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. 2024 May 23;7(1):617.
doi: 10.1038/s42003-024-06329-9.

The role of elasticity on adhesion and clustering of neurons on soft surfaces

Giovanni Marinaro  1 , Luigi Bruno  2 , Noemi Pirillo  3 , Maria Laura Coluccio  3 , Marina Nanni  4 , Natalia Malara  5 , Edmondo Battista  6 , Giulia Bruno  7 , Francesco De Angelis  7 , Laura Cancedda  4 , Daniele Di Mascolo  8   9 , Francesco Gentile  10
Affiliations

The role of elasticity on adhesion and clustering of neurons on soft surfaces

Giovanni Marinaro et al. Commun Biol. .

Abstract

The question of whether material stiffness enhances cell adhesion and clustering is still open to debate. Results from the literature are seemingly contradictory, with some reports illustrating that adhesion increases with surface stiffness and others suggesting that the performance of a system of cells is curbed by high values of elasticity. To address the role of elasticity as a regulator in neuronal cell adhesion and clustering, we investigated the topological characteristics of networks of neurons on polydimethylsiloxane (PDMS) surfaces - with values of elasticity (E) varying in the 0.55-2.65 MPa range. Results illustrate that, as elasticity increases, the number of neurons adhering on the surface decreases. Notably, the small-world coefficient - a topological measure of networks - also decreases. Numerical simulations and functional multi-calcium imaging experiments further indicated that the activity of neuronal cells on soft surfaces improves for decreasing E. Experimental findings are supported by a mathematical model, that explains adhesion and clustering of cells on soft materials as a function of few parameters - including the Young's modulus and roughness of the material. Overall, results indicate that - in the considered elasticity interval - increasing the compliance of a material improves adhesion, improves clustering, and enhances communication of neurons.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Experimental set-up.
Using micromachining and replica molding techniques, we fabricated soft PDMS substrates for cell culture and growth. The elasticity of the substrates was varied in the 0.55−2.65MPa interval (a). After detachment from the originating template (b) PDMS surfaces were incubated with primary neuronal cells (c) and placed on the stage of a fluorescence microscopy for investigation and analysis (d). Fluorescence images of cells were processed using networks science, topology analysis, and information theory techniques (e). For each considered value of elasticity, we determined the topological attributes of neuronal cell networks forming on the substrates over time, made and estimate of the amount of information exchanged in the net, and measured cell activity (f).
Fig. 2
Fig. 2. Characterization of soft PDMS substrates.
PDMS surfaces were mechanically characterized using conventional elongation tests (a) and micro-indentation techniques (b) to find the elasticity of the substrate as a function of the ratio of liquid PDMS phase to the binding agent r: the larger the value of r, the smaller the value of elasticity. While results from the elongation test and micro-indentation of samples are consistent, however the latter overestimates the values of elasticity of a factor of 1.3 (c). Using laser interferometry, we measured the topography of PDMS surface represented here in the form of a linear and 2D density plot (d) and of a 3D plot (e). The average (Ra) and root mean square (Rrms) values of roughness were determined from morphological data. Values of Ra greater than 0 (Ra~20nm) evidence that at the nanoscale the PDMS surface is not flat (f). For different surface-preparations the values of Ra deviate marginally from the central value Ra~20nm (g). Contact angle (CA) measurements of samples indicate the PDMS surface is moderately hydrophilic with values of CA<80∘ for all considered PDMS/curing agent ratios and values of elasticity E<2.65MPa (h). Data in Fig. 2f are quantitatively described by a whisker box plot, where the lower and upper boundary corresponds to the 25% and 75% quartiles of the distribution, while the central band marks the median value (sample size~50). Data in Fig. 2g are represented by mean ± standard deviation (sample size=10).
Fig. 3
Fig. 3. Soft PDMS surfaces as substrates for neuronal cell growth.
Neuronal cells were plated on soft PDMS surfaces and followed over time. At fixed times, growth was stopped, cells immobilized and examined by fluorescence microscopy. Image shows how cell number and layout is affected by substrate elasticity: cell-growth is hampered on substrates with larger values of elasticity (1.88MPa, rigth) compared to substrates with smaller ones (0.55 MPa, left) (a). The number of neuronal cells N measured on substrates 24h from incubation illustrates that N shows a nearly inverse relationship with E in the 0.55−2.65MPa interval (b). The negative correlation between N and E is exhibited for all considered times of incubation – 24 (c) 48 (d) 72 (e) and 96 h (f). Diagrams illustrate how the number of cells varies as a function of substrate elasticity (time) for all the times of the analysis (substrate elasticity) (g, h). Data in Fig. 3b–f are represented by mean ± standard deviation (sample size~50 for each data point). Data in Fig. 3g, h are quantitatively described by a whisker box plot, where the lower and upper boundary corresponds to the 25% and 75% quartiles of the distribution, while the central band marks the median value (sample size~50 for each data point).
Fig. 4
Fig. 4. Networks of neuronal cells on soft PDMS surfaces.
Visual examination of fluorescence images of neuronal cells suggests that surface stiffness can influence cell-clustering (a). We used image-analysis algorithms and networks-science to examine quantitatively the topological characteristics of cell-networks on the substrates. Fluorescence images of cells were gray-scale converted (b) and processed to extract cell-centers (c). Then, cell-centers were linked using the Waxman algorithm and a density-based rule (d). The small world coefficient (SW, a topological measure of networks) of neuronal-cell graphs as a function of surface elasticity, determined 24h from culture – the diagram suggests that the ability of cells to form structured networks decreases with E (e). The small world coefficient of neuronal-cell graphs as a function of time for a fixed value of the Young’s modulus E=2.65MPa (f). Correlation between the SW coefficient of neuronal cell networks forming on a substrate and the substrate elasticity, for different values of culture time: 24, 48, 72, 96h (g). Correlation between the SW coefficient and time, for different values of substrate elasticity: 0.55, 1, 1.88, 2.65MPa (h). Data in Fig. 4e–h are represented by mean ± standard deviation (sample size~50 for each data point).
Fig. 5
Fig. 5. Statistical analysis and simulations.
We used analysis of Variance (ANOVA) test to compare the small-world-ness of networks formed on different surfaces. Multiple-comparison post hoc Bonferroni test (a series of t-tests performed on each pair of groups corrected by the number of groups) indicates which samples means are significantly different from the control, i.e. random neuronal-networks cultured on rigid surfaces with SW=1. In the diagram, sample-means that are different at some significance level α, are marked by a bar. If α is less than 0.05, it is flagged with 1 star (*). If α is less than 0.01, it is flagged with 2 stars (**) (a). We used information theory to estimate the amount of information transported in networks of neuronal cells. We built connected graphs from fluorescence images of cells on the substrates (b) and examined how an initial disturbance propagates in those networks - resulting space and time patterns of signals were used to estimate the information processed over time in each node (c). Results of this theoretical analysis: total information I elaborated in neuronal cell graphs cultured on soft PDMS surfaces, as a function of surface elasticity (d). Data in Fig. 5a and d are represented by mean ± standard deviation. The sample size for data reported in Fig. 5d is 10.
Fig. 6
Fig. 6. Measuring neuronal-cell activity.
We used fMCI (functional multicalcium imaging) to measure the activity of neuronal cells on soft PDMS substrates. In the technique, calcium ions within neuronal cell networks are selectively targeted with a fluorescent compound - its transients (a, b) are then associated to the generation and release of action potentials in the system. Intensity of calcium-related fluorescence vs time measured at 6 different sites (neurons) of neuronal networks cultured on substrates with decreasing values of elasticity: 0.55, 1 and 2.65 MPa (c). Raster-plot of fluorescence intensity signals shown in c, d. Density of peaks of the fluorescence-intensity signals measured in neuronal-networks as a function of substrate elasticity (e). Data in Fig. 6e are represented by mean ± standard deviation (sample size = 12).
Fig. 7
Fig. 7. Understanding the mechanisms of cell adhesion and clustering on soft materials.
Schematics of the process of adhesion of a cell to rough soft materials (a). The adhesion of a cell to a surface is mediated by cell-adhesion molecules and steric interactions with a specific energy of adhesion γ. Following the Johnson, Kendall and Roberts model, cell-membrane adhesive forces (FJKR) depend on γ and the radius of curvature (χ) of the substrate (b). Since PDMS material is a non-flat surface, χ is not uniform on the substrate (c). As a result, following adhesion PDMS surface is loaded with non-uniform forces distributed irregularly on the surface (d). Nonuniform forces deform unevenly the PDMS surface exacerbating the original roughness (e). An increased value of roughness has, as a consequence, the increase of the overall energy density of adhesion at the cell-surface interface (f). An enhanced adhesion generates in turn, as a collateral effect, lateral forces in the system that perturb cell-equilibrium and cause cell aggregation and clustering (g).
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References

    1. Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science. 1999;283:381–387. - PubMed
    1. El-Gaby M, et al. An emergent neural coactivity code for dynamic memory. Nat. Neurosci. 2021;24:694–704. - PMC - PubMed
    1. Ma J, Tang J. A review for dynamics of collective behaviors of network of neurons. Sci. China Technol. Sci. 2015;58:2038–2045.
    1. Ma L, Hyman JM, Lindsay AJ, Phillips AG, Seamans JK. Differences in the emergent coding properties of cortical and striatal ensembles. Nat. Neurosci. 2014;17:1100–1106. - PMC - PubMed
    1. Ashourvan A, et al. Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states. Commun. Biol. 2021;4:210. - PMC - PubMed
-