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, 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 (µmBBN) 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 µmBBN 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.
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 (µmBBN) 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 µmBBN 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.
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 µm thick mold first and then notes will be given for the 200 µm thick mold.
2. Form and assemble the PDMS blood brain barrier (BBB) device
3. Seed the brain micro-environment into the device
4. Monitor progression of the endothelial layer formation
5. Seed cancer cells into the device
6. Image the tumor micro-environment by confocal imaging
7. Measure the tumor micro-environment via confocal tomography
8. Analyze the related characteristics using Artificial Intelligence
NOTE: Identify metastatic phenotypic features using artificial intelligence algorithms.
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 placed on top of the endothelial layer. Two breast cancer cell lines, a brain-seeking clone termed MDA-MB-231-BR-GFP, and parental MDA-MB-231-GFP cells were input into the µmBBN chip containing an astrocytic brain niche35,36,37,38,39,40,41. The µmBBN chips were imaged at 1, 2, and 9-Days using a confocal microscope with 3 channels (dsRed, GFP and Brightfield) using a 10x objective, with Z-slices every 9 µm and 1×9 XY stitching to cover the entire membrane area. This microscope maintained a temperature of 37 °C during the imaging process to minimize stress on the cells.
A representative image of a complete µmBBN chip (Figure 3A) and endothelial coverage (Figure 3B-D) is shown. Endothelial barriers with high and low coverage are quantified to ascertain µmBBN chips are suitable for the addition of cancer cells (Figure 3B). Representative images of a µmBBN chip with a confluent endothelial barrier that is acceptable for experimentation (Figure 3C) and a µmBBN chip with especially poor endothelial coverage that is not suitable for the addition of cancer cells are provided (Figure 3D). Long-term culturing of endothelial cells can result in coverage that spans beyond the membrane separating the top and bottom µmBBN chip chambers. Endothelial cells often grow both on top of and directly beneath the membrane, which does not affect cancer cell movement into the brain niche space but can make the plane fit difficult. Due to the variability of the µmBBN chip fabrication and endothelial coverage of the membrane, representative planes fit to a normal flat endothelial barrier (Figure 3E), and an atypical curved membrane are displayed for reference (Figure 3F). Drying the device in an oven at a temperature above 40 °C may cause a curved membrane as shown.
We observed phenotypic differences of the MDA-MB-231-BR-GFP and parental MDA-MB-231-GFP cell line when encountering astrocytic µmBBN that were quantified using the developed confocal tomographic analysis. The 4 phenotypic descriptors used for input into the machine learning algorithm (Table 1) are displayed in Figure 4.
Distance extravasated represents the distance in µm between the endothelial barrier and each cancer cell position in the µmBBN chip (Figure 4A). The endothelial barrier is located at 0 µm. Distances <0 µm represent cancer cells that remained in the flow chamber. Distances >0 µm indicate cancer cells that have extravasated through the endothelial barrier and entered the brain niche space. After 1-day of exposure to the µmBBN both MDA-MB-231-BR-GFP and MDA-MB-231-GFP were positioned within the endothelial barrier. However, after 2 and 9-Days of interaction a subset of the MDA-MB-231-BR-GFP migrated >100 µm into the astrocytic brain niche, whereas the parental MDA-MB-231-GFP cells remained in proximity to the endothelial barrier. We resolved the cancer cell positions at the endothelial barrier/brain niche interface by calculating the volume of each cancer cell that has passed through the endothelial barrier. The resulting percentage represents cancer cell extravasated by volume (Figure 4B). A 0% extravasated by cell volume indicates cancer cells that remain on top of the endothelial barrier, ranging to 100% when a cancer cell has completely extravasated through the endothelial barrier and resides in the brain niche space. The parental MDA-MB-231-GFP cells initially interact with the endothelial barrier after 1-Day of exposure to the µmBBN chip that were <50% extravasated by volume. At 2, and 9-Days the MDA-MB-231-GFP cells maintain a substantial proportion of cells that remained on top of the barrier away from the brain niche space. The MDA-MB-231-BR-GFP cells maintained a proportion of cells that were >100% extravasated, especially at the 2-Day timepoint.
The cancer cell shape changed dramatically over the duration of exposure to the µmBBN. Representative images of the cancer cells at each timepoint are depicted in Figure 4C. Morphological quantification of the cancer cell shape was calculated using sphericity, that accounts for cell volume and surface area (Figure 4D). The sphericity metric ranges from 0-1, with 1 representing a perfect sphere. Both MDA-MB-231-BR-GFP and MDA-MB-231-GFP cells were highly spherical 1-day post seeding into the µmBBN chip. After 2- and 9-days of interaction in the µmBBN chip, both cancer cell lines trended to decrease their spherical in shape, albeit at different rates. In addition to cancer cell shape, the volume in voxels of each cancer cell is also quantified using the confocal tomographic analysis. Each cancer cell line was stratified into two groups in Figure 4E-F: “out”, representing the cancer cells that transited through the endothelial barrier (>90% extravasated through the barrier) and “in”, the population of cells that interact with the endothelial barrier but do not extravasate through (<90% extravasated). The cancer cell subpopulations that extravasated into the astrocytic niche were smaller in size compared to the cancer cells that remained in interaction with the endothelial barrier but did not fully extravasate through to the brain.
The brain-seeking MDA-MB-231-BR-GFP revealed a phenotypic pattern in the µmBBN chips distinct from the parental MDA-MB-231-GFP that can be exploited to differentiate between brain-metastatic and non-brain metastatic cancer cells using machine learning. The data was randomly separated into training and validation datasets to train the model and perform validation tests. To train the model, a total of 38,859 cells were used after filtering the data and the model was tested against 9,714 individual cells. The trained model was applied to the cancer cell lines and PDX-generated cancer cells that were analyzed in astrocytic µmBBN chips (4 isolated from patient brain metastases of a variety of primary tumor types, and 1 primary breast cancer tumor) to generate an index of brain metastatic probability (Figure 5). Eight different machine learning classification methods were tested: Naïve bayes, random forest, decision tree, k-nearest-neighbor (kNN), stochastic gradient descent, neural network, and Adaboost. The result of each method is shown in Table 2. Neural network and Adaboost were the 2 best performing classification methods that are recommended for use with data generated using the µmBBN platform with an AUC of 0.920 and 0.928, respectively. Moreover, they showed an accuracy of 0.833 and 0.853. The average of precision and recall (F1) for the Neural network and Adaboost methods were 0.847 and 0.860. From previous work where we applied this approach to PDX samples of non-metastatic breast tumor and known metastatic samples (breast, lung, ovarian, tongue) we found that the same approach applied to the PDX samples enabled accurate identification of the metastatic cells from the non-metastatic ones. Table 2 shows the results for each machine learning algorithm applied to the PDX data wherein the same methods proved to be the most robust (Neural network and Adaboost (Random Forest). Of the 143 cells used in the testing set for the PDX samples, 71 were non-metastatic, 46 were metastatic breast, 11 were metastatic tongue, 13 were metastatic lung and 2 were metastatic ovarian. Each cell type produced an overall accuracy of 0.88 but individual had the following approximate accuracies: non-metastatic breast: 0.96, metastatic breast: 0.80, metastatic tongue: 0.80, metastatic lung: 0.92, metastatic ovarian: 1.0
Figure 1: Experimental workflow. (A) Schematic representation of the microfluidic device assembly process. A spinner is used to deposit a thin film of PDMS:toluene glue onto a 50 mm x 75 mm glass slide. Each half of the µmBBN device is stamped channel side facing the glue and then assembled with a polycarbonate membrane (5 µm pores) between the µmBBN device parts. The µmBBN devices are placed in a 37 °C oven for 24 h to cure the glue. Devices are then dried in a vacuum desiccator for at least 48 h prior to experimental use. A µmBBN device and 50 mm x 75 mm glass slide are activated with a plasma treatment and bonded together. Standard P200 pipettes cut at the tips are inserted into all µmBBN device inlets and outlets. The completed µmBBN device is then sterilized by an 8 min (200 W) plasma treatment then transferred to a sterile secondary container. (B) Schematic overview of utilizing the microfluidic device scaffolding to create a cellular blood brain barrier and brain niche micro-environment. Inside a biosafety cabinet, a mixture of astrocytes in collagen are seeded into the bottom µmBBN device chamber and allowed to solidify for 1 h at 37 °C. Matrigel is used to coat the membrane through the top flow chamber for 1 h at 37 °C. Then endothelial cells are seeded into one tip of the top chamber and allowed to flow and settle for 15 min. This seeding is repeated x4, alternating sides of the flow chamber. Please click here to view a larger version of this figure.
Figure 2: Confocal tomographic analysis overview. The software begins by converting a microscopic confocal Z-stack image into a 3-D model of the cells using segmentation and 3D meshes. The program then calculates the center position of each cell (centroid) and fits a plane to the endothelial barrier. Phenotypic measurements of each single cell are then tabulated. Please click here to view a larger version of this figure.
Figure 3: Endothelial barrier coverage and plane fitting. (A) Representative schematic and image of a µmBBN device. The dashed white line within the top view schematic indicates the area of the device represented in the cross-sectional view. (B) Comparison of high and low endothelial coverage of µmBBN devices prior to the application of cancer cells. Welch two-sample t-test, *** p < 0.1*10-4. (C) Representative image of high endothelial coverage. The dashed white box within the inset schematic of a µmBBN device indicates the location of the endothelial cells within the device. Scale bars of overview and inset images = 200 µm. (D) Representative image of low endothelial coverage. Scale bars of overview and inset images = 200 µm. (E) Example plane fit of a flat endothelial barrier. The green rectangle represents the position of the endothelial plane. Dots represent single endothelial cells comprising the barrier. Yellow dots are endothelial cells above the plane, and purple dots are cells that fall below the plane. Endothelial cells above the plane (yellow dots) exhibit a tendency to grow up the sidewalls and top of the device to form a tube. (F) Example plane fit of a µmBBN device with a curved plane. Please click here to view a larger version of this figure.
Figure 4: Quantification of cellular phenotypes of brain-metastatic and parental cell phenotypes in astrocytic blood brain niche microfluidic chips. (A) Strip plot of distance in µm of cancer cells from the endothelial barrier at 1, 2, and 9-Days. The dashed black line at 0 µm represents the endothelial barrier. Red boxes indicate a subset of MDA-MB-231-BR-GFP cells that migrated far into the brain niche. (B) Violin plot of the percent total volume of cancer cells extravasated through the endothelial barrier at 1, 2, and 9-Days. Short dashed lines represent quartiles, longer dashed line represents the mean. (C) Representative images of the morphology of cancer cells in µmBBN device. Scale bar = 25 µm. (D) Violin plot of the sphericity of cancer cells in µmBBN device at 1, 2, and 9-Days. Sphericity ranges from 1: spherical to 0: not spherical. (E) Box plot of MDA-MB-231-GFP cell volume in the µmBBN device in voxels for cells resting outside the endothelial barrier (out) and cells that extravasated through the barrier (in). (F) Box plot of MDA-MB-231-BR-GFP cell volume in the µmBBN device in voxels for cells resting outside the endothelial barrier (out) and cells that extravasated through the barrier (in). The box displays the quartiles and whiskers extend to show the proportion of the interquartile range past the low and high quartiles. Pairwise Wilcoxon Rank Sum and Kruskal-Wallis with Dunn’s multiple comparisons, *** p < 0.1*10-4. Reproduced from reference24 with permission from the Royal Society of Chemistry. Please click here to view a larger version of this figure.
Figure 5: Representative results of machine learning classification of cancer cells. (A) Machine learning overview. Demonstrates process of splitting data collected from confocal tomography, filtering the data, training the machine learning algorithm using 10-fold validation and then testing the model against a random sample of 20% of the data the was reserved. The selected model can then be applied on new data to collect the metastatic index of individual cells. (B) ROC curves showing the performance of 8 different machine learning algorithms for MDA-231-BR-GFP and MDA-231-GFP cells culture for 1, 2 and 9-Days before imaging. This is representative of the type of curve to be analyzed to understand the performance of the trained model. (C) ROC curves for 8 different machine learning algorithms applied to patient derived xenograft (PDX) dissociated cells cultured for 2-Days. This is representative of the type of curve to be analyzed to understand the performance of the trained model. Reproduced from reference24 with permission from the Royal Society of Chemistry. Please click here to view a larger version of this figure.
Feature | Descriptor |
Tumor cell | % extravasated into the niche |
Tumor cell | Volume |
Tumor cell | Sphericity |
Tumor cell | Distance extravasated |
Tumor cell | Live cell 2D migration |
Micro-metastasis | Porosity |
Micro-metastasis | Stromal interaction |
Micro-metastasis | Age |
Micro-metastasis | Growth rate |
Stromal cell | Volume |
Stromal cell | Distance from nearby cancer cells |
Stromal cell | Distance from endothelial barrier |
Stromal cell | Shape |
Table 1: List of descriptors by feature type. The phenotypic characterization of tumor cells is represented using a panel of descriptors. The red box indicates the descriptors that have been used to predict brain metastatic probability via machine learning.
Cancer cells | |||
Method | AUC | Accuracy | F1 |
Neural Network | 0.925 | 0.84 | 0.847 |
AdaBoost | 0.928 | 0.853 | 0.86 |
Random Forest | 0.925 | 0.849 | 0.855 |
Decision Tree | 0.898 | 0.817 | 0.827 |
kNN | 0.775 | 0.702 | 0.718 |
Logistic Regression | 0.769 | 0.735 | 0.751 |
Naïve Bayes | 0.745 | 0.715 | 0.73 |
SGD | 0.73 | 0.73 | 0.737 |
PDX Cancer cells | |||
Method | AUC | CA | F1 |
Neural Network | 0.972 | 0.881 | 0.878 |
Random Forest | 0.964 | 0.888 | 0.887 |
AdaBoost | 0.957 | 0.881 | 0.879 |
Tree | 0.954 | 0.867 | 0.865 |
Logistic Regression | 0.897 | 0.832 | 0.831 |
Naïve Bayes | 0.896 | 0.846 | 0.849 |
kNN | 0.882 | 0.818 | 0.814 |
SGD | 0.861 | 0.86 | 0.853 |
Table 2: Comparison of machine learning methods to classify cancer cells and PDX cancer cells by brain metastatic potential.
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 this technique. Distance extravasated and percent extravasated by volume permit the user to reconstruct the position of the cancer cells within the chip to assess extravasation across the barrier and migration within the tissue. Cell shape measurements such as sphericity and volume are related to the dynamic movements or function of the cell at each timepoint. Migratory MDA-MB-231-BR-GFP cells exhibit a high level of sphericity when initially introduced into the µmBBN device and become less spherical in shape as they decrease their movement and begin to colonize the niche. Overall cell volume of the MDA-MB-231-GFP and MDA-MB-231-BR-GFP differed due to the difference in shape of the cell lines. Cancer cells that cross the endothelial barrier are more rounded than the cells that do not traverse through the barrier, thus smaller round cells may be able to extravasate across the endothelial layer more efficiently.
Two critical steps exist within the protocol that facilitate success. The first occurs during the assembly of the µmBBN device upper and lower parts that are separated by the porous membrane. Upper and lower device parts must be mated so that the inlets and outlets overlap but are not occluded by the membrane in between to promote proper flow. All µmBBN devices with poor mating of the upper and lower device parts or membrane alignment are discarded to minimize fluctuations in quality. Subsequent baking to cure the PDMS:toluene glue to assemble the device is critical to perform at 37 °C to produce µmBBN devices with membranes that are flat. Baking temperatures that exceed 37 °C tend to produce devices with a curved membrane that makes image analysis difficult, especially when fitting a plane to the endothelial barrier. The second critical step happens during the seeding of the endothelial barrier. Seedings should occur at a minimum of fifteen minute intervals between the two inlets feeding the top part of the device to ensure random distribution of the endothelial cells onto the membrane of the device. Seeding of the collagen mixture containing astrocytes into the chip may require troubleshooting and modification to the lab-specific protocol. If the membrane surface of the device is not hydrophobic, the collagen will fill both the bottom and top parts of the device and set so that it clogs all flow through the top part of the device. The purpose of the final plasma gas treatment is to make the device surface hydrophobic. In this case, we recommend adjustment of the plasma gas treatment of the device to increase hydrophobicity.
We have observed some limitations with this approach. For example, the approach used to induce cell fluorescence can impact the imaging quality. When using live cell tracking dyes, the fluorescent pattern is made of small spots, while transfected or transduced expression of fluorophores produces a uniform pattern. The spotty pattern requires additional and sometimes error prone clustering of pixels. Additionally, the sensitivity of the measurement is dependent on the care taken during imaging. Higher resolution images and more z slices improves resolution, but also takes more time to image and analyze. Cells that are touching can also be incorrectly analyzed as a large single cell. This is a problem for many automated imaging systems but can be addressed in two ways. The first is that the endothelial layer has many cells touching, but their combination has negligible impact on the final location of the cutting plane. The second is the number of cancer cells is low enough that it is rare they are touching. Errors introduced when fitting the plane can occur for several reasons. The first is that some endothelial cells may have migrated away from the central membrane during cell culture. This can result in endothelial cells coating the entire channel or invading the collagen filled space, thereby skewing the measurement. Another source of error is, paradoxically, the manual adjusting of the plane to fix the previous error. However, repeatability and reproducibility studies have found this to have minimal impact. Finally, we observed that under certain circumstances the Boolean cutting of the cell mesh may fail or the algorithm to close the cut mesh may also fail. The techniques used here are the current “state of the art”, and these problems are currently being addressed by algorithmic scientists.
The results from training the machine learning algorithms (AI) against data collected by confocal tomography of the µmBBN demonstrate that the considerable number of individual cells analyzed by this approach may help address issues of capturing heterogeneity found in cancer cells. An AUC greater than 0.9 is considered a high performing classifier. Here we demonstrated an AUC of 0.928. We expect that as the method is improved upon performance will continue to increase. As with all AI methods, care must be taken to select the training data set carefully so that it represents broadly the type of data expected to be tested against. For this reason, we may expect that the performance would degrade, if the model were applied directly to patient samples for example without first exposing the model to a robust collection of patient samples. We demonstrate this here to some extent by including 1-Day, 2-Day, and 9-Day measurements for the cancer cells in the model compared to the 2-Day day used for the previous work. We observe that the broad sampling reduces the performance of the model slightly and shows how sensitive some of the poorer performing methods may be, thus suggesting that users may want to test several models on their data. Table 2 describing PDX results shows an overall good performance. However, individual PDX types demonstrate that the proportion of cells measured from each sample differ and may influence the performance for each site of origin. For example, the ovarian sample only produced two cells for the testing set. In contrast, metastatic breast cancer which had a larger population. This data set was intended to demonstrate that the cells survive in the niche and can be analyzed, but also highlights interpretive care is needed. Alterations to the target variable may also guide researchers in identifying sub-clones with other traits such as interactions with stromal cells or quiescent behavior.
This approach is important as more labs adopt membrane on-a-chip systems such as blood brain barrier on a chip, lung on a chip and gut on a chip11,42. The majority of these chips are assembled so that the membrane is parallel with the imaging system, which until now it has meant that it would be difficult to measure when and how many cells have moved from one side of the membrane to the other15,28. Moreover, when compared to horizontally oriented chips, vertically oriented chips provide a much larger membrane area increasing the dynamic range of the experiment. In addition, because orientation of the membrane is not critical for this analysis technique, confocal tomography could potentially enable new in vitro experiments. For instance, imaging the extravasation of breast cancer cells from the murine fat pad into the blood stream would be approachable by these methods. This could help researchers identify how the cancer cells probe the interface between the breast ECM and vessel.
In conclusion, we presented a methodology to construct a 3D microfluidic device that recapitulates the blood brain niche and demonstrate how to use confocal tomography and machine learning for analysis. Using this platform, we identified brain-metastatic cancer cell characteristics that differentiate between brain metastatic and non-metastatic PDX cancer cells based on their behavior within the µmBBN device. Future work will improve the clinical applicability of this platform as a diagnostic towards predicting brain metastases. We believe that having presented this platform will be useful and interesting to labs which need to measure cells of any type migrating or extravasating across a membrane. This is of importance as pre-clinical models of the tumor micro-environment become increasingly sophisticated so to must the engineering of the models and supporting software. To aid in robust analysis of the data we have packaged this tool as a simple to use, shared python notebook with installation instructions30.
The authors have nothing to disclose.
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
0.25% Trypsin-EDTA with phenol red | Thermo Fisher Scientific | 25200056 | |
1.5 mm biopsy punch with plunger | Integra LifeSciences Corporation | 33-31A-P/25 | |
10x MEM | Thermo Fisher Scientific | 11430030 | |
150 mm petri dishes | Fisher Scientific | FB0875714 | |
1x DPBS, without Ca and Mg | Thermo Fisher Scientific | 14190144 | |
200uL pipette tip | Fisher Scientific | 02-707-411 | |
4 inch silicon wafer | University Wafer | 452 | |
48 mm wide packing tape | Fisher Scientific | 19-072-097 | |
50 x 75 mm glass slide | Fisher Scientific | 12-550C | |
A1 confocal microscope | Nikon | ||
acetone | Fisher Scientific | A9-20 | |
antibiotic/antimycotic (penicillin/streptomycin/amphotericin) | Gibco | 15240062 | |
box cutter blade | Fisher Scientific | NC1721575 | |
dissection scissors | Fisher Scientific | 08-951-5 | |
DMEM with 4.5 g/L glucose | Thermo Fisher Scientific | 11960-044 | |
double sided tape | Fisher Scientific | NC0879005 | |
EGM-2 | Lonza | CC-3162 | |
Fetal Bovine Serum, Heat inactivated | Corning | MT35011CV | |
Fiji software | ImageJ | ||
glass vial | Fisher Scientific | 03-341-25D | |
glutamax | Thermo Fisher Scientific | 35050061 | |
hCMEC/D3 | EMD Millipore | SCC066 | |
Jupyter notebook | Anaconda | ||
L-glutamine | Thermo Fisher Scientific | 25030081 | |
Matrigel – growth factor reduced with phenol red | Corning | CB-40230A | |
MDA-MB-231 | ATCC | HTB-26 | |
MDA-MB-231-BR-GFP | Dr. Patricia Steeg, NIH | ||
N-2 growth supplement | Thermo Fisher Scientific | 17502048 | |
normal human astrocytes (NHA) | Lonza | CC-2565 | |
Orange software | University of Ljubljana | ||
Pasteur pipette | Fisher Scientific | 13-711-9AM | |
Photolithography masks | Photosciences Incorporated | ||
pLL3.7-dsRed | University of Michigan Vector Core | ||
pLL-EV-GFP | University of Michigan Vector Core | ||
pLOX-TERT-iresTK | Addgene | 12245 | |
pMD2.G | Addgene | 12259 | |
polycarbonate membrane, 5um pore size | Millipore | TMTP04700 | |
psPAX2 | Addgene | 12260 | |
PureCol, 3 mg/mL | Advanced Biomatrix | 5005 | Type I bovine collagen |
sodium bicarbonate | Thermo Fisher Scientific | 25080094 | |
sodium pyruvate | Thermo Fisher Scientific | 11360070 | |
Solo cup | Fisher Scientific | NC1416545 | |
SU-8 2075 | MicroChem Corporation | Y111074 0500L1GL | |
SU8 developer | MicroChem Corporation | Y020100 4000L1PE | |
Sylgard 184 | Ellsworth Adhesive Company | NC0162601 | |
Toluene | Sigma-Aldrich | 179965-1L | |
Tricholoro perfluoro octyl silane | Sigma-Aldrich | 448931-10G |