Abstract
This paper presents data acquired to study the dynamics and interactions of mitochondria and subcellular vesicles in living cardiomyoblasts. The study was motivated by the importance of mitochondrial quality control and turnover in cardiovascular health. Although fluorescence microscopy is an invaluable tool, it presents several limitations. Correlative fluorescence and brightfield images (label-free) were therefore acquired with the purpose of achieving virtual labelling via machine learning. In comparison with the fluorescence images of mitochondria, the brightfield images show vesicles and subcellular components, providing additional insights about sub-cellular components. A large part of the data contains correlative fluorescence images of lysosomes and/or endosomes over a duration of up to 400 timepoints (>30 min). The data can be reused for biological inferences about mitochondrial and vesicular morphology, dynamics, and interactions. Furthermore, virtual labelling of mitochondria or subcellular vesicles can be achieved using these datasets. Finally, the data can inspire new imaging experiments for cellular investigations or computational developments. The data is available through two large, open datasets on DataverseNO.