Label-free phenotyping of human microvessel networks

Abstract

Understanding the spatial heterogeneity in blood vessel formation and development is crucial for various biomedical applications. Traditional methods forin-vitromicrovessel segmentation rely on fluorescent labeling, which either interferes with the sample homeostasis, limits the study to a restricted set of precursor cells, or requires sample fixation, thus preventing live measurements. Moreover, these methods often focus on small, cropped images, neglecting global spatial heterogeneity of microvasculature, leading to biased data interpretation. To overcome these limitations, we present VascuMap, a deep-learning-based tool for label-free vessel segmentation and spatial analysis. VascuMap enables a comprehensive examination of entire vessel networks, capturing both morphological and topological features across the full vascular bed. Our method achieves high segmentation accuracy, comparable to the state-of-the-art fluorescence-based models. VascuMap’s capabilities extend to characterizing vasculature generated from label-free patient-derived samples, a vital step towards personalized medicine. Its compatibility with widefield label-free microscopy also accelerates sample acquisition, making it ideal for high-throughput systems crucial for drug toxicity and safety screens.

Self-organization of vascularized skeletal muscle from bovine embryonic stem cells

“…3-6 independent experiments showed similar staining patterns. c, Characterization of the endothelial cell networks using VascuMap 68 . After the IHC images were masked and segmented, the vessel segments (red edges) and branching points (white nodes) were identified and quantified using the connectivity analysis pipeline.…”

“…Endothelial network connectivity was analyzed with VascuMap software using a UNet architecture with a mixed vision transformer encoder 68 . The model, trained on endothelial cell images co-cultured with bovine muscle, labeled manually on an iPad 11 Pro with Photoshop and binarized via Python script.…”