VaMiAnalyzer: an open source, Python-based application for analysis of 3D in vitro vasculogenic mimicry assays

Abstract

Background: Vasculogenic mimicry (VM) is the phenomenon whereby non-vascular tumor cells develop vascular-like structures. VM is linked to more aggressive tumor phenotypes including higher rates of metastasis and invasion and is potentially resistant to anti-angiogenic cancer therapies. VM is investigated in vitro using 3D assays with microscopy images capturing the resulting VM structures, including loops, branch points, and tubes. The standard method to quantify endpoint data is to count various structural features manually, which is time-consuming and open to bias. At present, no software solutions have been developed to specifically address the analysis and quantification of VM structures.

Results: To address this limitation, we developed an open source, Python-based application, VaMiAnalyzer, allowing straightforward quantification of several VM structural features. The application follows a two-step approach that optionally corrects and enhances the raw input images and then analyzes and quantifies the VM features.

Conclusions: VaMiAnalyzer is stand-alone software that allows automated measurement of VM structural features from phase-contrast microscopy images. It produces results that are strongly consistent with manual counts but in a significantly shorter time, allowing quick, non-biased analysis of VM from microscopy images.

Fig. 1

Overview of VaMiAnalyzer two-step image analysis process. Raw images are analyzed for VM structures either directly or following background and shading correction. Representative images corresponding to each step in the workflow are shown below their respective stages in the analysis pipeline. (1) Raw image input. (2) Corrected image after background and shading correction. (3) Analyzed image featuring tubes (white lines), branch points (yellow dots), and loop counts (orange numbers)

Fig. 2

Comparison of output from VaMiAnalyzer, WimTube, and Angiogenesis Analyzer on a representative VM image. A VaMiAnalyzer output, featuring tubes (white lines), branch points (yellow dots), and loop counts (orange numbers). B WimTube, featuring tubes (red lines), branch points (white dots), loop counts (yellow numbers), and cell covered region (blue overlay). C Angiogenesis Analyzer, featuring tubes (green and blue lines), branch points (purple dots), and loops (area completely enclosed by yellow lines). The white arrows added post analysis, in C indicate loop examples in areas of confluent cell growth

Fig. 3

Comparison of VM analysis by VaMiAnalyzer, manual counting, and the WimTube tube formation image analysis program. Counts of loops, branches, and tubes did not differ significantly between the VaMiAnalyzer or manual counting of structures using 40 images (p > 0.05). The WinTube program results were significantly different from the other two groups for all three structural features ( *** p < 0.05)