In this article we introduce five angiogenesis quantification software solutions that can help you leverage the potential of AI-driven image analysis in vasculogenesis research. Find out how state-of-the art computer vision technology can assist you in the study of blood vessel growth processes. Using these advanced software applications you can easily evaluate vascular development and angiogenesis inhibition based on different quantitative parameters.
- The angiogenic blood vessel growth process
- Angiogenesis inhibition in cancer treatment
- The uses of quantification software in angiogenesis stimulation and inhibition research
The angiogenic blood vessel growth process
Angiogenesis, or the formation of new blood vessels, involves the strict regulation of multiple signalling pathways through which newly formed blood vessels emerge from the endothelial cells of pre-existing ones such as arteries, veins, and capillaries. Angiogenesis primarily occurs during embryogenesis and vessel reproduction in the form of vasculogenesis, but it can also be viewed as a salient process in different pathologic conditions, including cancer and inflammation, throughout the lifespan of an organism.
Ongoing angiogenesis is even considered an indication of cancer. In fact, the vascular endothelial growth factor pathway plays a pivotal role in tumor angiogenesis. Many cancers exploit this angiogenic activity to stimulate angiogenic tumor growth and supply nutrients to the tumor.
Further, tumor angiogenesis results in cancer cell invasion and metastasis. For this reason, tumor angiogenesis plays an important role in the regulation of cancer progression, although not completely understood at this point (Zhao and Adjei, 2015).
The study of angiogenesis is a crucial part of tumor research, because it can help reduce both morbidity and mortality from carcinomas. The discovery of angiogenic inhibitors in particular can help prevent neogenic blood vessel formation and tumor cell proliferation (Nishida et al., 2006).
Angiogenic growth factors
While angiogenesis takes place during embryo development above all, the major physical causes which stimulate angiogenic processes in fully-developed organisms, are tissue ischemia, hypoxia, inflammation and stress. There are a number of specific factors released by tumor cells known to stimulate or inhibit angiogenesis over time, including vascular growth factors, tumor angiogenesis growth factors, inflammatory cytokines etc.
VEGF – vascular endothelial growth factors – and VEGF receptors are a part of the major angiogenesis signalling pathways. There are five VEGF glycoproteins, which can be distinguished, namely VEGF-A, VEGF-B, VEGF-C, VEGF-D and VEGF-E (Lee et al., 2015).
The placental growth factors PLGF 1 and 2 are also a part of the VEGF family. VEGF-A and its receptors KLT/VEGFR1 and VEGFR-2 (a tumor angiogenesis receptor) are considered to be the main target areas of current antiangiogenic agents. VEGF-A, for example, can be targeted by applying a specific therapeutic agent to inhibit microvessel growth.
Recent articles suggest that VEGF may also have an additional effect in cancer progression due to the autocrine stimulation of VEGF-receptors in tumor cells. There is increasing evidence of the presence of VEGFRs in liquid and solid tumor cells, e.g. in melanoma, prostate cancer, breast cancer, as well as in leukemia (Lee et al., 2015).
However, the relevance of this expression pattern is still subject to further studies. Tumor growth might not only occur due to angiogenesis induced by VEGF, but can also be the result of direct stimulation via VEGFRs. Thus, endothelial cell-independent pathways may serve as the basis for useful future targets of cancer therapy methods that reach far beyond vascular endothelial growth factors (Lee et al., 2015).
Angiogenic markers and angiogenesis quantification
Several endothelial cell markers (e.g., PECAM-1/CD31, CD34, vWF) and angiogenesis protein markers are commonly used in immunohistochemistry (IHC) stains of human FFPE tumor sections. Quantitative data obtained from angiogenesis models can include the endothelial cell count or the expression levels of RNAs encoding proteinsassociated with neovascularization.
Angiogenesis markers are measured with standardized angiogenic protein assays on the basis of particular clinical parameters like VEGF levels (Rykala et al., 2011). Specialized image analysis software can play a central role in angiogenesis quantification based on these IHC protein markers. Such tools are commonly used for molecular tumor profiling, monitoring of tumor progression and estimating tumor malignancy. In addition to its clinical uses, IHC quantification software has proven to be an invaluable tool in a variety of experimental models for the study of pathological angiogenesis (Shih et al., 2002).
Angiogenesis inhibition in cancer treatment
Angiogenesis inhibition plays a vital role in cancer treatment. Angiogenesis inhibitory factors serve as cancer-fighting agents by interfering with various steps in the blood vessel growth. For example, they block the formation and growth of new blood vessels that support tumor progression.
Angiogenesis inhibitors may be used as monotherapy or in combination with other anti-cancer drugs. However, preclinical and clinical studies have shown higher therapeutic efficiency using a combined treatment regime in contrast with individual treatments (El-Kenawi & El-Remessy, 2013).
How do angiogenesis inhibitors work
Different angiogenesis inhibitors are currently applied in the treatment of many cancers (Petrovic et al., 2016; Goel and Mercurio, 2014). Angiogenesis inhibitors can be classified into direct inhibitors, which target endothelial cells in the growing vasculature or indirect inhibitors, which block the activity and expression of angiogenesis inducers. Indirect inhibitors include targeted therapy concepts against oncogenes, conventional chemotherapeutic agents or other drugs aiming at other cells of the tumor microenvironment (El-Kanawi & El-Remessy, 2013).
The suppression of vascular endothelial growth factors (VEGF) is often described in literature. This approach includes not only direct anti-VEGF treatments, either alone or in combination with chemotherapy, but also immunomodulatory drugs with antiangiogenic properties and receptor tyrosine kinase inhibitors, targeting VEGF receptors and their signaling.
Other approaches aim at the inhibition of VEGF receptor tyrosine kinase activity. Treatment with receptor tyrosine kinase inhibitors may appear relatively non-specific to angiogenesis as many other growth factor receptors share structural similarities in their tyrosine kinase domain.
The prevailing idea of all of these concepts is to specifically target angiogenesis and other endothelial cell functions. This aspect of VEGF-targeted therapy has been extensively studied (Goel and Mercurio, 2014).
Angiogenesis inhibitor factor examples
Among the most commonly used VEGF-targeting inhibitory agents are Avastin(Bevacizumab), Aflibercept (Zaltrap) and Ramucirumab (Cyramza). Current research gives insights into the antiangiogenic effects of novel angiogenic inhibitors. For example, promising preclinical studies revealed that Cilengitide, a selective integrin inhibitor, reduces vascular density, vascular permeability and increases survival rates in a model of orthotopically-implanted glioblastoma in rats. Inhibition of FGFR-1–4, PDGFRβ, and VEGFR-1–3 with Dovitinib demonstrated anti-tumor activity in xenografts models of renal cell carcinoma (Ramjiawan et al., 2017).
The uses of quantification software in angiogenesis stimulation and inhibition research
Using an elaborate angiogenesis analysis model allows researchers to examine the effects of stimulatory and inhibitory agents on vascular formation and growth.
In vitro angiogenic assays are performed on cell culture and are used to examine specific functions and processes. In vitro angiogenesis assays can be classified into categories such as:
- endothelial proliferation models,
- endothelial migration models,
- endothelial cell differentiation models.
In vivo assays provide a more thorough assessment of essential angiogenesis quantification parameters compared to in vitro and ex vivo assays, since they allow researchers to study angiogenesis dynamics in a living organism.
Ex vivo assays make use of organ or embryo culture to examine elaborate angiogenic processes. These models are more complex than in vitro assays, since they involve the interaction of vascular structures with different organ cells and surrounding tissue besides endothelial cells.
The following Table displays an overview of the most common types of in vitro, in vivo and ex vivo angiogenic assays discussed in recent research literature.
|In vitro assays||In vivo assays||Ex vivo assays|
|Boyden chamber assay||Martigel plug assay||Rat aortic ring assay|
|Endothelial tube formation assay (EFTA)||Corneal micropocket assay||Chick aortic arch assay|
|Phagokinetic track assay||Chick chorioallantoic membrane (CAM) assay||Choroid sprouting assay|
|MTT assay||Hindlimb ischemia assay||Retina model assay|
|Matrix invasion assay||Zebrafish assay||Human placental vessels assay|
|Fibrin bead assay||Disc assay (DAS)||Skeletal muscle explant assay|
|Skeletal muscle explant assayMatrix Metalloproteinase (MMP) assay||Sponge implantation method||Bovine/murine retinal explant assay|
How to choose the angiogenesis assay that fits your needs
There is not a single all-round angiogenesis assay applicable to every research design as the specifics of each method prevent the development of one standard procedure. Due to the heterogeneity and diversity of tissues and the complexities of angiogenic reactions, it seems to be an uphill task to develop a single assay for all experimental designs (Shahid et al., 2017). Depending on the purpose of your research, which aspect of angiogenesis you want to look at and which cells need to be included, different factors should be considered.
Tips and tricks
Choose an angiogenic assay based on the complexity of the processes you want to examine and the parameters you are interested in.
A number of angiogenesis assays enable an evaluation of pro- or anti-angiogenic activity of stimulating or inhibitory agents on the basis of their pro- or anti-proliferative, migratory and/or tube formation effects on ECs (Stryker et al., 2019). For this reason, in vitro and in vivo assays are used. In vivo assays allow early stage evaluations, while in vivo methods offer a living microenvironment. Here are some tips on choosing the right assay according to the current state of research.
First of all, the release rate [R] and the spatial and temporal concentration distribution [C] of a tested compound need to be estimated with the help of the chosen assay in order to evaluate dose-response curves. The method has to yield information on oncogene expression and angiogenic growth factor levels.
Next, the assays must be designed in a way that quantitative measuring parameters of the newly formed vessels can be defined. This means the chosen methodology must enable you to obtain quantitative data on parameters such as surface area [A], volume [V], vascular length [L], number of vessels in the network [N], fractal dimensions of the network [Df], and extent of basement membrane [BM].
In addition, the design of the assay should allow for weighing quantitative measures of morphological characteristics of new vessels such as endothelial cell migration [MR], proliferation rate [PR], canalization rate [CR], blood flow rate [F], and vascular permeability [P]. It is also vital that a clear demarcation between a newly formed vessel and the parent vessels can be detected with the help of the assay.
When doing the assessment, in vitro methods must always be verified by in vivo methods and an angiogenesis assay for long-term and non-invasive monitoring should be preferred. When choosing an assay, economic, ethical, robustness, and reliability aspects need to be considered as well in order to ensure a smooth workflow (Shahid et al., 2017; Norrby, 2006).
Tips and tricks
With the help of advanced deep learning solutions you can fully automate complete angiogenic assays workflows and obtain quantitative data on vascular formation processes and markers.
Such software products largely increase throughput and reproducibility in angiogenesis image analysis. Below we provide an overview of five IKOSA image analysis solutions that will help you obtain the optimal results while conducting the angiogenic assay of your choice. Each of these image analysis applications relies on a powerful deep learning algorithm that is able to vastly and reliably process huge amounts of image data.
CAM assay analysis software
The chorioallantoic membrane (CAM) method is widely used in ex ovo research to quantify neovascularization. Moreover, the CAM Assay is applied in in vivo cancer research for the quantitative analysis of the angiogenic and anti-angiogenic processes.
The CAM Assay model is commonly performed to study vascular growth patterns in the membrane lining developed around a chicken embryo on the inner surface of an egg shell. Current CAM Assay software enables researchers to automatically extract information on morphological and spatial parameters of the vascular area on the chorioallantoic membrane. Using the IKOSA CAM Assay App, a number of parameters can be easily quantified:
- vessel total area,
- vessel total length,
- vessel mean thickness,
- and number of branching points.
If you are currently studying angiogenic growth using the chorioallantoic membrane (CAM) model, the IKOSA CAM Assay application can provide you with an automated state-of-the-art solution to quantify the neovascularization process.
The IKOSA Prisma software portfolio features two easy-to-use application modules for the quantification of blood vessels on an avian chorioallantoic membrane (CAM). CAM Assay (left), CAM Grid Assay (right)
We developed this state-of-the-art algorithm for the analysis of CAM images in close cooperation with leading scientists from the Institute for Molecular and Cellular Anatomy at the University of Regensburg, the Otto Loewi Research Center at the Medical University of Graz, and the Ludwig Boltzmann Institute for Experimental and Clinical Traumatology in Vienna.
Learn how using the IKOSA CAM Assay analysis algorithm was put to practice in an article on the quantification of tumor-induced angiogenesis in 3D in vivo tutor model. One further publication of the research group showcases the use of our CAM Assay Application for quantifying renal cyst growth in kidney tissue.
Our cooperation with the team of Dr. Nassim Ghaffari Tabrizi-Wizsy at the Medical University of Graz gave rise to another automated tool for quantifying changes in vasculature in CAM images. The IKOSA CAM Grid Assay application has been developed for the segmentation of new blood vessels on a chorioallantoic membrane placed on a polymerized grid. This method allows you to collect quantitative data on parameters such as:
- number of vessels,
- total vessel area,
- mean vessel area,
- lmedian vessel area,
- and mean image intensity.
Software solutions for the analysis of angiogenic sprouting
Angiogenic sprouting refers to the morphogenesis of hierarchical networks of vascular sprouts such as arterioles, venules and highly branched capillaries providing efficient blood flow to body organs. Angiogenic sprouting models are widely applied by researchers to examine the dynamics of cancer cell invasion in blood vessel sprouting in vitro. The spheroid sprouting assay method makes use of endothelial cell spheroids or tumor organoids to study this process.
These methods are used to quantify the migration of cells as an indicator of angiogenic response. For this purpose spheroids are embedded on a collagen, matrigel or fibrin medium matrix. The migration of cells into the medium involves either the formation of single-cell sprouts or that of complex capillary-like structures.
The IKOSA Spheroid Sprouting Assay application allows you to study crucial sprouting parameters of endothelial cell spheroids based on time-lapse images. This allows you to extract spatial and temporal information on angiogenesis sprouting mechanisms. This particular image analysis software product is perfectly suited for the quantification of features such as:
- number of sprouts,
- sprouts total length,
- sprouts total area,
- body area,
- and body circularity.
This unique software tool for analyzing sheroid sprouts is the result of our collaboration Department of Obstetrics and Gynecology at the Medical University of Graz and the Ocular Angiogenesis Group at the Department of Medical Biology of Amsterdam UMC. The development of this application wouldn’t have been possible without the assistance of researchers Dr. Ursula Hiden, Jasmin Strutz MSc and Dr. Ingeborg Klaassen.
Our cooperation partners have a proven record of publications on endothelial cell response during angiogenesis including studies on:
- VEGFA signalling in human endothelial tip cells and non-tip cells
- The effect of capelin signalling in sprout progression
- Novel tip cell genes in microvascular endothelial cell monolayers
- The anti-angiogenic effect of crenolanib on cell viability, migration, sprouting, apoptosis and mitosis
- The regulatory effect of IGF-binding proteins 3 and 4 on angiogenic sprouting
Tube formation quantification software
Endothelial cell biology has found its rightful place in recent studies. Endothelial cell culture methods are widely used in the study of vascular network formation over time.In vitro vascular network formation research often makes use of endothelial cell samples to examine the development of vessel-like structures or tubes.
The endothelial tube formation assay (ETFA) is a widely used method to examine the capillary-like growth of endothelial cells on a fibrin matrix. EFTA is a popular used in vitro method, applied in experimental wound healing and angiogenesis research to study the induction or inhibition of tube formation.
Matrigel is a cell culture medium typically associated with the tube formation assay method. For this purpose endothelial cells are plated on the matrigel medium in an extracellular matrix. The formation of tube-like structures together with the differentiation of endothelial cells can be easily observed and quantified in this manner.
AI-driven microscopy image analysis software allows researchers to examine the pseudo-vascular structure of a 3-dimensional fibrin network. This specialized software enables scholars to automatically and precisely detect and quantify extremities, branch structures, segments and junctions of an endothelial cells tubular network.
The IKOSA Fibrin Tube Formation Assay application will help you gain valuable insights into vital parameters such as:
- number of tubes,
- total tubes area,
- total tubes length,
- number of tube branching points,
- number of loops,
- and total loop area.
We’d like to thank Dr. Ursula Hiden and Jasmin Strutz MSc from the Department of Obstetrics and Gynecology at the Medical University of Graz for their support on this project. Find out how their team applied the Fibrin Tube Formation Assay in an article examining outgrowth, proliferation, viability, angiogenesis and phenotype of primary human endothelial cells.
Angiogenesis analysis software and vascular network formation
Angiogenesis network formation research can greatly benefit from automated deep learning applications. For instance, these applications could support scientific studies on how to block the formation of new blood vessels in order to suppress tumor growth. In other words, researchers are seeking strategies to cut the adequate nutrient supply in the vascular network of cultured endothelial tissue.
Developed in collaboration with the Department of Obstetrics and Gynaecology at the Medical University of Graz, the IKOSA Network Formation Assay application allows users to automatically collect relevant information on multiple quantitative parameters such as:
- number of tubes,
- number of branching points,
- total areas covered by cells or tubes,
- and total tube length.
Find out how the research team at the Medical University of Graz applies the Network Formation Assay for studying placental angiogenesis.
We would like to thank the following project team for the opportunity to use CAM Assay images in this article:
Dr. Nassim Ghaffari Tabrizi-Wizsy (Otto Loewi Research Center, Immunology and Pathophysiology, Medical University of Graz) provided the expertise of working with the CAM Assay. Lorenz Faihs performed the experiments and imaging/analysis, as well as DI Dr. Peter Dungel and A.o. Univ.-Prof. Mag. DDr. Kurt Schicho (Ludwig Boltzmann Institute for Experimental and Clinical Traumatology and University Clinic for Cranio-, Maxillofacial and Oral Surgery, Medical University of Vienna), who planned the project.
Berndsen, R. H., Castrogiovanni, C., Weiss, A., Rausch, M., Dallinga, M. G., Miljkovic-Licina, M., … & Nowak-Sliwinska, P. (2019). Anti-angiogenic effects of crenolanib are mediated by mitotic modulation independently of PDGFR expression. British journal of cancer, 121(2), 139-149.
Bichlmayer, E. M., Mahl, L., Hesse, L., Pion, E., Haller, V., Moehwald, A., … & Haerteis, S. (2022). A 3D In Vivo Model for Studying Human Renal Cystic Tissue and Mouse Kidney Slices. Cells, 11(15), 2269.
Dallinga, M. G., Yetkin-Arik, B., Kayser, R. P., Vogels, I., Nowak-Sliwinska, P., Griffioen, A. W., … & Schlingemann, R. O. (2018). IGF2 and IGF1R identified as novel tip cell genes in primary microvascular endothelial cell monolayers. Angiogenesis, 21(4), 823-836.
Dallinga, M. G., Habani, Y. I., Kayser, R. P., Van Noorden, C. J., Klaassen, I., & Schlingemann, R. O. (2020). IGF-binding proteins 3 and 4 are regulators of sprouting angiogenesis. Molecular Biology Reports, 47(4), 2561-2572.
Dallinga, M. G., Habani, Y. I., Schimmel, A. W., Dallinga-Thie, G. M., van Noorden, C. J., Klaassen, I., & Schlingemann, R. O. (2021). The role of heparan sulfate and neuropilin 2 in VEGFA signaling in human endothelial tip cells and non-tip cells during angiogenesis in vitro. Cells, 10(4), 926.
El-Kenawi, A.E., El-Remessy, A.B. (2013). Angiogenesis inhibitors in cancer therapy: mechanistic perspective on classification and treatment rationales. British Journal of Pharmacology, 170, 712-729.
Goel H.L., Mercurio, A.M. (2013). VEGF targets the tumour cell. Nat Rev Cancer, 13(12), 871-882.
Kuri, P. M., Pion, E., Mahl, L., Kainz, P., Schwarz, S., Brochhausen, C., … & Haerteis, S. (2022). Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model—Establishment and Addition to Laser Speckle Contrast Imaging (LSCI). Cells, 11(15), 2321.
Lee, S.H., Jeong, D., Han, YS., Baek, M.J. (2015). Pivotal role of vascular endothelial growth factor pathway in tumor angiogenesis. Annals of Surgical Treatment and Research, 89(1), 1-8.
Loegl, J., Nussbaumer, E., Cvitic, S., Huppertz, B., Desoye, G., & Hiden, U. (2017). GDM alters paracrine regulation of feto-placental angiogenesis via the trophoblast. Laboratory Investigation, 97(4), 409-418.
Leopold, B., Strutz, J., Weiß, E., Gindlhuber, J., Birner-Gruenberger, R., Hackl, H., … & Hiden, U. (2019). Outgrowth, proliferation, viability, angiogenesis and phenotype of primary human endothelial cells in different purchasable endothelial culture media: feed wisely. Histochemistry and Cell Biology, 152(5), 377-390.
Nishida, N., Yano, H., Nishida, T., Kamura, T., Kojiro, M. (2006). Angiogenesis in cancer. Vascular Health and Risk Management, 2(3), 213-219.
Norrby, K. (2006). In vivo models of angiogenesis. J. Cell. Mol. Med., 10(3), 588-612.
Palm, M. M., Dallinga, M. G., van Dijk, E., Klaassen, I., Schlingemann, R. O., & Merks, R. M. (2016). Computational screening of tip and stalk cell behavior proposes a role for apelin signaling in sprout progression. PloS one, 11(11), e0159478.
Petrovic, N. (2016). Targeting Angiogenesis in Cancer Treatments: Where do we Stand? J Pharm Pharm Sci. 19(2), 226-238.
Ramjiawan, R.R., Griffioen, A.W., Duda, D.G. (2017). Anti-angiogenesis for cancer revisited: Is there a role for combinations with immunotherapy? Angiogenesis, 20(2), 185-204.
Rykala, J., Przybylowska, K., Majsterek, I., Pasz-Walczak, G., Sygut, A., Dziki, A., & Kruk-Jeromin, J. (2011). Angiogenesis markers quantification in breast cancer and their correlation with clinicopathological prognostic variables. Pathology & Oncology Research, 17(4), 809-817.
Shahid, I., AlMalki, W.H., AlRabia, M.W., Ahmed, M., Imam, M.T., Saifullah, M.K., Hafeez, M.H. (2017). Recent Advances in Angiogenesis Assessment Methods and their Clinical Applications, Physiologic and Pathologic Angiogenesis – Signaling Mechanisms and Targeted Therapy. Intechopen. doi:10-5772/66504.
Shih, S. C., Robinson, G. S., Perruzzi, C. A., Calvo, A., Desai, K., Green, J. E., … & Senger, D. R. (2002). Molecular profiling of angiogenesis markers. The American journal of pathology, 161(1), 35-41.
Stryker, Z. I., Rajabi, M., Davis, P.J., Mousa, S.A. (2019). Evaluation of Angiogenesis Assays. Biomedicines 7, 37, 1-13.
Zhao Y, Adjei AA. Targeting Angiogenesis in Cancer Therapy: Moving Beyond Vascular Endothelial Growth Factor. Oncologist. 2015 Jun;20(6):660-73.
Ask us your questions to learn more about IKOSA
Now it is time for you to pick the right application for your angiogenesis research project. KML Vision offers full flexibility using one of our IKOSA products. If you have recently developed new angiogenesis image analysis assays in your lab and you would like to have them automated, reach out to us.
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