Name: Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography (Video) | JoVE Journal
Uploaded: 2020-08-16T15:00:00.000+00:00
Duration: 9 min 53 s
Description: Explore machine learning quantification of brain metastatic tumor micro-environments using organ-on-a-chip 3D models and confocal tomography. This method enables precise analysis of cancer cell behaviors for diagnostic applications.

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We thank the Steeg Lab, at the National Cancer Institute for the generous donation of MDA-MB-231-BR-GFP cells. Confocal microscopy was performed at the University of Michigan Biointerfaces Institute (BI). Flow cytometry was performed at the University of Michigan Flow Cytometry Core. Viral vectors were created by the University of Michigan Vector Core. We also thank Kelley Kidwell for guidance in statistical analysis of these data.
FUNDING:
C.R.O. was partially supported by an NIH T-32 Training Fellowship (T32CA009676) and 1R21CA245597-01. T.M.W. was partially supported by 1R21CA245597-01 and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002240. Funding for materials and characterization was provided by National Cancer Institute of the National Institutes of Health under award number 1R21CA245597-01, P30CA046592, 5T32CA009676-23, CA196018, AI116482, METAvivor Foundation, and the Breast Cancer Research Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

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There are no disclosures to declare.

We have developed and presented a new method that adapts tools often utilized in clinical imaging analyses for measurement of extravasation and migration of cancer cells through an endothelial barrier into brain tissue. We pose this approach can be useful for both in vivo and in vitro measurements; we have demonstrated its use on a 3D microfluidic system recapitulating brain vasculature. Cancer cell measurements including distance extravasated, percent extravasated by volume, sphericity, and volume are quantified using t...

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type1,2. A principal question that arises when studying cancer metastasis is how sub clones migrate from the humoral environment of the bloodstream into an organ such as the brain3,4. This question has led to many variations of migration, invasion, and extravasation assays. All these methods share the critical step of counting or measuring properties of cells that move from one location to another in response to a stimulus. Most migration assays readily available are used to study two-dimensional (2D) migration of cancer cells. These have elucidated a wealth of knowledge; however, they do not recapitulate the three-dimensional nature of the in vivo system that other methods can provide5. Therefore, it is necessary to study the tumor micro-environment (TME) in three-dimensional (3D) systems, but the analysis approaches available for 3D structures are limited and often inconsistent.
One of the most popular 3D tools is a Boyden chamber that consists of a membrane suspended at the bottom of a well, separating two distinct regions. Boyden introduced the assay to study leukocyte chemotaxis4. The bottom regions may be varied by chemistry or other means6,7 to induce cells in the upper region to migrate to the lower region. The most common approach to quantifying the number of cells that have migrated is to release the cells from the bottom of the membrane using a buffer solution, lyse them, and then count them based on the quantity of DNA content in the solution7. This indirect approach is prone to operator error due to technique variability and the procedure destroys information about the cancer phenotype and the micro-environment. Variations of the Boyden chamber assay involve fixation of migratory cells that remain on the membrane, but only provides a count of cells that are no longer viable for continued study6,8,9.
Due to limitations of the Boyden chamber and the growth of innovations in the microfluidic community, migration assay chips have been developed which observe the motion of cells in response to a stimulus in one direction rather than three10,11,12. These migration assays facilitate control over factors such as flow or single cell separation13,14 that enable better interpretation of the results; however, their 2D format inevitably loses some dynamic information. Recent studies have focused on extravasation (i.e., the movement of cells from circulation into a tissue, such as the blood brain barrier) in a 3D environment14,15. The extravasation distance into tissue and probing behavior that occurs at the cellular barrier/membrane is more refined than measurements gleaned using either the Boyden chamber or a 2D microfluidic migration device16. Thus, devices that enable appropriate imaging and analysis of 3D extravasation are critical to capture these sophisticated measurements but are lacking in the literature.
Independent of migration assays, robust imaging techniques have been developed for magnetic resonance imaging (MRI) and tomography that are able to identify and accurately reconstruct tissue in 3D space17,18. These techniques acquire images in z-stacks and segment portions of the image based on the properties of the tissue and then convert the segmented images into three-dimensional meshes19,20,21. This allows physicians to visualize in 3D individual organs, bones, and vessels to aid in surgical planning or aid in diagnosis of cancer or heart disease22,23. Here, we will show that these approaches can be adapted for use on microscopic specimens and 3D extravasation devices.
To this end, we developed the innovative confocal tomography technique, presented herein, which affords flexibility to study the extravasation of tumor cells across a membrane by adapting existing tomography tools. This approach enables the study of the full gamut of cancer cell behaviors as they interact with a cellular barrier, such as an endothelial cell layer. Cancer cells exhibit probing behaviors; some may invade but remain close to the membrane, while others traverse the barrier readily. This technique is capable of yielding information about the phenotype of the cell in all dimensions24. Using this approach to study the TME is both relatively inexpensive, easy to interpret, and reproducible, when compared to more complex in vivo murine models. The presented methodology should provide a strong basis for the study of many types of tumors and micro-environments by adapting the stromal region.
We describe and demonstrate the use of a 3D microfluidic blood brain niche (&#181;mBBN) platform (Figure 1) where critical elements of the barrier and niche (brain microvascular endothelial cells and astrocytes) can be cultured for an extended period (approximately up to 9 days), fluorescently imaged by confocal microscopy, and the images reconstructed using our confocal tomography technique (Figure 2); all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment in a repeatable and quantitative manner. The blood brain barrier interface with the brain niche is composed of brain microvascular endothelial cells that are strengthened by basement membrane, astrocyte feet, and pericytes25. We selectively focused on the astrocyte and endothelial components given their importance in the formation and regulation of the blood brain barrier. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and tumor micro-environment cellular and humoral components, using this platform. Finally, we show how machine learning can be used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model &#181;mBBN and to assign them an objective index of brain metastatic potential24. The data sets generated by this method can be used to answer basic and translational questions about metastasis, therapeutic strategies, and the role of the TME in both.

<table><tbody><tr><td>0.25% Trypsin-EDTA with phenol red</td><td>Thermo Fisher Scientific</td><td>25200056</td><td></td></tr><tr><td>1.5 mm biopsy punch with plunger</td><td>Integra LifeSciences Corporation</td><td>33-31A-P/25</td><td></td></tr><tr><td>10x MEM</td><td>Thermo Fisher Scientific</td><td>11430030</td><td></td></tr><tr><td>150 mm petri dishes</td><td>Fisher Scientific</td><td>FB0875714</td><td></td></tr><tr><td>1x DPBS, without Ca and Mg</td><td>Thermo Fisher Scientific</td><td>14190144</td><td></td></tr><tr><td>200uL pipette tip</td><td>Fisher Scientific</td><td>02-707-411</td><td></td></tr><tr><td>4 inch silicon wafer</td><td>University Wafer</td><td>452</td><td></td></tr><tr><td>48 mm wide packing tape</td><td>Fisher Scientific</td><td>19-072-097</td><td></td></tr><tr><td>50 x 75 mm glass slide</td><td>Fisher Scientific</td><td>12-550C</td><td></td></tr><tr><td>A1 confocal microscope</td><td>Nikon</td><td></td><td></td></tr><tr><td>acetone</td><td>Fisher Scientific</td><td>A9-20</td><td></td></tr><tr><td>antibiotic/antimycotic (penicillin/streptomycin/amphotericin)</td><td>Gibco</td><td>15240062</td><td></td></tr><tr><td>box cutter blade</td><td>Fisher Scientific</td><td>NC1721575</td><td></td></tr><tr><td>dissection scissors</td><td>Fisher Scientific</td><td>08-951-5</td><td></td></tr><tr><td>DMEM with 4.5 g/L glucose</td><td>Thermo Fisher Scientific</td><td>11960-044</td><td></td></tr><tr><td>double sided tape</td><td>Fisher Scientific</td><td>NC0879005</td><td></td></tr><tr><td>EGM-2</td><td>Lonza</td><td>CC-3162</td><td></td></tr><tr><td>Fetal Bovine Serum, Heat inactivated</td><td>Corning</td><td>MT35011CV</td><td></td></tr><tr><td>Fiji software</td><td>ImageJ</td><td></td><td></td></tr><tr><td>glass vial</td><td>Fisher Scientific</td><td>03-341-25D</td><td></td></tr><tr><td>glutamax</td><td>Thermo Fisher Scientific</td><td>35050061</td><td></td></tr><tr><td>hCMEC/D3</td><td>EMD Millipore</td><td>SCC066</td><td></td></tr><tr><td>Jupyter notebook</td><td>Anaconda</td><td></td><td></td></tr><tr><td>L-glutamine</td><td>Thermo Fisher Scientific</td><td>25030081</td><td></td></tr><tr><td>Matrigel - growth factor reduced with phenol red</td><td>Corning</td><td>CB-40230A</td><td></td></tr><tr><td>MDA-MB-231</td><td>ATCC</td><td>HTB-26</td><td></td></tr><tr><td>MDA-MB-231-BR-GFP</td><td>Dr. Patricia Steeg, NIH</td><td></td><td></td></tr><tr><td>N-2 growth supplement</td><td>Thermo Fisher Scientific</td><td>17502048</td><td></td></tr><tr><td>normal human astrocytes (NHA)</td><td>Lonza</td><td>CC-2565</td><td></td></tr><tr><td>Orange software</td><td>University of Ljubljana</td><td></td><td></td></tr><tr><td>Pasteur pipette</td><td>Fisher Scientific</td><td>13-711-9AM</td><td></td></tr><tr><td>Photolithography masks</td><td>Photosciences Incorporated</td><td></td><td></td></tr><tr><td>pLL3.7-dsRed</td><td>University of Michigan Vector Core</td><td></td><td></td></tr><tr><td>pLL-EV-GFP</td><td>University of Michigan Vector Core</td><td></td><td></td></tr><tr><td>pLOX-TERT-iresTK</td><td>Addgene</td><td>12245</td><td></td></tr><tr><td>pMD2.G</td><td>Addgene</td><td>12259</td><td></td></tr><tr><td>polycarbonate membrane, 5um pore size</td><td>Millipore</td><td>TMTP04700</td><td></td></tr><tr><td>psPAX2</td><td>Addgene</td><td>12260</td><td></td></tr><tr><td>PureCol, 3 mg/mL</td><td>Advanced Biomatrix</td><td>5005</td><td>Type I bovine collagen</td></tr><tr><td>sodium bicarbonate</td><td>Thermo Fisher Scientific</td><td>25080094</td><td></td></tr><tr><td>sodium pyruvate</td><td>Thermo Fisher Scientific</td><td>11360070</td><td></td></tr><tr><td>Solo cup</td><td>Fisher Scientific</td><td>NC1416545</td><td></td></tr><tr><td>SU-8 2075</td><td>MicroChem Corporation</td><td>Y111074 0500L1GL</td><td></td></tr><tr><td>SU8 developer</td><td>MicroChem Corporation</td><td>Y020100 4000L1PE</td><td></td></tr><tr><td>Sylgard 184</td><td>Ellsworth Adhesive Company</td><td>NC0162601</td><td></td></tr><tr><td>Toluene</td><td>Sigma-Aldrich</td><td>179965-1L</td><td></td></tr><tr><td>Tricholoro perfluoro octyl silane</td><td>Sigma-Aldrich</td><td>448931-10G</td><td></td></tr></tbody></table>

quantifying the brain metastatic tumor micro-environment using an organ-on-a chip 3d model, machine learning, and confocal tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (&#181;mBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model &#181;mBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.

Using this technique, we analyzed cell types labeled with different fluorescent proteins or dyes. We demonstrate the use of this approach with a µmBBN chip formulated with hCMEC/D3-DsRed and non-fluorescent astrocytes. The brain microvascular endothelial cells were seeded onto a porous membrane (5 µm track etched pores) and placed in an incubator34 at 37 °C under 5% CO2. After three days the confluency of the endothelial layer was confirmed via microscopy and then cancer cells were plac...

Here, we present a protocol for preparing and culturing a blood brain barrier metastatic tumor micro-environment and then quantifying its state using confocal imaging and artificial intelligence (machine learning).

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, ...

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JoVE Journal

Cancer Research

We have developed 3D coculture models for live-cell imaging in real-time of interactions among breast tumor cells and other cells in their microenvironment that impact progression to an invasive phenotype.  These models can serve as preclinical screens for drugs to target paracrine-induced proteolytic, chemokine/cytokine and kinase pathways implicated in invasiveness.

MAME Models for 4D Live-cell Imaging of Tumor: Microenvironment Interactions that Impact Malignant Progression

This video demonstrates the development of a 3D co-culture system. Mouse brain slices are placed on a membrane, and fluorescently labeled human brain tumor cells are allowed to grow on it. Once the system is ready, it is stained for imaging.

Establishing a Brain Tumor in an Organotypic Slice Co-culture Model

The organotypic brain slice coculture with carcinoma cells enables visualizing morphological changes by fluorescence as well as bright field (video) microscopy during the process of carcinoma cell invasion of brain tissue. This model system also allows for cell exchange and replenishment approaches and offers a wide variety of manipulations and analyses.

Coculture System with an Organotypic Brain Slice and 3D Spheroid of Carcinoma Cells

Many types of human brain tumors are localized to specific regions within the brain and are difficult to grow in culture. This protocol addresses the role of tumor microenvironment and investigates new drug treatments by analyzing fluorescent primary brain tumor cells growing in an organotypic mouse brain slice.

A Brain Tumor/Organotypic Slice Co-culture System for Studying Tumor Microenvironment and Targeted Drug Therapies

Intracranial brain metastasis modeling is complicated by an inability to monitor tumor size and response to treatment with precise and timely methods. The presented methodology couples intracranial tumor injection with magnetic resonance imaging analysis, which when combined, cultivates precise and consistent injections, enhanced animal monitoring, and accurate tumor volume measurements.

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

In the age of immunotherapy and single-cell genomic profiling, cancer biology requires novel in vitro and computational tools for investigating the tumor-immune interface in a proper spatiotemporal context. We describe protocols to exploit tumor-immune microfluidic co-cultures in 2D and 3D settings, compatible with dynamic, multiparametric monitoring of cellular functions.

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

We describe a xenograft mouse model of breast cancer brain metastasis generated via tail-vein injection of an endogenously HER2-amplified inflammatory breast cancer cell line.

Modeling Brain Metastasis Via Tail-Vein Injection of Inflammatory Breast Cancer Cells

The present protocol develops an image-based technique for rapid, non-destructive, and label-free regional cell density and viability measurement within 3D tumor aggregates. Findings revealed a cell-density gradient, with higher cell densities in core regions than outer layers in developing aggregates and predominantly peripheral cell death in HER2+ aggregates treated with Trastuzumab.

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Brain metastasis is a cause of severe morbidity and mortality in cancer patients. Most brain metastasis mouse models are complicated by systemic metastases confounding analysis of mortality and therapeutic intervention outcomes. Presented here is a protocol for internal carotid injection of cancer cells that produces consistent intracranial tumors with minimal systemic tumors.

Modeling Brain Metastasis by Internal Carotid Artery Injection of Cancer Cells

The stereotactic intracranial injection technique enables precise and localized implantation of tumor cells into the mouse cortex, making it a powerful model for studying metastatic colonization of the brain.

Modelling Brain Metastasis: Standardized Analysis of Metastatic Colonization and Histological Growth Patterns by Stereotactic Intracortical Injection

We present a 3D glioblastoma migration model using patient derived magnetic gliomaspheres generated from newly diagnosed and recurrent tumors. This platform allows visualization of patient specific migration dynamics and assessment of therapeutic efficacy. The assay can be multiplexed post migration to evaluate protein localization and expression patterns.

Reproducible 3D Glioblastoma Migration Assay with Magnetic Nanoparticle Mediated Spheroid Localization Under Hypoxic Conditions

Explore machine learning quantification of brain metastatic tumor micro-environments using organ-on-a-chip 3D models and confocal tomography. This method enables precise analysis of cancer cell behaviors for diagnostic applications.

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography (Video) | JoVE Journal

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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