Here, we present a protocol for time-lapse imaging and analysis of vasculogenesis in vitro using phase contrast microscopy coupled with the open source software, Kinetic Analysis of Vasculogenesis. This protocol can be applied to quantitatively assess the vasculogenic potential of numerous cell types or experimental conditions that model vascular disease.
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.
The ability of endothelial progenitor cells to undergo vasculogenesis, or de novo vessel formation, is critical in establishing embryonic vasculature during development1. 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 body2. Every organ in the body is dependent on the vascular system for delivery of oxygen and nutrients, and for the removal of waste3. If vascular homeostasis is not maintained, such that blood vessel formation and repair are either insufficient or in excess, vascular diseases can result4. 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 vivo5,6,7. Modeling vessel formation involves plating vascular cells, such as endothelial cells, in basement membrane matrix that promotes cell organization and formation of vessel networks8,9,10. Typically, following overnight incubation, images of cell networks are captured at a single time point, resulting in a small number of images for analysis11,12,13. Therefore, analytic approaches have largely been developed and optimized for single time point assessments10,14. 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 of previously developed analytics to a multi-time point imaging approach would be inefficient and labor intensive14. Additionally, despite development of commercially available analyses, a pay-per-image fee renders this option cost-prohibitive for kinetic studies in which thousands of images are generated15. 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 min16. 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-in17. 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 skeletonization18. 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 motility16.
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.
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 GDM13. 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 three-dimensional (3D) models. Although the current version of KAV would be insufficient for quantitating 3D imaging data, a similarly designed time-lapse, 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.
The authors have nothing to disclose.
The authors acknowledge Lucy Miller, Leanne Hernandez, Dr. David Haas, Brittany Yeley (Indiana University School of Medicine), Dr. Karen Pollok, Julie Mund, Matthew Repass, and Emily Sims (Angio BioCore at the Indiana University Simon Cancer Center) for excellent technical assistance in deriving ECFC samples. The authors also acknowledge Drs. Maureen A. Harrington, Edward F. Srour, Richard N. Day, Mervin C. Yoder, and Matthias A. Clauss (Indiana University School of Medicine) for scholarly discussion as well as Janice Walls (Indiana University School of Medicine) for administrative support. All imaging was performed at the Indiana Center for Biological Microscopy, Indiana University School of Medicine. This work was supported by the National Institutes of Health (R01 HL094725, P30 CA82709, and U10 HD063094) and the Riley Children’s Foundation. Additionally, this publication was made possible with partial support from the National Heart, Lung, and Blood Institute of The National Institutes of Health under Award # T32 HL007910.
100mm Tissue Culture Plates | Fisher | 353003 | |
15ml conical tubes | Fisher | 1495949B | |
Angiogenesis 15-well micro-slides | Ibidi | 81506 | |
Matrigel | Fisher (Corning) | 354234 | |
EGM2 Medium | Lonza | CC3162 | |
Fetal Bovine Serum | Atlanta Biologicals | S11550 | |
PSA Antimyocytic, Antibiotic | Fisher | MT30004C1 | Added 5 milliliters per 500 milliter bottle of EGM2 medium. |
0.5% Trypsin/0.53nM EDTA in HBSS | Fisher (Corning) | MT25052CI | |
Type 1 Collagen | Fisher (Corning) | 354226 | 100mg of liquid, concentration range 3-4mg/mL. Dilute in 20nM acetic acid in ddH2O to use at a final concentration of 0.05mg/mL. |
Glacial Acetic Acid | Fisher | A38-212 | Used in Type 1 Collagen solution at a final concentration of 20nM. |
Kim Wipes | Fisher | 06-666 | |
Chambered slide | Ibidi | 80296 | This slide can be filled with distilled water to maintain humidity in the imaging chamber. |
Phosphate Buffered Saline (PBS) | Fisher (Gibco) | 20012027 | 1x solution, pH 7.2 |
Microscope | Nikon Instruments | MEA53100 and MEF55030 | Motorized TiE including Ph1 phase ring for phase contrast with objective |
Stage | Prior Instruments | ProScan II | Other OKOlab and Nikon Elements AR compatible stage may be used |
Objective | Nikon Instruments | 93178 | CFI Plan Fluor DL 10x |
Camera | Hamamatsu | C11440-42U | ORCA Flash 4.0LT |
Stage Blower | Precision Controls | N/A | This item has been discontinued, alternatives include Smart Air-Therm Heater(AIRTHERM-SMT-1W) from World Precision Instruments |
Stage-top Incubator | OKOLabs | H301 | BoldLine including a two position slide holder |
Computer | Hewlett-Packard | Z620 or equivalent | 4 core Intel Xeon processor, >32 GB RAM, >2 TB RAID 10, nVidia Quadro graphics card |
Nikon Elements Software | Nikon Instruments | AR v 4.20 | ND Acquisition is a multidimensional setup tool used to configure Elements software. |
FIJI (ImageJ) Software | N/A | N/A | Free, open-source software dowloaded online at the following URL: https://imagej.net/Fiji/Downloads |
KAV Plug-In | N/A | N/A | Free, open-source software dowloaded online at the following URL: https://github.com/icbm-iupui/kinetic-analysis-vasculogenesis |