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Human umbilical vein endothelial cells (HUVECs) and other primary endothelial cell types have been utilized for two decades to model blood vessel sprouting and development in vitro1. Such vascular platforms promise to illuminate molecular and tissue-level mechanisms of cardiovascular disease and may present physiological insight into the development of primitive vascular networks2,3. Though the field of vascular modeling has witnessed significant advances, a “gold standard” assay that can quantitatively model and assess physiological vascular development remains elusive. Most published protocols do not adequately recapitulate the vascular niche to encourage the formation of mature, functional blood vessels or do not have a method to quantitatively compare the vasculogenic potential of the assessed cell types in three dimensions (3D).
Many current vascular models are limited in their ability to mimic the physiological vascular niche. One of the most commonly employed in vitro platforms is the gelatinous protein mixture-based tube formation assay. Briefly, HUVECs are seeded as single cells on a thin layer of gel that consists of proteins harvested from murine sarcoma extracellular matrix (ECM); within one to two days, the HUVECs self-assemble into primitive tubes4. However, this process occurs in two-dimensions (2D) and the endothelial cells (ECs) utilized in this assay do not form enclosed, hollow lumens, thereby limiting the physiological significance of these studies. More recently, ECs and supporting cells (e.g., mesenchymal stem cells (MSCs) and pericytes) have been co-cultured in 3D microenvironments that simulate the fibrous architecture of the native ECM, such as collagen or fibrin hydrogels5. To model vascular development in this microenvironment, polymeric beads coated with ECs are typically employed6. The addition of exogenous growth factors and/or growth factors secreted by other cells interstitially embedded in the hydrogel can induce the ECs, coating the polymeric beads, to sprout and form single lumen; the number and diameter of sprouts and vessels can then be computed. However, these sprouts are singular and do not form an enclosed, connected network as is seen in physiological conditions and thus is more reminiscent of a tumor vasculature model. Microfluidic devices have also been utilized to mimic the vascular niche and to promote the formation of vasculature in EC-laden hydrogels7,8. Typically, an angiogenic growth factor-gradient is applied to the circulating cell culture medium to induce EC migration and sprouting. ECs that constitute the lumen of developed vessels are sensitive to the shear stress induced by the application of fluid flow through the microfluidic device; thus, these microfluidic devices capture key physiological parameters that are not accessible in the static models. However, these devices require costly microfabrication abilities.
Most importantly, all three vascular models (2D, 3D, microfluidic) overwhelmingly utilize primary ECs as well as primary supporting cell types. Primary cells cannot be developed into an effective cardiovascular therapy because the cells would engender an immune response upon implantation; furthermore, HUVECs and similar primary cell types are not patient-specific and do not capture vascular abnormalities that occur in patients with a genetic disposition or pre-existing health conditions, e.g., diabetes mellitus. Induced pluripotent stem cells (iPSCs) have emerged in the past decade as a patient-specific, proliferative cell source that can be differentiated into all somatic cells in the human body9. In particular, protocols have been published that outline the generation and isolation of iPSC-derived endothelial progenitors (iPSC-EPs)10,11; iPSC-EPs are bipotent and can, therefore, be further differentiated into endothelial cells and smooth muscle cells/pericytes, the building blocks of mature, functional vasculature. Only one study has convincingly detailed the development of a primary capillary plexus from iPSC-EPs in a 3D microenvironment12; though this study is critical to an understanding of iPSC-EP assembly and differentiation in natural and synthetic hydrogels, it did not quantitatively compare the network topologies of the resulting vasculature. Another recent study has used the polymeric bead model to compare the sprouting of HUVECs and iPSC-derived ECs5. Therefore, there is a clear need to further elucidate the physical and chemical signaling mechanisms that regulate iPSC-EP vasculogenesis in 3D microenvironments and to determine if these cells are suitable for ischemic therapy and cardiovascular disease modeling.
In the past decade, different open-source computational pipelines and skeletonization algorithms have been developed to quantify and compare vascular network length and connectivity. For example, Charwat et al. developed a Photoshop-based pipeline to extract a filtered, binarized image of vascular networks derived from a co-culture of adipose-derived stem cells and outgrowth ECs in a fibrin matrix13,14. Perhaps the most widely used topology comparison tool is AngioTool, a program published online by the National Cancer Institute15; despite the program’s widespread adoption and well-documented fidelity, the program is limited to analyzing vessel-like structures in two dimensions and other programs, including AngioSys and Wimasis, share the same dimensionality limitation16. Powerful software suites such as Imaris, Lucis, and Metamorph have been developed to analyze the network topology of engineered microvasculature; however, these suites are cost-prohibitive for most academic labs and limit access to the source code, which may hinder the ability of the end-user to customize the algorithm to their specific application. 3D Slicer, an open-source magnetic resonance imaging/computed tomography package, contains a Vascular Modeling Toolkit that can effectively analyze the topology of 3D vascular networks17; however, the analysis is dependent on the user manually placing the end-points of the network, which may become tedious when analyzing a large dataset and can be influenced by the user’s subconscious biases. In this manuscript, a computational pipeline that can quantify 3D vascular networks is described in detail. To overcome the above-outlined limitations, this open source computational pipeline utilizes ImageJ to pre-process acquired confocal images to load the 3D volume into a skeleton analyzer. The skeleton analyzer uses a parallel medial axis thinning algorithm, and was originally developed by Kerschnitzki et al. to analyze the length and connectivity of osteocyte networks18; this algorithm can be effectively applied to characterize the length and connectivity of engineered microvasculature.
Altogether, this protocol outlines the creation of microvascular networks in 3D microenvironments and provides an open-source and user-bias free computational pipeline to readily compare the vasculogenic potential of iPSC-EPs.