Name: generation of heterogeneous drug gradients across cancer populations on a microfluidic evolution accelerator for real-time observation
Uploaded: 2019-09-19T15:00:00
Description: Watch this Scientific Journal Video about Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation at JoVE.com

This work was supported by NSF PHY-1659940.

1. Fabrication of microfluidic device
<ol>
	<li>Generate the desired microfluidic pattern using a layout design software (see Supplemental Materials).</li>
	<li>Fabricate the photomask. See Table of Materials for more details.
	<ol>
		<li>Utilizing a laser writer, write the pattern on a soda-lime glass plate coated with 100 nm of Cr and 500 nm of photoresist AZ1518.</li>
		<li>Develop the photoresist with developer AZ300MIF for 60 s.</li>
		<li>Etch away the chromium without protection...

<ol>
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C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+heterogeneous+environment+and+docetaxel+gradient+in+the+emergence+of+polyploid,+mesenchymal+and+resistant+prostate+cancer+cells.">The role of heterogeneous environment and docetaxel gradient in the emergence of polyploid, mesenchymal and resistant prostate cancer cells.</a> Clinical &amp; Experimental Metastasis. 36 (2), 97-108 (2019).</li><li>Roca, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcription+factors+OVOL1+and+OVOL2+induce+the+mesenchymal+to+epithelial+transition+in+human+cancer.">Transcription factors OVOL1 and OVOL2 induce the mesenchymal to epithelial transition in human cancer.</a> PloS One. 8 (10), e76773 (2013).</li><li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>Tinevez, J. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trackmate:+An+open+and+extensible+platform+for+single-particle+tracking.">Trackmate: An open and extensible platform for single-particle tracking.</a> Methods. 115, 80-90 (2017).</li><li>Caballero, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organ-on-chip+models+of+cancer+metastasis+for+future+personalized+medicine:+From+chip+to+the+patient.">Organ-on-chip models of cancer metastasis for future personalized medicine: From chip to the patient.</a> Biomaterials. 149, 98-115 (2017).</li><li>Akhtar, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+flaws+and+human+harms+of+animal+experimentation.">The flaws and human harms of animal experimentation.</a> Cambridge Quarterly of Healthcare Ethics. 24 (4), 407-419 (2015).</li><li>Sontheimer-Phelps, A., Hassell, B. A., Ingber, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+cancer+in+microfluidic+human+organs-on-chips.">Modelling cancer in microfluidic human organs-on-chips.</a> Nature Reviews Cancer. 19 (2), 65-81 (2019).</li><li>Bogorad, M. I., DeStefano, J., Karlsson, J., Wong, A. D., Gerecht, S., Searson, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review:+in+vitro+microvessel+models.">Review: in vitro microvessel models.</a> Lab on a Chip. 15, 4242-4255 (2015).</li><li>Za&#241;udo, J. G. 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Conventional cell culture was developed almost a century ago and remains the most frequently used preclinical model in biomedical research, despite its proven limited ability to predict clinical results in cancer17. Animal models offer the highest physiological relevance and reasonable genetic similarity to humans, but have long been acknowledged to have significant limitations in predicting human outcomes18. Among all the existing preclinical models, microfluidic cancer-on...

Cancer has been increasingly recognized as a complex ecosystem that depends not only on the continued dysregulation of mutated cell populations but also on vital interactions between cancer cells and the host microenvironment. In this sense, cancer evolves on an adaptive landscape manifested by a combination of factors, including a heterogeneous tumor microenvironment and crosstalk with a variety of host cells, all of which contribute selective pressures for further genetic or epigenetic changes1,2,3. In the context of solid tumors, uneven distribution of chemotherapeutics and other resource gradients contributes to their molecular heterogeneity and may play a role in the development of drug resistance, increased angiogenesis to particular tumor subpopulations, and even metastasis4,5,6. Conventional in vitro 2D cell culture studies, while possessing large-scale, convenient experimental capacity, provides mean-field, uniform, and fixed conditions, often lacking the precise spatial and temporal environmental control necessary to truly emulate in vivo tumor dynamics. Thus, there is a need for more representative ex vivo models to reproduce the tumor microenvironment prior to animal models in the drug development pipeline in order for a better prediction of cancer progression as well as responses to drugs within dynamical stress landscapes. Microfluidics have been proposed to bridge the gap between 2D cell culture studies and more complex in vivo animal studies that may not be able to support controllable quantitative studies7,8,9.
An ideal in vitro system to characterize cancer cell dynamics should possess the ability to generate a heterogeneous microenvironment to mimic the adaptive cellular responses that may take place in a tumor, as well as allow for the observation of these dynamics at a single-cell resolution. In this article, we describe a microfluidic cell culture platform, a PDMS-based device called the &#34;Evolution Accelerator&#34; (EA), that allows for parallel in vitro studies of cancer cell dynamics at cellular resolution with real-time data acquisition over the course of weeks, while stably maintaining gradients of stress across the culture landscape. The design of this platform is based on our previous work, in which the evolutionary dynamics of organisms in a metapopulation can be accelerated10,11. Specifically, in a group of spatially separated populations that interact at some level, when exposed to a heterogeneous stress landscape, the most fit species can dominate in a local population faster compared with that of a large uniform population. The advantageous species then migrate to neighboring microhabitats in search of resources and space, and eventually dominate the entire population. As shown in Figure 1, the pattern of the microfluidic EA chip is composed of (i) a pair of serpentine channels that provide fresh media circulation and construct fixed boundary conditions for chemical diffusion, and (ii) the hexagonal cell culture region which consists 109 interconnected hexagonal and 24 half-hexagonal chambers in the center, resembling a honeycomb structure. The chip is 100 &#181;m in depth. Media channels and cell culture region are connected with small slits (about 15 &#181;m wide), which prevent direct media flow and the resulting shear stress across the cell culture area, yet still allow chemicals to diffuse through small slits and exchange nutrients, metabolic waste, etc. The generation of chemical gradients is demonstrated in Figure 1B, where one media channel contains 0.1 mM of fluorescein while the other channel is free of fluorescein. Cells are cultured on a gas permeable membrane, encapsulated by the microstructures through the positive back pressure on the membrane against the chip. The components of the device holder are illustrated in Figure 2, and the experimental setup is illustrated in Figure 3, where the culture is maintained on an inverted microscope at 37 &#176;C, with above 85% relative humidity, and conditioned under normoxia gas composition.
This system provides detailed observation of localized cellular interactions via brightfield and fluorescent channels and allows for spatially-resolved downstream assays such as immunofluorescence, Western blot, or mass spectrometry. We have previously demonstrated as a proof-of-principle of this microfluidic cancer-on-chip model on the long-term co-culture of epithelial and mesenchymal PC3 prostate cancer cells12 as well as the emergence of drug-resistance polyploid giant cancer cells using the epithelial PC3 cell line13. While we present the application of this platform to understand the spatiotemporal dynamics of epithelial PC3 and mesenchymal prostate cancer cells under a stress gradient of docetaxel, the microfluidic system can be easily applied to any combination of cell lines and resource (i.e., drug, nutrient, oxygen) gradients.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>10 mL BD Luer-Lok tip syringes</td>
			<td>BD</td>
			<td>14-823-16E</td>
			<td></td>
		</tr>
		<tr>
			<td>Antibiotic-Antimycotic</td>
			<td>Sigma-Aldrich</td>
			<td>A5955</td>
			<td>1x anti-anti</td>
		</tr>
		<tr>
			<td>AZ 300 MIF</td>
			<td>Merck KGaA</td>
			<td>18441123163</td>
			<td>Photoresist developer</td>
		</tr>
		<tr>
			<td>AZ1518</td>
			<td>Merck KGaA</td>
			<td>AZ1518</td>
			<td>Photoresist</td>
		</tr>
		<tr>
			<td>AZ4330</td>
			<td>Merck KGaA</td>
			<td>AZ4330</td>
			<td>Photoresist</td>
		</tr>
		<tr>
			<td>Cr Chromium Etchant</td>
			<td>Sigma-Aldrich</td>
			<td>651826</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum (FBS)</td>
			<td>Life Technologies Corporation</td>
			<td>10437028</td>
			<td></td>
		</tr>
		<tr>
			<td>Heidelberg DWL 66+ laserwriter</td>
			<td>Heidelberg Instruments</td>
			<td>DWL66+</td>
			<td>Writing photomask</td>
		</tr>
		<tr>
			<td>Hexamethyldisilazane (HMDS)</td>
			<td>Sigma-Aldrich</td>
			<td>379212</td>
			<td>For photoresist adhesion enhancement</td>
		</tr>
		<tr>
			<td>Hollow steel pins</td>
			<td>New England Small Tube</td>
			<td>NE-1300-01</td>
			<td>&#160;.025 OD .017 ID x .500 long / type 304 WD fullhard</td>
		</tr>
		<tr>
			<td>ibidi Heating System, Multi-Well Plates, K-Frame</td>
			<td>ibidi</td>
			<td>10929</td>
			<td>On-stage incubator&#160;</td>
		</tr>
		<tr>
			<td>Luer-Lok 23 G dispensing needle</td>
			<td>McMaster-Carr</td>
			<td>75165A684</td>
			<td>To connect syringes and tubings</td>
		</tr>
		<tr>
			<td>Lumox dish 35</td>
			<td>Sarstedt</td>
			<td>94.6077.331</td>
			<td>Gas-permeable cell culture dish</td>
		</tr>
		<tr>
			<td>Microposit Remover 1165</td>
			<td>Dow Electronic Materials</td>
			<td>Microposit Remover 1165</td>
			<td>Photoresist stripper</td>
		</tr>
		<tr>
			<td>Microseal B Adhesive Sealer</td>
			<td>Bio-Rad Laboratories</td>
			<td>MSB1001</td>
			<td>Adhesive sealer</td>
		</tr>
		<tr>
			<td>O-Ring (for Lumox plate sealing)</td>
			<td>McMaster-Carr</td>
			<td>9452K114</td>
			<td>Dash No. 27; 1-5/16&#34; ID x 1-7/16&#34; OD; Duro 70</td>
		</tr>
		<tr>
			<td>O-Ring (for bottom glass window sealing)</td>
			<td>McMaster-Carr</td>
			<td>9452K74</td>
			<td>Dash No. 20; 7/8&#34; ID x 1&#34; OD; Duro 70</td>
		</tr>
		<tr>
			<td>Plasma-Preen Plasma Cleaning/Etching System</td>
			<td>Plasmatic Systems, Inc</td>
			<td>Plasma-Preen</td>
			<td>Oxygen plasma system</td>
		</tr>
		<tr>
			<td>RPMI 1640</td>
			<td>Life Technologies Corporation</td>
			<td>11875-093</td>
			<td></td>
		</tr>
		<tr>
			<td>Samco RIE800iPB DRIE</td>
			<td>Samco</td>
			<td>RIE800iPB</td>
			<td>Deep reactive-ion etching system</td>
		</tr>
		<tr>
			<td>Suss MA6 mask aligner</td>
			<td>SUSS MicroTec</td>
			<td>MA6</td>
			<td>Mask aligner&#160;</td>
		</tr>
		<tr>
			<td>Sylgard 184 Silicone Elastomer</td>
			<td>Fisher Scientific</td>
			<td>NC9285739</td>
			<td>PDMS elastomer</td>
		</tr>
		<tr>
			<td>TePla M4L plasma etcher</td>
			<td>PVA TePla</td>
			<td>M4L</td>
			<td>Plasma etcher</td>
		</tr>
		<tr>
			<td>Trichloro-1H,1H,2H,2H-perfluorooctyl-silane (PFOTS)</td>
			<td>Sigma-Aldrich</td>
			<td>448931</td>
			<td>For silicon wafer silanization</td>
		</tr>
		<tr>
			<td>Tygon microbore tube (0.020&#34; x 0.060&#34;OD)</td>
			<td>Cole-Parmer</td>
			<td>EW-06419-01</td>
			<td>Tubings for media delivery</td>
		</tr>
	</tbody>
</table>

generation of heterogeneous drug gradients across cancer populations on a microfluidic evolution accelerator for real-time observation

Conventional cell culture remains the most frequently used preclinical model, despite its proven limited ability to predict clinical results in cancer. Microfluidic cancer-on-chip models have been proposed to bridge the gap between the oversimplified conventional 2D cultures and more complicated animal models, which have limited ability to produce reliable and reproducible quantitative results. Here, we present a microfluidic cancer-on-chip model that reproduces key components of a complex tumor microenvironment in a comprehensive manner, yet is simple enough to provide robust quantitative descriptions of cancer dynamics. This microfluidic cancer-on-chip model, the &#34;Evolution Accelerator,&#34; breaks down a large population of cancer cells into an interconnected array of tumor microenvironments while generating a heterogeneous chemotherapeutic stress landscape. The progression and the evolutionary dynamics of cancer in response to drug gradient can be monitored for weeks in real time, and numerous downstream experiments can be performed complementary to the time-lapse images taken through the course of the experiments.

Validation of optimum cell growth on chip 
A major goal of the experiment platform is to reproduce key components and interactions in a complex tumor microenvironment in a comprehensive manner, yet simple enough to provide quantitative, reliable and reproducible data. This goal can only be achieved if we have full control of the physical and biochemical environmental factors. We must either exclude the undesired factors or figure out a way to incorporate the uncontro...

Watch this Scientific Journal Video about Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation at JoVE.com

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

We present a microfluidic cancer-on-chip model, the &#34;Evolution Accelerator&#34; technology, which provides a controllable platform for long-term real-time quantitative studies of cancer dynamics within well-defined environmental conditions at the single-cell level. This technology is expected to work as an in vitro model for fundamental research or pre-clinical drug development.

Conventional cell culture remains the most frequently used preclinical model, despite its proven limited ability to predict clinical results in cancer. ...

Research

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