Skeletal muscle is responsible for voluntary force generation across animals, and muscle architecture largely determines the parameters of mechanical output. The ability to analyze muscle performance through muscle architecture is thus a key step towards better understanding the ecology and evolution of movements and morphologies. In pennate skeletal muscle, volume, fiber lengths and attachment angles to force transmitting structures comprise the most relevant parameters of muscle architecture. Measuring these features through tomographic techniques offers an alternative to tedious and destructive dissections, particularly as the availability of tomographic data is rapidly increasing. However, there is a need for streamlined computational methods to access this information efficiently. Here, we establish and compare workflows using partially automated image analysis for fast and accurate estimation of animal muscle architecture. After isolating a target muscle through segmentation, we evaluate freely available and proprietary fiber tracing algorithms to reconstruct muscle fibers. We then present a script using the Blender Python API to estimate attachment angles, fiber lengths, muscle volume and Physiological Cross-Sectional Area. We apply these methods to insect and vertebrate muscle and provide guided workflows. Results from fiber tracing are consistent compared to manual measurements but much less time-consuming. Lastly, we emphasize the capabilities of the open-source 3D software Blender as both a tool for visualization and a scriptable analytic tool to process digitized anatomical data. Across organisms, it is feasible to extract, analyze, and visualize muscle architecture from tomography data by exploiting the spatial features of scans and the geometric properties of muscle fibers. As digital libraries of anatomies continue to grow, the workflows and approach presented here can be part of the open-source future of digital comparative analysis.

Neurotransmission triggers Ca2+ signals in perisynaptic astrocytic processes (PAPs). As most PAPs are below the diffraction limit, the presence of Ca2+ stores in PAPs, notably the endoplasmic reticulum (ER), is unclear. Here, we create 46 three dimensional meshes of hippocampal tripartite synapses reconstructed from electron microscopy. We find that 75% of PAPs contain some ER, as close as 72 nm to the synapse, and quantify its geometrical properties. To discern the effect of ER shape and distribution on Ca2+ activity, we implemented an algorithm that automatically redistributes the ER within the reconstructed PAP meshes, with constant ER and PAP shape. Reaction-diffusion simulations in those meshes reveal that Ca2+ signals in PAPs are shaped by a complex interplay between the clustering of Ca2+ channels, Ca2+ buffering, ER shape and distribution. This study, by detecting ER in PAPs and linking its spatial properties to Ca2+ activity, sheds new light on mechanisms regulating signal transmission at tripartite synapses.