MyoFInDer: an AI-based tool for Myotube Fusion Index Determination.

The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error-prone and subjective. Several software tools have been proposed for addressing these limitations, but suffer from various drawbacks including unintuitive interfaces and limited performance. Here, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area, and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared to other tools, MyoFInDer stands out as it minimizes the inter-operator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.

Decellularized tissue exhibits large differences of extracellular matrix properties dependent on decellularization method: novel insights from a standardized characterization on skeletal muscle.

Decellularized matrices are an attractive choice of scaffold in regenerative medicine as they can provide the necessary extracellular matrix (ECM) components, signals and mechanical properties. Various detergent-based protocols have already been proposed for decellularization of skeletal muscle tissue. However, a proper comparison is difficult due to differences in species, muscle origin and sample sizes. Moreover, a thorough evaluation of the remaining acellular matrix is often lacking. We compared an in-house developed decellularization protocol to four previously published methods in a standardized manner. Porcine skeletal muscle samples with uniform thickness were subjected to in-depth histological, ultrastructural, biochemical and biomechanical analysis. In addition, 2D and three-dimensional cytocompatibility experiments were performed. We found that the decellularization methods had a differential effect on the properties of the resulting acellular matrices. Sodium deoxycholate combined with deoxyribonuclease I was not an effective method for decellularizing thick skeletal muscle tissue. Triton X-100 in combination with trypsin, on the other hand, removed nuclear material but not cytoplasmic proteins at low concentrations. Moreover, it led to significant alterations in the biomechanical properties. Finally, sodium dodecyl sulphate (SDS) seemed most promising, resulting in a drastic decrease in DNA content without major effects on the ECM composition and biomechanical properties. Moreover, cell attachment and metabolic activity were also found to be the highest on samples decellularized with SDS. Through a newly proposed standardized analysis, we provide a comprehensive understanding of the impact of different decellularizing agents on the structure and composition of skeletal muscle. Evaluation of nuclear content as well as ECM composition, biomechanical properties and cell growth are important parameters to assess. SDS comes forward as a detergent with the best balance between all measured parameters and holds the most promise for decellularization of skeletal muscle tissue.