AimSeg: A machine-learning-aided tool for axon, inner tongue and myelin segmentation.

Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements made manually from EM images-a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on transmission EM (TEM) images of tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results on TEM data from both healthy and remyelinating samples show good performance in segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth.

Intrinsic mutant HTT-mediated defects in oligodendroglia cause myelination deficits and behavioral abnormalities in Huntington disease.

White matter abnormalities are a nearly universal pathological feature of neurodegenerative disorders including Huntington disease (HD). A long-held assumption is that this white matter pathology is simply a secondary outcome of the progressive neuronal loss that manifests with advancing disease. Using a mouse model of HD, here we show that white matter and myelination abnormalities are an early disease feature appearing before the manifestation of any behavioral abnormalities or neuronal loss. We further show that selective inactivation of mutant huntingtin (mHTT) in the NG2+ oligodendrocyte progenitor cell population prevented myelin abnormalities and certain behavioral deficits in HD mice. Strikingly, the improvements in behavioral outcomes were seen despite the continued expression of mHTT in nonoligodendroglial cells including neurons, astrocytes, and microglia. Using RNA-seq and ChIP-seq analyses, we implicate a pathogenic mechanism that involves enhancement of polycomb repressive complex 2 (PRC2) activity by mHTT in the intrinsic oligodendroglial dysfunction and myelination deficits observed in HD. Our findings challenge the long-held dogma regarding the etiology of white matter pathology in HD and highlight the contribution of epigenetic mechanisms to the observed intrinsic oligodendroglial dysfunction. Our results further suggest that ameliorating white matter pathology and oligodendroglial dysfunction may be beneficial for HD.