Myelin disorders and stem cells: as therapies and models

Aydınlı, Fatmagül İlayda; Çelik, Eşref; Vatandaşlar, Burcu Kurt; Kerman, Bilal E.

doi:10.3906/biy-1512-83

Cited by 11 publications

(5 citation statements)

References 76 publications

(123 reference statements)

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“…Myelin is a vital structure for the function of the nervous system, thus, any damage to myelin disrupts the life of the individual leading to neurological diseases, such as, multiple sclerosis (MS). MS is a neuroinflammatory disease of the central neurvous system CNS affecting approximately 2.5 million individuals worldwide [11], [12]. Body's own immune system attack and destroy myelin [12], [13].…”

Section: Myelin and The Need For Automated Quantificationmentioning

confidence: 99%

“…Body's own immune system attack and destroy myelin [12], [13]. Current therapies reduce demyelination by suppressing the immune system but do not enhance remyelination and, thus, fail to cure the disease [11], [12], [14]. The innate myelin regeneration of the nervous system is inadequate to overcome the destructive potential of the immune attack [9].…”

Section: Myelin and The Need For Automated Quantificationmentioning

confidence: 99%

“…However, as myelin quantification is time consuming and labor intensive, it is not feasible to screen for such a high number of compounds. Therefore, rapid quantification of myelin will expedite drug development for MS and other myelin diseases by enabling testing of a large number of candidate chemicals quickly [11]. Additionally, the newly available automated tracing of the neurons from ultralarge images will assist in describing the synaptic connections in the nervous system [2].…”

Section: Myelin and The Need For Automated Quantificationmentioning

confidence: 99%

See 2 more Smart Citations

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Cimen¹,

Çapar

Ekinci

et al. 2018

Preprint

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Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors' knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.

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Section: Myelin and The Need For Automated Quantificationmentioning

confidence: 99%

Section: Myelin and The Need For Automated Quantificationmentioning

confidence: 99%

Section: Myelin and The Need For Automated Quantificationmentioning

confidence: 99%

See 1 more Smart Citation

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Cimen¹,

Çapar

Ekinci

et al. 2018

Preprint

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“…Myelin degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS) 1,2 . There are no remyelinating drugs.…”

Section: Introductionmentioning

confidence: 99%

A multi-spectral myelin annotation tool for machine learning based myelin quantification

et al. 2022

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Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.

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“…Furthermore, there are no therapies for many myelination diseases such as multiple sclerosis, Pelizaeus-Merzbacher disease, and Charcot-Marie-Tooth disease (24,25). Promotion of remyelination by existing glial progenitor cells or myelination-capable cell transplantation is the best strategy to reverse the neural damage.…”

mentioning

confidence: 99%

Motoneuron expression profiling identifies an association between an axonal splice variant of HDGF-related protein 3 and peripheral myelination

Kerman¹,

Genoud

Vatandaşlar³

et al. 2020

Journal of Biological Chemistry

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Disorders that disrupt myelin formation during development or in adulthood, such as multiple sclerosis and peripheral neuropathies, lead to severe pathologies, illustrating myelin’s crucial role in normal neural functioning. However, although our understanding of glial biology is increasing, the signals that emanate from axons and regulate myelination remain largely unknown. To identify the core components of the myelination process, here we adopted a microarray analysis approach combined with laser-capture microdissection of spinal motoneurons during the myelinogenic phase of development. We identified neuronal genes whose expression was enriched during myelination and further investigated hepatoma-derived growth factor-related protein 3 (HRP3 or HDGFRP3). HRP3 was strongly expressed in the white matter fiber tracts of the peripheral (PNS) and central (CNS) nervous systems during myelination and remyelination in a cuprizone-induced demyelination model. The dynamic localization of HPR3 between axons and nuclei during myelination was consistent with its axonal localization during neuritogenesis. To study this phenomenon, we identified two splice variants encoded by the HRP3 gene: the canonical isoform HRP3-I and a newly recognized isoform, HRP3-II. HRP3-I remained solely in the nucleus, whereas HRP3-II displayed distinct axonal localization both before and during myelination. Interestingly, HRP3-II remained in the nuclei of unmyelinated neurons and glial cells, suggesting the existence of a molecular machinery that transfers it to and retains it in the axons of neurons fated for myelination. Overexpression of HRP3-II, but not of HRP3-I, increased Schwann cell numbers and myelination in PNS neuron–glia co-cultures. However, HRP3-II overexpression in CNS co-cultures did not alter myelination.

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Myelin disorders and stem cells: as therapies and models

Cited by 11 publications

References 76 publications

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

A multi-spectral myelin annotation tool for machine learning based myelin quantification

Motoneuron expression profiling identifies an association between an axonal splice variant of HDGF-related protein 3 and peripheral myelination

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