Abstract
The ‘cardiosphere’ is a 3D cluster of cardiac progenitor cells recapitulating a stem cell niche-like microenvironment with a potential for disease and regeneration modelling of the failing human myocardium. In this multicellular 3D context, it is extremely important to decrypt the spatial distribution of cell markers for dissecting the evolution of cellular phenotypes by direct quantification of fluorescent signals in confocal microscopy. In this study, we present a fully automated method, named CARE (‘CARdiosphere Evaluation’), for the segmentation of membranes and cell nuclei in human-derived cardiospheres. The proposed method is tested on twenty 3D-stacks of cardiospheres, for a total of 1160 images. Automatic results are compared with manual annotations and two open-source software designed for fluorescence microscopy. CARE performance was excellent in cardiospheres membrane segmentation and, in cell nuclei detection, the algorithm achieved the same performance as two expert operators. To the best of our knowledge, CARE is the first fully automated algorithm for segmentation inside in vitro 3D cell spheroids, including cardiospheres. The proposed approach will provide, in the future, automated quantitative analysis of markers distribution within the cardiac niche-like environment, enabling predictive associations between cell mechanical stresses and dynamic phenotypic changes.
Highlights
Monitoring the differentiation process of stem/progenitor cells is important either to devise new regenerative medicine approaches, or to understand the molecular basis of chronic diseases involving modifications in tissue structure and property[1]
To bridge the gap of knowledge derived by a paucity of automatic solutions for the specific characterization of cells inside in vitro 3D aggregates, including cardiospheres, here an adaptive algorithm is presented, CARE (‘CARdiosphere Evaluation’), for automatic cardiosphere segmentation in fluorescence microscopy images
Manual annotations were compared with automatic results provided by CARE and two open-source software designed for cell detection (Fiji and CellProfiler)
Summary
Monitoring the differentiation process of stem/progenitor cells is important either to devise new regenerative medicine approaches, or to understand the molecular basis of chronic diseases involving modifications in tissue structure and property[1] Until now, this issue has remained relatively unaddressed, given the lack of systematic tools enabling quantitative investigation (even in real time) of cells dynamics inside the so-called stem cell niches[2] or in disease models[3,4]. The need for quantitative tools is being stimulated by the plethora of methods recently proposed to engineer tissue-specific 3D microenvironments mimicking the native architecture, i.e. the so-called ‘organoid’ approach[5] This approach is expected in the future to support ‘synthetic’ tissue/niche modelling[6] for enhanced regenerative medicine applications[7], pathology decryption[8] or fundamental cell differentiation programs in developmental processes[9,10]. Manual annotations were compared with automatic results provided by CARE and two open-source software designed for cell detection (Fiji and CellProfiler)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
