Identification of protein-protein interaction bridges for multiple sclerosis

Motivation: Identifying and prioritizing disease-related proteins is an important scientific problem to develop proper treatments. Network science has become an important discipline to prioritize such proteins. Multiple sclerosis, an autoimmune disease for which there is still no cure, is characterized by a damaging process called demyelination. Demyelination is the destruction of myelin, a structure facilitating fast transmission of neuron impulses, and oligodendrocytes, the cells producing myelin, by immune cells. Identifying the proteins that have special features on the network formed by the proteins of oligodendrocyte and immune cells can reveal useful information about the disease.Results: We investigated the most significant protein pairs that we define as bridges among the proteins providing the interaction between the two cells in demyelination, in the networks formed by the oligodendrocyte and each type of two immune cells (i.e. macrophage and T-cell) using network analysis techniques and integer programming. The reason, we investigated these specialized hubs was that a problem related to these proteins might impose a bigger damage in the system. We showed that 61%-100% of the proteins our model detected, depending on parameterization, have already been associated with multiple sclerosis. We further observed the mRNA expression levels of several proteins we prioritized significantly decreased in human peripheral blood mononuclear cells of multiple sclerosis patients. We therefore present a model, BriFin, which can be used for analyzing processes where interactions of two cell types play an important role

Combined segmentation and classificationbased approach to automated analysis of biomedical signals obtained from calcium imaging

Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand- crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy

Comparison of central and peripheral myelination dynamics

..

Diet choices

WOS: 000469887900034..

Myelin disorders and stem cells: As therapies and models

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

Two phases of macrophages: Inducing maturation and death of oligodendrocytes in vitro co-culture

Background: The plasticity of macrophages in the immune response is a dynamic situation dependent on external stimuli. The activation of macrophages both has beneficial and detrimental effects on mature oligodendrocytes (OLs) and myelin. The activation towards inflammatory macrophages has a critical role in the immune-mediated oligodendrocytes death in multiple sclerosis (MS) lesions. New method: We established an in vitro co-culture method to study the function of macrophages in the survival and maturation of OLs. Results: We revealed that M1 macrophages decreased the number of mature OLs and phagocytosed the myelin. Interestingly, non-activated as well as M2 macrophages contributed to an increase in the number of mature OLs in our in vitro co-culture platform. Comparison with existing methods: We added an antibody against an OL surface antigen in our in vitro co-cultures. The antibody presents the OLs to the macrophages enabling the investigation of direct interactions between macrophages and OLs. Conclusion: Our co-culture system is a feasible method for the investigation of the direct cell-to-cell interactions between OLs and macrophages. We utilized it to show that M2 and non-activated macrophages may be employed to enhance remyelination

Refractive index tomography of myelinating glial cells

Conference on Quantitative Phase Imaging V -- FEB 02-05, 2019 -- San Francisco, CAWOS: 000471821600014Refractive index tomography as an emerging technique enables the 3D morphological investigation of cells with no marker. Here, refractive index tomographic imaging of myelinating glial cells is presented. Myelin as a signal insulation layer around an axon is formed by the wrapping of Schwann cells or oligodendrocytes. Microscopic investigation of myelination traditionally requires fluorescent markers. Glial cells generally wrap the axon for more than ten layers. This multilayer formation has alternating and uniform layers of protein and lipid. Earlier studies on the structure of the myelin sheath have shown that the thickness period is lower than 20nm including the thickness of the extracellular medium after each layer. Direct observation of an individual layer is not possible (using classical microscopy techniques) due to dimensions being very small compared to the wavelength of the illumination light. However, periodic nature of the layers enables the differentiation of a myelinated axon from an unmyelinated one. Rapid change of the integrated refractive index and the Bragg fiber like structure alters the transmission behavior as a function of wavelength and incidence angle. With the 3D sectioning capability of refractive index tomography, these features can be easily identified.SPIE, Tomocube, Inc, Phi Optics, IncScientific and Technological Council of Turkey (TUBITAK) [116F437]; Turkish Academy of SciencesThis work is supported by the Scientific and Technological Council of Turkey (TUBITAK) under grant No. 116F437. B. E. Kerman gratefully acknowledges the support of the Turkish Academy of Sciences

Multiple sclerosis biomarker candidates revealed by cell-type-specific interactome analysis

Multiple sclerosis (MS) is a demyelinating disorder that affects multiple regions of the central nervous system such as the brain, spinal cord, and optic nerves. Susceptibility to MS, as well as disease progression rates, displays marked patient-to-patient variability. To date, biomarkers that forecast differences in clinical phenotypes and outcomes have been limited. In this context, cell-type-specific interactome analyses offer important prospects and hope for novel diagnostics and therapeutics. We report here an original study using bioinformatic analysis of MS data sets that revealed interaction profiles as well as specific hub proteins in white matter (WM) and gray matter (GM) that appear critical for disease mechanisms. First, cell-type-specific interactome analyses suggested that while interactions within the WM were focused on oligodendrocytes, interactions within the GM were mostly neuron centric. Second, hub proteins such as APP, EGLN3, PTEN, and LRRK2 were identified to be differentially regulated in MS data sets. Lastly, a comparison of the brain and peripheral blood samples identified biomarker candidates such as NRGN, CRTC1, CDC42, and IFITM3 to be differentially expressed in different types of MS. These findings offer a unique cell-type-specific cell-to-cell interaction network in MS and identify potential biomarkers by comparative analysis of the brain and the blood transcriptomics. From a study design and methodology perspective, we suggest that the cell-type-specific interactome analysis is an important systems science frontier that might offer new insights on other neurodegenerative and brain disorders as well

A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved]

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 colocalization 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, machinelearning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow

DeepMQ: A deep learning approach based myelin quantification in microscopic fluorescence images

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.316S026European Association for Signal ProcessingIEEE Signal Processing SocietyMathWorksAmazon Device