A Biomaterial Model to Assess the Effects of Age in Vascularization

Abstract

As humans age, there is an increased risk for developing age-associated diseases. Many of these diseases, such as cardiovascular disease, involve dysfunction in the vasculature. Cardiovascular disease stems from endothelial cell dysfunction and reduction in vascularization. Macrophages, prominent innate immune cells involved in orchestrating inflammation and wound healing, have a significant influence on vascularization. While much recent work has investigated the crosstalk between endothelial cells and macrophages, it is still not well defined. The interactions between the cell types are even less understood in specific disease states such as advanced age. Understanding how age influences macrophage/endothelial cell interaction is essential for understanding cardiovascular disease development in the elderly. In the polyethylene glycol (PEG)-based hydrogel system, we model the effects of age on vascularization by encapsulating endothelial cells, pericytes, and human donor macrophages. We created a biomaterial model system in which macrophages, either from young (<35 years old) or old (>65 years old) donors, interact with the modeled vasculature, termed microvessels. Confocal image analysis of vessel density, vessel length, and branch points were used to quantify microvessel growth depending on the age of the macrophage donor. Alongside this, soluble factor secretion and gene expression were evaluated using ELISA and NanoString to showcase biological mechanisms based on the age of each donor. Endothelial cells cultured with macrophages from old donors have reduced microvessel density. There also is reduced soluble factor secretion by the macrophages from old donors, which likely influenced microvessel growth. Altogether, we establish our PEG-based hydrogel vascular model as a system to evaluate patient-specific cell function as well as proposed mechanisms for how age influences microvessels.

Keywords: Aging; Biomaterial; Macrophages; Vasculogenesis.

Fig. 1.

Confocal images of endothelial cell (CD31, orange) forming microvessels inside Poly(ethylene glycol) based 3D hydrogels in co-culture with endothelial cells, pericytes, and donor macrophages (“Young” < 35 years old, “Old” > 65 years old). Vessel Density percentage was quantified using ImageJ’s Vessel Analysis Package (p value = 0.46). Average Vessel Length was quantified using ImageJ measurement tool (p value = 0.81). Branch Points were counted by hand with a single blind counter (p value = 0.64). All three methods compared “Old” vs “Young” data using a Student’s T-test statistical significance at p < 0.05. Scale Bar is 50 μm.

Fig. 2.

Gene expression data comparing the mRNA content tri-culture with macrophages encapsulated in the PEG-based hydrogel. A.) Volcano plot of every gene in the NanoString Myeloid Innate Immunity Panel. Genes in pink signify p<0.05. B.) Box and whisker plot showcasing general trends of gene expression of genes associated with the labelled cellular processes. Dark center line in each box represents the median, the box represents the middle 50% of genes, and error bars represent the min and max fold change expression. Some cellular processes such as cytokine and interferon signaling are notably down regulated.

Fig. 3.

A.) Enzyme-linked immunosorbent assay (ELISA) from MyBioSource Inflammatory Cytokine array was used to characterize the macrophage response in the hydrogel depending on the age of the macrophage donors B.) Relative soluble factor secretion comparing Young and Old. N=3 donors per group, Comparisons between Old and Young were made using a Student’s T-test. Significance at p < 0.05

Fig. 4.

A.) Relative concentration of soluble factors from Figure 3 compared to the B.) relative gene expression of the genes associated with these soluble factors.