Abstract
The extracellular space (ECS) consists of the narrow channels between brain cells together with their geometrical configuration and contents. Despite being only 20–60 nm in width, the ECS typically occupies 20% of the brain volume. Numerous experiments over the last 50 years have established that molecules moving through the ECS obey the laws of diffusion but with an effective diffusion coefficient reduced by a factor of about 2.6 compared to free diffusion. This review considers the origins of the diffusion barrier arising from the ECS and its properties. The paper presents a brief overview of software for implementing two point-source paradigms for measurements of localized diffusion properties: the real-time iontophoresis or pressure method for small ions and the integrative optical imaging method for macromolecules. Selected results are presented. This is followed by a discussion of the application of the MCell Monte Carlo simulation program to determining the importance of geometrical constraints, especially dead-space microdomains, and the possible role of interaction with the extracellular matrix. It is concluded that we can predict the impediment to diffusion of many molecules of practical importance and also use studies of the diffusion of selected molecular probes to reveal the barrier properties of the ECS.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Andĕrová M., Kubinová Š., Mazel T., Chvátal A., Eliasson C., Pekny M., Syková E.: Effect of elevated K +, hypotonic stress, and cortical spreading depression on astrocyte swelling in GFAP-deficient mice. Glia 35(3), 189–203 (2001)
Bobo R.H., Laske D.W., Akbasak A., Morrison P.F., Dedrick R.L., Oldfield E.H.: Convection-enhanced delivery of macromolecules in the brain. Proc. Natl. Acad. Sci. U.S.A. 91(6), 2076–2080 (1994)
Chen K.C., Nicholson C.: Changes in brain cell shape create residual extracellular space volume and explain tortuosity behavior during osmotic challenge. Proc. Natl. Acad. Sci. U.S.A. 97(15), 8306–8311 (2000)
Crank J.: The Mathematics of Diffusion, 2nd edn. Clarendon Press, Oxford (1975)
Fenstermacher J.D., Kaye T.: Drug “diffusion” within the brain. Ann. N.Y. Acad. Sci. 531, 29–39 (1988)
Goodknight R.C., Klikoff W.A., Fatt I.: Non-steady-state fluid flow and diffusion in porous media containing dead-end pore volume. J. Phys. Chem. 64, 1162–1168 (1960)
Hrabe J., Hrabĕtová S., Segeth K.: A model of effective diffusion and tortuosity in the extracellular space of the brain. Biophys. J. 87(3), 1606–1617 (2004)
Hrabĕtová S., Hrabe J., Nicholson C.: Dead-space microdomains hinder extracellular diffusion in rat neocortex during ischemia. J. Neurosci. 23(23), 8351–8359 (2003)
Hrabĕtová S., Masri D., Tao L., Xiao F., Nicholson C.: Calcium diffusion enhanced after cleavage of negatively charged components of brain extracellular matrix by chondroitinase ABC. J. Physiol. 587(Pt 16), 4029–4049 (2009)
Hrabĕtová S., Nicholson C.: Contribution of dead-space microdomains to tortuosity of brain extracellular space. Neurochem. Int. 45(4), 467–477 (2004)
Hrabĕtová S., Nicholson C.: Biophysical properties of brain extracellular space explored with ion-selective microelectrodes, integrative optical imaging and related techniques. In: Michael, A.C., Borland, L.M. (eds.) Electrochemical Methods for Neuroscience, pp. 167–204. CRC Press, Taylor Francis Group, Boca Raton (2007)
Kaur G., Hrabĕtová S., Guilfoyle D.N., Nicholson C., Hrabe J.: Characterizing molecular probes for diffusion measurements in the brain. J. Neurosci. Methods 171(2), 218–225 (2008)
Nicholson C.: Ion-selective microelectrodes and diffusion measurements as tools to explore the brain cell microenvironment. J. Neurosci. Meth. 48(3), 199–213 (1993)
Nicholson C.: Diffusion and related transport properties in brain tissue. Rep. Prog. Phys. 64, 815–884 (2001)
Nicholson C., Phillips J.M.: Ion diffusion modified by tortuosity and volume fraction in the extracellular microenvironment of the rat cerebellum. J. Physiol. 321, 225–257 (1981)
Nicholson C., Syková E.: Extracellular space structure revealed by diffusion analysis. Trends Neurosci. 21(5), 207–215 (1998)
Nicholson C., Tao L.: Hindered diffusion of high molecular weight compounds in brain extracellular microenvironment measured with integrative optical imaging. Biophys. J. 65(6), 2277–2290 (1993)
Ogston A.G., Preston B.N., Wells J.D.: On the transport of compact particles through solutions of chain-polymers. Proc. R. Soc. Lond. A 333, 297–316 (1973)
Parker K.H., Winlove C.P., Maroudas A.: The theoretical distributions and diffusivities of small ions in chondroitin sulphate and hyaluronate. Biophys. Chem. 32(2–3), 271–282 (1988)
Prokopová-Kubinová Š., Vargová L., Tao L., Ulbrich K., Šubr V., Syková E., Nicholson C.: Poly[N-(2-hydroxypropyl)methacrylamide] polymers diffuse in brain extracellular space with same tortuosity as small molecules. Biophys. J. 80(1), 542–548 (2001)
Rice M.E., Nicholson C.: Measurement of nanomolar dopamine diffusion using low-noise perfluorinated ionomer coated carbon fiber microelectrodes and high-speed cyclic voltammetry. Anal. Chem. 61, 1805–1810 (1989)
Rice M.E., Okada Y.C., Nicholson C.: Anisotropic and heterogeneous diffusion in the turtle cerebellum: implications for volume transmission. J. Neurophysiol. 70(5), 2035–2044 (1993)
Rodriguez-Carvajal M.A., Imberty A., Perez S.: Conformational behavior of chondroitin and chondroitin sulfate in relation to their physical properties as inferred by molecular modeling. Biopolymers 69(1), 15–28 (2003)
Siegel R.A., Langer R.L.: A new Monte Carlo approach to diffusion in constricted porous geometries. J. Colloid Interface Sci 109(2), 426–440 (1986)
Stiles J.R., Bartol T.M.: Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In: De Schutter, E. (ed.) Computational Neuroscience: Realistic Modeling for Experimentalists, pp. 87–127. CRC Press, London (2001)
Stiles J.R., Van Helden D., Bartol T.M. Jr., Salpeter E.E., Salpeter M.M.: Miniature endplate current rise times < 100 ms from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle. Proc. Natl. Acad. Sci. U.S.A. 93(12), 5747–5752 (1996)
Syková E., Nicholson C.: Diffusion in brain extracellular space. Physiol. Rev. 88(4), 1277–1340 (2008)
Tao A., Tao L., Nicholson C.: Cell cavities increase tortuosity in brain extracellular space. J. Theor. Biol. 234(4), 525–536 (2005)
Tao L., Nicholson C.: The three-dimensional point spread functions of a microscope objective in image and object space. J. Microsc. Oxf. 178(Part 3), 267–271 (1995)
Tao L., Nicholson C.: Diffusion of albumins in rat cortical slices and relevance to volume transmission. Neuroscience 75(3), 839–847 (1996)
Tao L., Nicholson C.: Maximum geometrical hindrance to diffusion in brain extracellular space surrounding uniformly spaced convex cells. J. Theor. Biol. 229(1), 59–68 (2004)
Thorne R.G., Hrabĕtová S., Nicholson C.: Diffusion of epidermal growth factor in rat brain extracellular space measured by integrative optical imaging. J. Neurophysiol. 92(6), 3471–3481 (2004)
Thorne R.G., Lakkaraju A., Rodriguez-Boulan E., Nicholson C.: In vivo diffusion of lactoferrin in brain extracellular space is regulated by interactions with heparan sulfate. Proc. Natl. Acad. Sci. U.S.A. 105(24), 8416–8421 (2008)
Thorne R.G., Nicholson C.: In vivo diffusion analysis with quantum dots and dextrans predicts the width of brain extracellular space. Proc. Natl. Acad. Sci. U.S.A. 103(14), 5567–5572 (2006)
Xiao F., Nicholson C., Hrabe J., Hrabĕtová S.: Diffusion of flexible random-coil dextran polymers measured in anisotropic brain extracellular space by integrative optical imaging. Biophys. J. 95(3), 1382–1392 (2008)
Yamaguchi Y.: Lecitans: organizers of the brain extracellular matrix. Cell. Mol. Life Sci. 57, 276–289 (2000)
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Gabriel Wittum.
Rights and permissions
About this article
Cite this article
Nicholson, C., Kamali-Zare, P. & Tao, L. Brain extracellular space as a diffusion barrier. Comput. Visual Sci. 14, 309–325 (2011). https://doi.org/10.1007/s00791-012-0185-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00791-012-0185-9