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Myelin disorders burden millions of people around the world, yet existing therapies are inadequate to cure them. Current remedies commonly treat the symptoms with minimal to no effect on the actual cause of the disorder. The basis and/or the mechanism of demyelination is not known for many of the disorders either. In recent years, stem cells of variable origin have been used in clinical trials as transplant agents to restore the defective biochemical process or the damaged tissue. We summarize the outcomes of these trials for demyelination disorders. The capability of reprograming mature cells into stem cells equips researchers with a new tool to replicate disease phenotypes in cell culture dishes for basic research and therapeutic screens. The applications of in vitro myelination disorder models are also discussed. The combined outcome of the discussed studies offers a promising future as stem cell transplantation generally results in decreased symptoms and improved quality of life. However, the mechanism of action of the interventions is not known and in cases of negative outcomes the reasons are usually obscure. Further basic science studies along with clinical interventions should close the knowledge gap and should help spread the positive results to a larger population. [ABSTRACT FROM AUTHOR]

Objective: Myelin is essential for a healthy nervous system. Myelin formed by oligodendrocytes, accelerates impulse propagation and supports neuronal survival. Demyelination leads to neurodegeneration. In multiple sclerosis (MS) immune attack results in demyelination. Our goal is to dissect interactions among oligodendrocytes, neurons, and immune cells to improve our understanding of myelination and the demyelinating diseases. We aim to discover new targets for remyelination therapies. Methods: We are building tools for analyzing protein-protein and cell-to-cell interactions. To identify genes involved in myelination and MS, we developed a bioinformatics-based strategy, interactome analysis, which combines proteome and gene expression methodologies. Identified genes are evaluated in peripheral blood of MS patients and in mouse models. To accelerate drug discovery, 23 machine learning-based methodologies were assessed for myelin identification in fluorescent microscopy images. Results: Interactome analysis identified hundreds of proteinprotein interactions between pairs of oligodendrocytes, neurons, macrophages, microglia, and T cells. Most significant interactions are further analyzed in vivo and in vitro. Our customizedconvolutional neural network and Boosted Tree methods segmented myelin at over 98% accuracy. Identified myelin can be quantified and visualized in 3D. Conclusion: The interactome analysis yielded novel genes that are likely to be linked to MS. Machine learning-based methodologies are very effective in accelerating myelin quantification and thus, drug screens against demyelinating diseases such as MS. Overall, our innovative analysis strategies employing computer assistance produced novel avenues of exploration for myelination and demyelinating diseases. This study was supported TUBITAK (218S495,316S026), Istanbul Medipol University (BAP2018/06), and Turkish Academy of Sciences (GEBIP). [ABSTRACT FROM AUTHOR]