DRG-Derived Neural Progenitors Differentiate into Functional Enteric Neurons Following Transplantation in the Postnatal Colon.
Hui HuYuanyuan DingWenbo MuYing LiYanpeng WangWeifang JiangYong FuJinfa TouWei ChenPublished in: Cell transplantation (2018)
Cell therapy has great promise for treating gastrointestinal motility disorders caused by intestinal nervous system (ENS) diseases. However, appropriate sources, other than enteric neural stem cells and human embryonic stem cells, are seldom reported. Here, we show that neural progenitors derived from the dorsal root ganglion (DRG) of EGFP mice survived, differentiated into enteric neurons and glia cells, migrated widely from the site of injection, and established neuron-muscle connections following transplantation into the distal colon of postnatal mice. The exogenous EGFP+ neurons were physiologically functional as shown by the activity of calcium imaging. This study shows that that other tissues besides the postnatal bowel harbor neural crest stem cells or neural progenitors that have the potential to differentiate into functional enteric neurons in vivo and can potentially be used for intestinal nerve regeneration. These DRG-derived neural progenitor cells may be a choice for cell therapy of ENS disease as an allograft. The new knowledge provided by our study is important for the development of neural crest stem cell and cell therapy for the treatment of intestinal neuropathy.
Keyphrases
- cell therapy
- stem cells
- spinal cord
- preterm infants
- mesenchymal stem cells
- embryonic stem cells
- high resolution
- induced apoptosis
- endothelial cells
- skeletal muscle
- gene expression
- drinking water
- machine learning
- adipose tissue
- photodynamic therapy
- risk assessment
- bone marrow
- signaling pathway
- mass spectrometry
- spinal cord injury
- cell cycle arrest
- climate change
- artificial intelligence
- oxidative stress
- pseudomonas aeruginosa
- ultrasound guided
- peripheral nerve
- big data
- insulin resistance
- metabolic syndrome
- biofilm formation
- fluorescence imaging
- deep learning