Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and α- Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.
Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and α- Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.
Despite the need for more effective drug treatments to address muscle atrophy and disease, physiologically accurate in vitro screening models and higher information content preclinical assays that aid in the discovery and development of novel therapies are lacking. To this end, MyoScreen was developed: a robust and versatile high-throughput high-content screening (HT/HCS) platform that integrates a physiologically and pharmacologically relevant micropatterned human primary skeletal muscle model with a panel of pertinent phenotypic and functional assays. MyoScreen myotubes form aligned, striated myofibers, and they show nerve-independent accumulation of acetylcholine receptors (AChRs), excitation–contraction coupling (ECC) properties characteristic of adult skeletal muscle and contraction in response to chemical stimulation. Reproducibility and sensitivity of the fully automated MyoScreen platform are highlighted in assays that quantitatively measure myogenesis, hypertrophy and atrophy, AChR clusterization, and intracellular calcium release dynamics, as well as integrating contractility data. A primary screen of 2560 compounds to identify stimulators of myofiber regeneration and repair, followed by further biological characterization of two hits, validates MyoScreen for the discovery and testing of novel therapeutics. MyoScreen is an improvement of current in vitro muscle models, enabling a more predictive screening strategy for preclinical selection of the most efficacious new chemical entities earlier in the discovery pipeline process.
ABSTRACT Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and α- Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.
Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and α- Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.
Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and α- Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.
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