Imaging mass cytometry analysis of Becker muscular dystrophy muscle samples reveals different stages of muscle degeneration.

Becker muscular dystrophy (BMD) is characterised by fiber loss and expansion of fibrotic and adipose tissue. Several cells interact locally in what is known as the degenerative niche. We analysed muscle biopsies of controls and BMD patients at early, moderate and advanced stages of progression using Hyperion imaging mass cytometry (IMC) by labelling single sections with 17 markers identifying different components of the muscle. We developed a software for analysing IMC images and studied changes in the muscle composition and spatial correlations between markers across disease progression. We found a strong correlation between collagen-I and the area of stroma, collagen-VI, adipose tissue, and M2-macrophages number. There was a negative correlation between the area of collagen-I and the number of satellite cells (SCs), fibres and blood vessels. The comparison between fibrotic and non-fibrotic areas allowed to study the disease process in detail. We found structural differences among non-fibrotic areas from control and patients, being these latter characterized by increase in CTGF and in M2-macrophages and decrease in fibers and blood vessels. IMC enables to study of changes in tissue structure along disease progression, spatio-temporal correlations and opening the door to better understand new potential pathogenic pathways in human samples.

Magnetic resonance imaging-based criteria to differentiate dysferlinopathy from other genetic muscle diseases.

The identification of disease-characteristic patterns of muscle fatty replacement in magnetic resonance imaging (MRI) is helpful for diagnosing neuromuscular diseases. In the Clinical Outcome Study of Dysferlinopathy, eight diagnostic rules were described based on MRI findings. Our aim is to confirm that they are useful to differentiate dysferlinopathy (DYSF) from other genetic muscle diseases (GMD). The rules were applied to 182 MRIs of dysferlinopathy patients and 1000 MRIs of patients with 10 other GMD. We calculated sensitivity (S), specificity (Sp), positive and negative predictive values (PPV/NPV) and accuracy (Ac) for each rule. Five of the rules were more frequently met by the DYSF group. Patterns observed in patients with FKRP, ANO5 and CAPN3 myopathies were similar to the DYSF pattern, whereas patterns observed in patients with OPMD, laminopathy and dystrophinopathy were clearly different. We built a model using the five criteria more frequently met by DYSF patients that obtained a S 95.9%, Sp 46.1%, Ac 66.8%, PPV 56% and NPV 94% to distinguish dysferlinopathies from other diseases. Our findings support the use of MRI in the diagnosis of dysferlinopathy, but also identify the need to externally validate "disease-specific" MRI-based diagnostic criteria using MRIs of other GMD patients.