In Situ Quantification of Surface Chemistry in Porous Collagen Biomaterials
Cells inside a 3D matrix (such as tissue extracellular matrix or biomaterials) sense their insoluble environment through specific binding interactions between their adhesion receptors and ligands present on the matrix surface. Despite the critical role of the insoluble matrix in cell regulation, the...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2016
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| Online Access: | http://hdl.handle.net/1721.1/105254 https://orcid.org/0000-0003-4698-6488 https://orcid.org/0000-0003-0151-708X |
| Summary: | Cells inside a 3D matrix (such as tissue extracellular matrix or biomaterials) sense their insoluble environment through specific binding interactions between their adhesion receptors and ligands present on the matrix surface. Despite the critical role of the insoluble matrix in cell regulation, there exist no widely-applicable methods for quantifying the chemical stimuli provided by a matrix to cells. Here, we describe a general-purpose technique for quantifying in situ the density of ligands for specific cell adhesion receptors of interest on the surface of a 3D matrix. This paper improves significantly the accuracy of the procedure introduced in a previous publication by detailed marker characterization, optimized staining, and improved data interpretation. The optimized methodology is utilized to quantify the ligands of integrins α[subscript 1]β[subscript 1], α[subscript 2]β[subscript 1] on two kinds of matched porous collagen scaffolds, which are shown to possess significantly different ligand density, and significantly different ability to induce peripheral nerve regeneration in vivo. Data support the hypothesis that cell adhesion regulates contractile cell phenotypes, recently shown to be inversely related to organ regeneration. The technique provides a standardized way to quantify the surface chemistry of 3D matrices, and a means for introducing matrix effects in quantitative biological models. |
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