Artificial intelligence approaches to the volumetric quantification of glycogen granules in EM images of human tissue

Abstract

Skeletal muscle, the major processor of dietary glucose, stores it in myriad glycogen granules. Their numbers vary with cellular location and physiological and pathophysiological states. AI models were developed to derive granular glycogen content from electron-microscopic images of human muscle. Two UNet-type semantic segmentation models were built: “Locations” classified pixels as belonging to different regions in the cell; “Granules” identified pixels within granules. From their joint output, a pixel fraction pf was calculated for images from patients positive (MHS) or negative (MHN) to a test for malignant hyperthermia susceptibility. pf was used to derive vf, the volume fraction occupied by granules. The relationship vf (pf) was derived from a simulation of volumes (“baskets”) containing virtual granules at realistic concentrations. The simulated granules had diameters matching the real ones, which were measured by adapting a utility devised for calcium sparks. Applying this relationship to the pf measured in images, vf was calculated for every region and patient, and from them a glycogen concentration. The intermyofibrillar spaces and the sarcomeric I band had the highest granular content. The measured glycogen concentration was low enough to allow for a substantial presence of non-granular glycogen. The MHS samples had an approximately threefold lower concentration (significant in a hierarchical test), consistent with earlier evidence of diminished glucose processing in MHS. The AI models and the approach to infer three-dimensional magnitudes from two-dimensional images should be adaptable to other tasks on a variety of images from patients and animal models and different disease conditions.