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.
1. Preparations
2. Protocol 1: Experimental Setup: Preparing the Slide
3. Protocol 2: Plating ECFCs on the Matrix
4. Protocol 3: Microscope Setup and Image Acquisition
NOTE: For our studies, Protocols 2 and 3 were conducted simultaneously by separate individuals. If Protocols 2 and 3 must be completed by the same individual, Protocol 3 Steps 4.1 and 4.2 below should be completed prior to initiating Protocol 2 above.
5. Protocol 4: Image Processing and Kinetic Analysis of Vasculogenesis (KAV)
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 utero16. 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 function13. 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 data16. 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 area16. 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 approaches16.
Five graphs depicting mean values of network structure from three separate experiments are shown for a single uncomplicated sample (UC) and the three GDM samples (Figure 4). The UC sample in these experiments performed similarly to previously analyzed UC samples16. Previously, it was identified that ECFC vasculogenesis in vitro is bi-phasic, consisting of Phase 1 (0 – 5 h) and Phase 2 (5 – 10 h)16. This pattern is consistent in the current studies, as evidenced by the graph depicting closed network data (Figure 4). The UC sample formed a greater number of closed networks compared to all three of the GDM samples. Interestingly, three of the four samples appear to have a similar time to maximal number of closed networks, which occurs between 2.5 and 3 hours. However, the GDM2 sample was slower to reach maximal closed networks, which occurred 5 hours post-plating. And, despite the GDM2 sample forming fewer maximal networks compared to the UC sample, the networks it forms were maintained similarly to the UC sample over time. Conversely, GDM1 and GDM3, which formed fewer networks compared to the UC sample, also exhibited an overall reduction in network number over time. Overall, from the graph depicting closed network number, it is evident that all ECFC samples displayed a bi-phasic pattern of network formation, however the rate of formation and the maximal number of networks achieved vary across samples.
Network area represents the average area within the closed networks formed by the ECFCs. Therefore, the greater the closed network number, the smaller the network areas. This pattern is reflected in the network area graphs where the UC sample, which formed a larger number of closed networks, had smaller network areas over time compared to the three GDM samples (Figure 4). GDM2, which reached maximal closed networks more slowly, exhibited high network area initially, however the area stabilized over time, and this was most similar to the UC sample at 15 hours. Over time, the average network area in all samples increased due to network de-stabilization. However, some samples, like GDM1 and GDM3, exhibited a more rapid increase in network area, which is likely indicative of decreased stability compared to other samples exhibiting a more gradual increase.
GDM-exposed ECFCs exhibit reduced network stability
The ratio of branches to nodes is a novel phenotype calculated by KAV and identified in our previous studies, and this is indicative of network connectivity16. In the current study, the UC sample had a low ratio, which represented a high level of network connectivity that was maintained over time (Figure 4). Conversely, the three GDM samples had a higher ratio of branches to nodes, especially in Phase 2 of network formation. This observation demonstrates reduced connectivity and de-stabilization of network structures.
Overall, GDM1 formed fewer nodes and branches compared to the other samples, with the reduction maintained over the course of the experiment. GDM2 and GDM3 formed and maintained a greater number of branches compared to the UC sample, especially between 5-15 h. The numbers of nodes detected in the GDM2 and GDM3 networks were more similar to the number of nodes in the UC sample, especially between 10-15 h. A greater number of branches, but a similar number of nodes, could account for the increased branch to node ratio evident at the later time points in the GDM samples. Importantly, simultaneous changes in branch and node number can be difficult to interpret in separate graphs. However, the novel branch to node ratio offers a way to assess how changes, including slight changes difficult to detect in the individual graphs, in both branch and node number, result in altered network connectivity.
Figure 1: Schematic outlining 10 parameters quantified by Kinetic Analysis of Vasculogenesis (KAV). KAV quantitates ten distinct parameters of network structure. All parameters are color-coded and outlined in the schematic numerically (1-10). Parameters include the number of branches (green, 1), closed networks (blue, 2), and nodes (red, 3), average network area (orange, 4), the number of network structures (black, 5), triple-branched nodes (yellow, 6), and quad-branched nodes (purple, 7), as well as the total (9) and average (10) branch length, and the ratio of the number of branches to the number of nodes (10). Please click here to view a larger version of this figure.
Figure 2: Kinetic Analysis of Vasculogenesis (KAV) quantitates network structure. (A) Skeleton and mask renditions of network structures identified by KAV using phase contrast images provide visual representation of network structures identified and quantified by KAV. Scale bar = 500 µm. (B) A representative phase contrast image of ECFC networks 10 h post-plating that has low contrast and grid marks from stitching individual images together. Different thresholding methods, such as Mean or Otsu, can be selected in the KAV plug-in to improve quantitation accuracy, if phase contrast images have low contrast or if gridding occurs. Skeleton and mask renditions of the phase contrast image are shown for both Mean and Otsu thresholding methods. Phase contrast images were captured using a 10X objective. Scale bar = 500 µm. Please click here to view a larger version of this figure.
Figure 3: Intrauterine GDM exposure alters ECFC network formation. Images of ECFC network formation were captured at 15 min intervals for 15 h by phase contrast microscopy. Representative phase contrast images at 5 h increments, starting at the time of plating (0.5 h), are shown for the UC and three GDM samples. Phase contrast images were captured using a 10X objective. Scale bar = 500 µm. Please click here to view a larger version of this figure.
Figure 4: ECFCs exposed to intrauterine GDM exhibit altered network formation kinetics. ECFCs were obtained from an uncomplicated pregnancy (UC, black) and three pregnancies complicated by gestational diabetes mellitus (GDM 1-3, red). Phase contrast images were captured at 15 min intervals for 15 h. Kinetic Analysis of Vasculogenesis (KAV) software quantitated closed networks, network areas, branches, nodes, and the ratio of branches to nodes. The data illustrated represent the mean ± standard error of the mean (SEM) of three separate experiments for each sample. Please click here to view a larger version of this figure.
Video 1: Intrauterine GDM exposure alters kinetics of ECFC network formation. Images of ECFC network formation were captured at 15 min intervals for 15 h by phase contrast microscopy. Phase contrast images are shown for the UC and three GDM samples over 15 h starting at 0.5 h. The scale bar represents 500 µm. Please click here to view this video. (Right-click to download.)
Image | Total Branch Networks | Nodes | Triples | Quadruples | Branches | Total Branch Length* | Avg Branch Length* | Branch to Node Ratio | Closed Networks | Network area** |
1 | 382 | 290 | 278 | 11 | 943 | 47210 | 124 | 3.25 | 9 | 480214 |
2 | 284 | 345 | 337 | 8 | 940 | 50699 | 179 | 2.72 | 16 | 264469 |
3 | 205 | 376 | 366 | 9 | 910 | 55728 | 272 | 2.42 | 27 | 150621 |
4 | 162 | 422 | 407 | 15 | 947 | 59692 | 368 | 2.24 | 40 | 98713 |
5 | 132 | 454 | 441 | 12 | 967 | 61923 | 469 | 2.13 | 53 | 72951 |
6 | 122 | 435 | 419 | 14 | 907 | 62587 | 513 | 2.09 | 68 | 56948 |
7 | 88 | 429 | 411 | 17 | 852 | 63983 | 727 | 1.99 | 74 | 51429 |
8 | 68 | 451 | 437 | 13 | 874 | 63960 | 941 | 1.94 | 74 | 51780 |
9 | 93 | 437 | 417 | 18 | 905 | 60901 | 655 | 2.07 | 49 | 82919 |
10 | 126 | 511 | 487 | 24 | 1075 | 61981 | 492 | 2.1 | 37 | 116857 |
11 | 49 | 397 | 384 | 12 | 737 | 61823 | 1262 | 1.86 | 79 | 48805 |
12 | 81 | 376 | 364 | 10 | 751 | 56817 | 701 | 2 | 63 | 64431 |
13 | 98 | 509 | 482 | 26 | 1028 | 60799 | 620 | 2.02 | 44 | 98056 |
14 | 93 | 449 | 416 | 31 | 909 | 58282 | 627 | 2.02 | 45 | 94081 |
*rounded to nearest micron, **rounded to nearest micron^2 |
Table 1: Representative raw data from one imaging study for one ECFC sample.
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 |