Name: hydrogel arrays enable increased throughput for screening effects of matrix components and therapeutics in 3d tumor models
Uploaded: 2022-06-16T13:10:00
Description: Watch this Scientific Journal Video about Hydrogel Arrays Enable Increased Throughput for Screening Effects of Matrix Components and Therapeutics in 3D Tumor Models at JoVE.com

The authors would like to specifically acknowledge Carolyn Kim, Amelia Lao, Ryan Stoutamore, and Itay Solomon for their contributions to earlier iterations of the photogelation scheme. Cell lines GS122 and GS304 were generously provided by David Nathanson. All figures were created with BioRender.com. UCLA core facilities, the Molecular Screening Shared Resources, and the Nano and Pico Characterization Laboratory were instrumental to the work. Chen Chia-Chun was supported by the UCLA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Training Program. Grigor Varuzhanyan was supported by a Tumor Cell Biology Training Program NIH Grant (T32 CA 009056).

Patient-derived GBM cell lines (GS122 and GS304) were provided by Professor David Nathanson (our collaborator), who developed these lines under a protocol approved by the UCLA Institutional Review Board (IRB# 10-000655). Cells were provided de-identified so that the cell lines could not be linked back to the individual patients.
1. Preparation of hydrogel solution
<ol>
	<li>Prepare HEPES-buffered solution by dissolving HEPES powder at 20 mM in Hank&#39;s balanced salt soluti...

<ol>
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The current work presents methods to generate 3D, miniaturized cultures within HA-based while simultaneously altering matrix stiffness and peptides available for integrin engagement. This technique enables the systematic study of how matrix parameters affect cellular phenotypes (e.g., the viability of cancer cells exposed to chemotherapy) with increased throughput. Previous approaches, including that presented herein, have tuned hydrogel stiffness by varying the percent total polymer in the final formulation, where stiff...

The expected cost of developing a new drug has steadily risen over the past decade, with over $1 billion in current estimates1. Part of this expense is the high failure rate of drugs entering clinical trials. Approximately 12% of drug candidates ultimately earn approval from the United States (US) Food &#38; Drug Administration (FDA) in 2019. Many drugs fail in Phase I due to unanticipated toxicity2, while others that pass safety trials may fail due to a lack of efficacy3. This attrition due to non-efficacy can partly be explained by the fact that cancer models used during drug development are notoriously non-predictive of clinical efficacy4.
Functional disparities between in vitro and in vivo models may be attributed to removing cancer cells from their native microenvironment, including non-tumor cells and the physical ECM5,6. Commonly, research groups use commercially available culture matrices, such as Matrigel (a proteinaceous basement membrane matrix derived from mouse sarcomas) to provide cultured tumor cells with a 3D matrix microenvironment. Compared to 2D culture, 3D culture in membrane matrix has improved the clinical relevance of in vitro results7,8. However, culture biomaterials from decellularized tissues, including the membrane matrix, typically exhibit batch-to-batch variability that may compromise reproducibility9. Furthermore, matrices derived from tumors with different tissue origins from those studied may not provide the appropriate physiological cues10. Finally, cancers with high degrees of intratumoral heterogeneity have microenvironmental features that vary on a submicron-size scale and which the membrane matrix cannot be tuned to recapitulate11.
Glioblastoma (GBM), a uniformly lethal brain tumor with a median survival time of approximately 15 months, is a cancer for which treatment development has been particularly difficult12,13. The current standard of care for GBM consists of primary tumor resection, followed by radiotherapy, and then chemotherapy using temozolomide (TMZ)14. Yet, more than half of clinical GBM tumors exhibit treatment resistance through various mechanisms15,16,17. Predicting the efficacy of a treatment regimen for an individual patient is extremely difficult. Standard preclinical models used to predict individual outcomes consist of patient-derived tumor cells xenografted orthotopically into immunocompromised mice. While patient-derived xenografts can recapitulate many aspects of clinical GBM tumors and are valuable for preclinical models18, they are inherently expensive, low throughput, time-consuming, and involve ethical concerns19. Cultures of patient-derived cells, on 2D plastic surfaces or as spheroids, mostly avoid these issues. While patient-derived cells preserve&#160;genetic aberrations, their cultures in 2D or as suspended spheroids have been largely poor representations of patient-derived xenografts in rodents and original patient tumors20. Previously, we, and others, have shown that GBM cells cultured in a 3D ECM that mimics the mechanical and biochemical properties of brain tissue can preserve drug resistance phenotypes10,21,22,23.
Interactions between hyaluronic acid (HA), a polysaccharide abundant in the brain ECM and overexpressed in GBM tumors, and its CD44 receptor modulate the acquisition of drug resistance in vitro21,24,25,26,27.&#160;For example, the inclusion of HA within soft, 3D cultures increased the ability of patient-derived GBM cells to acquire therapeutic resistance. This mechano-responsivity was dependent on HA binding to CD44 receptors on GBM cells21. Additionally, integrin binding to RGD-bearing peptides, incorporated into 3D culture matrices, amplified CD44-mediated chemoresistance in a stiffness-dependent manner21. Beyond HA, the expression of several ECM proteins, many containing RGD regions, vary between normal brain and GBM tumors28. For example, one study reported that 28 distinct ECM proteins were upregulated in GBM tumors29. Within this complex tumor matrix microenvironment, cancer cells integrate mechanical and biochemical cues to yield a particular resistance phenotype, which depends on relatively small differences (e.g., less than an order of magnitude) in Young&#39;s modulus or density of integrin-binding peptides28,29,30.
The present protocol characterizes how tumor cells interpret unique combinations of matrix cues and identify complex, patient-specific matrix microenvironments that promote treatment resistance (Figure 1A). A photochemical method for generating miniaturized, precisely tuned matrices for 3D culture provides a large, orthogonal variable space. A custom-built array of LEDs, run by a microcontroller, was incorporated to photocrosslink hydrogels within a 384-well plate format to increase automation and reproducibility. Exposure intensity was varied across well to alter micro-mechanical properties of resulting hydrogels, as assessed using atomic force microscopy (AFM). While this manuscript does not focus on constructing the illumination array itself, a circuit diagram (Figure 1B) and parts list (Table of Materials) are provided as aids for device reproduction.
This report demonstrates the rapid generation of an array of GBM cells cultured in unique, 3D microenvironments in which Young&#39;s modulus (four levels across a single order of magnitude) and integrin-binding peptide content (derived from four different ECM proteins) were varied orthogonally. The approach was then used to investigate the relative contributions of hydrogel mechanics and ECM-specific integrin engagement on the viability and proliferation of patient-derived GBM cells as they acquire resistance to temozolomide (TMZ) chemotherapy.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1.1 kOhm resistors, 6 W</td>
			<td>Digikey</td>
			<td>35601k1ft</td>
			<td></td>
		</tr>
		<tr>
			<td>1.7 mL microcentrifuge tube</td>
			<td>Genesse Scientific</td>
			<td>21-108</td>
			<td></td>
		</tr>
		<tr>
			<td>15 mL conical tube</td>
			<td>Fisher Scientific</td>
			<td>14-959-70C</td>
			<td></td>
		</tr>
		<tr>
			<td>365 nm LED</td>
			<td>Digikey</td>
			<td>ltpl-c034uvh365</td>
			<td></td>
		</tr>
		<tr>
			<td>384 well plate</td>
			<td>Bio Greiner One</td>
			<td>781090</td>
			<td></td>
		</tr>
		<tr>
			<td>40 &#181;m cell strainer</td>
			<td>MTC bio</td>
			<td>C4040</td>
			<td></td>
		</tr>
		<tr>
			<td>4-Armed thiol terminated polyethlene glycol (20 kDa)</td>
			<td>Laysan Bio</td>
			<td>4arm-PEG-SH-20K-1g</td>
			<td></td>
		</tr>
		<tr>
			<td>6 NPN BJTs</td>
			<td>Digikey</td>
			<td>2n5550ta</td>
			<td></td>
		</tr>
		<tr>
			<td>80 Ohm resistors, 0.125 W</td>
			<td>Digikey</td>
			<td>erjj-6enf80r6v</td>
			<td></td>
		</tr>
		<tr>
			<td>8-Armed norbornene terminated polyethylene glycol (20 kDa)</td>
			<td>Jenkem Technology</td>
			<td>A7025-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Accutase</td>
			<td>Innovative Cell Technologies</td>
			<td>AT104500</td>
			<td>&#160;cell dissociation&#160; reagent</td>
		</tr>
		<tr>
			<td>AFM Probes</td>
			<td>Novascan</td>
			<td></td>
			<td>0.01 N/m Nominal spring constant, 2.5 &#181;m SiO2 particle</td>
		</tr>
		<tr>
			<td>Arduino IDE</td>
			<td>Arduino</td>
			<td>1.8.19</td>
			<td></td>
		</tr>
		<tr>
			<td>Arduino Nano</td>
			<td>Makerfire</td>
			<td>Mini Nano V3.0 ATmega328P Microcontroller Board</td>
			<td></td>
		</tr>
		<tr>
			<td>bFGF</td>
			<td>Peprotech</td>
			<td>100-18B</td>
			<td>20 ng/mL</td>
		</tr>
		<tr>
			<td>CCK8</td>
			<td>Abcam</td>
			<td>ab228554</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Thermoscientific</td>
			<td>sorvall legend xtr</td>
			<td></td>
		</tr>
		<tr>
			<td>CP100ST</td>
			<td>Gilson</td>
			<td>F148415</td>
			<td>Pipette tips for positive displacement pipette</td>
		</tr>
		<tr>
			<td>Cubis Semi-Micro Balance</td>
			<td>Sartorius</td>
			<td>MSA225S100DI</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM - F12 (50-50)</td>
			<td>Life Technologies</td>
			<td>11330057</td>
			<td>1x</td>
		</tr>
		<tr>
			<td>DMSO</td>
			<td>Fisher Scientific</td>
			<td>BP231-100</td>
			<td></td>
		</tr>
		<tr>
			<td>DPBS Ca (-) Mg (-)</td>
			<td>Genesse Scientific</td>
			<td>25-508</td>
			<td></td>
		</tr>
		<tr>
			<td>EGF</td>
			<td>Peprotech</td>
			<td>AF100-15</td>
			<td>50 ng/mL</td>
		</tr>
		<tr>
			<td>Ethanol, Anhydrous</td>
			<td>Fisher Scientific</td>
			<td>A405P</td>
			<td>Add DI water to dilute to 70%</td>
		</tr>
		<tr>
			<td>Fisherbrand Class B Amber Glass threaded vials</td>
			<td>Fisher Scientific</td>
			<td>03-339-23C</td>
			<td></td>
		</tr>
		<tr>
			<td>Fisherbrand Weighing Paper</td>
			<td>Fisher Scientific</td>
			<td>09-898-12B</td>
			<td></td>
		</tr>
		<tr>
			<td>G21 Supplement</td>
			<td>Gemini Bio</td>
			<td>400-160</td>
			<td>50x</td>
		</tr>
		<tr>
			<td>Hanks Balanced Salt Solution</td>
			<td>Thermo Fisher Scientific</td>
			<td>14175095</td>
			<td></td>
		</tr>
		<tr>
			<td>HCl, ACS, 12M</td>
			<td>Sigma Aldrich</td>
			<td>S25838A</td>
			<td>Add DI water to dilute to 1 M</td>
		</tr>
		<tr>
			<td>Heparin sodium salt from porcine intestinal mucosa</td>
			<td>Sigma Aldrich</td>
			<td>H3149-100Ku</td>
			<td>25 &#181;g/mL</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Sigma Aldrich</td>
			<td>H7006-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Hot Air Gun</td>
			<td>Wagner</td>
			<td>HT1000</td>
			<td></td>
		</tr>
		<tr>
			<td>Integrin-binding sialoprotein (IBSP) peptide</td>
			<td>Genscript</td>
			<td>Custom Order</td>
			<td>GCGYGGGGNGEPRGDTYRAY</td>
		</tr>
		<tr>
			<td>Lithium phenyl-2,4,6 trimethylbenzoylphosphinate (LAP) , &#62;95%</td>
			<td>Sigma Aldrich</td>
			<td>900889-1G</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnetic stir plate</td>
			<td>Thermo Scientific</td>
			<td>SP194715</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcentrifuge</td>
			<td>Thermo Scientific</td>
			<td>Sorvall legend micro 21R</td>
			<td></td>
		</tr>
		<tr>
			<td>Microman E single Channel Pipettor</td>
			<td>Gilson</td>
			<td>FD10004</td>
			<td>Positive displacement pipette</td>
		</tr>
		<tr>
			<td>Micropipette Tips</td>
			<td>Various Manufacturs</td>
			<td></td>
			<td>Various sizes</td>
		</tr>
		<tr>
			<td>mLine micropipette</td>
			<td>Sartorious</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>N-acetyl Cysteine</td>
			<td>Sigma Aldrich</td>
			<td>A7250-10G</td>
			<td></td>
		</tr>
		<tr>
			<td>Nanowizard 4</td>
			<td>Bruker</td>
			<td></td>
			<td>AFM microscope</td>
		</tr>
		<tr>
			<td>NaOH</td>
			<td>Fisher Scientific</td>
			<td>ss255-1</td>
			<td>Add DI water to dilute to 1 M</td>
		</tr>
		<tr>
			<td>Normoicin</td>
			<td>Invivogen</td>
			<td>ant-nr-1</td>
			<td>500x</td>
		</tr>
		<tr>
			<td>Osteopontin Peptide</td>
			<td>Genscript</td>
			<td>Custom Order</td>
			<td>GCGYGTVDVPDGRGDSLAYG</td>
		</tr>
		<tr>
			<td>Pipet Aid</td>
			<td>Drummond</td>
			<td>4000102</td>
			<td></td>
		</tr>
		<tr>
			<td>Plain Microscope Slides</td>
			<td>Globe Scientific</td>
			<td>1301</td>
			<td></td>
		</tr>
		<tr>
			<td>Press-To-Seal silicone Isolator, 12-4.5mm diam x 2mm deep</td>
			<td>Grace Bio Labs</td>
			<td>664201-A</td>
			<td>Cut so that 8 individual molds are made from a single sheet</td>
		</tr>
		<tr>
			<td>Processing</td>
			<td>Processing</td>
			<td>3.5.4</td>
			<td></td>
		</tr>
		<tr>
			<td>Repeater M4</td>
			<td>Eppendorf</td>
			<td>4982000322</td>
			<td></td>
		</tr>
		<tr>
			<td>Repeater Pipette Tips</td>
			<td>Sartorious</td>
			<td>30089430</td>
			<td>1 mL sizes</td>
		</tr>
		<tr>
			<td>RGD Peptide</td>
			<td>Genscript</td>
			<td></td>
			<td>GCGYGRGDSPG</td>
		</tr>
		<tr>
			<td>Scoth Tape</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Serological Pipettes</td>
			<td>Genesse Scientific</td>
			<td>12-102,12-104</td>
			<td>5,10 mL Pipettes</td>
		</tr>
		<tr>
			<td>Solder Paste</td>
			<td>Digikey</td>
			<td>315-NC191LT15T5-ND</td>
			<td></td>
		</tr>
		<tr>
			<td>Solder Wire</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Straight dissecting forceps</td>
			<td>VWR Scientific</td>
			<td>82027-408</td>
			<td></td>
		</tr>
		<tr>
			<td>Synergy H1 Plate Reader</td>
			<td>Biotek</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>T-75 Cell Culture Treated Flask</td>
			<td>Genesee Scientific</td>
			<td>25-209</td>
			<td></td>
		</tr>
		<tr>
			<td>Temozolomide</td>
			<td>Sigma Aldrich</td>
			<td>T2577</td>
			<td>Typically used from 10 &#181;M to 100 &#181;M</td>
		</tr>
		<tr>
			<td>Tenascin-C Peptide</td>
			<td>Genscript</td>
			<td></td>
			<td>GCGYGRSTDLPGLKAATHYTITIR 
			GV</td>
		</tr>
		<tr>
			<td>Thiolated Hyaluronic Acid (700 kDa), 6-8% modified</td>
			<td>Lifecore Biomedical</td>
			<td>HA700K5</td>
			<td></td>
		</tr>
		<tr>
			<td>VWR Spinbar, Flea Micro</td>
			<td>VWR</td>
			<td>58948-375</td>
			<td></td>
		</tr>
	</tbody>
</table>

hydrogel arrays enable increased throughput for screening effects of matrix components and therapeutics in 3d tumor models

Cell-matrix interactions mediate complex physiological processes through biochemical, mechanical, and geometrical cues, influencing pathological changes and therapeutic responses. Accounting for matrix effects earlier in the drug development pipeline is expected to increase the likelihood of clinical success of novel therapeutics. Biomaterial-based strategies recapitulating specific tissue microenvironments in 3D cell culture exist but integrating these with the 2D culture methods primarily used for drug screening has been challenging. Thus, the protocol presented here details the development of methods for 3D culture within miniaturized biomaterial matrices in a multi-well plate format to facilitate integration with existing drug screening pipelines and conventional assays for cell viability. Since the matrix features critical for preserving clinically relevant phenotypes in cultured cells are expected to be highly tissue- and disease-specific, combinatorial screening of matrix parameters will be necessary to identify appropriate conditions for specific applications. The methods described here use a miniaturized culture format to assess cancer cell responses to orthogonal variation of matrix mechanics and ligand presentation. Specifically, this study demonstrates the use of this platform to investigate the effects of matrix parameters on the responses of patient-derived glioblastoma (GBM) cells to chemotherapy.

AFM measurements confirmed precise control of hydrogel mechanics as a function of UV irradiance (mW/cm2) during photo-crosslinking using a custom-built, Arduino-controlled LED array (Figure 2A). The hydrogel formulation used in this protocol can be found in Table 2. The spacing of the LEDs on the provided template matches the spacing for every other well of a 384-well plate, allowing for the formation of gels inside the plate (Figure 2B</stron...

Watch this Scientific Journal Video about Hydrogel Arrays Enable Increased Throughput for Screening Effects of Matrix Components and Therapeutics in 3D Tumor Models at JoVE.com

Hydrogel Arrays Enable Increased Throughput for Screening Effects of Matrix Components and Therapeutics in 3D Tumor Models

The present protocol describes an experimental platform to assess the effects of mechanical and biochemical cues on chemotherapeutic responses of patient-derived glioblastoma cells in 3D matrix-mimetic cultures using a custom-made UV illumination device facilitating high-throughput photocrosslinking of hydrogels with tunable mechanical features.

Cell-matrix interactions mediate complex physiological processes through biochemical, mechanical, and geometrical cues, influencing pathological changes ...

Research

JoVE Journal

Bioengineering

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

The mechanical microenvironment has been shown to act as a crucial regulator of tumor growth behavior and signaling, which is itself remodeled and ...

Porous Silicon Microparticles for Delivery of siRNA Therapeutics

Small interfering RNA (siRNA) can be used to suppress gene expression, thereby providing a new avenue for the treatment of various diseases. However, the ...

3D Hydrogel Scaffolds for Articular Chondrocyte Culture and Cartilage Generation

Human articular cartilage is highly susceptible to damage and has limited self-repair and regeneration potential. Cell-based strategies to engineer ...

3D Microtissues for Injectable Regenerative Therapy and High-throughput Drug Screening

To upgrade traditional 2D cell culture to 3D cell culture, we have integrated microfabrication with cryogelation technology to produce macroporous ...

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

In the central nervous system, numerous acute injuries and neurodegenerative disorders, as well as implanted devices or biomaterials engineered to enhance ...

Fabricating a Kidney Cortex Extracellular Matrix-Derived Hydrogel

Extracellular matrix (ECM) provides important biophysical and biochemical cues to maintain tissue homeostasis. Current synthetic hydrogels offer robust ...

A Human 3D Extracellular Matrix-Adipocyte Culture Model for Studying Matrix-Cell Metabolic Crosstalk

The extracellular matrix (ECM) plays a central role in regulating tissue homeostasis, engaging in crosstalk with cells and regulating multiple aspects of ...

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording

Implantable microelectrode technologies have been widely used to elucidate neural dynamics at the microscale to gain a deeper understanding of the neural ...

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion

Current mixing steps of viscous materials rely on repetitive and time-consuming tasks which are performed mainly manually in a low throughput mode. These ...

Photodegradable Hydrogel Interfaces for Bacteria Screening, Selection, and Isolation

Biologists have long attempted to understand the relationship between phenotype and genotype. To better understand this connection, it is crucial to ...