MDA-MB-231 Breast Cancer Cells and Their CSC Population Migrate Towards Low Oxygen in a Microfluidic Gradient Device

Most cancer deaths are caused by secondary tumors formed through metastasis, yet due to our limited understanding of this process, prevention remains a major challenge. Recently, cancer stem cells (CSCs) have been proposed as the source of metastases, but only little is known about their migratory behavior. Oxygen gradients in the tumor have been linked to directional migration of breast cancer cells. Here, we present a method to study the effect of oxygen gradients on the migratory behavior of breast CSCs using a microfluidic device. Our chip contains a chamber in which an oxygen gradient can be generated between hypoxic (<1%) and ambient (21%) conditions. We tracked the migration of CSCs obtained from MDA-MB-231 breast cancer cells, and found that their migration patterns do not differ from the average MDA-MB-231 population. Surprisingly, we found that the cells migrate towards low oxygen levels, in contrast with an earlier study. We hypothesize that in our device, migration is exclusively due to the pure oxygen gradient, whereas the effects of oxygen in earlier work were obscured by additional cues from the tumor microenvironment (e.g., nutrients and metabolites). These results open new research directions into the role of oxygen in directing cancer and CSC migration

Metastasis in context : Modeling the tumor microenvironment with cancer-on-a-chip approaches

Most cancer deaths are not caused by the primary tumor, but by secondary tumors formed through metastasis, a complex and poorly understood process. Cues from the tumor microenvironment, such as the biochemical composition, cellular population, extracellular matrix, and tissue (fluid) mechanics, have been indicated to play a pivotal role in the onset of metastasis. Dissecting the role of these cues from the tumor microenvironment in a controlled manner is challenging, but essential to understanding metastasis. Recently, cancer-on-a-chip models have emerged as a tool to study the tumor microenvironment and its role in metastasis. These models are based on microfluidic chips and contain small chambers for cell culture, enabling control over local gradients, fluid flow, tissue mechanics, and composition of the local environment. Here, we review the recent contributions of cancer-on-a-chip models to our understanding of the role of the tumor microenvironment in the onset of metastasis, and provide an outlook for future applications of this emerging technology

Metastasis in context : Modeling the tumor microenvironment with cancer-on-a-chip approaches

Most cancer deaths are not caused by the primary tumor, but by secondary tumors formed through metastasis, a complex and poorly understood process. Cues from the tumor microenvironment, such as the biochemical composition, cellular population, extracellular matrix, and tissue (fluid) mechanics, have been indicated to play a pivotal role in the onset of metastasis. Dissecting the role of these cues from the tumor microenvironment in a controlled manner is challenging, but essential to understanding metastasis. Recently, cancer-on-a-chip models have emerged as a tool to study the tumor microenvironment and its role in metastasis. These models are based on microfluidic chips and contain small chambers for cell culture, enabling control over local gradients, fluid flow, tissue mechanics, and composition of the local environment. Here, we review the recent contributions of cancer-on-a-chip models to our understanding of the role of the tumor microenvironment in the onset of metastasis, and provide an outlook for future applications of this emerging technology

Compression and reswelling of microgel particles after an osmotic shock

We use dedicated microfluidic devices to expose soft hydrogel particles to a rapid change in the externally applied osmotic pressure and observe a non-monotonic response: After an initial rapid compression the particle slowly reswells to approximately its original size. Using a simple phenomenological and a more elaborate poroelastic model, we extract important material properties from a single microfluidic experiment, including the compressive modulus, the gel permeability and the diffusivity of the osmolyte inside the gel. We expect our approach to be relevant to applications such as controlled release, chromatography, and responsive materials

Compression and reswelling of microgel particles after an osmotic shock

We use dedicated microfluidic devices to expose soft hydrogel particles to a rapid change in the externally applied osmotic pressure and observe a non-monotonic response: After an initial rapid compression the particle slowly reswells to approximately its original size. Using a simple phenomenological and a more elaborate poroelastic model, we extract important material properties from a single microfluidic experiment, including the compressive modulus, the gel permeability and the diffusivity of the osmolyte inside the gel. We expect our approach to be relevant to applications such as controlled release, chromatography, and responsive materials

The pressure-driven viscous fingering method used to generate the cylindrical collagen gel in the 3D BBB chip.

<p>A) Schematic diagram of the PDMS structure used to generate the 3D BBB chip (left) and an illustration of a cross-section through the chip showing the PDMS channel containing the collagen gel made with viscous fingering and a central lumen (right). B) Photograph of the 3D BBB chip on the stage of an inverted microscope. C) Time-lapse images of the fingering method showing the microchannel before (t = 1) and after infusion of a neutralized collagen gel containing dispersed human astrocytes (t = 2), which was then followed by injection of a low viscosity liquid (dyed blue here) driven by hydrostatic pressure to initiate “finger” formation in the center of the gel (t = 3), and eventually a continuous hollow cylindrical lumen throughout the length of the device (t = 4). The time course from t = 1 to 4 is user dependent but normally less than 30 sec (bar, 500 μm). D) Graph showing the correlation between the hydrostatic pressures used to drive the fingering process and the resulting lumen diameter (* p<0.05 Student’s t-test, n = 3). E) Low magnification micrograph of an entire device containing a lumen filled with blue fluid, formed as described in C (dashed lines, delineate the edges of the channel; black dotted rectangle indicates where images shown in F and G were recorded (bar, 3 mm). F) Second harmonic generation image of the collagen distribution in the 3D BBB chip, and an intensity generated voxel illustration of the lumen based on this information (G) (bar, 100 μm). H) High magnification of the second harmonic generation image showing of collagen microstructure in the cylindrical gel within the 3D BBB chip (bar, 50 μm).</p

Co-culture of human brain microvascular endothelial cells, pericytes and astrocytes in the 3D BBB chip.

<p>Schematic illustrations of the cells populating the 3D vessel structures for the three experimental set-ups are shown at the top, and fluorescence confocal micrographs of the engineered brain microvessel viewed from the top (A, D, G) or shown in cross-section at either low (B, E, H) or high (C, F, I) magnification (rectangles in lower magnifications images indicate respective areas shown at higher magnification below). The fluorescence micrographs show the cell distributions in 3D BBB chips containing brain microvascular endothelium alone (A-C), endothelium with prior plating of brain pericytes on the surface of the gel in the central lumen (D-F) or endothelium with brain astrocytes embedded in the surrounding gel (G-I). High-magnification cross-sections are projections of confocal stacks (bars, 200 μm in A,B,D,E,G,H and 30 μm in C, F, I). Green indicates F-actin staining, blue represents Hoechst-stained nuclei, and magenta corresponds to VE-Cadherin staining, except for G where morphology and intensity masks were used to discriminate astrocytes (green) from endothelial cells (magenta); original image can be seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150360#pone.0150360.s007" target="_blank">S2 Movie</a>. Arrows indicate contact points between endothelium and pericytes (F) or astrocytes (I).</p

Production of an abluminal basement membrane by brain endothelial cells in the 3D BBB chip.

<p>A) Perspective view of a 3D reconstruction of a confocal fluorescence micrograph showing a monolayer of brain microvascular endothelial cells lining the lumen of a engineered vessel in the 3D BBB chip (green, F-actin staining; magenta, collagen IV staining). Higher magnification views of staining for F-actin (B) and collage IV (C), and a cross-sectional view (D) showing the accumulation of a linear pattern of basement membrane collagen IV (magenta) staining beneath the F-actin (green) containing endothelial cells (bars, 100 μm in A; 80 μm in B, C; 40 μm in D).</p