Name: quantifying the brain metastatic tumor micro-environment using an organ-on-a chip 3d model, machine learning, and confocal tomography
Uploaded: 2020-08-16T15:00:00
Description: Watch this Scientific Journal Video about Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography at JoVE.com

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

1. Prepare the blood brain barrier niche mold
NOTE: The culturing device used in this platform is a PDMS based scaffold that we build a cellular blood brain barrier niche upon. It is made of two parts separated by a porous membrane. To prepare the blood brain barrier niche two SU-8 molds made using photolithography are necessary26,27. The protocol will be described for the 100 &#181;m thick mold first and then notes will be given for the...

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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

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 &#181;mBBN chip formulated with hCMEC/D3-DsRed and non-fluorescent astrocytes. The brain microvascular endothelial cells were seeded onto a porous membrane (5 &#181;m track etched pores) and placed in an incubator34 at 37 &#176;C under 5% CO2. After three days the confluency of the endothelial layer was confirmed via microscopy and then cancer ...

Watch this Scientific Journal Video about Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography at JoVE.com

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

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, ...

Confocal tomography was developed to obtain the full gamut of cancer cell behaviors, as they interact with the cellular barrier and niche. This method provides a basis for studying metastases as they occur. This approach is useful for quantifying live cell behavior.Confocal tomography, when combined with machine learning, is useful for a variety of organ-on-a-chip platforms. Analyzing primary or recurrent tumor or circulating tumor cells can constitute an important diagnostic tool to predict brain metastases and to discern brain metastatic from non brain metastatic cells. To assemble the microfluidic BBN device, transfer a device from the vacuum desiccator onto an appropriate surface with the inlets facing down and place the entire setup into a plasma chamber.Place the entire setup into a plasma chamber and pull a vacuum before treating the device with plasma for 30 seconds at 80 Watts. At the end of the treatment, use a guide to quickly place the device, inlet side up onto the glass slide on the lab bench to create a permanent bond between the PDMs of the device and the slide. Next, insert 200 microliter pipette tips, cut two millimeters from the tip into all of the inlets and outlets and place the device into the plasma chamber for an eight minute, 200 watt plasma treatment.After the device has cooled, place the device into a sterile secondary container and within 15 minutes of plasmid treatment, transfer 120 microliters of a collagen astrocyte solution into the device through the pipette tip for the bottom chamber. Allow the solution to wick across the chamber into the opposite pipette tip and fill the next channel of the device. When all four channels of the device have been filled, place the chip in the cell culture incubator for one hour.When the collagen has set, bill all of the pipette tips feeding the bottom chamber with the appropriate complete cell culture medium and coat the upper chamber with 2%growth factor reduced matrigel, in complete endothelial medium. When both chambers have been coded, place the device into the incubator for one hour before rinsing the upper chamber with the appropriate medium. Alternating tips, see 30 microliters of one times 10 to the sixth endothelial cells per milliliter of appropriate medium, through the tips into the upper chamber every 15 minutes, for a total of four aliquots of cells per tip.Incubate the device in between seedings. When all of the endothelial cells have been seeded, fill all of the tips with the medium and return the device to the cell culture incubator for 48 hours. When the endothelial layer has matured, replace the medium in the chip to replenish the cell culture medium and seed each top chamber channel with 30 microliters of one times 10 to the six cancer cells per milliliter of appropriate medium.After each 30 microliter aliquot of cells has been seeded, return the device to the incubator for 15 minutes then fill all tips with the appropriate medium return the device to the cell culture incubator for 24 to 48 hours. After the device has been cultured and imaged, to measure the phenotype of the cancer cells, open the provided software and read the confocal image into memory. The software will save each color channel from the 3D confocal image into a separate TIFF file.Select Change opacity values for each microscope channel'and adjust the channel alpha value for the color channels present in the image, such that the background is removed and only the fluorescents within the cells remains. To visualize the effect, select View a 3D rendering'to verify the threshold are set correctly and if the image looks correct, save the opacity values in the experiment tracker spreadsheet. Then, use marching cubes to convert the volume image into individual 3D triangular meshed objects saved in the VTK data format.To fit a plane to the endothelial barrier, first locate the cell centroids. Use the polydata connectivity filter to iterate through the list of meshes in the VTK file to extract the regions that are not connected. Calculate the centroid of each mesh and add the measurement to a list of centroids filtering for meshes that are too large or too small.To fit a plane to the list of centroids for the endothelial cells, use a minimization of error method, run Visualize RFP centroids and plane fit'to generate and plot the plane fit and inspect the fit of the plane, adjusting the fit manually as necessary. When the plane has been properly fitted, save the normal of the plane to the experiment tracker file. To measure each cancer cells phenotype after characterizing the endothelial layer with a plane, load the cell analysis function and run Read in the experiment tracker information and analyze the channels that exist"to iterate over and analyze each region in the VTK file.For each region, the function will clip each cell so that the mesh below the membrane is calculated. To measure the cellular phenotype, calculate the shape, volume and position of each cancer cell. Then measure the volume and position of each clipped cancer cell to calculate the percentage of cell that extravasated through the endothelial barrier.After performing these steps for several experiments run Export the data as a single xlsx file'to save the data in a spreadsheet. A microfluidic BBN chip with a confluent endothelial barrier is acceptable for experimentation, while a microfluidic BBN chip with especially poor endothelial coverage, is not. In this representative analysis the brain seeking cancer cell line, exhibits a subpopulation of cells that extravasates across the endothelial barrier and migrates deep into the brain niche space of the microfluidic BBN chip At two and nine days after exposure, the parental cancer cells maintained a substantial proportion of cells on top of the barrier away from the brain niche space, while the brain metastatic cancer cell population maintained a proportion of cells that were greater than 100%extravasated.Morphological quantification of the cancer cell shape indicated that parental cells demonstrated fewer spherically shaped cells day one post-seeding, while after two and nine days of interaction both cancer cell lines trended toward decreasing their sphericity. In addition, the cancer cell subpopulations that extravasated into the astricydic niche were smaller in size, compared to the cancer cells that remained in interaction with the endothelial barrier without extravasating into the brain. Brain seeking cancer cell lines and patient derived xenografts, exhibit a phenotypic pattern in the microfluidic BBN chip that could be exploited to differentiate between the brain metastatic and non brain metastatic cancer cells by machine learning.Self-selection is important. Endothelial cells with a higher, low passage number exhibited variable barrier behaviors. In addition, cancer cells and their fluorescent expression may change over time.After this procedure, researchers may employ molecular profiling of the secret's home or cells in the device in order to study molecular mechanisms of metastases. This technique enables the exploration of complex interactions between cancer cells and their microenvironments, by combining an engineered microenvironment with a quantitative imaging of niche components over time.

quantifying-brain-metastatic-tumor-micro-environment-using-an-organ

Research

JoVE Journal

Cancer Research

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the context of a whole organism. Sophisticated techniques to generate genetic mosaics facilitate induction of visually marked, genetically defined clones surrounded by normal tissue. The clones can be analyzed through diverse molecular, cellular and omics approaches. This study describes how to generate fluorescently labeled clonal tumors of varying malignancy in the eye/antennal imaginal discs (EAD) of Drosophila larvae using the Mosaic Analysis with a Repressible Cell Marker (MARCM) technique. It describes procedures how to recover the mosaic EAD and brain from the larvae and how to process them for simultaneous imaging of fluorescent transgenic reporters and antibody staining. To facilitate molecular characterization of the mosaic tissue, we describe a protocol for isolation of total RNA from the EAD. The dissection procedure is suitable to recover EAD and brains from any larval stage. The fixation and staining protocol for imaginal discs works with a number of transgenic reporters and antibodies that recognize Drosophila proteins. The protocol for RNA isolation can be applied to various larval organs, whole larvae, and adult flies. Total RNA can be used for profiling of gene expression changes using candidate or genome-wide approaches. Finally, we detail a method for quantifying invasiveness of the clonal tumors. Although this method has limited use, its underlying concept is broadly applicable to other quantitative studies where cognitive bias must be avoided.

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

Native Chromatin Immunoprecipitation Using Murine Brain Tumor Neurospheres

Epigenetic modifications may be involved in the development and progression of glioma. Changes in methylation and acetylation of promoters and regulatory regions of oncogenes and tumor suppressors can lead to changes in gene expression and play an important role in the pathogenesis of brain tumors. Native chromatin immunoprecipitation (ChIP) is a popular technique that allows the detection of modifications or other proteins tightly bound to DNA. In contrast to cross-linked ChIP, in native ChIP, cells are not treated with formaldehyde to covalently link protein to DNA. This is advantageous because sometimes crosslinking may fix proteins that only transiently interact with DNA and do not have functional significance in gene regulation. In addition, antibodies are generally raised against unfixed peptides. Therefore, antibody specificity is increased in native ChIP. However, it is important to keep in mind that native ChIP is only applicable to study histones or other proteins that bind tightly to DNA. This protocol describes the native chromatin immunoprecipitation on murine brain tumor neurospheres.

Epigenetic mechanisms are frequently altered in glioma. Chromatin immunoprecipitation could be used to study the consequences of genetic alterations in glioma that result from changes in histone modifications which regulate chromatin structure and gene transcription. This protocol describes native chromatin immunoprecipitation on murine brain tumor neurospheres.

Epigenetic modifications may be involved in the development and progression of glioma. Changes in methylation and acetylation of promoters and regulatory ...

Utilization of Ultrasound Guided Tissue-directed Cellular Implantation for the Establishment of Biologically Relevant Metastatic Tumor Xenografts

Preclinical testing of anticancer therapies relies on relevant xenograft models that mimic the innate tendencies of cancer. Advantages of standard subcutaneous flank models include procedural ease and the ability to monitor tumor progression and response without invasive imaging. Such models are often inconsistent in translational clinical trials and have limited biologically relevant characteristics with low proclivity to produce metastasis, as there is a lack of a native microenvironment. In comparison, orthotopic xenograft models at native tumor sites have been shown to mimic the tumor microenvironment and replicate important disease characteristics such as distant metastatic spread. These models often require tedious surgical procedures with prolonged anesthetic time and recovery periods. To address this, cancer researchers have recently utilized ultrasound-guided injection techniques to establish cancer xenograft models for preclinical experiments, which allows for rapid and reliable establishment of tissue-directed murine models. Ultrasound visualization also provides a noninvasive method for longitudinal assessment of tumor engraftment and growth. Here, we describe the method for ultrasound-guided injection of cancer cells, utilizing the adrenal gland for NB and renal sub capsule for ES. This minimally invasive approach overcomes tedious open surgery implantation of cancer cells in tissue-specific locations for growth and metastasis, and abates morbid recovery periods. We describe the utilization of both established cell lines and patient derived cell lines for orthotopic injection. Pre-made commercial kits are available for tumor dissociation and luciferase tagging of cells. Injection of cell suspension using image-guidance provides a minimally invasive and reproducible platform for the creation of preclinical models. This method is utilized to create reliable preclinical models for other cancers such as bladder, liver and pancreas exemplifying its untapped potential for numerous cancer models.

Here, we present a protocol to utilize ultrasound-guided injection of neuroblastoma (NB) and Ewing's sarcoma (ES) cells (established cell lines and patient-derived tumor cells) at biologically relevant sites to create reliable preclinical models for cancer research.

Preclinical testing of anticancer therapies relies on relevant xenograft models that mimic the innate tendencies of cancer. Advantages of standard ...

Live-Cell Imaging Assays to Study Glioblastoma Brain Tumor Stem Cell Migration and Invasion

Glioblastoma (GBM) is an aggressive brain tumor that is poorly controlled with the currently available treatment options. Key features of GBMs include rapid proliferation and pervasive invasion into the normal brain. Recurrence is thought to result from the presence of radio- and chemo-resistant brain tumor stem cells (BTSCs) that invade away from the initial cancerous mass and, thus, evade surgical resection. Hence, therapies that target BTSCs and their invasive abilities may improve the otherwise poor prognosis of this disease. Our group and others have successfully established and characterized BTSC cultures from GBM patient samples. These BTSC cultures demonstrate fundamental cancer stem cell properties such as clonogenic self-renewal, multi-lineage differentiation, and tumor initiation in immune-deficient mice. In order to improve on the current therapeutic approaches for GBM, a better understanding of the mechanisms of BTSC migration and invasion is necessary. In GBM, the study of migration and invasion is restricted, in part, due to the limitations of existing techniques which do not fully account for the in vitro growth characteristics of BTSCs grown as neurospheres. Here, we describe rapid and quantitative live-cell imaging assays to study both the migration and invasion properties of BTSCs. The first method described is the BTSC migration assay which measures the migration toward a chemoattractant gradient. The second method described is the BTSC invasion assay which images and quantifies a cellular invasion from neurospheres into a matrix. The assays described here are used for the quantification of BTSC migration and invasion over time and under different treatment conditions.

Here, we describe live-cell imaging techniques to quantitatively measure the migration and invasion of glioblastoma brain tumor stem cells over time and under multiple treatment conditions.

Glioblastoma (GBM) is an aggressive brain tumor that is poorly controlled with the currently available treatment options. Key features of GBMs include ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as tumors. Because digital polymerase chain reactions (PCR) enable the precise quantification of DNA copy number variants, they are becoming an essential tool for detecting rare genetic variations, such as drug-resistant mutations. It is expected that molecular diagnoses using digital PCR (dPCR) will be available in clinical practice in the near future; thus, how to efficiently conduct dPCR with human genetic material is a hot topic. Here, we introduce a method to detect Adenomatous polyposis coli (APC) somatic mosaicism using dPCR with the chip-in-a-tube format, which allows eight dPCR reactions to be simultaneously conducted. Care should be taken when filling and sealing the reaction mixture on the chips. This article demonstrates how to avoid the over- and underestimation of positive partitions. Furthermore, we present a simple procedure for collecting the dPCR product from the partitions on the chips, which can then be used to confirm the specific amplification. We hope that this methods report will help promote the dPCR with the chip-in-a-tube method in genetic research.

Digital polymerase chain reaction (PCR) is a useful tool for the high-sensitivity detection of single nucleotide variants and DNA copy number variants. Here, we demonstrate key considerations for measuring rare variants in the human genome using digital PCR with the chip-in-a-tube format.

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

The Clinical Application of Tumor Treating Fields Therapy in Glioblastoma

Glioblastoma is the most common and lethal form of brain cancer, with a median survival of 15 months after diagnosis and a 5 year survival rate of only 5% with current standard of care. Tumors often recur within 9 months following initial surgery, radiation and chemotherapy, at which point treatment options become limited. This highlights the pressing need for the development of better therapeutics to prolong survival and increase the quality of life for these patients.
Tumor Treating Fields (TTFields) therapy was developed to take advantage of the effect of low frequency alternating electrical fields on cells for cancer therapy. TTFields have been demonstrated to disrupt cells during mitosis and slow tumor growth. There is also growing evidence that they act through stimulating immune responses within exposed tumors. The advantages of TTFields therapy include its noninvasive approach and increased quality of life compared to other treatment modalities such as cytotoxic chemotherapies. The Food and Drug Administration approved TTFields therapy for the treatment of recurrent glioblastoma in 2011 and for newly diagnosed glioblastoma in 2015. We report on the effects of TTFields during mitosis, the results of electric fields modeling, and proper transducer array placement. Our protocol outlines the clinical application of TTFields on a patient post-surgery, using the second-generation device.

Glioblastoma is the most common and aggressive primary brain malignancy in adults, with most tumors recurring after initial treatment. Tumor Treating Fields (TTFields) therapy is the newest treatment modality for glioblastoma. Here, we describe the proper application of TTFields-transducer arrays on patients and discuss theory and aspects of treatment.

Glioblastoma is the most common and lethal form of brain cancer, with a median survival of 15 months after diagnosis and a 5 year survival rate of only 5% ...

Characterization of the Effects of Migrastatic Inhibitors on 3D Tumor Spheroid Invasion by High-resolution Confocal Microscopy

Drug discovery and development in cancer research is increasingly being based on drug screens in a 3D format. Novel inhibitors targeting the migratory and invasive potential of cancer cells, and consequently the metastatic spread of disease, are being discovered and considered as complementary treatments in highly invasive cancers such as gliomas. Thus, generating data enabling the detailed analyses of cells in a 3D environment following the addition of a drug is required. The methodology described here, combining spheroid invasion assays with high-resolution image capture and data analysis by confocal laser scanning microscopy (CLSM), enabled detailed characterization of the effects of the potential anti-migratory inhibitor MI-192 on glioma cells. Spheroids were generated from cell lines for invasion assays in low adherent 96-well plates and then prepared for CLSM analysis. The described workflow was preferred over other commonly used spheroid-generating techniques due to both ease and reproducibility. This, combined with the enhanced image resolution attained by confocal microscopy compared to conventional wide-field approaches, allowed the identification and analysis of distinct morphological changes in migratory cells in a 3D environment following treatment with the migrastatic drug MI-192.

The effects of migrastatic inhibitors on glioma cancer cell migration in three-dimensional (3D) invasion assays using a histone deacetylase (HDAC) inhibitor are characterized by high-resolution confocal microscopy.

Drug discovery and development in cancer research is increasingly being based on drug screens in a 3D format. Novel inhibitors targeting the migratory and ...

A Novel Stromal Fibroblast-Modulated 3D Tumor Spheroid Model for Studying Tumor-Stroma Interaction and Drug Discovery

Tumor-stroma interactions play an important role in cancer progression. Three-dimensional (3D) tumor spheroid models are the most widely used in vitro model in the study of cancer stem/initiating cells, preclinical cancer research, and drug screening. The 3D spheroid models are superior to conventional tumor cell culture and reproduce some important characters of real solid tumors. However, conventional 3D tumor spheroids are made up exclusively of tumor cells. They lack the participation of tumor stromal cells and have insufficient extracellular matrix (ECM) deposition, thus only partially mimicking the in vivo conditions of tumor tissues. We established a new multicellular 3D spheroid model composed of tumor cells and stromal fibroblasts that better mimics the in vivo heterogeneous tumor microenvironment and its native desmoplasia. The formation of spheroids is strictly regulated by the tumor stromal fibroblasts and is determined by the activity of certain crucial intracellular signaling pathways (e.g., Notch signaling) in stromal fibroblasts. In this article, we present the techniques for coculture of tumor cells-stromal fibroblasts, time-lapse imaging to visualize cell-cell interactions, and confocal microscopy to display the 3D architectural features of the spheroids. We also show two examples of the practical application of this 3D spheroid model. This novel multicellular 3D spheroid model offers a useful platform for studying tumor-stroma interaction, elucidating how stromal fibroblasts regulate cancer stem/initiating cells, which determine tumor progression and aggressiveness, and exploring involvement of stromal reaction in cancer drug sensitivity and resistance. This platform can also be a pertinent in vitro model for drug discovery.

A novel three-dimensional spheroid model based on the heterotypic interaction of tumor cells and stromal fibroblasts is established. Here, we present coculture of tumor cells and stromal fibroblasts, time-lapse imaging, and confocal microscopy to visualize the formation of spheroids. This three-dimensional model offers a pertinent platform to study tumor-stroma interactions and test cancer therapeutics.

Tumor-stroma interactions play an important role in cancer progression. Three-dimensional (3D) tumor spheroid models are the most widely used in vitro ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...