GEO series
Development of a split-toxin CRISPR screening platform to systematically identify upstream regulators of human myoblast fusion
GSE293514
Homo sapiens
Expression profiling by high throughput sequencing; Other
57 samples
2025/10/24
GPL34281GPL29480
Summary
Muscle defects are a common feature in human developmental disorders and often lead to severe functional impairment. These defects arise from intricate tissue crosstalk and rare genetic mutations, underscoring the need to systematically identify cell-autonomous mechanisms regulating human myogenesis. Despite the clinical significance, our understanding of human development remains limited, due in part to the absence of a scalable genetic approach to study this process. Here, we introduce a rationally designed high-throughput CRISPR screening platform that integrates human myoblast models, muscle-specific CRISPR knockout libraries, and a split-toxin strategy that acts as a functional readout for myoblast fusion—a fundamental step of human myogenesis. This screening strategy enables selection of CRISPR-induced fusion-defective myocytes in a quantifiable manner. Leveraging this platform, our initial genetic screen uncovered a large group of new hits essential for human myoblast fusion. The majority of these hits converge into 23 protein complexes, most of which have not previously been functionally linked to myogenesis in any species. Notably, mutations in 41 of our fusion screen hits cause human diseases presenting abnormal skeletal muscle morphology. Applying a new single-cell CRISPR & RNA-seq approach, we show that majority of these hits control human myoblast fusion as well as influence early-stage myogenic differentiation. Together, this work presents a new systematic approach to study human myogenesis and uncovers promising candidates governing human muscle differentiation and fusion. A broader application of this split-toxin based CRISPR screening platform would accelerate the study of cell-autonomous mechanisms of human muscle development and diseases at scale.
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