Estimating the 3D pore size distribution of biopolymer networks from directionally biased data

Nadine R. Lang, Stefan Münster, Claus Metzner, Patrick Krauss, Sebastian Schürmann, Janina Lange, Katerina E. Aifantis, Oliver Friedrich, Ben Fabry

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

The pore size of biopolymer networks governs their mechanical properties and strongly impacts the behavior of embedded cells. Confocal reflection microscopy and second harmonic generation microscopy are widely used to image biopolymer networks; however, both techniques fail to resolve vertically oriented fibers. Here, we describe how such directionally biased data can be used to estimate the network pore size. We first determine the distribution of distances from random points in the fluid phase to the nearest fiber. This distribution follows a Rayleigh distribution, regardless of isotropy and data bias, and is fully described by a single parameter - the characteristic pore size of the network. The bias of the pore size estimate due to the missing fibers can be corrected by multiplication with the square root of the visible network fraction. We experimentally verify the validity of this approach by comparing our estimates with data obtained using confocal fluorescence microscopy, which represents the full structure of the network. As an important application, we investigate the pore size dependence of collagen and fibrin networks on protein concentration. We find that the pore size decreases with the square root of the concentration, consistent with a total fiber length that scales linearly with concentration.

Original languageEnglish (US)
Pages (from-to)1967-1975
Number of pages9
JournalBiophysical Journal
Volume105
Issue number9
DOIs
StatePublished - Nov 5 2013

ASJC Scopus subject areas

  • Biophysics

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    Lang, N. R., Münster, S., Metzner, C., Krauss, P., Schürmann, S., Lange, J., Aifantis, K. E., Friedrich, O., & Fabry, B. (2013). Estimating the 3D pore size distribution of biopolymer networks from directionally biased data. Biophysical Journal, 105(9), 1967-1975. https://doi.org/10.1016/j.bpj.2013.09.038