Tubulator : an automated approach to analysis of t-tubule and dyadic organization in cardiomyocytes.
Michael FriskPer Andreas NorsengEmil Knut Stenersen EspeWilliam Edward LouchPublished in: Philosophical transactions of the Royal Society of London. Series B, Biological sciences (2022)
During cardiac disease, t-tubules and dyads are remodelled and disrupted within cardiomyocytes, thereby reducing cardiac performance. Given the pathological implications of such dyadic remodelling, robust and versatile tools for characterizing these sub-cellular structures are needed. While analysis programs for continuous and regular structures such as rodent ventricular t-tubules are available, at least in two dimensions, these approaches are less appropriate for assessment of more irregular structures, such as dyadic proteins and non-rodent t-tubules. Here, we demonstrate versatile, easy-to-use software that performs such analyses. This software, called Tubulator , enables automated analysis of t-tubules and dyadic proteins alike, in both tissue sections and isolated myocytes. The program measures densities of subcellular structures and proteins in individual cells, quantifies their distribution into transversely and longitudinally oriented elements, and supports detailed co-localization analyses. Importantly, Tubulator provides tools for three-dimensional assessment and rendering of image stacks, extending examinations from the single plane to the whole-myocyte level. To provide insight into the consequences of dyadic organization for synchrony of Ca 2+ handling, Tubulator also creates 'distance maps', by calculating the distance from all cytosolic positions to the nearest t-tubule and/or dyad. In conclusion, this freely accessible program provides detailed automated analysis of the three-dimensional nature of dyadic and t-tubular structures. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.
Keyphrases
- high resolution
- deep learning
- left ventricular
- public health
- machine learning
- healthcare
- heart failure
- high glucose
- induced apoptosis
- oxidative stress
- data analysis
- risk assessment
- cell cycle arrest
- endothelial cells
- health information
- atrial fibrillation
- cell death
- cell proliferation
- climate change
- endoplasmic reticulum stress
- human health