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Front. Bioeng. Biotechnol. | doi: 10.3389/fbioe.2018.00131

Microenvironmental rigidity of 3D scaffolds and influence on Glioblastoma cells: A biomaterial design perspective

  • 1Istituto di Nanotecnologia (NANOTEC), Italy
  • 2Consiglio Nazionale Delle Ricerche (CNR), Italy

With the increasing number of studies on three-dimensional (3D) scaffolds for cell culture one question, which remains desperately unanswered, is which parameters can ideally define a 3D culture model? Key micro-environmental cues involve a balanced complexity of spatial configuration of cells, chemical and biomechanical gradients, ECM, topography, hypoxia and cell–cell interactions. Moreover, the choice depends on the research question to be addressed. For a brain-like environment, the degree of chemical or physical cross-linking of any set of material, is renowned to affect the porosity, density, and stiffness of the scaffold, which constitute individually important considerations in glioblastoma (GBM) tissue engineering. A significant role is played by the scaffold’s stiffness, which has important implications for development, differentiation, disease, and regeneration. However, reaction to more physiologically relevant 3D environments are still not well understood. Current work in literature to whether GBM brain cancer cell migration and invasion are rigidity-sensitive or not are still contradictory. For example, GBM cell invasion was shown to be inhibited on soft brain-like matrices, whereas invasion was promoted on harder matrices. Instead, primary patient-derived GBM lines showed a rigidity-independent behaviour thus.
Here, we discuss the properties and design of 3D micro-topographic scaffolds, for brain tissue engineering. We address the rigidity-response of different GBM cell lines to different rigidities. Furthermore, we focus on the challenges and where future research is directed in this rapidly advancing field.

Keywords: 3D scaffolds; Glioblastoma; Stiffness

Glioblastoma (GBM), or grade IV glioma, is an extremely aggressive tumour that infiltrates through the brain leaving the patient with a median survival time from 12 to 15 months (Ostrom et al., 2013). Individual aspects of the microenvironment features play a critical role on GBM cells dynamics and treatment resistance (Bellail et al., 2004; Zamecnik, 2005; Calabrese et al., 2007). Because of GBM’s aggressive and invasive behaviour, inhibition of GBM migration is envisaged as an important therapeutic objective (Bravo-Cordero et al., 2012; Wells et al., 2013). However current models fail to account for the complex brain microenvironment. The demand of preclinical models that can faithfully recapitulate the clinical scenario may bridge the discrepancy between preclinical and clinical data and aid to develop more effective treatments.
The extracellular matrix (ECM) of the GBM microenvironment is constitutively composed of the polysaccharide hyaluronic acid (HA), and in a distinctive minor degree of tenascin-C, collagen IV and V, fibronectin and laminin (Giese and Westphal, 1996; Rape et al., 2014). Also, typically with high glioma grade, the HA’s cellular receptor CD44 is overexpressed, suggesting that CD44-enriched cells invade more efficiently the brain parenchyma (Bellail et al., 2004). GBM malignancy is furthermore promoted through interactions with the other aforementioned ECM components through different biochemical pathways (Sarkar et al., 2006; Lathia et al., 2012) which trigger an increase of the concentration of the non-cellular components (Bellail et al., 2004; Lathia et al., 2012). This increased density of the tumour ECM consequently increases the mechanical stiffness of the microenvironment (Ananthanarayanan et al., 2011; Wiranowska, 2011; Pathak and Kumar, 2012; Pedron and Harley, 2013; Kim and Kumar, 2014; Umesh et al., 2014; Heffernan et al., 2015). Evidence that cell behaviour is influenced by matrix stiffness has led to many studies of variation of the mechanical stiffness and corresponding cell responses in 2D cultures. This response of cells to gradients of the microenvironmental stiffness is renowned as mechanotaxis or durotaxis (Lo, 2000; Cortese et al., 2009; Palama et al., 2012; Palamà, 2016).
However, 2D platforms do not adequately mimic the in vivo tumour environment. Recent work has therefore focused on 3D scaffolds and matrix influence on cells with different materials and cells, as reported in Table 1. 3D culture platforms can promote cell–cell and cell–matrix interactions within a diffusion-limited environment, which instead cannot be obtained within in a 2D culture. But, as with 2D cultures, inconsistencies on how scaffold stiffness affect cell proliferation and influence drug delivery and treatment resistance are reported in literature (Wang et al., 2014; Heffernan et al., 2015; Pedron et al., 2015; Donglai Lv, 2016; Kui et al., 2016; Palamà et al., 2017). In order to tune the mechanical properties of different materials the most common method is to alter the crosslink density or base polymer concentration which consequently alters different parameters such as the ECM architecture, stiffness, pore size, diffusion of soluble factors of the scaffolds and ligand density. A 3D culture platform that aims to mimic the native GBM microenvironment should also contain HA. Yet pure HA lacks mechanical strength and the ability to promote cell adhesion due to its anionic properties (Wang et al., 2012). Moreover, it does not allow control over mechanical stiffness. These downsides have been partly overcome by using synthetic ECM polymers (Lutolf and Hubbell, 2005; Seliktar, 2012). One semi-synthetic material, predominantly used, is HA-based hydrogels functionalized to favour cell adhesion. In fact, HA-based hydrogels allow to independently tune the stiffness of the scaffold. For example, Ananthanarayanan studied HA gels of varying stiffness embedded with GBM spheroids and corroborated that their invasive capacity and morphological patterns were similar to what was seen in vivo in human brain slices, in opposition to glioma cells cultured in 2D and 3D collagen contexts (Ananthanarayanan et al., 2011). Differences were theorized to be related to the variation of expression of CD44. This was confirmed by Harley and co-workers, whom identified CD44 as a key driver of glioma malignancy with cells encapsulated in gelatin and PEG-based hydrogels grafted with a HA hydrogel network (Pedron et al., 2013). Analogous observations were made by Erikson’s, using a porous chitosan–hyaluronic acid (CHA) scaffolds of different stiffness, obtained varying the chitosan content. With a higher polymer content, the pore walls were thicker, with reduced interconnections between pores as well as the pore size (Erickson et al., 2018). The morphology of the cell aggregates changed with the stiffness as well as the expression of drug resistance, hypoxia, and invasion-related genes. Discrepancies with other studies showed to be drug/toxin type dependent (Mih et al., 2011). 3D alginate-based scaffolds of varying stiffness were used to investigate the GBM response to cytotoxic compounds (Zustiak et al., 2016). The diffusivity in all alginate hydrogels studied was showed to be the same but the cells showed to be more resistant to drugs in a stiffer matrix as compared to the softer scaffolds. Many differences of reported works showed also to be cell type dependent (Zustiak et al., 2014). For example, most reports use dissociated single cells (Hachet et al., 2012). A stiffness cell type dependency was observed with glioblastoma stem-like cells (GSC) on 2D Matrigel coated polyacrylamide matrices of varying stiffness (200 Pa– 50 kPa) and also within soft (~400 Pa) 3D Matrigel hydrogels. This work also assumed that neuronal phenotypes favoured soft matrices while astrocytic phenotypes supported stiffer substrates (Grundy et al., 2016)-(Saha et al., 2008). Hybrid scaffolds of HA and collagen I, have been used to investigate on migration of a few patient-derived GBM neurosphere lines (Rao et al., 2013; Cha et al., 2016). Rao et al. used interpenetrating networks of hydrogels and collagen I to evaluate effects of HA content on cell migration. They showed that adhesion and speed of OSU-2 GBM cells decreased with increasing of the HA concentration. In contrast, Kim’s group demonstrated that addition of HA to collagen I hydrogels facilitated cell migration. Multicellular spheroids were used in 3D engineered hydrogel networks of collagen I and compared with 2D tissue culture plates coated with HA. Here too, migration of cells were showed to be favoured on softer scaffolds in contrast to 2D cultures (Ulrich et al., 2010). Aggregated cultures would mimic better the GBM microenvironment allowing cell–cell contacts and collective migration. Non-adherent cultures lack the cell–matrix interactions present in the tumour stroma, whereas complex spherical cancer models (i.e. nonadherent cancer cell line-derived spheroids, or spheroids derived from primary tumour dissociation) can promote cell–cell interactions. Patient-derived cells cultured as neurospheres is a significant advance respect to glioma cell lines, however they do not accurately reproduce the original tumour composition due to heterogeneity loss and lack of an adhesive matrix. A solution could be represented by GBM organoids (Hubert et al., 2016), although these require months for generation while neurosphere cultures can be established within only few weeks, thus becoming useless in aid of patient treatment and not necessarily being an improvement to the patient outcomes (Oh et al., 2014).
Many reports have involved also incorporation of degradable polymers, such as matrix metalloprotease (MMP) or hyaluronidase-susceptible sites, which has shown to facilitate cell migration and to support degradation of scaffolds over time. However also contrasting results have been reported on how mechanics influence MMP secretion. For example, an increase of MMP-9 production in hyaluronic acid-based hydrogels with increasing stiffness was reported by Pedron’s group while Wang and colleagues described an opposite behaviour (Pedron et al., 2013; Wang et al., 2014; Pedron et al., 2015).
Although these studies have confirmed an influence of the mechanical properties on cell response, only a few works report a selected degree of decoupling of the mechanical properties, porosity and/or biochemical cues. The interference of other compounding stimuli in the design of functional cell culture substrates should be minimized if not isolated. Changes in the ligand density and the pore size of the matrix, may obstruct migration of cells and alter solute diffusion (Shu et al., 2002). For example, Cha used a different molecular weight of HA to simply coat the collagen I fibres without modifications and crosslinking (Cha et al., 2016). A higher molecular weight and 3D structure of the polymer may have induced different cell responses. Rao’s work reported a high degree of thiolation, which may have altered the bioactivity of the substrate (Rao et al., 2013). Kumar and co-workers work managed to decouple the effects by assembling hydrogel networks of collagen I and agarose and increasing the stiffness by increasing the concentration of nonadhesive agarose while keeping collagen I levels constant (Ulrich et al., 2010). However, increasing the concentration of agarose may result in smaller pores that restricted migration on stiffer hydrogels. Moreover, the presence of the agarose interfering with collagen fibre deformation and bundling, may have thereby restricted local ability of tumour cells to stiffen their microenvironment. Kumar’s group also engineered a hydrogel ECM platform in which biochemical and biophysical properties may be varied independently and simultaneously, suggesting that adipogenic differentiation was mainly favoured on soft matrix conditions, whereas osteogenic differentiation was preferably found on the stiff (Kilian and Mrksich, 2012; Rape et al., 2015). Divergences however, may be related to the fact the normal brain ECM is mostly composed of a dense non-fibrillar matrix based on a HA-proteoglycan-tenascin network, lacking fibrillar collagens (Thorne and Nicholson, 2006).
In conclusion there is still no existing artificial GBM microenvironment which can replace an in vivo model. It is essential to ask if it is worth to complicate the ECM environment and which definite parameters are at least required to achieve a physiologically relevant model ex vivo? Tuning matrix stiffness would allow investigation of cell behaviour during tumorigenesis thereby providing an important tool to target and investigate the more effective therapy at different stages of cancer progression. This invites further study and highlights the importance of conducting parallel measurements using spheroid cell lines in highly multiplexed conditions as well as comparisons with patient outcomes. Treatment resistance should be also investigated.

Keywords: 3D scaffolds, Glioblastoma, stiffness, Neurosphere, microenvironment

Received: 01 Aug 2018; Accepted: 03 Sep 2018.

Edited by:

Gianni Ciofani, Politecnico di Torino, Italy

Reviewed by:

Piergiorgio Gentile, Newcastle University, United Kingdom
Luca Ceseracciu, Fondazione Istituto Italiano di Technologia, Italy
Giulia Suarato, Fondazione Istituto Italiano di Technologia, Italy  

Copyright: © 2018 Palamà, D'Amone and Cortese. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
PhD. Ilaria Elena Palamà, Istituto di Nanotecnologia (NANOTEC), Lecce, Italy,
PhD. Barbara Cortese, Consiglio Nazionale Delle Ricerche (CNR), Rome, 56124, Lazio, Italy,