Dynamical properties of water in living cells

With the aim of studying the effect of water dynamics on the properties of biological systems, in this paper, we present a quasi-elastic neutron scattering study on three different types of living cells, differing both in their morphological and tumor properties. The measured scattering signal, which essentially originates from hydrogen atoms present in the investigated systems, has been analyzed using a global fitting strategy using an optimized theoretical model that considers various classes of hydrogen atoms and allows disentangling diffusive and rotational motions. The approach has been carefully validated by checking the reliability of the calculation of parameters and their 99% confidence intervals. We demonstrate that quasi-elastic neutron scattering is a suitable experimental technique to characterize the dynamics of intracellular water in the angstrom/picosecond space/time scale and to investigate the effect of water dynamics on cellular biodiversity

Monitoring glioma heterogeneity during tumor growth using clustering analysis of multiparametric MRI data

International audienceSynopsis Brain tumor heterogeneity plays a major role during gliomas growth and for the tumors resistance to therapies. The goal of this study was to demonstrate the ability of clustering analysis applied to multiparametric MRI (mpMRI) data to summarize and quantify intralesional heterogeneity during tumor growth. A mpMRI dataset of rats bearing glioma was acquired during the tumor growth (5 maps, 8 animals and 6 time points). After co-registration of every MR data over time, a clustering analysis was performed using a Gaussian mixture distribution model. Although preliminary, our results show that clustering analysis of mpMRI has a great potential to monitor quantitatively intralesional heterogeneity during the growth of tumors. Introduction For tumor diagnosis, histology often remains the reference, but due to tumor heterogeneity, it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in monitoring quantitatively intralesional brain tumor heterogeneity. MRI has demonstrated its ability to quantitatively map structural information like diiusion (ADC) as well as functional characteristics such as the blood volume (BVf), vessel size (VSI), the oxygen saturation of the tissue (StO), or the blood brain barrier permeability. In a recent study (1), these MR parameters were analyzed independently from each other to demonstrate the great potential of a multiparametric MR (mpMRI) protocol to monitor combined radio-and chemo-therapies. However, to summarize and quantify all the information contained in an mpMRI protocol while preserving information about tumor heterogeneity, new methods to extract information need to be developed. The goal of this study is to demonstrate the ability of clustering analysis (2) applied to longitudinal mpMRI to summarize and quantify intralesional heterogeneity during tumor growth. Methods Animal model: The local IRB committee approved all studies. 9L tumors were implanted in 8 rats and imaging was performed every 2 days between day 7 and day 17 post tumor implantation on a 4.7T Bruker system (D7, D9, D11, D13, D15 and D17; respectively). The following mpMRI protocol was acquired at each MR session: a T2-weighted spin echo sequence to obtain structural information over the whole brain, a diiusion weighted EPI sequence to map the Apparent Diiusion Coeecient (ADC) and multiple spin/gradient echo sequences to map T2 and T2*. A Gradient Echo Sampling of the FID and Spin Echo (GESFIDE) sequence was acquired pre-and post-injection of USPIO (133 µmol/kg). A dynamic contrast enhancing sequence was acquired using a RARE sequence (T1w images; n=15, 15.6 sec per image). After the acquisition of 4 images, a bolus of gadolinium-chelate was administered (100µmol/kg). Parametric maps: for each MR session, BVf and VSI maps were computed using the approach described in (3), StO using the method described in (4) and the vessel permeability maps (Perm) was calculated as the percentage of enhancement (voxel-wise) within 3 min post injection of gadolinium (cf. g1-a). Co-registration: each parametric map of each MR session was co-registered to that acquired at the previous time point using rigid registration (SPM toolbox and Matlab). ROI: tumor was manually delineated using the T2w images (Tumor-ROI; Red line in g1-a). Cluster analysis: parameter values were centered and normalized. Then, a Gaussian mixture distribution (Matlab function called: tgmdist) was use to performed the clustering analysis of all voxels included in the tumor-ROI. The number of classes inside the mixture was selected by minimizing the Bayesian information criterion (BIC). Results Firstly, we performed the clustering analysis 9 times using 1 to 9 classes. The optimal classes number, deened by the BIC was 5. Each cluster may be seen as a tissue type, as described Fig.1-E. The result of the clustering analysis is illustrated Fig1-A for one animal. For each of the ve clusters (labeled K1 to K5), the evolution of the mean cluster volume over the entire population of tumor is presented Fig 1-B. Note that the sum of the ve cluster volumes represents the whole tumor volume. Fig.1-C illustrates the longitudinal evolution of the 5 clusters in 2 animals with diierent tumor growth rate (slow on the top and high on the bottom). Although the cluster analysis analyzed every voxel independently from each other, one can see that the clustering results are spatially consistent at 1 time point but also over time. Indeed, clusters are spatially grouped: for example, the green cluster is mostly located in the center of the tumor (Fig1-C). Our result shows a diierence in cluster composition between the slow and the high growth rate tumors (Fig.1-C,D). For example, in the slow growth rate tumor, the yellow cluster takes more and more space in the tumor overtime (up to 49% at D17) whereas, in the high growth rate tumor, it is the green one. The main diierence between the yellow and the green cluster is the strong reduction in StO in the green cluster versus the yellow cluster (cf. Fig.1-E). Conclusions To our knowledge, it is a rst study demonstrating the feasibility of performing a clustering analysis on mpMRI data to monitor the evolution of brain tumor heterogeneity in vivo. This approach highlights the type of tissue, which mostly contributes to the development of the tumor. The composition in tissue type could be used to reene the evaluation of chemo and radiotherapies and could contribute to improve tumor prognosis

The Rho-associated protein kinase p160ROCK is required for centrosome positioning

The p160–Rho-associated coiled-coil–containing protein kinase (ROCK) is identified as a new centrosomal component. Using immunofluorescence with a variety of p160ROCK antibodies, immuno EM, and depletion with RNA interference, p160ROCK is principally bound to the mother centriole (MC) and an intercentriolar linker. Inhibition of p160ROCK provoked centrosome splitting in G1 with the MC, which is normally positioned at the cell center and shows little motion during G1, displaying wide excursions around the cell periphery, similar to its migration toward the midbody during cytokinesis. p160ROCK inhibition late after anaphase in mitosis triggered MC migration to the midbody followed by completion of cell division. Thus, p160ROCK is required for centrosome positioning and centrosome-dependent exit from mitosis

Manganese Cytotoxicity Assay on Hippocampal Neuronal Cell Culture

Compared to an in vivo experiment, neuronal cell cultures are immediately accessibleto observation and manipulation. In this protocol, we describe a technique to evaluate thecytotoxicity of a metal, manganese (Mn2+), on hippocampal neuronal cell cultures. Interestingly, this protocol is easily adaptable to any type of primary culture (e.g., cortical neurons) and any type of toxic compound (e.g., chemical product).Fil: Daoust, Alexia. Inserm; Francia. Universite Joseph Fourier; FranciaFil: Saoudi, Yasmina. Inserm; Francia. Universite Joseph Fourier; FranciaFil: Brocard, Jacques. Inserm; Francia. Universite Joseph Fourier; FranciaFil: Collomb, Nora. Inserm; Francia. Universite Joseph Fourier; FranciaFil: Batandier, Cecile. Laboratoire de Bioénergétique Fondamentale et Appliquée; FranciaFil: Bisbal, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina. Inserm; Francia. Universite Joseph Fourier; FranciaFil: Salome, Murielle. European Synchrotron Radiation Facility; FranciaFil: Andrieux, Annie. Inserm; Francia. Universite Joseph Fourier; FranciaFil: Bohic, Sylvain. Inserm; Francia. Universite Joseph Fourier; Francia. European Synchrotron Radiation Facility; FranciaFil: Barbier, Emmanuel. Inserm; Francia. Universite Joseph Fourier; Franci

Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI Data

International audienceWhen analyzing brain tumors, two tasks are intrinsically linked, spatial localization and physiological characterization of the lesioned tissues. Automated data-driven solutions exist, based on image segmentation techniques or physiological parameters analysis, but for each task separately, the other being performed manually or with user tuning operations. In this work, the availability of quantitative magnetic resonance (MR) parameters is combined with advanced multivariate statistical tools to design a fully automated method that jointly performs both localization and characterization. Non trivial interactions between relevant physiological parameters are captured thanks to recent generalized Student distributions that provide a larger variety of distributional shapes compared to the more standard Gaussian distributions. Probabilistic mixtures of the former distributions are then considered to account for the different tissue types and potential heterogeneity of lesions. Discriminative multivariate features are extracted from this mixture modelling and turned into individual lesion signatures. The signatures are subsequently pooled together to build a statistical fingerprint model of the different lesion types that captures lesion characteristics while accounting for inter-subject variability. The potential of this generic procedure is demonstrated on a data set of 53 rats, with 36 rats bearing 4 different brain tumors, for which 5 quantitative MR parameters were acquired

Monitoring brain tumor evolution using multiparametric MRI

International audience— Analysing brain tumor tissue composition can improve the handling of tumor growth and resistance to therapies. We show on a 6 time point dataset of 8 rats that multiparametric MRI can be exploited via statistical clustering to quantify intra-lesional heterogeneity in space and time

Suivi de l'hétérogénéité de la croissance des gliomes par IRM multiparamétrique analysée par clustering

National audienceL'hétérogénéité intra-lésionnelle des tumeurs cérébrales joue un rôle majeur dans la croissance et la résistance des gliomes aux thérapies. L'objectif de cette étude est de démontrer la capacité d'une analyse par clusters, appliquée aux données d'IRM multiparamétriques (IRMmp), pour suivre quantitativement l'hétérogénéité intra-lésionnelle au cours de la croissance tumorale. Un jeu de données d'IRMmp a été acquis durant la croissance d'un modèle de gliome chez le rat et analysé par clustering

Monitoring glioma heterogeneity during tumor growth using clustering analysis of multiparametric MRI data

International audienceSynopsis Brain tumor heterogeneity plays a major role during gliomas growth and for the tumors resistance to therapies. The goal of this study was to demonstrate the ability of clustering analysis applied to multiparametric MRI (mpMRI) data to summarize and quantify intralesional heterogeneity during tumor growth. A mpMRI dataset of rats bearing glioma was acquired during the tumor growth (5 maps, 8 animals and 6 time points). After co-registration of every MR data over time, a clustering analysis was performed using a Gaussian mixture distribution model. Although preliminary, our results show that clustering analysis of mpMRI has a great potential to monitor quantitatively intralesional heterogeneity during the growth of tumors. Introduction For tumor diagnosis, histology often remains the reference, but due to tumor heterogeneity, it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in monitoring quantitatively intralesional brain tumor heterogeneity. MRI has demonstrated its ability to quantitatively map structural information like diiusion (ADC) as well as functional characteristics such as the blood volume (BVf), vessel size (VSI), the oxygen saturation of the tissue (StO), or the blood brain barrier permeability. In a recent study (1), these MR parameters were analyzed independently from each other to demonstrate the great potential of a multiparametric MR (mpMRI) protocol to monitor combined radio-and chemo-therapies. However, to summarize and quantify all the information contained in an mpMRI protocol while preserving information about tumor heterogeneity, new methods to extract information need to be developed. The goal of this study is to demonstrate the ability of clustering analysis (2) applied to longitudinal mpMRI to summarize and quantify intralesional heterogeneity during tumor growth. Methods Animal model: The local IRB committee approved all studies. 9L tumors were implanted in 8 rats and imaging was performed every 2 days between day 7 and day 17 post tumor implantation on a 4.7T Bruker system (D7, D9, D11, D13, D15 and D17; respectively). The following mpMRI protocol was acquired at each MR session: a T2-weighted spin echo sequence to obtain structural information over the whole brain, a diiusion weighted EPI sequence to map the Apparent Diiusion Coeecient (ADC) and multiple spin/gradient echo sequences to map T2 and T2*. A Gradient Echo Sampling of the FID and Spin Echo (GESFIDE) sequence was acquired pre-and post-injection of USPIO (133 µmol/kg). A dynamic contrast enhancing sequence was acquired using a RARE sequence (T1w images; n=15, 15.6 sec per image). After the acquisition of 4 images, a bolus of gadolinium-chelate was administered (100µmol/kg). Parametric maps: for each MR session, BVf and VSI maps were computed using the approach described in (3), StO using the method described in (4) and the vessel permeability maps (Perm) was calculated as the percentage of enhancement (voxel-wise) within 3 min post injection of gadolinium (cf. g1-a). Co-registration: each parametric map of each MR session was co-registered to that acquired at the previous time point using rigid registration (SPM toolbox and Matlab). ROI: tumor was manually delineated using the T2w images (Tumor-ROI; Red line in g1-a). Cluster analysis: parameter values were centered and normalized. Then, a Gaussian mixture distribution (Matlab function called: tgmdist) was use to performed the clustering analysis of all voxels included in the tumor-ROI. The number of classes inside the mixture was selected by minimizing the Bayesian information criterion (BIC). Results Firstly, we performed the clustering analysis 9 times using 1 to 9 classes. The optimal classes number, deened by the BIC was 5. Each cluster may be seen as a tissue type, as described Fig.1-E. The result of the clustering analysis is illustrated Fig1-A for one animal. For each of the ve clusters (labeled K1 to K5), the evolution of the mean cluster volume over the entire population of tumor is presented Fig 1-B. Note that the sum of the ve cluster volumes represents the whole tumor volume. Fig.1-C illustrates the longitudinal evolution of the 5 clusters in 2 animals with diierent tumor growth rate (slow on the top and high on the bottom). Although the cluster analysis analyzed every voxel independently from each other, one can see that the clustering results are spatially consistent at 1 time point but also over time. Indeed, clusters are spatially grouped: for example, the green cluster is mostly located in the center of the tumor (Fig1-C). Our result shows a diierence in cluster composition between the slow and the high growth rate tumors (Fig.1-C,D). For example, in the slow growth rate tumor, the yellow cluster takes more and more space in the tumor overtime (up to 49% at D17) whereas, in the high growth rate tumor, it is the green one. The main diierence between the yellow and the green cluster is the strong reduction in StO in the green cluster versus the yellow cluster (cf. Fig.1-E). Conclusions To our knowledge, it is a rst study demonstrating the feasibility of performing a clustering analysis on mpMRI data to monitor the evolution of brain tumor heterogeneity in vivo. This approach highlights the type of tissue, which mostly contributes to the development of the tumor. The composition in tissue type could be used to reene the evaluation of chemo and radiotherapies and could contribute to improve tumor prognosis

SAR comparison between CASL and pCASL at high magnetic field and evaluation of the benefit of a dedicated labeling coil

International audienceTo investigate the heating induced by (pseudo)-continuous arterial spin labeling ((p)CASL) sequences in vivo at 9.4T and to evaluate the benefit of a dedicated labeling coil

SAR comparison between CASL and pCASL at high magnetic field and evaluation of the benefit of a dedicated labeling coil

International audienceTo investigate the heating induced by (pseudo)-continuous arterial spin labeling ((p)CASL) sequences in vivo at 9.4T and to evaluate the benefit of a dedicated labeling coil