Distinct morphometric features of cardiomyocytes isolated from mouse hypertrophy models: An ImageJ analysis combined with machine learning algorithms - Hoang Duc Minh Pham; Marie‐Thérèse Daher; Onnik Agbulut; Zhenlin Li; Ara Parlakian (Physiological Reports, 2025)
Abstract This study aims at defining a standardized workflow based on a customized ImageJ macro combined with a machine‐learning algorithm to analyze morphometric features of isolated cardiomyocytes using high‐resolution/high‐content photomicrographs and to identify key and specific morphological features of cardiomyocytes isolated from various murine cardiac hypertrophy models. For that purpose, we set up and optimized a Langendorff based protocol for isolating cardiomyocytes from mouse hearts. This optimized protocol yielded in a significantly high number of formaldehyde‐fixed cardiomyocytes, with more than 97% of rod shaped cells. Moreover, our method allowed for reliable gene expression analysis and conservation of cell integrity through multiple freeze–thaw cycles. Next, we successfully applied our analytical workflow on formaldehyde‐fixed cardiomyocytes isolated from various murine cardiac hypertrophy models and defined distinct morphological features in Angiotensin II, Isoproterenol, and age‐induced hypertrophy. Taken together, our study provides an effective and standardized workflow for high‐throughput morphological and molecular characterization of isolated cardiomyocytes, and could constitute a robust and reliable analytical tool to distinguish healthy versus diseased states and assess the ability of a potential therapeutic agent or strategy to reverse the situation.