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Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Vasculogenesis is a complex process by which endothelial stem and progenitor cells undergo de novo vessel formation. Quantitative assessment of vasculogenesis has become a central readout of endothelial progenitor cell functionality, and therefore, several attempts have been made to improve both in vitro and in vivo vasculogenesis models. However, standard methods are limited in scope, with static measurements failing to capture many aspects of this highly dynamic process. Therefore, the goal of developing this novel protocol was to assess the kinetics of in vitro vasculogenesis in order to quantitate rates of network formation and stabilization, as well as provide insight into potential mechanisms underlying vascular dysfunction. Application of this protocol is demonstrated using fetal endothelial colony forming cells (ECFCs) exposed to maternal diabetes mellitus. Fetal ECFCs were derived from umbilical cord blood following birth, cultured, and plated in slides containing basement membrane matrix, where they underwent vasculogenesis. Images of the entire slide wells were acquired using time-lapse phase contrast microscopy over 15 hours. Images were analyzed for derivation of quantitative data using an analysis software called Kinetic Analysis of Vasculogenesis (KAV). KAV uses image segmentation followed by skeletonization to analyze network components from stacks of multi-time point phase contrast images to derive ten parameters (9 measured, 1 calculated) of network structure including: closed networks, network areas, nodes, branches, total branch length, average branch length, triple-branched nodes, quad-branched nodes, network structures, and the branch to node ratio. Application of this protocol identified altered rates of vasculogenesis in ECFCs obtained from pregnancies complicated by diabetes mellitus. However, this technique has broad implications beyond the scope reported here. Implementation of this approach will enhance mechanistic assessment and improve functional readouts of vasculogenesis and other biologically important branching processes in numerous cell types or disease states.


Introduction
The ability of endothelial progenitor cells to undergo vasculogenesis, or de novo vessel formation, is critical in establishing embryonic vasculature during development 1 . Additionally, further vessel formation and maturation of pre-existing vessels, which is known as angiogenesis, is also a key process in development and in postnatal life to maintain blood flow and homeostasis throughout the body 2 . Every organ in the body is dependent on the vascular system for delivery of oxygen and nutrients, and for the removal of waste 3 . If vascular homeostasis is not maintained, such that blood vessel formation and repair are either insufficient or in excess, vascular diseases can result 4 . Therefore, vascular formation and adaptation are commonly studied, as they are essential in the maintenance of organ health and are implicated in the development of numerous pathologic states.
Due to an increased understanding of the involvement of the vascular system in development, as well as in disease manifestation and progression, assays have been developed to model vasculogenesis and angiogenesis in vitro and in vivo 5,6,7 . Modeling vessel formation involves plating vascular cells, such as endothelial cells, in basement membrane matrix that promotes cell organization and formation of vessel networks 8,9,10 . Typically, following overnight incubation, images of cell networks are captured at a single time point, resulting in a small number of images for analysis 11,12,13 . Therefore, analytic approaches have largely been developed and optimized for single time point assessments 10,14 .
However, static analyses are simply insufficient at capturing the dynamic process of vessel formation and provide limited insight into potential mechanisms involved. Although increasing imaging frequency would likely provide the necessary data to identify formation kinetics, application . Therefore, there is a need in the field for a streamlined and efficient approach to capture and quantify vasculogenesis in vitro, including the ability to analyze large image sets generated by time-lapse live cell microscopy.
To overcome these limitations, a new approach was developed with the purpose of expanding single time point imaging to enable dynamic assessment of vasculogenesis with images acquired every 15 min 16 . By capturing multiple time points over several hours, this approach provides a more detailed depiction of the process of vasculogenesis, as well as insight into potential mechanisms contributing to the formation and maintenance of vessel networks. In addition to improving the frequency and quality of image acquisition, this approach incorporates novel software in the form of an open-source plug-in 17 . The software, referred to as Kinetic Analysis of Vasculogenesis (KAV), is a streamlined application that incorporates image processing and analysis specifically for large image sets generated from multi-time point acquisitions. KAV analyzes phase contrast images through image segmentation followed by skeletonization 18 . Ten parameters of network structure are quantified by KAV including: branches, closed networks, nodes, network areas, network structures, triple-branched nodes, quad-branched nodes, total branch length, average branch length, and the branch to node ratio (see schematic in Figure 1) 16 . Application of the KAV approach includes a novel, calculated phenotype of in vitro vasculogenesis, referred to as the branch to node ratio. Our recent work demonstrated that this ratio is indicative of network connectivity and may be associated with other cellular processes involved in network formation, such as motility 16 .
Although fetal endothelial colony forming cell (ECFC) vasculogenesis was assessed in these studies, this image acquisition and analysis approach can be readily applied to evaluate any cell types that undergo vasculogenesis or angiogenesis. Additionally, this approach can be used to identify altered vascular function resulting from a variety of pathologic states, such as gestational diabetes mellitus, as shown in these studies. Furthermore, this method could be adapted to assess network formation and branching, which are important for other biologically relevant processes. Thus, the potential impact of applying this novel approach to unique biological systems is yet to be determined.

Culture endothelial colony forming cells (ECFCs)
NOTE: Fetal ECFC samples used in these experiments were isolated from human umbilical cord blood and cultured by the Angio BioCore (ABC) at the Indiana University School of Medicine as previously described 6 . Routine quality control phenotyping was conducted within the ABC to confirm expression of endothelial antigens as previously described 19,20,21 . Samples used in experiments were passaged minimally (2-3 times). All tissue culture work was performed in a tissue culture hood. All reagents, including tissue culture plates and solutions, were sterile.
1. Two days prior to the experiment, coat 100 mm round tissue culture plates with 6 mL of 0.05 mg/mL type 1 collagen, and incubate at 37°C overnight. NOTE: Type 1 collagen is diluted in double-distilled water containing 20 nM acetic acid to a working concentration of 0.05 mg/mL. 2. The day prior to the experiment, trypsinize and replate ECFCs on the type 1 collagen coated plates in 1.1.1. (See Note under 1.1). 3. To trypsinize ECFCs, aspirate media from plated cells and wash with 7 mL of PBS. Aspirate PBS, then add 2-3 mL of 0.5% trypsin and incubate cells for 2-5 min at 37 °C. 4. Immediately following incubation with trypsin, add 3 mL of EGM2 and pipet up and down to dislodge all adherent cells. Transfer cell suspension to a 15-mL conical tube. 5. Centrifuge all samples at 500 x g for 5 min at room temperature. 6. Aspirate supernatant and re-suspend cell pellets in 1-2 mL of EGM2. Mix the cell suspension thoroughly and remove a small aliquot (20-40 μL) for counting. 7. Add equal parts trypan blue to the cell aliquot and pipette 10 µL onto a hemocytometer. Count the number of cells in the four primary quadrants of the hemocytometer. NOTE: Use both sides of the hemocytometer for all sample counts to increase accuracy. 8. Wash the collagen-coated tissue culture plates, which were prepared in step 1.1.1, twice with 7 mL of PBS. After aspirating the second PBS wash, add 10 mL of endothelial growth medium 2 (EGM2) culture media supplemented with 10% fetal bovine serum to the plates. 9. Using the cell counts obtained in step 1.1.7, calculate the volume of cell suspension required to plate 4 x 10 5 cells. Plate 4 x 10 5 ECFCs on the tissue culture plates prepared in step 1.1.8, and incubate at 37 °C and 5% carbon dioxide (CO 2 ) overnight.
2. Prior to the experiment, store supplies at 4 °C overnight 1. Store a 3" x 2" metal plate, slide (keep in package until open in hood), a box of sterile 20 microliter (μL) tips, and basement membrane matrix (matrix). NOTE: The matrix is kept at -80 °C for long-term storage; always thaw it at 4 °C overnight or for several hours prior to use.
3. Confirm availability of adequate space for the images on the computer's hard-drives. NOTE: Using the configuration outlined in this protocol, approximately 60 gigabytes (GB) of space were required per experiment (4 GB/well). However, space estimates are based on hardware configuration and will need to be determined for each system.

7.
Save the KAV-generated data 1. Once the plug-in has finished the analysis, two windows will pop-up on the screen including a data table with numerical values and a stack of fused images depicting skeleton and mask renditions. 2. Save the numerical values by navigating to the upper left corner of the data table and clicking 'Edit' then 'Copy' or 'Cut' to paste values into an excel spreadsheet. 3. Click on the fused skeleton and mask image. Save the fused skeleton/mask image stack as a Tagged Image File Format (TIFF) file by clicking 'File,' 'Save As,' 'TIFF.' 4. Save the cropped phase contrast image stack (created using ROI) by clicking 'File,' 'Save As,' 'TIFF.' 5. Click on the ROI Manager window and select the ROI used to crop the images. Click 'More' followed by 'Save' to save the ROI for future use. NOTE: Using the same ROI will help maintain consistency across analyses.

KAV produces visual representations of network structure
The contrast between the ECFCs and the matrix background in the phase contrast images enables KAV to identify cell-specific structures. ECFC networks identified by KAV are represented pictorially as skeleton and mask renditions to illustrate structures used by the software for quantification (Figure 2A). Importantly, qualitative assessment of the skeleton and mask renditions enables rapid identification of KAV sensitivity and accuracy, which can be useful in determining optimal threshold settings and interpreting analysis outcomes. When the quality of the phase contrast images is high and sufficient contrast is achieved, KAV accurately identifies ECFC networks as indicated by similarity between the phase contrast image and the KAV-generated skeleton and mask renditions (Figure 2A and Video 1).
Alternatively, if phase contrast images do not have high contrast and/or artefacts of imaging such as gridding occur, network detection accuracy is reduced and outcomes become ambiguous ( Figure 2B). Additionally, formation of air bubbles within the cell media can also obscure detection accuracy (Figure 2). However, problems with image quality can often be overcome through selection of different thresholding methods included in the KAV software. For example, the phase contrast image in Figure 2B was analyzed using identical image processing settings except for the thresholding method. From the Skeleton and Mask renditions, it is evident that the Otsu thresholding resulted in a more accurate detection of the ECFC networks shown in the phase contrast image ( Figure 2B). Therefore, image quality is critical to achieving accurate and meaningful results in this assay. However, different thresholding methods included in the KAV user interface allow adjustment of image analysis based on the quality of the input images.

Time-lapse microscopy identifies qualitative and quantitative differences in ECFC vasculogenesis following intrauterine gestational diabetes mellitus exposure
Recently, the KAV analytic approach was applied to assess fetal ECFC function following exposure to maternal type 2 diabetes mellitus (T2DM) in utero 16 . Using KAV, altered kinetics of vasculogenesis were identified in fetal ECFCs exposed to T2DM. However, in addition to T2DM exposure, which occurs throughout the entire gestation, gestational diabetes mellitus (GDM), or the development of glucose intolerance commonly in the third trimester of pregnancy, also impairs ECFC function 13 . Therefore, KAV was applied to determine if GDM-exposed ECFCs also display altered kinetics of ECFC network formation. Phase 1 (0-5 h) and Phase 2 (5+ h) were assessed using time lapse microscopy coupled with KAV analysis (Figure 3). Representative phase contrast images acquired at the start of image acquisition (0.50 h) and throughout the time course (5.00 and 10.00 h) depict ECFC network formation in the four samples tested from a single experimental day (Figure 3 and Video 1). Despite equivalent cell loading, as observed in the phase contrast images at 0.50 hours, qualitative differences in network structure are evident at the 5.00 and 10.00-hour time points. ECFCs from the uncomplicated pregnancy (UC) form a complex and intricate network 5 hours post-plating, similar to our previously published data 16 . Conversely, ECFCs from GDM sample 1 (GDM1) form very few network structures that are not interconnected. However, this pattern is not reflected in all ECFC samples obtained from GDM pregnancies, indicative of heterogeneity between samples. Samples GDM2 and GDM3 display greater network formation compared to GDM1, although the patterns of connectivity appear altered compared to the UC sample. Importantly, KAV measures several structural components of ECFC networks to identify both obvious and subtle phenotypes.
In addition to generating skeleton and mask renditions of network structure, KAV quantitates ten metrics of network structure, including the number of individual network structures, nodes, triple-branched nodes, quadruple-branched nodes, branches, total branch length, average branch length, branch to node ratio, total closed networks, and network area 16 . KAV compiles the data for each sample into a single table of values for all images of the time course. Table 1 includes representative raw data from one imaging study for one ECFC sample. Parameters of network structure measured by KAV are organized into columns, and the data for sequential images acquired over time are organized into rows. For example, in our studies, image 1 was obtained 30 min post-plating with subsequent images being collected every 15 min. Raw values generated by KAV can then be graphed or further analyzed using more complex statistical approaches

KAV enables evaluation of large data sets with multiple time points
Traditionally, quantitation of vasculogenesis in vitro has consisted of a single, or a few time point measurements. This static approach is simply inadequate for capturing and quantifying a dynamic and complex process. Therefore, this novel method was developed to enable efficient analyses of network formation kinetics to gain insight into potential molecular mechanisms involved in the dynamic process of vasculogenesis. Efficiency and automation are important components in the development of novel techniques to generate and analyze large quantities of imaging data. KAV was developed specifically to analyze large image stacks consisting of hundreds of images to decrease the time and labor required to derive biologically meaningful conclusions from time-lapse data sets. Importantly, KAV conducts image processing and data generation in a matter of seconds for small (less than 100 images) image stacks and minutes for larger (greater than 100 images) image stacks, resulting in unparalleled efficiency. Additionally, data organization into spreadsheets by time and parameters measured enables rapid generation of graphical presentation and statistical analysis.

Challenges in successful application of this approach
Although this assay includes improvements to both image acquisition and analysis, some challenges may impede successful implementation. The four main obstacles include humidity stability, timing precision from plating to image acquisition, software and hardware reliability, and management of large data sets generated for each experiment. Stable live cell imaging over several hours can be challenging. Specifically, appropriate and consistent humidification is required for the imaging chambers. Angiogenesis slides are used in this assay, which are designed for parallelizing matrix-based angiogenesis assays. Their low volume, approximately 50 µL, makes evaporation and condensation significant considerations for extended culture conditions. To combat this experimental issue, it is necessary to use an incubator that regulates humidity. However, it may also be required to augment this regulation if excessive drying of the imaging chamber occurs over time. We found that the simplest approach to increase humidity is to increase water surface area in the chamber. To accomplish this goal, our protocol suggests the following three water sources: a second chambered slide filled with water, which is also where the incubator temperature is measured, water in the area surrounding the wells on the slides, and wetted filter paper or wipes. Conversely, condensation occurs when the air temperature in the room cools the bottom of the slide and the humidity inside the chamber condenses on the slides and the wells. This complication can lead to a dilution of the cell media and a lensing effect that interferes with the phase contrast. In these studies, condensation was minimized through application of heated air (39-42 °C) onto the bottom of the slide.
A key consideration for any time-lapse study is consistency between experiment initiation and imaging. The timing of when imaging starts is critical for interpretation of downstream events. To ensure the timing precision of this assay, the time between initial plating and the first imaged time point should be tightly controlled. In practice, this "dead time" can be minimized, but more importantly, it needs to be consistent to allow experiments on different days to be compared. For instance, in this protocol we expect a dead time of approximately 30 min. This can be facilitated by the proximity of experimental preparation and imaging facilities.
What might seem like mundane details at first glance are important for software, hardware stability, and data management. The software and associated hardware drivers, network environment and automated software updates all affect the stability of the software. It is worth testing this protocol in a "dry run" to identify bottlenecks and potential sources of problems in the image acquisition; for instance, check for unreliable live cell setup, stage, shutter, and camera reliability, and the institutional information technology policies on automated operating system and software updates.
Data management is an ongoing concern of any imaging experiment that generates hundreds to thousands of images. Fortunately, commercial software often checks for available space prior to starting an imaging experiment. However, this assay also includes post-capture image processing and analysis that can add to the hard drive space burden and interfere with imaging experiments, if available space is limited. Further, the security and stability of the computer cannot be guaranteed, even with hardware solutions such as a redundant array of identical disks. Thus, a data management strategy to ensure space on the computer's hard drives for ongoing experiments and a robust remote archiving, for instance with an institutional archiving resource, is necessary.

Strategies to Maximize Image Quality
Although the ability of KAV to accurately discern ECFCs from the matrix background is robust, the sensitivity of the assay is dependent on image quality. For example, if the image has low contrast, the software will detect cell networks with less accuracy (Figure 2B). The quality of the phase contrast is impacted by several factors, including the settings of the microscope used for image acquisition, loading of the matrix and the cell suspension media during assay preparation, and maintenance of media levels within the wells throughout imaging. To optimize the phase contrast for these assays, minor adjustments to phase ring alignment in the microscope were made to improve contrast. Additionally, sample preparation was rigorously tested to ensure equal and consistent media loading. If the amount of matrix and/or media loaded into the wells is either below or above the optimum range, a meniscus can form leading to altered phase contrast. Finally, due to the small volume of liquid in the wells, it is critical to maintain media levels by minimizing evaporation and condensation, as noted above. Overall, assay optimization is critical to generate high quality contrast images for analysis.
In addition to phase contrast quality, other factors can also impact assay outcomes including media contamination, imperfections in the matrix, and particles or debris in the matrix or media. Contamination is a hazard of this assay since it involves live cell imaging over several hours in a microscopy chamber. To reduce the risk of contamination, the matrix and cell suspensions are loaded onto the slide in a sterile hood. In addition, each sample is typically plated in duplicate or triplicate for an experiment to minimize the risk of data loss due to unforeseen issues such as debris or particles confounding the analysis.

Sample heterogeneity drives need for increased assay sensitivity
Heterogeneity has been observed and reported in previous studies of ECFCs exposed to GDM 13 . A high level of heterogeneity in cell function observed in these samples is speculated to be attributable to several factors including severity of maternal disease, duration of disease, and management style used to regulate blood glucose levels. Importantly, this analytic approach captures the dynamic range of ECFC vasculogenesis, making it feasible to identify phenotypic differences due to functional heterogeneity in samples from GDM pregnancies. Overall, the use of primary patient samples, such as the ones used in these studies, can introduce larger variability compared to a cell line. As greater emphasis is placed on translational studies involving multiple primary animal or human samples with functional variability, assay sensitivity is essential for detection and derivation of biologically meaningful measurements and conclusions. Therefore, development of approaches like KAV will improve the quantity and quality of data generated by in vitro vasculogenesis and angiogenesis assays to enable more robust observations and conclusions. Furthermore, these data will facilitate future investigation of underlying molecular mechanisms that contribute to altered ECFC vasculogenesis following intrauterine GDM exposure.

Future applications of this approach
Although this method was applied to assess fetal ECFC vasculogenesis in vitro in these studies, the potential applications of this approach are numerous. This technique can be readily implemented to study any cell population that participates in the processes of vasculogenesis or angiogenesis. Specifically, it can be used to study individual cell populations, as was demonstrated in this study, but it could also be applied to co-culture systems. In the future, it would be beneficial to expand this approach beyond the two-dimensional in vitro assay to assess threedimensional (3D) models. Although the current version of KAV would be insufficient for quantitating 3D imaging data, a similarly designed timelapse, multi-parametric analytic approach specifically for 3D or in vivo models would inform if observations made in two-dimensions in vitro are representative of cell function in a more biologically relevant setting.

Disclosures
The authors have nothing to disclose.