Bioengineered 3D Brain Tumor Model To Elucidate the Effects of

Apr 8, 2014 - Here we report the development of a bioengineered 3D brain tumor model to help elucidate the effects of matrix stiffness on GBM cell fat...
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Bioengineered 3D Brain Tumor Model To Elucidate the Effects of Matrix Stiffness on Glioblastoma Cell Behavior Using PEG-Based Hydrogels Christine Wang,† Xinming Tong,‡ and Fan Yang*,†,‡ †

Department of Bioengineering, Stanford University, Stanford, California 94305, United States Department of Orthopaedic Surgery, Stanford University, Stanford, California 94305, United States

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ABSTRACT: Glioblastoma (GBM) is the most common and aggressive form of primary brain tumor with a median survival of 12−15 months, and the mechanisms underlying GBM tumor progression remain largely elusive. Given the importance of tumor niche signaling in driving GBM progression, there is a strong need to develop in vitro models to facilitate analysis of brain tumor cell-niche interactions in a physiologically relevant and controllable manner. Here we report the development of a bioengineered 3D brain tumor model to help elucidate the effects of matrix stiffness on GBM cell fate using poly(ethylene-glycol) (PEG)-based hydrogels with brain-mimicking biochemical and mechanical properties. We have chosen PEG given its bioinert nature and tunable physical property, and the resulting hydrogels allow tunable matrix stiffness without changing the biochemical contents. To facilitate cell proliferation and migration, CRGDS and a MMP-cleavable peptide were chemically incorporated. Hyaluronic acid (HA) was also incorporated to mimic the concentration in the brain extracellular matrix. Using U87 cells as a model GBM cell line, we demonstrate that such biomimetic hydrogels support U87 cell growth, spreading, and migration in 3D over the course of 3 weeks in culture. Gene expression analyses showed U87 cells actively deposited extracellular matrix and continued to upregulate matrix remodeling genes. To examine the effects of matrix stiffness on GBM cell fate in 3D, we encapsulated U87 cells in soft (1 kPa) or stiff (26 kPa) hydrogels, which respectively mimics the matrix stiffness of normal brain or GBM tumor tissues. Our results suggest that changes in matrix stiffness induce differential GBM cell proliferation, morphology, and migration modes in 3D. Increasing matrix stiffness led to delayed U87 cell proliferation inside hydrogels, but cells formed denser spheroids with extended cell protrusions. Cells cultured in stiff hydrogels also showed upregulation of HA synthase 1 and matrix metalloproteinase-1 (MMP-1), while simultaneously downregulating HA synthase 2 and MMP-9. This suggests that varying matrix stiffness can induce differential ECM deposition and remodeling by employing different HA synthases or MMPs. Furthermore, increasing matrix stiffness led to simultaneous upregulation of Hras, RhoA, and ROCK1, suggesting a potential link between the mechanosensing pathways and the observed differential cell responses to changes in matrix stiffness. The bioengineered 3D hydrogel platform reported here may provide a useful 3D in vitro brain tumor model for elucidating the mechanisms underlying GBM progression, as well as for evaluating the efficacy of potential drug candidates for treating GBM. KEYWORDS: tumor model, hydrogels, glioblastoma, tumor microenvironment, three-dimensional

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INTRODUCTION Glioblastoma (GBM) is the most common and aggressive form of primary brain tumor in adults, accounting for 60−70% of malignant glioma with an annual incidence of 14,000 cases per year in the United States alone.1 Patients with GBM often undergo aggressive therapy with a median survival of 12 to 15 months.1 Given the poor clinical outcomes of this devastating disease, there is a strong need to better understand the © 2014 American Chemical Society

Special Issue: Engineered Biomimetic Tissue Platforms for in Vitro Drug Evaluation Received: Revised: Accepted: Published: 2115

January 26, 2014 April 4, 2014 April 8, 2014 April 8, 2014 dx.doi.org/10.1021/mp5000828 | Mol. Pharmaceutics 2014, 11, 2115−2125

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Scheme 1. Glioma Cell Encapsulation in 3D Biomimetic Hydrogela

a

Hydrogel was formed from 8-arm PEG-norbornene, which was crosslinked with linear PEG-dithiol and MMP-cleavable sequence (mixed at 1:1 ratio). RGD peptide (CRGDS) was covalently linked to the hydrogel network at a final concentration of 0.914 mM. Sodium hyaluronate (HA) was added to a final concentration of 0.266% (w/v) prior to crosslinking, allowing HA to be physically entrapped after gelation. Cells were homogeneously mixed in the precursor solution at a final concentration of 0.5 M cells/mL. Hydrogel was crosslinked via UV photopolymerization (365 nm and 4 mW/cm2).

mechanisms underlying tumor progression. While previous research has largely focused on elucidating the intracellular processes that drive tumor growth, increasing evidence has highlighted the tumor microenvironment as a significant player in influencing GBM cell fate and tumor progression.2,3 The brain tumor microenvironment is a complex and dynamic system, consisting of various biochemical and mechanical cues that act in conjunction to fuel GBM cell growth and invasion. Extracellular matrix proteins (ECM), such as collagen and hyaluronic acid (HA), provide structural support, as well as influence intracellular signaling.3 Concentrations of HA have been found to be elevated in GBM patients, which can dictate glioma cell fate.4,5 Previous literature also suggests that HA can stimulate expression and secretion of proteins, including matrix metalloproteases (MMP) or osteopontin, which can drive GBM cell migration and invasion.6 In addition to biochemical cues, mechanical cues, such as matrix stiffness, can also significantly increase during tumor growth, as GBM cells actively secrete fibrous ECM proteins, including laminin, collagen, and fibronectin.3 The stiffness of GBM tissues has been reported to be 26 kPa, which is much higher than the stiffness of normal brain tissue (0.1 to 1 kPa).7,8 The current gold standard methods for studying cancer biology include using 2D monolayer culture, organotypic culture, or animal models. Culturing cells in 2D monolayer offers ease of use, but tissue culture plastic has a stiffness on the GPa scale, which is orders of magnitude higher than brain tissues. Furthermore, 2D monolayer culture fails to mimic key aspects of the tumor microenvironment, such as 3D architecture and presence of the ECM. Organotypic culture, on the other hand, is rich in ECM cues but has limited control over varying the inputs and are difficult for mechanistic studies. Animal models provide the most sophisticated model for the tumor microenvironment, but are generally lengthy to produce, costly, and only suitable for low throughput studies. Furthermore, studying human cancer cell behavior in animal models requires the use of immunocompromised animals, which eliminates the potential roles of immune cells, such as macrophages, in tumor progression. The limitations of current platforms for studying cancer biology call for the development of alternative in vitro models to facilitate analysis of tumor cellniche interactions in a physiologically relevant, controllable manner.

Biomaterials have been widely used as artificial niches to support culturing cells in 3D for tissue engineering. Hydrogels are particularly attractive for studying brain cells due to their high, tissue-mimicking water content, as well as tunable biochemical and physical properties. Furthermore, many hydrogel platforms can be formed using cytocompatible conditions and permit transport of nutrients and waste. Recent studies have employed hydrogels as in vitro models for studying brain tumor cell behavior in response to microenvironment cues and showed stiffness-dependent GBM cell motility and proliferation.9,10 Previous work have primarily employed naturally derived materials, such as collagen or hyaluronic acid (HA), to study cancer cell behavior in 3D in vitro hydrogels.10,11 Despite their highly biomimetic nature, naturally derived materials do not support independent tuning of microenvironment cues and are subject to batch-to-batch variability. In one study, Ananthanarayanan et al. used HAbased hydrogels to study GBM cell invasion as a function of matrix stiffness. To tune the matrix stiffness, HA concentration was varied, which introduced simultaneous changes in the biochemical ligand concentration sensed by GBM cells. This makes it difficult to differentiate the respective contribution of biochemical or physical cues on observed cell fate changes. In addition, to achieve broad tunable matrix stiffness, the concentration of HA used in the hydrogel exceeded that found in brain tissues.4,10 In another study, Pedron et al. used gelatin-based hydrogels to study GBM cell behavior.12 Similar to HA, gelatin lacks decoupled tunability and may be less physiologically relevant, as concentrations of fibrous ECM proteins like collagen are lower in normal brain tissue compared to connective tissue outside the central nervous system. To overcome the limitations of naturally derived polymers, recent research efforts have explored hydrogels based on synthetic polymers, such as poly(ethylene-glycol) (PEG) or polyacrylamide, as 3D in vitro cancer models that offer more control and reliability.13−15 Unlike natural polymers, synthetic polymers, such as PEG, are bioinert and present a blank slate. Biological epitopes can also be incorporated to mimic native ECM and support desirable cell fate processes, such as adhesion, proliferation, or migration. Hydrogel platforms that can capitalize on the advantages of both naturally derived and synthetic materials offer an attractive tool for engineering 3D in vitro cancer niches with enhanced biomimicry and tunability. The goal of this study was to develop and characterize a hydrogel platform with independently tunable biochemical and 2116

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mechanical cues and employs both naturally derived and synthetic materials as a 3D in vitro model for studying GBM cell-niche interactions (Scheme 1). To achieve independently tunable biochemical and mechanical properties, we employed a combination of synthetic and naturally derived materials. Hydrogel stiffness was controlled by varying concentrations of 8-arm PEG-norbornene (PEG-NB). To allow for cellmediated degradation and migration, hydrogel network was cross-linked using both linear PEG-dithiol (PEG-SH) and a MMP-cleavable sequence (CGPQGIWGQC). To facilitate cell adhesion in 3D, a cell adhesive peptide (CRGDS) was conjugated to the hydrogel network. Lastly, to mimic the brain ECM content, sodium hyaluronate (HA) (20−40 kDa) was mixed with the hydrogel precursor solution prior to crosslinking, allowing the HA to be physically entrapped after gelation. We chose thiol-ene photopolymerization to cross-link the hydrogel, as this process is cytocompatible and permits spatial and temporal control. The resulting hydrogels were characterized using mechanical testing and quantification of equilibrium swelling ratio. Using this hydrogel platform, we then studied the effect of varying matrix stiffness on modulating the cell fate of U87 cells, a commercially available GBM cell line, in 3D over 3 weeks. Cell fates in 3D hydrogels were analyzed by monitoring cell proliferation, morphology, and gene expression.

Table 1. Chemical Structures of Hydrogel Components

material testing system’s position read-out, respectively. Before each test, a preload of approximately 2 mN was applied. The upper plate was then lowered at a rate of 1% strain/sec to a maximum strain of 30%. Load and displacement data were recorded at 100 Hz. The modulus was determined for strain ranges of 10−20% from linear curve fits of the stress vs strain curve in each strain range. Swelling and Mesh Size Calculation. Acellular hydrogels (3% or 14% PEG, n = 3) prepared as above were allowed to equilibrate in PBS at room temperature overnight. The equilibrium swelling ratio Q was calculated as the ratio of the mass of the swollen hydrogel to the mass of the dry components after lyophilization. The theoretical hydrogel mesh size was calculated as done previously.18 Hydrogel Degradation. To verify that the hydrogels synthesized are MMP-degradable, acellular hydrogels (14% PEG) were prepared as above and equilibrated in PBS at 37 °C overnight. On day 0, the wet weight of each hydrogel was measured after equilibration. Additional hydrogels were used to measure the dry weight to calculate the average initial polymer concentration in the hydrogels. On days 1 through 5, hydrogels were placed in a fresh solution of collagenase (Type II, 5 U/ mL, Worthington Biochemical Corp, Lakewood, NJ, USA) or PBS overnight at 37 °C. At each time point, the wet weight of the hydrogel was measured (n = 3). The equilibrium swelling ratio Q was calculated at each time point using the initial polymer concentration in the hydrogel and the hydrogel wet weight. Cell Encapsulation in 3D Hydrogels. U87-mg/Luc +/GFP+ cells (U87-mg-df) were expanded in cell culture medium consisting of Dulbecco’s minimal essential medium (DMEM, Life Technologies, Grand Island, NY), supplemented with 10% (v/v) fetal bovine serum (FBS, Gibco, Life Technologies), 100 U/mL penicillin, and 100 μg/mL streptomycin at 37 °C in 5% CO2. To mimic the mechanical stiffness found in normal brain and GBM tissue, we chose 3% PEG (SOFT) and 14% PEG (STIFF) based on the mechanical testing results. U87-mg-df cells were trypsinized, pelleted, and resuspended in the hydrogel precursor solution (3% or 14% PEG) at a final concentration of 0.5 million cells/mL. Then 75 μL of the cell-

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EXPERIMENTAL SECTION Materials. 8-Arm PEG (MW ≈ 40 kDa) was purchased from JenKem Technology USA (Allen, TX, USA). Linear PEG (MW ≈ 1.5 kDa) was purchased from Sigma-Aldrich USA (St. Louis, MO). RGD peptide (CRGDS) was purchased from Bio Basic, Inc. (Amherst, NY, USA). Sodium hyaluronate (HA) (MW ≈ 20−40 kDa) was purchased from Lifecore Biomedical (Chaska, MN, USA). MMP-cleavable peptide (CGPQGIWGQC) was synthesized by GenScript (Piscataway, NJ, USA). All other reagents and solvents were obtained from Fisher Scientific (Pittsburgh, PA, USA) unless otherwise noted. Hydrogel Formation. 8-Arm PEG-norbornene (PEG-NB) and linear PEG-dithiol (PEG-SH) were synthesized as previously reported.16,17 To form the hydrogel network, 8Arm 40 kDa PEG-NB was mixed with linear 1.5 kDa PEG-SH and an MMP-cleavable sequence at a molar ratio of 2:3:3 in the presence of photoinitiator Igracure D2959 (0.05% w/v, Ciba Specialty Chemicals, Tarrytown, NY, USA). RGD peptide (CRGDS) was covalently linked to the hydrogel network at a final concentration of 0.914 mM. Sodium hyaluronate (20−40 kDa) was added at a final concentration of 0.266% (w/v), which was selected based on reported values of hyaluronic acid content in human brain tumor stroma.4 Chemical structures of each component are shown in Table 1. Each hydrogel sample contained 75 μL of hydrogel precursor solution, which was loaded in a cylindrical-shaped mold (3 mm in height, 5 mm in diameter). The hydrogel was exposed to UV light (365 nm and 4 mW/cm2) for 5 min at RT to induce gelation. Mechanical Testing. Acellular hydrogels (n = 3) prepared as above were allowed to equilibrate in PBS at room temperature overnight. To measure the stiffness of hydrogels, unconfined compressive tests were conducted using an Instron 5944 materials testing system (Instron Corporation, Norwood, MA). The test setup consisted of custom-made aluminum compression plates lined with PTFE to minimize friction. All tests were conducted in PBS solution at RT. Hydrogel diameter and thickness were measured using digital calipers and the 2117

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Table 2. Primer Sequences Used in RT-PCR gene

forward primer (5′ → 3′)

reverse primer (5′ → 3′)

ref

GAPDH CD44 RHAMM HAS1 HAS2 HAS3 MMP1 MMP2 MMP9 Hras Kras Nras RhoA ROCK1 ROCK2

ACAGTCAGCCCGCATCTTCTT GGTCCTATAAGGACACCCCAAAT GTTTCTGGAGCTGGCCGTC GGTGGGGACGTGCGGATC GTGGATTATGTACAGGTTTGTGA CTCTACTCCCTCCTCTATATGTC AGCTAGCTCAGGATGACATTGATG CCACTGCCTTCGATACAC TGGGCTACGTGACCTATGACAT ACATCCACCAGTACAGGGA TTCCTACAGGAAGCAAGTAG GGTGAAACCTGTTTGTTGGA GGCTGGACTCGGATTCGTT GACCTGTAACCCAAGGAGAT ATGAAGATACAGCAAAACCAGTC

CGACCAAATCCGTTGACTC AATCAAAGCCAAGGCCAAGA ACTGGTCCTTTCAATACTTCTAAAGT ATGCAGGATACACAGTGGAAGTAG TCCAACCATGGGATCTTCTT AACTGCCACCCAGATGGA GCCGATGGGCTGGACAG GAGCCACTCTCTGGAATCTTAAA GCCCAGCCCACCTCCACTCCTC TGCAGCCAGGTCACACTTGTTC CACAAAGAAAGCCCTCCCCA ATACACAGAGGAAGCCTTCG CACAGGCTCCATCACCAACA GGAAAGTGGTAGAGTGTAGG CACCTTGAATAATGACTGCTTTC

38 39 40 41 42 41 43 44 45 46 47 47 48 49 50

Technologies) per manufacturer’s instructions. Relative expression levels of target genes was determined using the comparative CT method. Target gene expression was first normalized to an endogenous gene GAPDH, followed by a second normalization to the gene expression level in the control group (3% PEG, day 1). Histology and Immunohistochemical Staining. To prepare samples for histology, cell-laden or acellular hydrogels (n = 2) were fixed in 4% paraformaldehyde (Sigma) overnight at 4 °C, incubated in PBS containing 30% sucrose overnight at 4 °C, and cyropreserved in optimum cutting temperature (OCT, Tissue-Tek) solution in liquid nitrogen. Samples were stored at −80 °C and sectioned at −20 °C. Sections were stained with H&E (Sigma) to visualize cell morphology. For immunostaining of the cell cytoskeleton, antigen retrieval was performed via incubation in 10 mM sodium citrate buffer (0.05% Tween 20, pH 6.0, Sigma) at 100 °C for 20 min. Cells were then permeabilized in PBS containing 1% Triton X-100 (Sigma) for 15 min at 37 °C. Nonspecific binding was blocked using 1% BSA in PBS for 60 min at RT. To stain for actin, sections were then stained with phalloidin-rhodamine (50 μg/ mL, Sigma) for 60 min at 37 °C. Cell nuclei were counterstained with Hoechst dye 33342 (0.25 μg/mL, Cell Signaling Technologies, Danvers, MA) for 60 min at RT. Sections were then mounted (Vectashield, Vector Laboratories, Burlingame, CA) and imaged using a Zeiss fluorescence microscope. Statistical Analyses. GraphPad Prism (GraphPad software, San Diego, CA, USA) was used to perform statistical analysis on cell proliferation and theoretical mesh size data. Unpaired student’s t tests (assuming Gaussian distribution) and two-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test were used to determine statistical significance (p < 0.05). Error was reported as standard deviation unless otherwise noted.

containing hydrogel solution was pipetted into a cylindricalshaped mold and UV cross-linked as described above. The samples were then cultured in growth medium as described above for 21 days at 37 °C in 5% CO2 with medium change every other day. Cell Viability. U87-mg cells (GFP-/Luc-) were encapsulated in soft and stiff hydrogels, and cell viability (n = 1) was assessed 2 h after encapsulation using the Live/Dead Cell Viability Assay kit (Life Technologies). Briefly, the Live/Dead reagent was prepared per manufacturer’s instructions. Hydrogels were immersed in Live/Dead reagent solution for 30−40 min and imaged using a Zeiss fluorescence microscope. Cell Proliferation. Cell proliferation inside soft vs stiff hydrogels was monitored over time using bright field microscopy. To quantify the cell proliferation, we measured the DNA content inside hydrogels at days 1 and 21 using the Quant-iT PicoGreen assay (Life Technologies). Briefly, lyophilized hydrogel samples (n = 3) were rehydrated and digested using papain (Worthington Biochemical Corp) at 60 °C for 16 h. After cooling to room temperature, samples were vortexed and centrifuged at 10,000 rpm for 5 min. The supernatant was used to measure DNA content using the PicoGreen assay per manufacturer’s instructions. Acellular hydrogels were used as control. Gene Expression. To examine the effects of varying matrix stiffness on U87 cell fates, we analyzed gene expressions of U87 cells after being cultured inside soft vs stiff hydrogels for 21 days. A panel of 14 genes was examined including matrix metalloproteinases (MMP1, MMP2, and MMP9), HA receptors (CD44 and RHAMM), HA synthases (HAS1, HAS2, and HAS3), Ras proteins (Hras, Nras, and Kras), and mechanotransduction proteins RhoA and ROCK (ROCK1 and ROCK2). Primer sequence of all tested genes are listed in Table 2. To measure the expression of target genes, total RNA was extracted from the hydrogels (n = 3), and RT-PCR was performed. Briefly, hydrogel samples were homogenized in TRIzol (Life Technologies). RNA was extracted by the addition of chloroform and precipitated using RNeasy Mini Kit columns (Qiagen, Valencia, CA). cDNA was synthesized from extracted RNA using SuperScript III First-Strand Synthesis kit (Life Technologies) per manufacturer’s instructions. RT-PCR was then performed on an Applied Biosystems7900 Real-Time PCR system (Applied Biosystems, Life Technologies) using Power SYBR Green PCR Master Mix (Applied Biosystems, Life

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RESULTS Characterization of Hydrogels. To determine the optimal hydrogel formulations that mimic the stiffness of normal brain and GBM tissues, we first varied the concentration of PEG from 2% to 14% (w/v) and measured the resulting hydrogel stiffness using an unconfined compression test. As expected, increasing PEG concentration led to increased stiffness, ranging from 0.5 to 26 kPa (Figure 1A), 2118

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view in stiff hydrogels due to swelling. Cell proliferation over time was monitored using bright field microscopy (Figure 2C). Cell number increased dramatically in soft hydrogels, and large cell aggregates were distributed homogeneously, almost reaching confluency throughout the 3D hydrogels. In contrast, increases in cell density and spheroid size in stiff hydrogels was markedly slower. Also, most spheroids in the stiff hydrogel remained relatively isolated by day 21. A consistent trend was also observed in total DNA content (Figure 2B). While soft and stiff hydrogel groups showed comparable DNA content at day 1, cells in the soft hydrogels proliferated much more dramatically over 21 days, resulting in a 5−6-fold higher DNA content than that in the stiff hydrogels (p < 0.05). Effects of Hydrogel Stiffness on GBM Cell Spreading. GBM cells are unique in terms of their extreme ability to spread and invade. While U87 cells remained round on day 1 in both the soft and stiff hydrogels, cells in the soft hydrogel began to spread and send out protrusions as early as day 3, which continued to increase over time, ultimately forming an extensive cell network by day 14 (Figure 3A). In stiff hydrogels, noticeable cell spreading was observed starting on day 7, and cell protrusions became progressively longer by day 14 (Figure 3A). In addition, H&E and cytoskeletal immunostaining were performed on day 21 to examine cell morphology. Cells in the soft hydrogels appeared more fibroblastic-like with a bipolar, elongated morphology, whereas spheroid periphery in stiff hydrogels were more spindle-like with longer, thinner actin-rich protrusions (Figure 3B). Effects of Hydrogel Stiffness on Gene Expression. HA Receptors and Synthases. The expression of HA receptors CD44 and RHAMM was measured on days 1 and 21 (Figure 4A,B). CD44 expression was much lower in stiff hydrogels at both time points compared to that in soft hydrogels. However, within each group, no marked changes in CD44 expression were observed over 21 days. RHAMM gene expression increased over time in both soft and stiff hydrogels, but varying hydrogel stiffness had little effect on RHAMM expression. To determine the effect of matrix stiffness on HA production, we then analyzed gene expression of HA synthases (HAS1, HAS2, and HAS3) (Figure 4C−E). Varying hydrogel stiffness resulted in differential expression patterns for HAS1 and HAS2, but little change in HAS3. At day 21, HAS1 was upregulated substantially in stiff hydrogels, whereas a greater increase in HAS2 gene expression was observed in soft hydrogels. Matrix-Degrading Enzymes. To analyze the effect of matrix stiffness on matrix degradation, we also quantified expression of MMPs (MMP1, 2, and 9) in soft and stiff hydrogels (Figure 4F−H). MMP1, 2, and 9 were selected, as these MMPs are expressed by U87 cells and have been reported to play key roles in facilitating glioma cell fate processes.19−21 As seen in Figure 4F−H, MMP9 was highly upregulated in the soft hydrogels as compared to the stiff hydrogels by day 21, while moderate differences were observed for MMP1 and MMP2. Mechanosensing and Ras Proteins. Furthermore, we measured the expression levels of RhoA, ROCK1 and 2, and Nras/Hras/Kras, as previous literature has suggested that tumor cell proliferation may be linked to the mechanosensing pathways.22 Varying hydrogel stiffness did not result in any significant differences in the expression levels of Nras and Kras, but Hras was significantly upregulated in stiff hydrogels by day 21 (Figure 5A−C). RhoA and ROCK1 expression was also significantly upregulated in the stiff hydrogels by day 21, while

Figure 1. (A) Effect of PEG concentration on hydrogel stiffness as measured using an unconfined compression test (n = 3). 3% and 14% PEG (w/v) were selected to mimic normal (1 kPa, soft) and glioblastoma (26 kPa, stiff) brain tissues. (B) Equilibrium swelling ratio Q for soft and stiff hydrogels as measured via wet weight/dry weight (n = 3). (C) Degradation of stiff hydrogels with and without collagenase as measured using equilibrium swelling ratio Q (n = 3). (D) Theoretical mesh sizes of soft (1 kPa) and stiff (26 kPa) hydrogels were calculated using equilibrium swelling ratio Q and the Flory Rehner calculation (n = 3).

which covers the stiffness range of normal brain and GBM tissues. Based on the results, we chose 3% and 14% PEG formulations to mimic the stiffness of normal brain (1 kPa) and GBM (26 kPa) tissues, respectively. Henceforth, 3% and 14% PEG hydrogels will be referred to as soft and stiff hydrogels, respectively. We then measured the equilibrium swelling ratio (Q) and calculated the theoretical mesh sizes of soft and stiff hydrogels using the Flory−Rehner calculation.18 Increasing the PEG concentration from 3% to 14% led to a marked decrease in Q from 40.83 ± 0.04 to 12.87 ± 0.23 (Figure 1B). Using the Flory−Rehner calculation, this corresponded to a decreased theoretical hydrogel mesh size from 16.53 to 12.87 nm (*, p < 0.05) (Figure 1D). To verify that our hydrogels were indeed MMP-degradable, stiff hydrogels were incubated in PBS control or collagenase solution for up to 5 days. Hydrogel wet weights were measured on a daily basis to determine the hydrogel equilibrium swelling ratio Q. In the presence of collagenase, Q increased substantially as early as day 1 and plateaued by day 5 (Figure 1C). In contrast, no changes in Q were observed in hydrogels incubated in PBS control. These results confirmed that our hydrogels can degrade specifically in a MMPresponsive manner. Effects of Hydrogel Stiffness on GBM Cell Proliferation. We first assessed the effect of varying hydrogel stiffness on GBM cell proliferation by encapsulating U87 cells in 3D at an initial concentration of 0.5 M cells/mL. Cell viability was evaluated using a Live/Dead fluorescence assay on day 1 (Figure 2A). On day 1, most cells remained viable after encapsulation. Fewer cells were observed in the same field of 2119

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Figure 2. (A) Live/Dead assay of glioma cells on day 1 for soft and stiff hydrogels. Live = green; dead = red. Scale bar = 250 μm. (B) Total DNA content per hydrogel (μg) for soft and stiff hydrogels on days 1 and 21, as measured using PicoGreen assay (n = 3); *, p < 0.05. (C) Glioma cell proliferation in soft and stiff hydrogels on days 1, 5, 12, and 21. Scale bar = 200 μm.

and 12.87 nm, respectively (Figure 1D). Both of these values are much larger than the hydrodynamic radius of most soluble growth factors, which is estimated to be 4 nm.23 Thus, it is unlikely that nutrient diffusion rate would be limited in both soft and stiff hydrogels, and slower cell proliferation in stiff hydrogel was likely not caused by differences in nutrients diffusion. Consistent with our finding, Loessner et al. also reported that increasing matrix stiffness led to decreased proliferation of epithelial ovarian cancer cells in 3D.15 In a stiffer environment, the cells are subjected to increased retractive force from the surrounding matrix due to a higher cross-linking density per volume. Therefore, in order for single cells and spheroids to expand in volume, more MMP-cleavable cross-links need to be degraded. Cells in the soft environment have more room and fewer cross-links to degrade. It is also worth noting that our observed trend of varying matrix stiffness on cell proliferation is opposite to those found when culturing glioma cells on 2D hydrogel substrates. When culturing glioma cells on a 2D polyacrylamide hydrogel substrate, Ulrich et al. showed that increasing hydrogel stiffness led to enhanced glioma cell proliferation.9 The difference in cell response when cultured in 2D versus 3D suggests that dimensionality is an important factor to consider when designing in vitro models for studying glioma cell−niche interactions. For tumor cells to grow and migrate in vivo, they need to overcome the physical confinement of the surrounding extracellular matrix. However, such physical constraint is absent in 2D culture, and cells are not subjected to increases in mechanical stress induced by physical constraint in 3D. As such, 3D biodegradable hydrogels with tissue-mimicking biochemical and biophysical cues may

cells in both soft and stiff hydrogels upregulated ROCK2 expression by Day 21 (Figure 5D−F).

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DISCUSSION In this study, we developed and characterized a PEG-based, biomimetic hydrogel platform with decoupled biochemical and mechanical properties as an artificial niche for culturing GBM cells in 3D. We then examined the effect of varying matrix alone, without changing biochemical content, on brain tumor cell behavior in 3D using U87 glioma cells as a model cell type. Specifically, we chose two matrix stiffnesses, soft and stiff, to mimic the tissue stiffness of normal brain and GBM tissues, respectively. Using a combination of qualitative and quantitative methods, we evaluated cell fate by analyzing cell proliferation, morphology, and gene expression. Our results show that U87 cells were able to proliferate, spread, and form spheroids in our biomimetic hydrogels and actively express genes for matrix synthesis and remodeling. Furthermore, matrix stiffness mimicking normal brain versus GBM tissue induces U87 cells to respond differentially in terms of cell proliferation, spreading, and gene expression. Consistent with previous reports, we observed decreased cell proliferation in hydrogels within stiffer hydrogels in 3D (Figure 2B).11 Possible factors that may have contributed to reduced cell proliferation in the stiff hydrogels include decreased nutrient diffusion or increased physical constraints due to the higher cross-linking density. To assess the effect of varying matrix stiffness on hydrogel mesh size, we measured the equilibrium swelling ratio and calculated the theoretical mesh size for both soft and stiff hydrogels. The mesh sizes of soft and stiff hydrogels were found to be 16.53 2120

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Figure 3. (A) Glioma cell morphology in soft and stiff hydrogels on days 1, 3, 5, 7, and 14. Scale bar = 50 μm. (B) H&E stain for soft and stiff hydrogels on day 21. Actin (red) and nuclei (blue) stain for soft and stiff hydrogels on day 21. Scale bar = 75 μm.

Figure 4. Normalized gene expression of glioma cells in soft vs stiff hydrogels at days 1 and 21 (n = 3) for HA receptors ((A) CD44 and (B) RHAMM), HA synthases ((C) HAS1, (D) HAS2, and (E) HAS3), and matrix metalloproteinases ((F) MMP1, (G) MMP2, and (H) MMP9); *, p < 0.05.

provide a more physiologically relevant model to mimic tumor progression in vivo.

We further assessed the effect of varying matrix stiffness on modulating extracellular matrix deposition and remodeling by 2121

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Figure 5. Normalized gene expression of glioma cells in soft vs stiff hydrogels at days 1 and 21 (n = 3) for Ras proteins ((A) Hras, (B) Nras, and (C) Kras) and mechanosensing proteins ((D) RhoA, (E) ROCK1, and (F) ROCK2); *, p < 0.05.

included in the precursor solution, future studies will chemically conjugate HA to the hydrogel network. To facilitate tumor growth and migration, extracellular matrix remodeling is also an important contributing factor. Among the three MMPs we examined, MMP9 expression showed the greatest differential response to varying matrix stiffness (Figure 4H). MMP9 is a gelatinase that cleaves type IV collagen and gelatin and has been found to be highly elevated in primary GBM samples.29 The enhanced MMP9 expression in soft hydrogels suggests faster degradation and more active ECM remodeling, which clears space for cell proliferation and migration. This is consistent with the trend we observed where soft hydrogel supported faster cell proliferation and spheroid formation. Consistent with our finding, a previous study has also reported increased MMP expression when culturing U87 cells in softer gelatin hydrogels.12 The upregulation of MMP9 in the soft hydrogels suggests that glioma cells may employ a mesenchymal mode of migration, in which cells generate paths in the matrix via secretion of MMPs to degrade the matrix. Cell migration may also occur through amoeboid migration, in which cells employ morphological adaptations, such as constriction rings, to move through tight spaces in the matrix.30 Given the differential expression of MMP9 in soft and stiff hydrogels, this suggests that cells may be using different modes of migration in response to the matrix stiffness. Our results show that increasing matrix stiffness led to up-regulation of MMP1 while simultaneously down-regulating MMP9. Furthermore, increasing matrix stiffness resulted in upregulation of RhoA and ROCK1. Previous literature has suggested a potential link between the mechanosensing and cell proliferation pathways, which may regulate MMP9 expres-

quantifying the gene expression of three HA synthase isoforms that synthesize HA, a key component of the brain ECM. Previous literature suggested that HA may play a role in glioma cell fate processes, including proliferation and migration.6,24,25 Despite similar function, the three HA synthase isoforms produce HA of varying molecular weights and have different enzymatic activity. HAS1 is responsible for synthesizing HA of molecular weight (MW) 2 × 105 to 2 × 106 Da, while HAS2 is responsible for synthesizing HA of MW greater than 2 × 106 Da.26 Increased HAS2 expression in soft hydrogels suggests that the cells are laying down high MW HA, which can potentially enhance cell adhesion and migration (Figure 4D). In contrast, HAS1 expression was substantially higher in stiff hydrogels than in soft hydrogels, suggesting more low MW HA was being deposited in stiff hydrogels (Figure 4C). The MW of HA fragments has been shown to play a role in many cell fate processes.27 For example, HA of 4−6-mers has been shown to upregulate mRNAs of various MMPs, while HA of 6−36-mers may have angiogenic effects and may modulate tumor cell migration or proliferation. These results suggest that varying matrix stiffness can induce differential ECM deposition by employing different HA synthase isoforms. Furthermore, the retention of HA within hydrogels synthesized by the cells or initially included in the hydrogel precursor solution will likely depend on the molecular weight of the HA. Previous literature has reported a Stokes radius of ∼5.9 nm for sodium hyaluronate with MW of ∼39 kDa.28 Given that the theoretical mesh size of our soft and stiff hydrogels is 16.53 and 12.87 nm, respectively, we do expect HA with radius smaller than the theoretical mesh sizes to diffuse out of the hydrogel network over time. To maintain stable incorporation of HA initially 2122

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sion.22,31 Specifically, MMP9 is a downstream effector of Ras, which may interact with the Rho/ROCK signaling axis. Our results demonstrate that U87 cell morphology and spreading are differentially modulated in 3D hydrogels with varying matrix stiffness. Cells in stiff hydrogels showed a more spindle-like morphology with longer and thinner actin protrusions, whereas in soft hydrogels, cells displayed fewer and shorter protrusions (Figure 3B). These results may be explained by differential expression of RhoA and ROCK1, two key players in mechanotransduction pathways (Figure 5D−E). RhoA is a small GTPase that is known to regulate the actin cytoskeleton and the formation of stress fibers via downstream effectors. RhoA is regulated via the balance between GTP-ase activating protein (GAP) and guanine nucleotide exchange factor (GEF) proteins. ROCK1 and 2 are activated by GTPbound Rho and share more than 30 intermediate downstream substrates.32 Despite their similarities, ROCK1 and 2 may play different roles in cell fate processes. For example, in mouse embryonic fibroblasts, ROCK1 deficiency inhibited actin cytoskeleton remodeling, while ROCK2 downregulation led to increased periphery membrane folding and altered cell adhesion.32 In glioma cells, ROCK1 and 2 inhibition has been reported to lead to differences in cell cycle progression.33 Also, Zohrabian et al. found that the addition of a ROCK inhibitor to glioblastoma cultures in 2D culture led to decreased radial migration.34 Increased ROCK1 expression in the stiff hydrogels may explain the increase in the number of actin protrusions at the periphery of the spheroid in stiff hydrogels. Although the matrix stiffness is significantly higher, the cells may actively reorganize their actin cytoskeleton to probe and invade the matrix, a phenomenon that has been observed in breast cancer cell lines when compressed in 2D.35 Previous literature has suggested that matrix stiffness sensed by Rho may also affect cell proliferation via the Ras pathway.22 Zohrabian et al. found that addition of ROCK inhibitors to glioblastoma cultures reduced the levels of phosphorylated ERK, a downstream effector of Ras, which may ultimately lead to reduced cell proliferation.34 Therefore, there may be cooperation between the Rho/ROCK and Ras pathways to regulate cell motility and growth. In our studies, the upregulation of Rho/ROCK proteins may be correlated with the significant upregulation in Hras expression. U87 cells have been shown to express the three isoforms of Ras, namely, Hras, Nras, and Kras.36 These three RAS genes encode for highly homologous Ras proteins, which have identical effector binding domains and can interact with the same set of downstream effectors. However, each Ras protein exhibits different posttranslational modifications, which results in different trafficking routes and localization in domains of the plasma membrane.37 In our study, we observed upregulated Hras expression in stiff hydrogels (Figure 5A). However, despite increased expression of Hras, cell proliferation rates seemed to be lower in the stiff hydrogels. This may be partially caused by other factors that influence cell proliferation, such as the greater physical constraint that cells need to overcome in stiff hydrogels. In summary, here we report a PEG-based 3D hydrogel platform as an artificial niche for studying the effect of matrix cues on GBM brain tumor cell fate in 3D. Using U87 cells as a model GBM cell line, we demonstrate that such biomimetic hydrogels support U87 cell growth, spreading, and migration in 3D over the course of 3 weeks, and cells remain active in depositing new ECM and continue to upregulate matrix remodeling genes. Unlike previously reported hydrogels, our

platform allows varying matrix stiffness to mimic the physiologically relevant matrix stiffness of normal brain and GBM tissues without changing biochemical content. Our results showed that soft and stiff hydrogels induced differential tumor cell fates, such as proliferation, spreading, and gene expressions, suggesting that changes in matrix stiffness in tumors play an important role in modulating GBM progression. While the present study focuses on varying matrix stiffness alone, our hydrogel platform allows decoupled tunability of other niche cues, such as biochemical ligands or matrix degradability, for examining complex interactions between cancer cells and tumor niche. The platform reported here may provide a useful 3D in vitro model for elucidating the mechanisms underlying GBM progression in a more physiologically relevant and controlled manner, as well as for evaluating efficacy of potential drug candidates.

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AUTHOR INFORMATION

Corresponding Author

*(F.Y.) Tel: 650-725-7128. Fax: 650-723-9370. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank Stanford Child Health Research Institute (CHRI) Faculty Scholar Award for funding. C.W. would like to thank Stanford Graduate Fellowship for support. The authors also appreciate the technical assistance from Anthony Behn on mechanical testing.

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REFERENCES

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