Work

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

Year: 2025

Type: article

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.

Source: BMC Bioinformatics

DOI: 10.1186/s12859-025-06280-4

Authors: Stephen P.G. Moore, Xinyu Zhang, Olivia Chika Jonathan, Anqi Zou, Deborah Lang, Chao Zhang

Institutions: Boston University, Northeastern University

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990