Assessing and enhancing migration of human myogenic progenitors using directed iPS cell differentiation and advanced tissue modelling.
SungWoo ChoiGiulia FerrariLouise A MoyleKirsty Ml MackinlayNaira NaouarSalma JalalSara BenedettiChristine A WellsFrancesco MuntoniFrancesco Saverio TedescoPublished in: EMBO molecular medicine (2022)
Muscle satellite stem cells (MuSCs) are responsible for skeletal muscle growth and regeneration. Despite their differentiation potential, human MuSCs have limited in vitro expansion and in vivo migration capacity, limiting their use in cell therapies for diseases affecting multiple skeletal muscles. Several protocols have been developed to derive MuSC-like progenitors from human induced pluripotent stem (iPS) cells (hiPSCs) to establish a source of myogenic cells with controllable proliferation and differentiation. However, current hiPSC myogenic derivatives also suffer from limitations of cell migration, ultimately delaying their clinical translation. Here we use a multi-disciplinary approach including bioinformatics and tissue engineering to show that DLL4 and PDGF-BB improve migration of hiPSC-derived myogenic progenitors. Transcriptomic analyses demonstrate that this property is conserved across species and multiple hiPSC lines, consistent with results from single cell motility profiling. Treated cells showed enhanced trans-endothelial migration in transwell assays. Finally, increased motility was detected in a novel humanised assay to study cell migration using 3D artificial muscles, harnessing advanced tissue modelling to move hiPSCs closer to future muscle gene and cell therapies.
Keyphrases
- skeletal muscle
- single cell
- cell migration
- endothelial cells
- induced apoptosis
- stem cells
- rna seq
- cell cycle arrest
- high throughput
- insulin resistance
- induced pluripotent stem cells
- endoplasmic reticulum stress
- pluripotent stem cells
- high glucose
- cell therapy
- signaling pathway
- transcription factor
- type diabetes
- staphylococcus aureus
- biofilm formation
- adipose tissue
- metabolic syndrome
- escherichia coli
- deep learning
- bone marrow
- big data
- growth factor