[en] In this study, we conduct a multiscale, multiphysics modeling of the brain gray matter as a poroelastic composite. We develop a customized representative volume element based on cytoarchitectural features that encompass important microscopic components of the tissue, namely the extracellular space, the capillaries, the pericapillary space, the interstitial fluid, cell–cell and cell-capillary junctions, and neuronal and glial cell bodies. Using asymptotic homogenization and direct numerical simulation, the effective properties at the tissue level are identified based on microscopic properties. To analyze the influence of various microscopic elements on the effective/macroscopic properties and tissue response, we perform sensitivity analyses on cell junction (cluster) stiffness, cell junction diameter (dimensions), and pericapillary space width. The results of this study suggest that changes in cell adhesion can greatly affect both mechanical and hydraulic (interstitial fluid flow and porosity) features of brain tissue, consistent with the effects of neurodegenerative diseases.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
DEHGHANI, Hamidreza ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Holzapfel, Gerhard
MITTELBRONN, Michel ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Neuropathology
ZILIAN, Andreas ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
yes
Language :
English
Title :
Cell adhesion affects the properties of interstitial fluid flow: A study using multiscale poroelastic composite modeling
Publication date :
27 February 2024
Journal title :
Journal of the Mechanical Behavior of Biomedical Materials
ISSN :
1751-6161
eISSN :
1878-0180
Publisher :
Elsevier, Oxford, United Kingdom
Volume :
153
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
R-AGR-3440 - PRIDE17/12252781 DRIVEN_Common - ZILIAN Andreas
Budday, S., Nay, R., de Rooij, R., Steinmann, P., Wyrobek, T., Ovaert, T.C., Kuhl, E., Mechanical properties of gray and white matter brain tissue by indentation. J. Mech. Behav. Biomed. Mater. 46 (2015), 318–330.
Budday, S., Ovaert, T.C., Holzapfel, G.A., Steinmann, P., Kuhl, E., Fifty shades of brain: A review on the mechanical testing and modeling of brain tissue. Arch. Comput. Methods Eng. 27 (2020), 1187–1230.
Budday, S., Sarem, M., Starck, L., Sommer, G., Pfefferle, J., Phunchago, N., Kuhl, E., Paulsen, F., Steinmann, P., Shastri, V.P., Holzapfel, G.A., Towards microstructure-informed material models for human brain tissue. Acta Biomater. 104 (2020), 53–65.
Changede, R., Sheetz, M., Integrin and cadherin clusters: A robust way to organize adhesions for cell mechanics. Bioessays 39 (2017), 1–12.
Dehghani, H., Noll, I., Penta, R., Menzel, A., Merodio, J., The role of microscale solid matrix compressibility on the mechanical behaviour of poroelastic materials. Eur. J. Mech. – A/Solids, 83, 2020, 103996.
Dehghani, H., Penta, R., Merodio, J., The role of porosity and solid matrix compressibility on the mechanical behavior of poroelastic tissues. Mater. Res. Express, 6, 2018, 035404.
Dehghani, H., Zilian, A., Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio. Comput. Mech. 66 (2020), 625–649.
Dehghani, H., Zilian, A., Finite strain poro-hyperelasticity: an asymptotic multi-scale ALE-FSI approach supported by ANNs. Comput. Mech. 71 (2023), 695–719.
Franceschini, G., Bigoni, D., Regitnig, P., Holzapfel, G., Brain tissue deforms similarly to filled elastomers and follows consolidation theory. J. Mech. Phys. Solids 54 (2006), 2592–2620.
Guertler, C.A., Okamoto, R.J., Schmidt, J.L., Badachhape, A.A., Johnson, C.L., Bayly, P.V., Mechanical properties of porcine brain tissue in vivo and ex vivo estimated by MR elastography. J. Biomech. 69 (2018), 10–18.
Hamley, I.W., The amyloid beta peptide: A chemist's perspective. Role in alzheimer's and fibrillization. Chem. Rev. 112:10 (2012), 5147–5192.
Hellas, J.A., Andrew, R.D., Neuronal swelling: A non-osmotic consequence of spreading depolarization. Neurocrit. Care 35 (2021), 112–134.
Hladky, S.B., Barrand, M.A., The glymphatic hypothesis: the theory and the evidence. Fluids Barriers CNS, 19(1), 2022, 9.
Holzapfel, G.A., On large strain viscoelasticity: Continuum formulation and finite element applications to elastomeric structures. Internat. J. Numer. Methods Engrg. 39 (1996), 3903–3926.
Holzapfel, G.A., Nonlinear Solid Mechanics. A Continuum Approach for Engineering. 2000, John Wiley & Sons, Chichester.
Huang, X., Zhou, W., Deng, D., Effective diffusion in fibrous porous media: A comparison study between lattice Boltzmann and pore network modeling methods. Materials, 14(14), 2021, 756.
Iliff, J.J., Wang, M., Liao, Y., Plogg, B.A., Peng, W., Gundersen, G.A., Benveniste, H., Vates, G.E., Deane, R., Goldman, S.A., Nagelhus, E.A., Nedergaard, M., A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci. Transl. Med., 4(147), 2012, 147ra111.
Jamal, A., Mongelli, M.T., Vidotto, M., Madekurozwa, M., Bernardini, A., Overby, D.R., De Momi, E., Rodriguez y Baena, F., Sherwood, J.M., Dini, D., Infusion mechanisms in brain white matter and their dependence on microstructure: An experimental study of hydraulic permeability. IEEE Trans. Biomed. Eng. 68:4 (2021), 1229–1237.
Jin, B.-J., Smith, A.J., Verkman, A.S., Spatial model of convective solute transport in brain extracellular space does not support a “glymphatic” mechanism. J. Gen. Physiol. 148 (2016), 489–501.
Kang, J., Lemaire, H.-G., Unterbeck, A., Salbaum, J.M., Masters, C.L., Grzeschik, K.-H., Multhaup, G., Beyreuther, K., Müller-Hill, B., The precursor of alzheimer's disease amyloid A4 protein resembles a cell-surface receptor. Nature 325 (1987), 733–736.
Kedarasetti, R.T., Drew, P.J., Costanzo, F., Arterial vasodilation drives convective fluid flow in the brain: a poroelastic model. Fluids Barriers CNS, 19(1), 2022, 34.
Lau, L.W., Cua, R., Keough, M.B., Haylock-Jacobs, S., Yong, V.W., Pathophysiology of the brain extracellular matrix: a new target for remyelination. Nat. Rev. Neurosci. 14 (2013), 722–729.
Lowery, L.A., Vactor, D.V., The trip of the tip: understanding the growth cone machinery. Nat. Rev. Mol. Cell Biol. 10 (2009), 332–343.
Lu, Y.-B., Franze, K., Seifert, G., Steinhäuser, C., Kirchhoff, F., Wolburg, H., Guck, J., Janmey, P., Wei, E.-Q., Käs, J., Reichenbach, A., Viscoelastic properties of individual glial cells and neurons in the CNS. Proc. Natl. Acad. Sci. 103 (2006), 17759–17764.
Miller, K., Chinzei, K., Orssengo, G., Bednarz, P., Mechanical properties of brain tissue in-vivo: experiment and computer simulation. J. Biomech. 33:11 (2000), 1369–1376.
Miller, L., Penta, R., Homogenized balance equations for nonlinear poroelastic composites. Appl. Sci., 11(14), 2021.
Miller, L., Penta, R., Micromechanical analysis of the effective stiffness of poroelastic composites. Eur. J. Mech. A Solids, 98, 2023, 104875.
Mogilner, A., Manhart, A., Intracellular fluid mechanics: Coupling cytoplasmic flow with active cytoskeletal gel. Annu. Rev. Fluid Mech. 50 (2018), 347–370.
Niessen, C.M., Gottardi, C.J., Molecular components of the adherens junction. Biochim. Biophys. Acta 1778 (2008), 562–571.
Ow, S.-Y., Dunstan, D.E., A brief overview of amyloids and alzheimer's disease. Prot. Sci. 23 (2014), 1315–1331.
Quang, B.-A.T., Mani, M., Markova, O., Lecuit, T., Lenne, P.-F., Principles of E-cadherin supramolecular organization in vivo. Curr. Biol. 23 (2013), 2197–2207.
Sáez, A.E., Perfetti, J.C., Rusinek, I., Prediction of effective diffusivities in porous media using spatially periodic models. Transp. Porous Media 6:2 (1991), 143–157.
Song, M., Yu, S.P., Ionic regulation of cell volume changes and cell death after ischemic stroke. Transl. Stroke Res. 5 (2014), 17–27.
Sosa, L.J., Cáceres, A., Dupraz, S., Oksdath, M., Quiroga, S., Lorenzo, A., The physiological role of the amyloid precursor protein as an adhesion molecule in the developing nervous system. J. Neurochem. 143 (2017), 11–29.
Spedden, E., White, J.D., Naumova, E.N., Kaplan, D.L., Staii, C., Elasticity maps of living neurons measured by combined fluorescence and atomic force microscopy. Biophys. J. 103 (2012), 868–877.
Spocter, M.A., Hopkins, W.D., Barks, S.K., Bianchi, S., Hehmeyer, A.E., Anderson, S.M., Stimpson, C.D., Fobbs, A.J., Hof, P.R., Sherwood, C.C., Neuropil distribution in the cerebral cortex differs between humans and chimpanzees. J. Comp. Neurol. 520 (2012), 2917–2929.
Syková, E., Nicholson, C., Diffusion in brain extracellular space. Physiol. Rev. 88:4 (2008), 1277–1340 PMID: 18923183.
Tang, V.W., Collagen, stiffness, and adhesion: the evolutionary basis of vertebrate mechanobiology. Mol. Biol. Cell 31 (2020), 1823–1834.
Togashi, H., Sakisaka, T., Takai, Y., Cell adhesion molecules in the central nervous system. Cell Adhes. Migr. 3 (2009), 29–35.
Uchida, N., Honjo, Y., Johnson, K.R., Wheelock, M.J., Takeichi, M., The catenin/cadherin adhesion system is localized in synaptic junctions bordering transmitter release zones. J. Cell Biol. 135 (1996), 767–779.
van Hoek, A.N., Yang, B., Kirmiz, S., Brown, D., Freeze-fracture analysis of plasma membranes of CHO cells stably expressing aquaporins 1-5. J. Membr. Biol. 165 (1998), 243–254.
Ventura-Antunes, L., Herculano-Houzel, S., Energy supply per neuron is constrained by capillary density in the mouse brain. Front. Integr. Neurosci., 16, 2022, 760887.
Washbourne, P., Dityatev, A., Scheiffele, P., Biederer, T., Weiner, J.A., Christopherson, K.S., El-Husseini, A., Cell adhesion molecules in synapse formation. J. Neurosci. 24:42 (2004), 9244–9249.
Wu, Y., Kanchanawong, P., Zaidel-Bar, R., Actin-delimited adhesion-independent clustering of E-cadherin forms the nanoscale building blocks of adherens junctions. Dev. Cell 32 (2015), 139–154.
