Application of Machine Learning for Volumetric Analysis of Atherosclerotic Burden

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide. Preclinical studies to research and validate therapeutic interventions for CVD often depend on two-dimensional histological surveys. The use of light sheet fluorescent microscopy together with optical clearing methods amenable to immunofluorescent staining are recent advances, all of which deliver detailed three-dimensional rendering of vessels. This offers the ability to describe and quantify features critical in CVD models, specifically, atherosclerotic plaque burden in atherosclerotic animal models. The main challenge for this approach remains the lengthy, hands-on, analysis time. Labkit is a user-friendly Fiji plugin that applies a machine-learning algorithm to create three-dimensional renderings from large microscopy data. The application of this plugin is expected to decrease the hands-on analysis time required to generate accurate volumetric renderings of atherosclerotic plaque burden in athero-prone mice. For analysis, Ldlr -/- (C57/Bl6) mice aged 6-8 weeks were fed a high-fat diet for 15 weeks to allow the development of atherosclerotic plaque along the aorta. Aged-matched chow-fed C57/Bl6 mice were used as athero-free controls. Aortic roots were sectioned and stained with hematoxylin and eosin, or oil red o stains, and later imaged and analyzed using ImageJ. AdipoClear and immunolabeling together with light-sheet fluorescent microscopy allowed for three-dimensional visualization. Both Imaris software v9.9.1 and the built-in bridge to ImageJ/Labkit were used to quantify the plaque burden in the mice manually or automatically, respectively. Our findings indicate that Labkit offers an effective and user-friendly platform for the segmentation of atherosclerotic plaque in aortas.