Cation-Specific Effects on the Self-Assembly of Collagen Molecules Mediated by Acetate on Mica Surface Observed with Atomic Force Microscopy

Abstract

The well-organized collagen layers on mica surface have drawn extensive attention for its essential applications and studies on the process of self-assembly as a model system. In this work, collagen extracted from fish scales by acid-base method was used to explore the self-assembly characters, and atomic force microscopy was applied to observe the collagen assembled on mica surface mediated by acetate with four different cations, including K + , Na + , Mg 2+ , and Ca 2+ . It showed that cations might influence the interaction between collagen fibrils and mica surface at high ionic concentration. And a similar network structure was acquired with uniform pore size for four kinds of acetates; nearly ranged collagen fibrils in the same direction were collected in Mg 2+ solutions, while flat films with some fibrils were achieved in K + solutions. The Hofmeister series and Collins' model were adapted to explain the effects of cations and acetate on the self-assembly of collagen. These results and analysis would be helpful for directing the pattern of collagen assembly on a solid surface with a potential application in food science and engineering.

Combined effects of glutaraldehyde and riboflavin/uv365 on the self-assembly of type I collagen molecules observed with atomic force microscopy

“…Cross-linking techniques have been reported to enhance the mechanical properties of collagen. [4,5] Glutaraldehyde is a kind of common cross-linking agent applied in protein chemistry and tissue engineering. [6] The free amino acid residues of collagen can react in cross-linking with glutaraldehyde and change the structural stability of collagen assembly by adding the glutaraldehyde to collagen solution.…”

Simplified computational model for generating biological networks

“…Images of biological networks produced by Barnard et al, Bos et al, Wang et al, and Yurchenco et al taken between 1984 and 2017 were analysed. [32][33][34][35][36] A subset of the results are shown in Table 1, and the complete data can be found in the ESI. † The experimental networks show a mean node coordination hki ¼ 2.78, with a standard deviation of 0.237.…”

“…16 Although the polygon side count distributions (characterised by the mean number of sides, hni and its second moment, m 2 (n)) have been extensively used to characterise more regular Fig. 1 Images of biological network from (left to right) Barnard et al, 32 Bos et al, 33 and Wang et al 34 respectively. The lower panels show networks generated by our method that appear similar, from left to right with {k l , K q } ¼ {0.01, 0.01}, {10, 0.01} and {100, 100}.…”

Simplified Computational Model for Generating Biological Networks

“…Images of biological networks produced by Barnard et al, Bos et al, Wang et al, and Yurchenco et al taken between 1984 and 2017 were analysed [32][33][34][35][36] . A subset of the results are shown in Table 1, and the complete data can be found in the Supplementary Information.…”

“…Missing edges, either from low contrast or when terminated by an edge, leads polygons to be counted as too large or to not be counted at all respectively. These issues are more significant in smaller or lower-detail im- Barnard et al 32 2.961 0.378 5.574 -0.109 135 Bos et al 33 2.780 0.335 5.825 -0.209 40 Wang et al 34 2.945 0.805 5.735 -0.057 498 Yurchenco and Furthmayr 35 2.721 0.284 6.254 -0.181 61 Yurchenco and Furthmayr 35 2.667 0.287 6.593 -0.184 54 Table 1 Statistics collected for a number of images of biological networks (imaged under different conditions) showing the mean node coordination, k , the second moment, µ 2 (k), the mean number of polygon edges, n , the number of polygons in each configuration, N, and the assortativity, r. Two different analyses of the networks imaged by Yurchenco et al 35 are shown in order to highlight the sensitivity of the obtained metrics. A more detailed table is available in the supplementary information.…”