Extracellular Matrix Density Regulates the Rate of Neovessel Growth and Branching in Sprouting Angiogenesis

<div><p>Angiogenesis is regulated by the local microenvironment, including the mechanical interactions between neovessel sprouts and the extracellular matrix (ECM). However, the mechanisms controlling the relationship of mechanical and biophysical properties of the ECM to neovessel growth during sprouting angiogenesis are just beginning to be understood. In this research, we characterized the relationship between matrix density and microvascular topology in an <i>in vitro</i> 3D organ culture model of sprouting angiogenesis. We used these results to design and calibrate a computational growth model to demonstrate how changes in individual neovessel behavior produce the changes in vascular topology that were observed experimentally. Vascularized gels with higher collagen densities produced neovasculatures with shorter vessel lengths, less branch points, and reduced network interconnectivity. The computational model was able to predict these experimental results by scaling the rates of neovessel growth and branching according to local matrix density. As a final demonstration of utility of the modeling framework, we used our growth model to predict several scenarios of practical interest that could not be investigated experimentally using the organ culture model. Increasing the density of the ECM significantly reduced angiogenesis and network formation within a 3D organ culture model of angiogenesis. Increasing the density of the matrix increases the stiffness of the ECM, changing how neovessels are able to deform and remodel their surroundings. The computational framework outlined in this study was capable of predicting this observed experimental behavior by adjusting neovessel growth rate and branching probability according to local ECM density, demonstrating that altering the stiffness of the ECM via increasing matrix density affects neovessel behavior, thereby regulated vascular topology during angiogenesis.</p></div

Visualization of <i>in vitro</i> experiments and simulation results.

<p>The computational model was designed to simulate angiogenic outgrowth and neovascularization within 3D organ culture of microvessel fragments with a type-I collagen gel. All images in this figure depict the 3.8×2.5×0.2 mm region that was imaged during the experiments.(A) Z-projection mosaic of 3D confocal image data showing microvessels cultured in a 3.0 mg/ml collagen gel after Day 6 of culture. Endothelial cells within the culture were labeled with Isolectin IB4-Alexa 488 and imaged using a confocal microscope with a 10× objective. (B) Skeletonized vessel data obtained from the confocal image data of a vascularized collagen gel in Panel A. (C) Results of a simulation using the computational model. Microvessels were represented as a collection of line segments, and growth was simulated by the addition of new segments to the free ends of existing segments. (Scale bar = 350 µm).</p

Microvasculatures observed at different levels of collagen density.

<p>Increasing the density of the ECM reduced neovascularization in both the experiments and computational simulations. Top Row: Z-projection mosaic of 3D confocal image data showing vascularized collagen gels taken at Day 6 of growth. Bottom Row: Results of the comparable computational simulations, presented as 3D volume-renderings of the line segment data. The three levels of collagen density assessed in this study were: 2.0 mg/ml (A, D), 3.0 mg/ml (B, E), and 4.0 mg/ml (C, F). (Scale bar = 350 µm).</p

Matrix density scaling factor.

<p>A scaling factor was calculated from experimental data and used to scale growth rate and branching probability within the computational model based on local ECM density. The factor was calculated by taking the average total vascular length measured for the 2.0, 3.0, and 4.0/ml vascularized constructs and normalizing by the total vascular length for the 3.0 mg/ml construct. Experimental data is presented as the solid points on the graph. This data was then fit to the exponential function described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085178#pone.0085178.e003" target="_blank">Eq. 3</a> (R² = 1.0). The fitted function is presented as the dashed line on the graph.</p

Predictive simulation of angiogenesis within a density gradient.

<p>In this simulation, matrix density runs from 1.0/ml to 10.0 mg/ml along the horizontal axis (<i>x</i>-axis) while remaining uniform along the other two directions. (A) Z-projection of the matrix density field and the initial microvessel fragments. (B) Growth at Day 6. Initial microvessel fragments are shown in white. The computational framework predicted high amounts of neovascularization in the low density portion of the domain. Growth significantly reduced as density increased along the x-axis. Additionally, vessels that grew towards the low density regions grew at a faster rate than vessels growing towards the high density regions.</p

Branch points and free ends per unit length.

<p>A branching point was defined as any node that connected to three or more vessel segments. Branching points were created by either a new vessel sprout (branching) or two separate vessels fusing into one (anastomosis). Measurements from the experimental cultures are presented in black and predictions from the computational model are presented in gray. (A) The number of branch points was normalized by the total vascular length in order to isolate the tendency of microvessels to form a branch point per unit length of growth. Branching per unit length was observed to decrease as matrix density was increased. An end point was defined as a node that was associated with only one vessel segment and represents the terminal end of a vessel. Measurements from the experimental cultures are presented in black and predictions from the computational model are presented in gray. (B) Normalizing the number of end points by the total vascular length revealed that the number of free ends per unit length increased along with matrix density. There was a significant effect of matrix density on branch points and free ends per unit length for both experimental and computational results (One-way ANOVA, p<0.05 in all 4 cases). No statistical difference was detected between any experimental and computational morphometric at each matrix density level by T-test. Statistical equivalence as detected by a TOST-test is indicated by the bracket and equal sign.</p

Phase contrast light micrographs of rat microvessel fragments.

<p>(Left) Isolated microvessel fragment at Day 0 with a visible lumen, indicated by the arrow. (Right) Angiogenic microvessel fragments within a type I collagen gel at Day 6 of growth. The thicker initial fragments are indicated by the arrows. The thinner protrusions extending from the initial fragments are neovessels formed through angiogenesis.</p

Total vascular length and network interconnectivity.

<p>(A) The total vascular length decreased as matrix density increased. Measurements from the experimental cultures are presented in black and predictions from the computational model are presented in gray. (B) Vessel interconnectivity, a measure of the percentage of microvessels within the domain that are connected into the largest continuous vascular network, decreased as a function of matrix density, indicating a reduction in network formation. There was a significant effect of matrix density on total vascular length and network connectivity for both experimental and computational results (One-way ANOVA, p<0.05 in all 4 cases). No statistical difference was detected between any experimental and computational morphometric at each matrix density level by T-test. Statistical equivalence as detected by a TOST-test is indicated by the bracket and equal sign.</p

Volumetric renderings of collagen and elastin layers within in the lymphatic vessel wall imaged using multiphoton microscopy.

<p>(A) Collagen signal as viewed from the interior of the vessel. (B) Elastin signal as viewed from the interior of the vessel. The bottom panels show composite renderings of collagen (white) and elastin (green) within the interior surface of the vessel (C) and the exterior surface (D). Volumetric renderings of multiphoton image data were performed using the software FluoRender [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183222#pone.0183222.ref025" target="_blank">25</a>]. Scale bar 100 μm.</p

Collagen and elastin orientation as quantified by FFT.

<p>The mean collagen fibre orientation from the 6 specimens is given in blue, and the mean elastin orientation is given in red. Error bars indicate standard deviation. Orientation angles from -90° to 90°, with the axial length of the vessel orientated at 0°. Asterisks indicate a significant statistical difference between collagen and elastin orientation as detected via T-test.</p