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.

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.

Duchenne muscular dystrophy is a genetic disease produced by mutations in the dystrophin gene characterized by early onset muscle weakness leading to severe and irreversible disability. The cellular and molecular consequences of the lack of dystrophin in humans are only partially known, which is crucial for the development of new therapies aiming to slow or stop the progression of the disease. Here we have analyzed quadriceps muscle biopsies of seven DMD patients aged 2 to 4 years old and five age and gender matched controls using single nuclei RNA sequencing (snRNAseq) and correlated the results obtained with clinical data. SnRNAseq identified significant differences in the proportion of cell population present in the muscle samples, including an increase in the number of regenerative fibers, satellite cells, and fibro-adipogenic progenitor cells (FAPs) and a decrease in the number of slow fibers and smooth muscle cells. Muscle samples from the younger patients with stable mild weakness were characterized by an increase in regenerative fibers, while older patients with moderate and progressive weakness were characterized by loss of muscle fibers and an increase in FAPs. An analysis of the gene expression profile in muscle fibers identified a strong regenerative signature in DMD samples characterized by the upregulation of genes involved in myogenesis and muscle hypertrophy. In the case of FAPs, we observed upregulation of genes involved in the extracellular matrix regeneration but also several signaling pathways. Indeed, further analysis of the potential intercellular communication profile showed a dysregulation of the communication profile in DMD samples identifying FAPs as a key regulator of cell signaling in DMD muscle samples. In conclusion, our study has identified significant differences at the cellular and molecular levels in the different cell populations present in skeletal muscle samples of patients with DMD compared to controls.

Background: The diagnosis of patients with mutations in the VCP gene can be complicated due to their broad phenotypic spectrum including myopathy, motor neuron disease and peripheral neuropathy. Muscle MRI guides the diagnosis in neuromuscular diseases (NMDs); however, comprehensive muscle MRI features for VCP patients have not been reported so far. Methods: We collected muscle MRIs of 80 of the 255 patients who participated in the “VCP International Study” and reviewed the T1-weighted (T1w) and short tau inversion recovery (STIR) sequences. We identified a series of potential diagnostic MRI based characteristics useful for the diagnosis of VCP disease and validated them in 1089 MRIs from patients with other genetically confirmed NMDs. Results: Fat replacement of at least one muscle was identified in all symptomatic patients. The most common finding was the existence of patchy areas of fat replacement. Although there was a wide variability of muscles affected, we observed a common pattern characterized by the involvement of periscapular, paraspinal, gluteal and quadriceps muscles. STIR signal was enhanced in 67% of the patients, either in the muscle itself or in the surrounding fascia. We identified 10 diagnostic characteristics based on the pattern identified that allowed us to distinguish VCP disease from other neuromuscular diseases with high accuracy. Conclusions: Patients with mutations in the VCP gene had common features on muscle MRI that are helpful for diagnosis purposes, including the presence of patchy fat replacement and a prominent involvement of the periscapular, paraspinal, abdominal and thigh muscles.

The prevalence and progression of respiratory muscle dysfunction in patients with limb girdle muscular dystrophies (LGMDs) has been only partially described to date. Most reports include cross-sectional data on a limited number of patients making it difficult to gain a wider perspective on respiratory involvement throughout the course of the disease and to compare the most prevalent LGMD subtypes. We reviewed the results of spirometry studies collected longitudinally in our cohort of patients in routine clinical visits from 2002 to 2020 along with additional clinical and genetic data. A linear mixed model was used to investigate the factors associated with the progression of respiratory dysfunction.

Gaucher disease (GD) is a genetic lysosomal disorder characterized by high bone marrow (BM) involvement and skeletal complications. The pathophysiology of these complications is not fully elucidated. Magnetic resonance imaging (MRI) is the gold standard to evaluate BM. This study aimed to apply machine-learning techniques in a cohort of Spanish GD patients by a structured bone marrow MRI reporting model at diagnosis and follow-up to predict the evolution of the bone disease. In total, 441 digitalized MRI studies from 131 patients (M: 69, F:62) were reevaluated by a blinded expert radiologist who applied a structured report template. The studies were classified into categories carried out at different stages as follows: A: baseline; B: between 1 and 4 y of follow-up; C: between 5 and 9 y; and D: after 10 years of follow-up. Demographics, genetics, biomarkers, clinical data, and cumulative years of therapy were included in the model. At the baseline study, the mean age was 37.3 years (1–80), and the median Spanish MRI score (S-MRI) was 8.40 (male patients: 9.10 vs. female patients: 7.71) (p < 0.001). BM clearance was faster and deeper in women during follow-up. Genotypes that do not include the c.1226A>G variant have a higher degree of infiltration and complications (p = 0.017). A random forest machine-learning model identified that BM infiltration degree, age at the start of therapy, and femur infiltration were the most important factors to predict the risk and severity of the bone disease. In conclusion, a structured bone marrow MRI reporting in GD is useful to standardize the collected data and facilitate clinical management and academic collaboration. Artificial intelligence methods applied to these studies can help to predict bone disease complications.

Genetic neuromuscular diseases are a group of disorders characterized by a progressive loss of muscle fibres and their substitution by fibrotic and fat tissue. Magnetic resonance imaging is helpful for the study of these diseases, as it identifies fat replacement in the muscles. Several studies have described patterns of muscle involvement that can guide patient diagnosis. In this project, we present a pipeline for the automatic diagnosis of muscle diseases using magnetic resonance imaging. We applied Deep Learning techniques to identify individual muscles in the images, defined an algorithm for the automated quantification of muscle tissue, and created a Machine Learning diagnosis model. This project was made possible thanks to an international network of 16 centres across 4 continents, which shared the required data for training the different models. We developed multiple tools for the processing and cohesion of the data, creating a large repository of data that will be useful for future projects.

Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. Our aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs. We collected 976 pelvic and lower limbs T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by 4 specialists in the field. A total of 976 lower limbs MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts on the field, the diagnostic accuracy of the model generated was significantly higher in a new set of 20 MRIs. Machine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests. This study provides Class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies.