Glu) and N-acetylaspartate (NAA) are relevant in the context of neurodegenerative diseases as they are considered as good neuronal markers [START_REF] Gill | Brain metabolites as 1H NMR markers of neuronal and glial disorders[END_REF][START_REF] Petroff | Metabolic assessment of a neuron-enriched fraction of rat cerebrum using high-resolution 1H and 13C NMR spectroscopy[END_REF][START_REF] Simmons | Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies[END_REF]. In addition, myo-inositol (Ins) and glutamine (Gln) are potential markers of glial proliferation [START_REF] Brand | Multinuclear NMR studies on the energy metabolism of glial and neuronal cells[END_REF]. Numerous studies have already demonstrated the interest of 1 H-MRS to detect early onset of neurodegenerative disorders [START_REF] Jenkins | 1H NMR spectroscopy studies of Huntington's disease: correlations with CAG repeat numbers[END_REF][START_REF] Schuff | N-acetylaspartate as a marker of neuronal injury in neurodegenerative disease[END_REF][START_REF] Tkac | Neurochemical changes in Huntington R6/2 mouse striatum detected by in vivo 1H NMR spectroscopy[END_REF]. Unfortunately, 1 H-MRS has very low sensitivity and hence limited spatial resolution.

Therefore, bridging the gap between biological information provided by 1 H-MRS and spatial information provided by anatomical MRI remains a key challenge. Recently, CEST (Chemical Exchange Saturation Transfer) has been proposed to image dilute molecules in vivo with a good spatial resolution [START_REF] Ward | A new class of contrast agents for MRI based on proton chemical exchange dependent saturation transfer (CEST)[END_REF][START_REF] Wolff | NMR imaging of labile proton exchange[END_REF]. The potential of CEST imaging, and particularly CEST imaging of glutamate (gluCEST) has already been demonstrated in human [START_REF] Cai | Mapping glutamate in subcortical brain structures using high-resolution GluCEST MRI[END_REF][START_REF] Roalf | Glutamate imaging (GluCEST) reveals lower brain GluCEST contrast in patients on the psychosis spectrum[END_REF] and animal models of neurodegenerative diseases [START_REF] Haris | Imaging of glutamate neurotransmitter alterations in Alzheimer's disease[END_REF][START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF][START_REF] Carrillo-De Sauvage | The neuroprotective agent CNTF decreases neuronal metabolites in the rat striatum: an in vivo multimodal magnetic resonance imaging study[END_REF]. 1 H-MRS and gluCEST are promising methods to identify in vivo biomarkers. While both methods are usually used for cross validation, to date no preclinical study has actually demonstrated specific interests of each method.

In addition, the wide heterogeneity of animal models for a same pathology can render difficult the choice of the appropriate method for monitoring disease progression or evaluating treatment efficacy. In this study, we used two mouse models of HD (Ki140CAG and R6/1) exhibiting different hallmarks of the pathology [START_REF] Farshim | Mouse Models of Huntington's Disease[END_REF] in order to highlight the complementarity of 1 H-MRS and gluCEST for the study of neurodegenerative diseases.

Materials and methods

Two mouse models of HD

The first model was a knock-in model expressing chimeric mouse/human exon 1 containing 140 CAG repeats inserted in the murine huntingtin (Htt) gene (Ki140CAG) [START_REF] Menalled | Time course of early motor and neuropathological anomalies in a knock-in mouse model of Huntington's disease with 140 CAG repeats[END_REF]. It is characterized by slow progression of motor and cognitive symptoms as well as cellular and molecular abnormalities leading to neurodegeneration [START_REF] Menalled | Time course of early motor and neuropathological anomalies in a knock-in mouse model of Huntington's disease with 140 CAG repeats[END_REF]. Twelve-month old mice were divided in 3 groups: heterozygous (Ki140-Hetero., n = 5), homozygous (Ki140-Homo., n = 5) for the Htt gene and wild type littermate controls (Ki140-WT, n = 5).

The Ki140CAG mice dataset is the same as the one already published in a previous study [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF]. The second model was a transgenic model expressing mouse/human exon 1 containing 115 CAG repeats in the human huntingtin (HTT) gene (R6/1) [START_REF] Mangiarini | Exon 1 of the HD gene with an expanded CAG repeat is to cause a progressive neurological phenotype in transgenic mice[END_REF]. Mice exhibit motor deficits and striatal atrophy as early as 4 months and decline in motor performances correlates with the appearance of huntingtin aggregates in striatal neurons [START_REF] Ferrante | Mouse models of Huntington's disease and methodological considerations for therapeutic trials[END_REF]. The disease progression is more aggressive than in the Ki140CAG model and mimics the juvenile form of HD. Seven-month old mice were divided in two groups: heterozygous for Htt gene (R6/1-Hetero., n = 4) and wild type littermate controls (R6/1-WT, n = 3).

Animal studies were conducted according to the French regulation (EU Directive 2010/63/EU -French Act Rural Code R 214-87 to 131) and procedures received approval from the ethical committee (APAFIS#770).

Mice were anesthetized using around 1.5% isoflurane in a 1:1 gas mixture of air/O2. Mice temperature was monitored and maintained at 37 °C and respiratory rate was continuously monitored using PC SAM software (SA Instruments, USA).

H-MRS and gluCEST data acquisition

Acquisitions were performed on a horizontal 11.7 T scanner (Bruker, Ettlingen, Germany). 1 H-MRS spectra were acquired using a quadrature cryoprobe (Bruker, Ettlingen, Germany) with a LASER (Localization by Adiabatic Selective Refocusing) sequence (echo time (TE)/repetition time (TR) = 20/5000 ms) combined with VAPOR water suppression in a voxel of 8 µL (2 x 2 x 2 mm 3 ) located in the left striatum. Spectra were acquired using 128 repetitions in 10 min. Metabolite concentrations were quantified using LCModel [START_REF] Provencher | Estimation of metabolite concentrations from localized in vivo proton NMR spectra[END_REF], including the macromolecule (MM) spectrum of a control mouse that was determined by metabolite nulling and included in LCModel's basis-set. The following metabolites were reliably quantified (Cramér-Rao lower bound ≤ 5%): total choline (tCho), total creatine (tCr), glutamate (Glu), glutamine (Gln), myo-inositol (Ins), total N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (tNAA) and taurine (Tau). Metabolite concentrations were normalized with respect to 8 mM tCr.

GluCEST data were acquired with a volume coil for radiofrequency transmission and a quadrature surface coil for reception (Bruker, Ettlingen, Germany). The sequence consisted in a 2D fast spin-echo preceded by a frequency-selective continuous wave saturation pulse (150 × 150 μm 2 in-plane resolution, field of view = 19.2 x 19.2 mm 2 , matrix size = 128 x 128, 0.6 mm slice thickness, 3 contiguous slices, TR = 5000 ms, 10 echoes and effective TE = 30 ms). The saturation module was composed by 10 broad pulses of 100 ms, with 20-μs inter-delay (Tsat = 1 s) and amplitude B1 = 5 μT. The frequency of the saturation pulse Δω was applied within a range from -5 ppm to 5 ppm with a step of 0.5 ppm. B0 correction was performed using WASSR method [START_REF] Kim | Water Saturation Shift Referencing (WASSR) for Chemical Exchange Saturation Transfer (CEST) Experiments[END_REF]. The specific glutamate contribution was isolated using Asymmetrical Magnetization Transfer Ratio (MTRasym) centered at ±3 ppm. Variation maps of gluCEST contrast between WT littermates and HD mice were calculated in each Region of Interest (ROI) as already described [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF] were used for striatal volume measurements. For comparison of gluCEST and 1 H-MRS data, we calculated the mean gluCEST contrast in a 2 x 2 mm² square ROI located in the left striatum in three contiguous slices.

The volume of the ROI used for gluCEST analysis was 2 x 2 x 1.8 mm 3 , which was comparable to the spectroscopic voxel.

Results

Striatal volume

We observed a significant 20.9% decrease (p = 0.002) of striatal volume in Ki140CAG homozygous mice as compared to WT littermates (Fig. 1). A milder atrophy was observed in Ki140CAG heterozygous mice but this did not reach statistical significance (-10.1%, p = 0.11, Fig. 1). A marked striatal atrophy was measured in R6/1 mice as compared to their respective WT littermates (-25.7%, p = 0.00002, Fig. 1). Noticeably, we observed a larger striatal volume in control R6/1 mice (22.8 ± 0.9 mm 3 ) than in control Ki140CAG mice (18.7 ± 0.7 mm 3 ), possibly due to the difference in age (7-month and 12-month old for R6/1 and Ki140CAG mice respectively) and/or mouse strains.

H-MRS

We observed a significant decrease of tNAA in the left striatum in both models (-17.4% and -24.3% in Ki140-Hetero and Ki140-Homo respectively and -36.4% in R6/1-Hetero, Fig. 2) as compared to their respective WT littermates. Taurine exhibited a significant 25.9% and 33.3% decrease in heterozygous and homozygous Ki140CAG mice respectively. A significant 13.0% decrease of Glu was observed in heterozygous Ki140CAG mice. Glu concentration was also decreased in homozygous Ki140CAG mice (-14.9%) even though the variation was not significant (p = 0.06). Finally, significant variations of Gln (+27.4%) and tCho (-21.7%) were measured in homozygous Ki140CAG mice. Surprisingly, tNAA was the only metabolite showing significant variation in R6/1 mice (Fig. 2). In order to study potential correlations between biological information provided by 1 H-MRS and anatomical information, the concentration of tNAA measured in the left striatum was plotted as a function of the striatal volume Vstriat for each mouse cohort (Fig. 3). We observed a proportional relationship between the tNAA concentration and the striatal volume for both models ([tNAA] = 0.31 Vstriat + 1.55 (R² = 0.97) for Ki140CAG mice and

[tNAA] = 0.37 Vstriat -2.15 for R6/1 mice). No other metabolite exhibited similar dependency to the striatal volume.

GluCEST

An example of gluCEST images acquired in one mouse of each cohort is shown in Fig. 4.a. As already described, Ki140CAG mice exhibited decreased levels of gluCEST contrast across the whole brain with a stronger effect observed in the homozygous mouse (Fig. 4.a, top panel). This "gene-dose" effect was confirmed by the mean MTRasym curves calculated for each group of mice in a volume comparable to the voxel used in 1 H-MRS mostly located into the left striatum (Fig. Eight ROIs were drawn manually in the brain of each mice (Fig. 5, top left panel) and mean gluCEST contrast was calculated in each ROI for each mice group. Variations of gluCEST contrast between WT and HD mice were calculated as described previously [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF]. Ki140-Homo mice exhibited significant decreases of gluCEST contrast in the striatum and the piriform cortex (Fig. 5, bottom right). On the contrary, these structures did not reach statistical significance in Ki140-Hetero mice (Fig. 5, bottom left), confirming a "gene-dose" effect in this mouse model. The most striking result was the strong and significant decrease of gluCEST contrast in the corpus callosum in both heterozygous and homozygous Ki140CAG mice (-21.8% and -28.4% respectively) as mentioned in our previous work [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF]. In contrast, no variation of gluCEST contrast was found in R6/1 mice in any brain region (Fig. 5, top right).

Discussion

Animal models are valuable tools for neuroscientists as they offer the possibility to specifically focus on one or several aspects of the pathology. Numerous studies have demonstrated the interest of animal models to study common hallmarks of HD pathogenesis like striatum atrophy, presence of huntingtin aggregates, motor and behavioral symptoms or modification of metabolic profiles [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF][START_REF] Lerner | Striatal atrophy and dendritic alterations in a knock-in mouse model of Huntington's disease[END_REF][START_REF] Tkac | Homeostatic adaptations in brain energy metabolism in mouse models of Huntington disease[END_REF][START_REF] Zacharoff | Cortical metabolites as biomarkers in the R6/2 model of Huntington's disease[END_REF]. However, these studies also highlighted the wide heterogeneity of animal models and variety of relevant biomarkers for a same pathology. Thus, the choice of the appropriate method to study one particular model is not straightforward.

In this study, we used two mouse models of HD known to mimic different stages of the pathology in order to evaluate the complementarity of 1 H-MRS and gluCEST to identify relevant in vivo biomarkers of HD. The Ki140CAG model is considered as a slowly progressive model reproducing the progression of the disease in HD patients [START_REF] Menalled | Systematic behavioral evaluation of Huntington's disease transgenic and knock-in mouse models[END_REF] whereas the transgenic R6/1 model is considered as a severe HD model that mimics juvenile onset of HD [START_REF] Mangiarini | Exon 1 of the HD gene with an expanded CAG repeat is to cause a progressive neurological phenotype in transgenic mice[END_REF].

Cell specific alterations observed by MRS

1 H-MRS has unique ability to measure intracellular metabolite concentrations, providing plenty of information about biological alterations. In particular, as tNAA is mostly located in the neuronal compartment, it is considered has a marker of neuronal integrity and function [START_REF] Gill | Brain metabolites as 1H NMR markers of neuronal and glial disorders[END_REF][START_REF] Petroff | Metabolic assessment of a neuron-enriched fraction of rat cerebrum using high-resolution 1H and 13C NMR spectroscopy[END_REF][START_REF] Simmons | Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies[END_REF]. In these models, tNAA levels were systematically lower in HD animals. Interestingly, the decrease seemed correlated with disease severity as tNAA decreases measured in Ki140CAG mice (-17.4% and -24.3% in Ki140-Hetero and Ki140-Homo respectively, Fig. 2) were smaller than that measured in the more severe R6/1 model (-36.4%, Fig. 2).

In addition, variations of tNAA levels were correlated with striatal volumes and proportionality coefficients were comparable in both models (Fig. 3). Interestingly, R6/1 and Ki140CAG mice exhibited decrease of tNAA level even in absence of actual neuronal loss measured at 7 months (31, 32) and 12 months [START_REF] Menalled | Time course of early motor and neuropathological anomalies in a knock-in mouse model of Huntington's disease with 140 CAG repeats[END_REF] respectively. The decrease of striatal volume can be explained by neurons shrinkage, reduced cell complexity and dendritic arborization [START_REF] Lerner | Striatal atrophy and dendritic alterations in a knock-in mouse model of Huntington's disease[END_REF]. The reduction in tNAA levels in mouse models is coincident with decreased levels of mRNA and proteins that are considered as molecular markers of the striatum, i.e. preferentially expressed in striatal medium sized spiny neurons [START_REF] Langfelder | Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice[END_REF][START_REF] Kuhn | Mutant huntingtin's effects on striatal gene expression in mice recapitulate changes observed in human Huntington's disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage[END_REF]. For example, reduced levels of the mRNA and/or protein of striatal markers such as Dopamine and 3′,5′-Cyclic Adenosine Monophosphate-Regulated Neuronal Phosphoprotein (known as DARPP-32), phosphodiesterase 10 (PDE10) or the recently studied doublecortin-like kinase 3 (DCLK3) have been observed in Ki140CAG and R6/1 mice [START_REF] Langfelder | Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice[END_REF][START_REF] Kuhn | Mutant huntingtin's effects on striatal gene expression in mice recapitulate changes observed in human Huntington's disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage[END_REF][START_REF] Hodges | Regional and cellular gene expression changes in human Huntington's disease brain[END_REF][START_REF] Galvan | The striatal kinase DCLK3 produces neuroprotection against mutant huntingtin[END_REF]. The levels of these markers have also been found to be reduced in post-mortem samples in HD patients [START_REF] Galvan | The striatal kinase DCLK3 produces neuroprotection against mutant huntingtin[END_REF].

In spite of the absence of neuronal loss at 12 months, both models showed behavioral phenotypes [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF][START_REF] Menalled | Time course of early motor and neuropathological anomalies in a knock-in mouse model of Huntington's disease with 140 CAG repeats[END_REF][START_REF] Li | The use of the R6 transgenic mouse models of Huntington's disease in attempts to develop novel therapeutic strategies[END_REF][START_REF] Naver | Molecular and behavioral analysis of the R6/1 Huntington's disease transgenic mouse[END_REF]. Thus, such results are in line with the idea that tNAA level is related to neuronal cell suffering and could be a potential marker of neuronal functions alteration [START_REF] Dautry | Early N-acetylaspartate depletion is a marker of neuronal dysfunction in rats and primates chronically treated with the mitochondrial toxin 3-nitropropionic acid[END_REF]. It is interesting to note that basal levels of tNAA and striatal volumes were not comparable between the two models, especially between WT mice of each cohort (Fig. 3). However, such discrepancies can be explained by the difference in age of the cohorts as well as the difference in genetic background used to generate the models and breed the colonies.

Moreover, breeding conditions and genetic drift can lead to different phenotypes of mouse colonies bred in different laboratories [START_REF] Ferrante | Mouse models of Huntington's disease and methodological considerations for therapeutic trials[END_REF]. Consequently, it is difficult to compare brain metabolites levels and structures volumes between control animals of each cohort.

Glutamate is one of the main neurotransmitters into the brain. Even if Glu released in the synaptic cleft is taken up by astrocytes, most of Glu is located in the neuronal compartment. Thus, Glu is often considered as a potential neuronal marker [START_REF] Gill | Brain metabolites as 1H NMR markers of neuronal and glial disorders[END_REF][START_REF] Petroff | Metabolic assessment of a neuron-enriched fraction of rat cerebrum using high-resolution 1H and 13C NMR spectroscopy[END_REF][START_REF] Simmons | Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies[END_REF]. However, in this study, no correlation between striatal volume and Glu level was found in R6/1 mice (data not shown). Comparable results have already been reported in R6/2 mice, a similar but more aggressive HD model than R6/1, where Glu levels measured in the cortex and the striatum of R6/2 were stable as compared to WT littermates, while cortical and striatal volumes were decreased by ~20% and ~32% respectively, and tNAA levels were decreased by ~33% and ~29% respectively (29) at 15 weeks. In Ki140CAG mice, we observed a decrease of Glu levels but no neuronal loss was observed on histological sections using NeuN staining, a neuronal maker [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF]. However, the decrease in Glu levels was associated with increase in Gln in Ki140CAG mice whereas they were stable in R6/1 mice. Interestingly, Glu could be used as a potential marker of reorganization of energy metabolism and metabolic fluxes. Indeed, such results could indicate an imbalance in glutamate-glutamine cycle in Ki140CAG mice which is important for energy homeostasis [START_REF] Choi | Application of MRS to mouse models of neurodegenerative illness[END_REF]. In addition, Glu can also be implicated in energy production through its equilibrium with alpha-ketoglutarate, an intermediate of the Krebs cycle. One can hypothesize that dynamic adaptations of metabolic fluxes may take place during the animal lifespan in order to counterbalance disturbance of homeostasis and energy production due to mutant huntingtin [START_REF] Dubinsky | Towards an Understanding of Energy Impairment in Huntington's Disease Brain[END_REF].

One can assume that such compensatory mechanisms would be more efficient in slowly progressive models than in severe forms where the aggravation of the alterations of brain cells are probably faster and might not be counterbalanced. The exact mechanisms underlying the differences between R6/1 and KI140CAG are not known but likely results from the differences in terms of the proper genetic modifications found in the two models: while KI140CAG express one copy (heterozygous) of the whole mouse Htt gene with a part of the exon 1 of mutant human HTT gene under the murine Htt promoter, the R6/1 model express 4 to 5 copies of only the exon 1 of human HTT, leading more rapidly to neurotoxicity [START_REF] Farshim | Mouse Models of Huntington's Disease[END_REF].

Complementarity of 1 H-MRS and gluCEST imaging

While 1 H-MRS can provide rich and specific information about biological processes in vivo, it suffers from a lack of spatial resolution. It requires large voxels which can be problematic for studying small or heterogeneous structures. Moreover, voxel positioning is based on a priori knowledge about the pathology.

Consequently, potential alterations in unsuspected structures can be missed. On the contrary, gluCEST imaging can provide high resolution metabolic imaging where partial volume effect and surrounding structures contamination are minimized. Results already published [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF] obtained in the Ki140CAG model demonstrated this advantage by highlighting strong alterations of glutamate levels in the corpus callosum of HD mice. Interestingly, variation of gluCEST contrast was even more pronounced in homozygous mice than in heterozygous, confirming a "gene-dose" effect in this model (Fig. 5). Such measurements would have been hardly achievable using 1 H-MRS due to the important contamination from surrounding structures.

On the other hand, the gluCEST variation map acquired in R6/1 mice was very stable, the strongest variation being -2.4% (but not significant, p = 0.746) measured in the left somatosensory cortex. Such variations are below the detection threshold estimated at ±4% in our previous study [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF]. Nonetheless, the stability of gluCEST map in R6/1 mice was very informative as it suggested that no brain region exhibited alteration of Glu levels in this model. Note that some individual examples of gluCEST maps exhibited a slight heterogeneity of gluCEST contrast, especially in the cortical region (Fig. 4.a). However, we did not observe a systematic bias over all animals and no difference was observed between left and right hemispheres at the scale of mice cohorts [START_REF] Pepin | In vivo imaging of brain glutamate defects in a knock-in mouse model of Huntington's disease[END_REF], ruling out a possible bias due to mouse positioning or coil inhomogeneity.

Further investigations would be necessary to elucidate the origin of such heterogeneity at the individual scale.

Finally, we compared Glu concentrations measured by 1 H-MRS in the left striatum (Fig. 2.b) with gluCEST contrasts measured in a comparable volume in Ki140CAG-WT and R6/1-WT (Fig. 4.c and .e respectively).

Interestingly, R6/1-WT mice exhibited a gluCEST contrast that was 19.6% lower than that of Ki140CAG-WT mice (MTRasym = 11.9%, Fig. 4.e and MTRasym = 14.8%, Fig. 4.c, respectively). This was consistent with the 20.0% difference of Glu concentration measured by 1 H-MRS between wild type Ki140CAG and wild type R6/1 (6.44 mM and 8.05 mM respectively, Fig. 2.b). In addition, comparison of Z-spectra upfield from the water resonance acquired in Ki140-CAG (Fig. 4.b) and R6/1 mice (Fig. 4.d) showed very small differences. This means that magnetization transfer between water and non-exchangeable protons was unaltered in HD animals and the asymmetrical approach used to characterize gluCEST contrast was valid. While other contributions to the CEST contrast by non-specific magnetization transfer effects or by endogenous metabolites such as GABA or taurine cannot be excluded, such observations seems to indicate that gluCEST contrast is dominated by Glu concentration, as already described for comparable saturation parameters [START_REF] Carrillo-De Sauvage | The neuroprotective agent CNTF decreases neuronal metabolites in the rat striatum: an in vivo multimodal magnetic resonance imaging study[END_REF][START_REF] Cai | Magnetic resonance imaging of glutamate[END_REF].

Conclusion

By combining 1 H-MRS and gluCEST, we have shown striking differences between these two mouse models.

The decrease of tNAA levels measured in R6/1 mice suggested an alteration of the neuronal compartment but the stability of other metabolites argued in favor of integrity of the astrocytic compartment and energy metabolism. In Ki140CAG mice, a slowly progressive model, modifications of several metabolite levels could reflect homeostatic changes to compensate deficits that occurred early in mouse's life. Moreover, gluCEST was able to evidence alterations in unexpected brain regions such as the corpus callosum. However, this method may also be blind to disease process in certain situations where glutamate levels are preserved. This highlights the complementarity of both methods to identify relevant biomarkers, especially in the context of HD. Acquisition of 1 H-MRS and gluCEST data within a same protocol could provide various readouts to monitor disease progression or treatment efficacy and improve the use of animal models for the study of a large spectrum of neurodegenerative diseases.

Figures and legends

List of figure legends

  : Variation = 100 x (MTRasym(WT) -MTRasym(HD)) / MTRasym(WT). ROI segmentation was performed manually by a single operator with help of the Allen mouse brain atlas using the reference CEST image acquired without saturation. T2-weighted images acquired with Multi Slices Multi Echoes (MSME) sequence (TE/TR = 5/2500 ms, 17 echoes, effective TE = 45 ms, in-plane resolution = 70 × 70 μm 2 , 25 slices with thickness = 300 μm)

  4.c). As illustrated by individual examples(Fig.4.a, bottom panel) and mean MTRasym curves (Fig.4.e), no variation of gluCEST contrast was measured in R6/1 mice as compared to their respective WT littermates.

1 :

 1 Figure 1: Striatal atrophy measured by MRI in Ki140CAG and R6/1 mice. Striatum volume was measured in Ki140-WT (n = 5), Ki140-Hetero. (n = 5), Ki140-Homo. (n = 5), R6/1-WT (n = 3) and R6/1-Hetero (n = 4) mice. Data are presented as mean ± SEM. Statistical analysis was performed by Student's t-test for R6/1 mice and by one-way ANOVA (significant threshold set to 0.05) with Fisher's LSD post hoc test for Ki140CAG mice (*p < 0.05; **p < 0.01; ***p < 0.001).

Figure 2 : 1 H

 21 Figure 2: 1 H-MRS of Ki140CAG and R6/1 mice. a) Examples of 1 H-spectrum acquired in a voxel of 8 µL located in the left striatum (see inset) of one mouse from each group of Ki140CAG mice (top panel) and R6/1 mice (bottom panel). The following metabolites, total choline (tCho), total creatine (tCr), glutamate (Glu), glutamine (Gln), myo-inositol (Ins), total N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (tNAA) and taurine (Tau), were reliably quantified (CRLB < 5%). b) Metabolic profiles measured by 1 H-MRS in Ki140CAG and R6/1 mice. Concentration of metabolites normalized with respect to 8 mM tCr were measured in left striatum of Ki140-WT (n = 5), Ki140-Hetero. (n = 5), Ki140-Homo. (n = 5), R6/1-WT (n = 3) and R6/1-Hetero (n = 4) mice. Data are presented as mean ± SEM. Statistical analysis was performed by Student's t-test for R6/1 mice and by one-way ANOVA (significant threshold set to 0.05) with Fisher's LSD post hoc test for Ki140CAG mice (*p < 0.05; **p < 0.01; ***p < 0.001).

Figure 3 :

 3 Figure 3: Variation of tNAA concentration as a function of the striatal volume in Ki140CAG and R6/1 mice.

Figure 4 :

 4 Figure 4: GluCEST imaging in Ki140CAG and R6/1 mice. a) Example of gluCEST images acquired at 3 ppm in Ki140CAG (top panel) and R6/1 (bottom panel) mice in each genotype group (WT littermate control mouse: left panel images; heterozygous mouse: middle panel images; homozygous mouse: right panel images). b) Z-spectra acquired in a 2x2x1.8 mm 3 voxel located in the left striatum of WT littermate, heterozygous and homozygous Ki140CAG mice (mean±SEM). c) Corresponding MTRasym spectra acquired in Ki140CAG mice. d) Z-spectra acquired in a 2x2x1.8 mm 3 voxel located in the left striatum of WT littermate and heterozygous R6/1 mice (mean±SEM). e) Corresponding MTRasym spectra acquired in R6/1 mice.

Figure 5 :

 5 Figure 5: Variation maps of gluCEST contrast in Ki140CAG and R6/1 mice. Mice brain was segmented into 8 ROIs (top left). Variation of gluCEST contrast calculated in each ROI between WT littermate controls and HD mice as follows: Variation = 100 x (MTRasym(WT) -MTRasym(HD)) / MTRasym(WT). MTRasym(HD) was either mean gluCEST contrast measured in R6/1-Hetero mice (top right), in Ki140-Hetero mice (bottom left) or in Ki140-Homo mice (bottom right). Statistical analysis was performed by Student's t-test for R6/1 mice and by one-way ANOVA (significant threshold set to 0.05) with Fisher's LSD post hoc test for Ki140CAG mice (*p < 0.05; **p < 0.01; ***p < 0.001).

Figure 1 :

 1 Figure 1: Striatal atrophy measured by MRI in Ki140CAG and R6/1 mice. Striatum volume was measured in Ki140-WT (n = 5), (n = 5), Ki140-Homo. (n = 5), R6/1-WT (n = 3) and R6/1-Hetero (n = 4) mice. Data are presented as mean ± SEM. Statistical analysis was performed by Student's t-test for R6/1 mice and by one-way ANOVA (significant threshold set to 0.05) with Fisher's LSD post hoc test for Ki140CAG mice (*p < 0.05; **p < 0.01; ***p < 0.001).

Figure 2 : 1 H

 21 Figure 2: 1 H-MRS of Ki140CAG and R6/1 mice. a) Examples of 1 H-spectrum acquired in a voxel of 8 µL located in the left striatum (see inset) of one mouse from each group of Ki140CAG mice (top panel) and R6/1 mice (bottom panel). The following metabolites, total choline (tCho), total creatine (tCr), glutamate (Glu), glutamine (Gln), myo-inositol (Ins), total N-acetyl-aspartate + N-acetyl-aspartyl-glutamate (tNAA) and taurine (Tau), were reliably quantified (CRLB < 5%). b) Metabolic profiles measured by 1 H-MRS in Ki140CAG and R6/1 mice. Concentration of metabolites normalized with respect to 8 mM tCr were measured in left striatum of Ki140-WT (n = 5), Ki140-Hetero. (n = 5), Ki140-Homo. (n = 5), R6/1-WT (n = 3) and R6/1-Hetero (n = 4) mice. Data are presented as mean ± SEM. Statistical analysis was performed by Student's t-test for R6/1 mice and by one-way ANOVA (significant threshold set to 0.05) with Fisher's LSD post hoc test for Ki140CAG mice (*p < 0.05; **p < 0.01; ***p < 0.001).

Figure 3 :

 3 Figure 3: Variation of tNAA concentration as a function of the striatal volume in Ki140CAG and R6/1 mice.

Figure 4 :

 4 Figure 4: GluCEST imaging in Ki140CAG and R6/1 mice. a) Example of gluCEST images acquired at 3 ppm in Ki140CAG (top panel) and R6/1 (bottom panel) mice in each genotype group (WT littermate control mouse: left panel images; heterozygous mouse: middle panel images; homozygous mouse: right panel images). b) Z-spectra acquired in a 2x2x1.8 mm 3 voxel located in the left striatum of WT littermate, heterozygous and homozygous Ki140CAG mice (mean±SEM). c) Corresponding MTRasym spectra acquired in Ki140CAG mice. d) Z-spectra acquired in a 2x2x1.8 mm 3 voxel located in the left striatum of WT littermate and heterozygous R6/1 mice (mean±SEM). e) Corresponding MTRasym spectra acquired in R6/1 mice.

Figure 5 :

 5 Figure 5: Variation maps of gluCEST contrasts in Ki140CAG and R6/1 mice. Mice brain was segmented into 8 ROIs (top left). Variation of gluCEST contrast was calculated in each ROI between WT littermate controls and HD mice as follows: Variation = 100 x (MTRasym(WT) -MTRasym(HD)) / MTRasym(WT). MTRasym(HD) was either mean gluCEST contrast measured in R6/1-Hetero mice (top right), in Ki140-Hetero mice (bottom left) or in Ki140-Homo mice (bottom right). Statistical analysis was performed by Student's t-test for R6/1 mice and by one-way ANOVA (significant threshold set to 0.05) with Fisher's LSD post hoc test for Ki140CAG mice (*p < 0.05; **p < 0.01; ***p < 0.001).

Acknowledgments

This work was supported by two grants from Agence Nationale pour la Recherche ("HDeNERGY" project ANR-14-CE15-0007-01 and "epiHD" project ANR-17-CE12-0027) and one grant from the ERA-Net for Research Programs on Rare Diseases ("TreatPolyQ" project ANR-17-RAR3-0008-01). The 11.7T MRI scanner was funded by a grant from NeurATRIS: A Translational Research Infrastructure for Biotherapies in Neurosciences ("Investissements d'Avenir", ANR-11-INBS-0011).

Grants and sponsors

"HDeNERGY" project (ANR-14-CE15-0007-01) "epiHD" project (ANR-17-CE12-0027) "TreatPolyQ" project (ANR-17-RAR3-0008-01)

The 11.7T MRI scanner was funded by a grant from NeurATRIS "Investissements d'Avenir" (ANR-11-INBS-0011)

List of Abbreviations


Amani Nefzi

Lynn Carr

Claire Dalmay

Arnaud Pothier

Philippe Lévêque

Delia Arnaud-Cormos
email: delia.arnaud-cormos@xlim.fr

P Leveque

D Arnaud

Microdosimetry Using Rhodamine B Within Macro-and Microsystems for Radiofrequency Signals Exposures of Biological Samples

come   L'archive ouverte pluridisciplinaire

Microdosimetry Using Rhodamine B Within Macroand Microsystems for Radiofrequency Signals Exposures of Biological Samples

Amani Nefzi, Lynn Carr, Claire Dalmay, Arnaud Pothier, Philippe Leveque, and Delia Arnaud-Cormos,

Abstract-Exposing living cells to a certain level of electromagnetic field (EMF) might induce some biological effects including temperature elevation. In this article, we studied two exposure systems at the macro and microscopic levels, allowing the study of the EMF effect on the biological samples exposed to 1.8-GHz signals. The macrosystem was an open transverse electromagnetic (TEM) cell that served as a dosimetry reference for defining limitations and optimal conditions for the temperature calibration using Rhodamine B (RhodB). The microfluidic microsystem was based on the coplanar waveguide (CPW) electrodes. Temperature measurements are carried out with a fluorooptic probe to extract specific absorption rate (SAR) values that are compared with numerical dosimetry, based on an FDTD method. After calibration, the fluorescence fits well with the temperature variation measured by the probe. To investigate dosimetry at a microscopic level, the fluorescence of the temperature-dependent dye RhodB was measured by fluorescence microscopy within the microfluidic channel or the biological cells. Results evidenced that the technique is applicable for RhodB concentrations higher than 1 µm with a value of 50 µm recommended for reliable experiments. For steady detection and SAR assessments, temperature variations of a few tenths of degrees were required.

Index Terms-Exposure systems, fluorescence, microdosimetry, microelectrodes, microfluidics, radiofrequency (RF), rhodamine B (RhodB).

I. INTRODUCTION

T HE interaction of electromagnetic fields (EMF) with living entities has been the subject of a considerable amount of research in the past three decades. The studies concerned a broad panel of topics ranging, nonexhaustively, from the effects of telecommunications systems on health [START_REF] Paffi | Considerations for developing an RF exposure system: A review for in vitro biological experiments[END_REF] to biomedical applications, such as hyperthermia, electropo-ration [START_REF] Yarmush | Electroporation-based technologies for medicine: Principles, applications, and challenges[END_REF], nanosecond pulsed electric fields [START_REF] Schoenbach | Ultrashort electrical pulses open a new gateway into biological cells[END_REF], as well as microfluidic [START_REF] Dalmay | A microfluidic biochip for the nanoporation of living cells[END_REF] and lab-on-a-chip devices. To reach rigorous conclusions, these studies require the accurate assessment of EMF parameters such as electric field, temperature, and specific absorption rate (SAR) [START_REF]Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz)[END_REF]. However, due to the lack of appropriate instrumentation, assessing these exposure parameters is challenging, in particular at microscopic scales.

This article aims to investigate dosimetry at a micro-scopic level and further contribute to the characterization of exposure systems. The microdosimetry is based on the fluorescence intensity of the dye, Rhodamine B (RhodB), which is temperature-dependent. Few studies have reported RhodB temperature measurements in the context of 900-MHz exposures [START_REF] Bermingham | A measurement and modeling study of temperature in living and fixed tissue during and after radiofrequency exposure[END_REF]- [START_REF] Moreau | Rhodamine B as an optical thermometer in cells focally exposed to infrared laser light or nanosecond pulsed electric fields[END_REF].

Biological samples were exposed in this article to dif-ferent radiofrequency (RF) incident powers at a frequency of 1.8 GHz commonly used in mobile phone telecommuni-cation systems and for bioelectromagnetic investigations [START_REF] Paffi | Considerations for developing an RF exposure system: A review for in vitro biological experiments[END_REF]. A first complete study was carried out in an open transverse electromagnetic (TEM) cell exposure system that contains a FluoroDish. Although TEM cells have been used as a macrosystem in bioelectromagnetic studies for in vitro experiments [START_REF] Paffi | Considerations for developing an RF exposure system: A review for in vitro biological experiments[END_REF], [START_REF] Kohler | Characterization of a TEM cell-based setup for the exposure of biological cell suspensions to high-intensity nanosecond pulsed electric fields (nsPEFs)[END_REF], [START_REF] Iftode | Design and validation of a TEM cell used for radiofrequency dosimetric studies[END_REF], real-time microscopic observations studies with FluoroDishes are rather uncommon.

In recent years, microsystems, such as thin interdigitated arrays of electrodes with micrometer gaps, have been used for the impedancemetry real-time monitoring of the cellular changes caused by the exposure to RF [START_REF] García-Fernández | Dosimetric characteristics of an EMF delivery system based on a real-time impedance measurement device[END_REF]. In such microsystems, numerical simulations evidenced a significant difference between the SAR values of the whole volume and the local SAR at the electrodes and the cellular level.

Microfluidic and lab-on-chip technologies are currently the subjects of an important research effort [START_REF] Verpoorte | Microfluidics meets MEMS[END_REF]. By reducing analyte and reagent volumes and allowing for multiplexing, they have a large range of potential applications in chemical, biomedical, or environmental fields [START_REF] Whitesides | The origins and the future of microfluidics[END_REF]. In most lab-on-chip applications, effective control of temperature is a key factor in the optimization of the chip's performance, especially when biological samples are exposed to RF [START_REF] Inoue | Concentration of bacteria in high conductive medium using negative dielectrophoresis[END_REF], [START_REF] Manczak | Tracking cancer cells with microfluidic high frequency DEP cytometer implemented on BiCMOS lab-on-chip platform[END_REF]. Under common conditions, RF power is limited to avoid significant heating [START_REF] Li | Distributed effect in high-frequency electroporation of biological cells[END_REF]. Microexposure systems can, however, provide high EMFs that can induce temperature increase not perfectly controlled. To avoid potential temperature increase, very low conductivity media that do not provide the best conditions for biological sample experiments can be used [START_REF] Leroy | Microfluidic biosensors for microwave dielectric spectroscopy[END_REF]. In these microsystems, it is, therefore, important to characterize temperature variations accurately. Temperature measurements in a microfluidic channel, with a very limited volume of analysis that includes biological materials, require the development of specific protocols, as conventional physical probes are not suitable [START_REF] Shah | Generalized temperature measurement equations for rhodamine B dye solution and its application to microfluidics[END_REF]. Although challenging, microdosimetric measure-ments using the temperature-dependent dye, RhodB, at the electrode/cell level would be highly useful in assessing local exposure parameters.

In this article, we first propose a dosimetric characterization of an exposure system based on a TEM cell that contains a FluoroDish. This exposure system served as a dosime-try reference for defining limitations and optimal conditions for the RhodB-based temperature calibration. A microfluidic microsystem was specifically designed for RhodB fluorescence measurements. As an intermediate step, a glass slide, similar to the microfluidic system support, holding a sample drop was exposed and characterized in the TEM cell.

This article is organized as follows. Section II presents the exposure systems, the setup and exposure protocols, and the numerical modeling and RhodB fluorescence measurements. Section III details the results in terms of SAR values and distri-butions, RhodB fluorescence, and temperature measurements. Conclusions are given in Section IV.

II. MATERIALS AND METHODS

A. RF Exposure Setup

The setup for exposures to RF at 1.8 GHz of biological samples is shown in Fig. 1. It is composed of a continuous-wave (CW) RF signal generator (HP8648B, Hewlett-Packard, USA) connected to a 44-dB gain power amplifier (M.19.40.50, Nuclétudes, France). The amplifier is connected to a bidirectional coupler that monitors incident and reflected powers at the exposure system input. Depending on the exposure system (e.g., TEM cell), a 50-high-power load can be connected to the output port to absorb the transmitted power and match the impedance.

B. Macroexposure System

To calibrate the RhodB-based temperature technique, exper-iments on biological samples exposed to RF were first con-ducted with an open TEM cell exposure system [START_REF] Soueid | Delivery devices for exposure of biological cells to nanosecond pulsed electric fields[END_REF], [START_REF] Soueid | Electromagnetic analysis of an aperture modified TEM cell including an ITO layer for real-time observation of biolog-ical cells exposed to microwaves[END_REF] (Fig. 1). The TEM cell contains a FluoroDish (World Precision Instruments #FD35-100) as a biological holder. A 20-mm aperture on the TEM lower plate allows real-time microscopic fluorescence measurements.

C. Microexposure System

Fig. 2 shows the microsystem designed for microflu-idic applications and for microdosimetric characterization in response to RF fields in the microwave range that was investigated with the proposed RhodB technique.

The microelectrodes for the microdosimetry experiments are based on a coplanar waveguide (CPW) designed to expose the fluid injected in the microfluidic channel. The microelectrodes were designed to ensure impedance matching of the CPW when the microchannel is filled with a biological sample, usually an aqueous saline-based solution presenting a relative permittivity of 76 and an electrical conductivity equal to 2.3 S/m at 1.8 GHz and room temperature.

The developed microsystem is based on 2-μm gold electrodes evaporated and patterned by standard photolithography on a 500-μm-thick fused silica substrate. The microfluidic channels were defined using a negative photosensitive polymer, named DF 1050. This polymer was laminated on the chip to form a 50-μm-high microchannel network [See Fig. 2(c)]. First, experiments were led in static conditions with no flow in the channel. Hence, the fluid was injected in the microchannels using a micropipette and channels were closed with a cover glass slide to prevent evaporation during exposure and monitoring of the fluorescent level.

To adapt the microsystem with the real-time monitoring of the RhodB fluorescence setup on the microscope stage (Fig. 1), a printed circuit board (PCB) was designed allowing applying the RF signal through SMA connectors. Fig. 2(a) shows the system composed of the microelectrodes connected to the PCB that was dimensioned to be inserted on the microscope stage. Copper lines adapted at 50 have been designed to ensure the connections between the CPW and the SMA connectors through wire-bonding [See Fig. 2(b)].

D. Numerical Modeling and Simulations 1) TEM Cell Containing a FluoroDish:

The numerical dosimetry was performed with a 3-D electromagnetic software package based on the FDTD method. The TEM and the FluoroDish, with the biological sample, were modeled, as shown in Fig. 3. Perfect electric conductors were assigned to model the metallic plates of the TEM cell. The 50-impedance ports were used for the input feed source and the output load. The FluoroDish glass and the plastic permittivities were set to 7.5 and 2.5, respectively. The biological buffer solution containing the RhodB was modeled with the following dielectric properties at 1.8 GHz: conductivity of 2.3 S/m, relative permittivity of 76, and mass density of 1000 kg/m 3 .

These properties were measured at room temperature using a dielectric probe immersed in the buffer solution (85070E Dielectric Probe, Agilent, USA). The simulations were performed at a frequency of 1.8 GHz. During the experiments, the temperature increases for a few degrees, inducing a modification of the liquid's dielectric properties of less than 5% that can also be reported on the SAR. The TEM structure was meshed with a nonuniform mesh size. For increased accuracy, the biological sample and the FluoroDish glass bottom were meshed with a grid mesh of 100 μm. The top of the microscope objective is also considered in the simulations as a perfect conductor with a lens modeled as a glass with a relative permittivity of 7.5.

2) TEM Cell Containing a Glass Slide With a Drop of Biological Sample:

The TEM cell containing a glass slide with a drop of biological medium was also modeled and simulated. The relative permittivity of the glass slide was 3.8. Its dimensions were 50 mm × 50 mm × 0.5 mm. A half ellipse with a radius of 3 mm and a height of 2 mm was implemented to model the small drop corresponding to the 35-μL volume of the biological sample. All other geometric, dielectric, and simulation parameters were set as for the modeling of the TEM cell containing the FluoroDish.

3) Microfluidic Microsystem: The designed microsystem illustrated in Fig. 2 was modeled, as shown in Fig. 2(b). The 2-μm-thick gold electrodes, the metallic parts of the PCB that ensured the connections with the SMA connectors, and the wire bonding were considered as perfect electric conductors. The relative permittivities of the 500-μm-thick fused silica substrate, the 50-μm polymer, and the PCB substrate were 3.8, 3.58, and 4.3. Compared with the losses induced in the liquid, the losses of the DF 1050 and other related material are small and were often neglected in the simulations. A nonuniform meshing was implemented with a mesh size of 5 μm for the simulations of the CPW microelectrodes.

E. Exposure Protocol

Temperature variation following exposure to 1.8-GHz RF at different incident powers was measured by optical fiber probes. The RhodB fluorescence variation was also measured in parallel. For reference and calibration purposes, two optical fiber temperature probes (Luxtron One, Lumasense Technologies, CA, USA) were used. The probes were placed in the center of the FluoroDish containing the biological solution and outside the exposure system, respectively. Images for measuring fluorescence intensity were collected using two microscope objectives: a 63× or a 20× objective.

F. Rhodamine B Fluorescence Measurements and Analysis

RhodB fluorescence was either measured in solution, by adding 3 mL of RhodB with different concentrations in HEPES-buffered salt solution (HBSS) to a FluoroDish, or in RhodB loaded cells. The 50-μm RhodB concentration was used for experiments except for the study of RhodB concentration influence where 10, 1, 0.1, and 0.01 μm were explored. RhodB (excitation: 553 nm, emission: 627 nm) was observed by epifluorescence using a solid-state light engine (Spectra 7, Lumencor), coupled to the microscope (DMI6000, Leica) by a 1-mm quartz fiber. Emitted light was separated by excitation with a dichroic beam-splitter (89100BS, Chroma) and selected using emission filters controlled by a filter wheel (MAC 6000, Ludl). Images were captured using an electronmultiplying charge-coupled device camera (EMCCD Evolve 512, Roper) with 512 × 512 pixels. Images were collected every 1 or 15 s depending on the experiments. ImageJ was used for image analysis. For cell-free experiments, fluorescence intensity was measured using a region of interest that covered the whole imaged area. For experiments using cells, which were adherent to the glass bottom of the FluoroDish, images were background-corrected and fluorescence intensity was measured in the regions of interest manually drawn around cells.

G. Biological Sample Labeling

For experiments with cells, the human glioblastoma cell line U-87 MG (brain cancer cells) was used. Cells were grown in complete culture medium (MEM supplemented with 10% fetal bovine serum, glutamine, 100 units/mL penicillin, and 100 μg/mL streptomycin; Invitrogen) in an incubator at 37

• C and 5% CO2. Cells were subcultured onto plastic FluoroDishes (WPI) at a density of 1-2 10 6 cells/mL and allowed to adhere and grow for 48 h prior to experiments. Before imaging, cells were loaded with RhodB (Sigma) using a slightly modified protocol from [START_REF] Moreau | Rhodamine B as an optical thermometer in cells focally exposed to infrared laser light or nanosecond pulsed electric fields[END_REF] by incubating with 50-μm RhodB in HBSS (NaCl 121 mM, KCl Following this incubation, cells were washed with HBSS to remove excess unloaded dye. A volume of 3 mL of HBSS was used for imaging.

H. Dosimetric Assessments

Along with the electric field and temperature, the SAR is a commonly determined parameter to assess the exposure of biological samples to RF. The SAR was extracted from measurements and simulations using the following equations:

SAR = C ∂ T (W/kg) ( 1
)
∂ t t =t0
where C is the specific heat capacity of the biological sample, 4186 J/(kg • K) and ∂ T/∂ t is the initial slope of the temperature

III. RESULTS

A. Dosimetry of the TEM Cell Containing a FluoroDish and a Glass Slide With a Biological Sample Drop

Fig. 4 shows the numerical simulations results in terms of SAR distribution (a transversal cut along the center of the FluoroDish and the glass slides are shown). The distance corresponding to the gap between the bottom of the biological holder and the top of the microscope objective is defined as the working distance. All SAR values are normalized per 1-W incident power. For the FluoroDish [See Fig. 4(a)], the SAR value averaged over the whole biological solution volume is 41.7 ± 43.8 W/kg with a 200-μm working distance. The computed SAR value averaged over a cylinder of 1-mm diameter and height corresponding to the Luxtron probe measurement volume is defined as the probe volume. The probe volume SAR is equal to 43.1 ± 10.1 W/kg. The microfluidic support is formed from a glass slide. To model this system, a glass slide containing a drop of the biological sample, which was covered to limit evaporation, was first studied under exposure within Table I shows the SAR values obtained from simulations and measurements for two microscope objectives 63× and 20× placed in contact, at 200 and 400 μm, respectively. In simulations, the standard deviation is computed from the voxels in the selected volume of the SAR, as illustrated in the inset of Fig. 4(a). In the experiments, the standard deviation is due to different temperature acquisitions and from the SAR estimation using temperature measurements [START_REF] Yarmush | Electroporation-based technologies for medicine: Principles, applications, and challenges[END_REF].

Results show good agreement between simulations and measurements for all configurations. the TEM cell. This setup served as a calibration for further measurements with the microfluidic system. For the glass slide containing the drop [See Fig. 4(b)], the SAR value averaged over the whole drop volume is 0.51 ± 0.15 W/kg with a 200-μm working distance. The SAR at the bottom layer of the drop, averaged over a slice of 100-μm height corresponding to the simulation mesh size, is equal to 0.36 ± 0.02 W/kg. The inhomogeneous SAR distribution within the whole volume is not critical as we conduct temperature measurements and fluorescence acquisitions over small volumes and thin imaging slices. In the fluorescence measurement area, the SAR values on a horizontal plane are rather homogenous. Considering a height of 1 mm, the probe volume SAR standard deviation remains within acceptable values lower than 25%. Fig. 5 shows SAR values obtained from numerical simulations (whole volume, bottom layer at the cells level, probe volume) versus the working distance. The probe volume corresponds to the simulation SAR over the equivalent Luxtron probe volume located at the probe tip [See Fig. 4(a)]. For the FluoroDish [See Fig. 5(a)], the SAR values depend on the microscope objective position. The interference of the objective on the SAR values can be explained by the coupling between the objective and the hole of the TEM cell. We have considered reducing the hole; however, this would limit the use of the 63× objective, which requires to be placed in contact with the biological holder. Thus, the objective working distance must be considered for an

B. Optimizations of the RhodB-Based Dosimetric Technique

RhodB fluorescence measurements at the level of biological cells or thin imaging slices are a rather complex process. We observed that the control of the room temperature is important. For instance, the air-conditioning can cause ±1

• C in room temperature. In addition, high temperature varia-tions can modify the focus of the microscope. The setup of the RhodB measurement technique thus requires simul-taneous fine-tuning and optimization of different parameters related to the RF exposure system, the imaging fluorescence acquisitions, and the biological sample preparation. Different parameters were studied with the TEM cell containing the FluoroDish such as the influence of the incident power, the concentration of RhodB, and the duration of RF exposures. Moreover, the relation between the % change in fluorescence and temperature rise is dependent on several factors. In our experiments, no significant variations depending, for example, on the pH of the culture medium were observed [START_REF] Moreau | Rhodamine B as an optical thermometer in cells focally exposed to infrared laser light or nanosecond pulsed electric fields[END_REF], [START_REF] Shah | Generalized temperature measurement equations for rhodamine B dye solution and its application to microfluidics[END_REF].

1) Influence of the Incident Power: Different measurements were carried out to study the sensitivity of the RhodB to the incident power. For these experiments, we used 3 mL of HBSS containing 50-μm RhodB in a FluoroDish. The FluoroDish was placed inside the TEM cell, and measurements at the different incident powers (10, 5, 1, and 0.5 W) were carried out with exposure durations (RFon) varying from 30 s to 10 min.

To extract the temperature, a conversion coefficient α was defined as the ratio between temperature variation and normalized fluorescence variation, i.e., the ratio between the

( • C/u).
(3) The normalized fluorescence variation was slightly less than 2%/

• C, and the conversion coefficient α was assessed at 57 ± 2.5 (mean ± SD). Typical results presented in Fig. 6 show a temperature elevation of 3

• C and 1.5

• C, for 10-W 30-s RFon and 0.5-W 10-min RFon, respectively. Plots evidence good agreement between the temperature and fluorescence values even for low incident powers (0.5 W).

2) Influence of the RhodB Concentration:

To study the influence of RhodB concentration on its fluorescence, measurements were performed with concentrations of 50, 10, 1, 0.1, and 0.01 μm. The incident power was varied from 0.5 to 10 W, and the same protocol of 10 min or 30 s of RFon was applied. The results reveal a good concordance between the temperature and the RhodB fluorescence for concentrations higher than 1 μm for a significant increase of temperature (typically 1

• C). These experiments point out the dependence of the RhodB fluorescence to its bulk concentration.

Table II shows the SAR values obtained from measurements at 1.8 GHz for different incident power and RhodB concen-trations. As observed, the SAR values normalized per 1-W incident power extracted from all fluorescence measurements present a high level of consistency. The results of this macrodosimetry are reproducible, and this measurement technique thus calibrated and validated can inform us about the temperature variation of a sample at the microscopic scale. Fig. 7 shows the experimental results of temperature versus Rhod B fluorescence variation obtained for clusters of few U-87 MG adherent cells. The cells are exposed to 1.8 GHz at 10-W incident power, for durations of 30 s, in the FluoroDish placed in the TEM cell. Results evidence the fluorescence variation that can be correlated with the temperature variation. It should be noted that for the fluorescence acquisitions, only the area of each cell is considered. This can explain the noise on the fluorescence measurements. As the cell clusters differ, it is necessary to normalize the fluorescence-level reference. Indeed, within the different clusters, there are regions without fluorescence. As the mean value of the fluorescence is different from one cluster to another, a normalization was applied to merge all the clusters. Therefore, the calibration factor cannot be applied.

C. Dosimetry of the Microfluidic Microsystem

1) Electromagnetic Frequency Characterization: Fig. 8 shows S-parameters that characterize the transmitted (S21) and reflected (S11) powers by the microfluidic system. Sparameters were obtained from measurements achieved by conventional on wafer probing with ACP-40-A-GSG Cascade probes connected to a calibrated Rohde & Schwarz ZVA-24 network analyzer. The microfluidic channel is either empty or filled with the biological sample, i.e., a drop of HBSS. Fig. 8(a) shows that the results of the empty or filled microfluidic channel slightly differ particularly in terms of |S11| frequency position minima that can be related to the resonance frequency of the microsystem. For both configurations, however, the reflection coefficient |S11| remains lower than -10 dB for the considered frequency band, ensuring a good impedance matching. Fig. 8(b) shows that the transmission (S21) coefficients for both configurations are higher than -1 dB for frequencies up to 3 GHz. These measurements validated the relevance of using such a device for the exposure of biological cells, since the energy will be effectively transferred to the biological medium inside the microfluidic channel.

Fig. 9 shows the SAR spatial distribution within the microfluidic channel filled with a drop of HBSS covered by 2) RhodB Fluorescence Measurements: The RhodB fluorescence variation measured within the microfluidic chan-nel is shown in Fig. 10. The microsystem is exposed to 1.8 GHz at 0.2-W incident power during 30 s. For these measurements, the drop of HBSS that fills the channel was labeled with 100μm RhodB to enhance fluorescence emis-sion. The fluorescence result is related to the RF exposure with a fast variation during the 30-s RF delivery. An SAR value of 14.5 kW/kg was extracted from the initial slope of the fluorescence/temperature. Indeed, the fluorescence signal measurements depend on the sensitive volume. For small microchannels, very high SAR values and, therefore, high tem-perature variation differences between the simulations and the measurements may exist. Two phenomena can be evidenced. First, the initial slope of the temperature may be very steep and SAR determination from this slope can be limited by the camera temporal resolution (1 s in our experiments). Second, as the involved volume is very small, the temperature variation and dispersion are significant. In this context, the calibration for small channels may require tuning of the fluorescence-sensitive volume and comparisons with simulated SAR. For macroscopic systems (FluoroDish and drop), the sensitive volume is not critical as the SAR is rather homogenous over the 1-mm bottom layer.

IV. CONCLUSION

In this article, a temperature dosimetric technique based on RhodB fluorescence variation was set up and optimized. The technique serves to complete numerical dosimetry results for RF exposure systems. The exposure setup was based on a FluoroDish, containing 3 mL of HBSS, inserted into the TEM cell. The dosimetry assessment was carried out both numerically, with the FDTD method to obtain SAR distribution values, and experimentally, using a Luxtron probe and RhodB for temperature measurement.

For the setup and optimization of the RhodB-based dosi-metric technique, different parameters such as RhodB con-centration and the incident power directly related to the SAR thresholds were studied. From the results, it can be evidenced that the technique is applicable for RhodB concentrations higher than 1 μm with a value of 50 μm recommended for reliable experiments. As for the measurements with the optical fiber temperature probe, a temperature variation of a few tenths of degrees is required for detection and SAR assessment. One experiment with cells adherent to a FluoroDish exposed in a TEM cell illustrating that fluorescence variation can be correlated with the temperature variation was reported.

This technique is highly relevant for dosimetric characterizations at the microscopic level and suitable for the microexposure system assessment of local exposure parameters. In this article, a microfluidic microsystem was specifically designed for RF exposures and RhodB fluorescence measurements.

Fig. 1 .

 1 Fig. 1. Biological sample RF exposure in a TEM cell with a FluoroDish, temperature measurements setup, and real-time microscopic measurements of RhodB fluorescence.

Fig. 2 .

 2 Fig. 2. Microsystem design and dimensions. (a) Photographs of the microfluidic microsystem based on CPW electrodes and of the microsys-tem mounted on a PCB designed to be inserted on the microscope stage. (b) 3-D view of the global system with electrical connections (numerical model extracted from the FDTD software). (c) Schematic top view and sectional view. The center CPW line is narrow at the beginning to allow conventional on-wafer probing with ACP-40-A-GSG Cascade probes connected to a vector network analyzer.

Fig. 3 .

 3 Fig. 3. TEM cell with (a) FluoroDish that contains the biological sample (numerical model extracted from the FDTD software, FluoroDish dimensions) and (b) glass slide with a drop of biological sample.

5. 4

 4 mM, MgCl2 0.8 mM, NaHCO3 6 mM, D-glucose 5.5 mM, HEPES 25 mM, CaCl2 1.8 mM, pH 7.3) for 1 h at 37• C.

Fig. 4 .

 4 Fig. 4. SAR spatial distribution within the biological sample inside (a) FluoroDish placed in the TEM cell. Inset: Probe volume 1-mm cylinder for comparisons with Luxtron probe measured SAR. (b) Drop placed on a glass slide inside the TEM cell. SAR values are normalized per 1-W incident power for an objective at a working distance of 200 μm.

  where E is the electric field amplitude (V/m), ρ is the mass density (kg/m3 ), and σ is the electrical conductivity (S/m) of the biological sample.

Fig. 5 .

 5 Fig. 5. Variation of SAR values (whole volume, bottom layer at the cell level, and temperature probe volume) versus the microscope objective position (the working distance corresponds to the gap between the bottom of the biological holder and the top of the objective). SAR values are normalized per 1-W incident power. (a) Within the biological sample inside the FluoroDish placed in the TEM cell. (b) Within a drop placed on a glass slide inside the TEM cell.

Fig. 6 .

 6 Fig. 6. Experimental results of temperature obtained with the optical fiber probe and extracted from Rhod B fluorescence measurements. The biological samples, 3 mL of HBSS with 50-μm RhodB, were exposed to 1.8 GHz. Fluorescence measurements were performed with the 20× objective. (a) Data at incident power of 10 W for an RF exposure duration of 30 s. (b) Data at an incident power of 0.5 W for an RF exposure duration of 10 min.

Fig. 7 .

 7 Fig. 7. Experimental results of temperature versus Rhod B fluorescence variation obtained with the 20× objective at a working distance of 200 μm for four different U-87 MG clusters of 4-5 cells. The cells were exposed to 1.8 GHz, at 10-W incident power, for a duration of 30 s, in the FluoroDish placed in the TEM cell.

Fig. 8 .

 8 Fig. 8. S-Parameters over a wide frequency range of the microfluidic microsystem; the microfluidic channel is empty or filled with the biological sample, i.e., a drop of HBSS. (a) Reflection (S11) coefficient. (b) Transmission (S21) coefficient.

Fig. 9 .

 9 Fig. 9. SAR spatial distribution within the microfluidic channel filled a drop of HBSS covered by a glass slide. SAR values are normalized per 1-mW incident power.

Fig. 10 .

 10 Fig.10. RhodB fluorescence variation obtained with the 20× objective within the microfluidic channel containing a drop of HBSS with 100-μm RhodB. The microsystem is exposed to 1.8 GHz at 0.2-W incident power for a duration of 30 s.

TABLE II SAR

 II VALUES AT 1.8 GHz FOR DIFFERENT INCIDENT POWERS AND RhodB CONCENTRATIONS. SAR VALUES ARE NORMALIZED PER 1-W INCIDENT POWER

This work was supported by the European Union's Horizon 2020 Research and Innovation Program under Grant 737164, by Region Limousin, the CNRS, and ANR Labex SigmaLim.

Arnaud Pothier (M'10) received the Ph.D. degree in electrical engineering from the University of Limoges, Limoges, France, in 2003.

He is currently a Full-Time Senior Researcher with the XLIM Research Institute. His current research interest includes RF biosensors for labelfree cell analysis. Since 2007, she has been with the Bioelectromagnetics Team, XLIM Institute, University of Limoges/C.N.R.S., Limoges, France, as an Associate Professor. In 2012, she joined the Pulsed Power Group, Department of Electrical Engineering, University of Southern California (USC), Los Angeles, SC, USA, where she developed research with the Biological Applications Research Team. Since 2018, she has been a Junior Member with the Institut Universitaire de France (IUF), Paris, France, and a member of the International Bioelectrics Consortium. Her current research interests include nanosecond pulses/microwave exposure system setup and dosimetric characterization for bioelectromagnetic studies.