In Vitro Testing of Biomaterials for Neural Repair: Focus on Cellular Systems and High-Content Analysis

Abstract

Biomimetic materials are designed to stimulate specific cellular responses at the molecular level. To improve the soundness of in vitro testing of the biological impact of new materials, appropriate cell systems and technologies must be standardized also taking regulatory issues into consideration. In this study, the biological and molecular effects of different scaffolds on three neural systems, that is, the neural cell line SH-SY5Y, primary cortical neurons, and neural stem cells, were compared. The effect of poly(L-lactic acid) scaffolds having different surface geometry (conventional two-dimensional seeding flat surface, random or aligned fibers as semi3D structure) and chemical functionalization (laminin or ECM extract) were studied. The endpoints were defined for efficacy (i.e., neural differentiation and neurite elongation) and for safety (i.e., cell death/survival) using high-content analysis. It is demonstrated that (i) the definition of the biological properties of biomaterials is profoundly influenced by the test system used; (ii) the definition of the in vitro safety profile of biomaterials for neural repair is also influenced by the test system; (iii) cell-based high-content screening may well be successfully used to characterize both the efficacy and safety of novel biomaterials, thus speeding up and improving the soundness of this critical step in material science having medical applications.

Keywords: Good Laboratory Practice guidelines; neural cell lines; neural primary culture; neural stem cells high-content analysis; poly(lactic acid).

FIG. 1.

Scaffold characterization: morphology and cell seeding. SEM micrographs of (A) semi3D-random scaffold; (B) semi3D-aligned scaffold; (C) semi3D-random scaffold coated with laminin; (D) semi3D-random scaffold coated with Cultrex. Scale bar: 10 μm. (E–P) Conventional fluorescence micrographs showing the SH-SY5Y cell deposition (labeled by β-III-tubulin) on the semi3D uncoated (E–J) and laminin-coated (K–P; laminin is in red) PLLA scaffolds, having random (E–G; K–M) and aligned (H–J; N–P) topography (E, H, K, N); HCS visualization of the same cell preparations (semi3D, uncoated, random: F,G; semi3D, uncoated, aligned: I,J; semi3D, laminin-coated, random: L,M; semi3D, laminin-coated, aligned: O,P). HCS, high-content analysis; PLLA, poly(L-lactic acid); SEM, scanning electron microscopy.

FIG. 2.

Scaffold characterization: wettability. (A) mean WCA values; (B) representative WCA behavior for semi3D-random scaffold (white diamonds), semi3D-random scaffold laminin coated (black diamonds), PLLA-FILM (black squares) and PLLA-FILM laminin coated (white squares); representative images of the water drop are also reported. WCA, water contact angle.

FIG. 3.

Effect of different scaffolds on neural differentiation. (A) Neurite length of SH-SY5Y cells grown on different scaffolds, as measured by conventional quantitative microscopy and HCS. (B) Neurite length of SH-SY5Y cell line, as measured by HCS; (C) Neurite length of primary cortical neurons, as measured by HCS; (D) Neurite length of neurons derived from NSCs, as measured by HCS. In all cell systems, conventional 2D, semi3D-random and semi3D-aligned scaffolds having different coatings are compared. Data are expressed as mean ± SD, and represent the mean of three independent experiments. Statistical analysis: two-way ANOVA (SH-SY5Y F[9, 100] = 5.165; primary cortical neurons F[14,45] = 6.317; NSCs F[18,60] = 15.40) followed by Tukey's post hoc test. Asterisks represent the difference between the semi3D group versus the relative 2D control group within the same DIV (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001), letters indicate the difference between coated semi3D groups versus the relative uncoated semi3D control group (a = p < 0.05; b = p < 0.01; d = p < 0.0001). 2D, two-dimensional seeding; SD, standard deviation.

FIG. 4.

Effect of different scaffolds in the neural and astroglial specification of NSCs. (A, B) Representative images of plated NSCs at 7DIV grown on 2D (A) and semi3D scaffolds (B), stained with β-III-tubulin (green), GFAP (red) and Hoechst (blue). (C, D) The graphs indicate the percentage of GFAP (C) and β-III-tubulin (D)-positive cell, according to the PLLA topography and coating. Data are expressed as mean ± SD, and represent the mean of three independent experiments. Statistical analysis: two-way ANOVA (GFAP F[7, 32] = 3.404; β-III-tubulin F[7, 30] = 7.136) followed by Tukey's post test. Asterisks represent the difference between the semi3D group versus the relative 2D control group within the same DIV (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).

FIG. 5.

Effect of different scaffolds on oligodendroglial specification of NSCs. (A, B) Representative images of plated NSCs at 7DIV grown on 2D (A) and semi3D scaffolds (B), stained with MBP (red) and Hoechst (blue). (C) The graph represents the percentage of MBP positive cells. Data are expressed as mean ± SD, and represent the mean of three independent experiments. Statistical analysis: two-way ANOVA (F[7, 28] = 4.739) followed by Tukey's post test. Asterisks represent the difference between the semi3D group versus the relative 2D control group within the same DIV (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).

FIG. 6.

Effect of different scaffolds on cell viability of SH-SY5H cell line (A), primary cortical neurons (B), and NSCs (C). The graphs represent the percentage of pyknotic nuclei measured using HCS technology. Data are expressed as mean ± SD, and represent the mean of three independent experiments. Statistical analysis: two-way ANOVA (n = 3; SH-SY5Y F[9, 60] = 5.682; primary cortical neurons F[14, 40] = 4.164; NSCs F[18, 60] = 23.57) followed by Tukey's post test. Asterisks represent the difference between the semi3D group versus the relative 2D control group within the same DIV (*p < 0.05; ***p < 0.001; ****p < 0.0001), letters indicate the difference between coated semi3D groups versus the relative uncoated semi3D control group (a = p < 0.05; b = p < 0.01; d = p < 0.0001).