MyoFuse: A fully AI-based workflow for automated quantification of skeletal muscle cell fusion in vitro

BackgroundThe myogenic fusion index (FI) is commonly used in skeletal muscle cell culture to assess the ability of myoblasts to form myotubes, as the ratio of myoblast nuclei fused with myotubes over the total number of myoblasts. The manual quantification of the FI from 2D microscopy images is tedious and biased, thus several automated methods have been developed. However, they still face challenges such as efficient nucleus segmentation and classification of fused and isolated myoblast nuclei. Here, we developed a novel workflow entirely based on AI for fully automated and unbiased quantification of the FI. ResultsUsing current methods, we show that myoblast nuclei located above or below myotubes can significantly corrupt accurate FI computation. To circumvent this issue, we developed MyoFuse which enables an accurate and high-throughput segmentation and classification of myonuclei. It comprises a nuclei segmentation step using Cellpose, followed by a classification network trained with Svetlana. MyoFuse demonstrated strong accuracy when tested against manual annotation in mouse C2C12 and human primary myotubes. The trained classifier is able to differentiate myotube nuclei from myoblast nuclei based on myotube cytoplasm staining only. Experimental comparisons also highlighted that the previously developed methods lead to a significant overestimation of the FI. ConclusionIn summary, we underscore the lack of accuracy of traditional methods for automated FI quantification. MyoFuse enables a direct and accurate segmentation of nuclei even in nuclei clusters frequently observed in myotubes. This workflow thus offers a new and more reliable method to evaluate the FI. It also limits the selection bias by processing large images.