Dataset Information

Application of Urine-Derived Stem Cells to Cellular Modeling in Neuromuscular and Neurodegenerative Diseases.

ABSTRACT: Neuromuscular and neurodegenerative diseases are mostly modeled using genetically modified animals such as mice. However, animal models do not recapitulate all the phenotypes that are specific to human disease. This is mainly due to the genetic, anatomical and physiological difference in the neuromuscular systems of animals and humans. The emergence of direct and indirect human somatic cell reprogramming technologies may overcome this limitation because they enable the use of disease and patient-specific cellular models as enhanced platforms for drug discovery and autologous cell-based therapy. Induced pluripotent stem cells (iPSCs) and urine-derived stem cells (USCs) are increasingly employed to recapitulate the pathophysiology of various human diseases. Recent cell-based modeling approaches utilize highly complex differentiation systems that faithfully mimic human tissue- and organ-level dysfunctions. In this review, we discuss promising cellular models, such as USC- and iPSC-based approaches, that are currently being used to model human neuromuscular and neurodegenerative diseases.

SUBMITTER: Sato M

PROVIDER: S-EPMC6915080 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Application of Urine-Derived Stem Cells to Cellular Modeling in Neuromuscular and Neurodegenerative Diseases.

Sato Mitsuto M Takizawa Hotake H Nakamura Akinori A Turner Bradley J BJ Shabanpoor Fazel F Aoki Yoshitsugu Y

Frontiers in molecular neuroscience 20191205

Neuromuscular and neurodegenerative diseases are mostly modeled using genetically modified animals such as mice. However, animal models do not recapitulate all the phenotypes that are specific to human disease. This is mainly due to the genetic, anatomical and physiological difference in the neuromuscular systems of animals and humans. The emergence of direct and indirect human somatic cell reprogramming technologies may overcome this limitation because they enable the use of disease and patient ...[more]

PMID: 31920531

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Project description:Background: Neuromuscular disorders (NMDs) are a heterogeneous group of genetic diseases, caused by mutations in genes involved in spinal cord, peripheral nerve, neuromuscular junction, and muscle functions. To advance the knowledge of the pathological mechanisms underlying NMDs and to eventually identify new potential drugs paving the way for personalized medicine, limitations regarding the availability of neuromuscular disease-related biological samples, rarely accessible from patients, are a major challenge. Aim: We characterized urinary stem cells (USCs) by in-depth transcriptome and protein profiling to evaluate whether this easily accessible source of patient-derived cells is suitable to study neuromuscular genetic diseases, focusing especially on those currently involved in clinical trials. Methods: The global transcriptomics of either native or MyoD transformed USCs obtained from control individuals was performed by RNA-seq. The expression of 610 genes belonging to 16 groups of disorders (http://www.musclegenetable.fr/) whose mutations cause neuromuscular diseases, was investigated on the RNA-seq output. In addition, protein expression of 11 genes related to NMDs including COL6A, EMD, LMNA, SMN, UBA1, DYNC1H1, SOD1, C9orf72, DYSF, DAG1, and HTT was analyzed in native USCs by immunofluorescence and/or Western blot (WB). Results: RNA-seq profile of control USCs shows that 571 out of 610 genes known to be involved in NMDs, are expressed in USCs. Interestingly, the expression levels of the majority of NMD genes remain unmodified following USCs MyoD transformation. Most genes involved in the pathogenesis of all 16 groups of NMDs are well represented except for channelopathies and malignant hyperthermia related genes. All tested proteins showed high expression values, suggesting consistency between transcription and protein representation in USCs. Conclusion: Our data suggest that USCs are human cells, obtainable by non-invasive means, which might be used as a patient-specific cell model to study neuromuscular disease-causing genes and that they can be likely adopted for a variety of in vitro functional studies such as mutation characterization, pathway identification, and drug screening.

Project description:Cellular senescence is a homeostatic biological process characterized by a permanent state of cell cycle arrest that can contribute to the decline of the regenerative potential and function of tissues. The increased presence of senescent cells in different neurodegenerative diseases suggests the contribution of senescence in the pathophysiology of these disorders. Although several factors can induce senescence, DNA damage, oxidative stress, neuroinflammation, and altered proteostasis have been shown to play a role in its onset. Oxidative stress contributes to accelerated aging and cognitive dysfunction stages affecting neurogenesis, neuronal differentiation, connectivity, and survival. During later life stages, it is implicated in the progression of cognitive decline, synapse loss, and neuronal degeneration. Also, neuroinflammation exacerbates oxidative stress, synaptic dysfunction, and neuronal death through the harmful effects of pro-inflammatory cytokines on cell proliferation and maturation. Both oxidative stress and neuroinflammation can induce DNA damage and alterations in DNA repair that, in turn, can exacerbate them. Another important feature associated with senescence is altered proteostasis. Because of the disruption in the function and balance of the proteome, senescence can modify the proper synthesis, folding, quality control, and degradation rate of proteins producing, in some diseases, misfolded proteins or aggregation of abnormal proteins. There is an extensive body of literature that associates cellular senescence with several neurodegenerative disorders including Alzheimer's disease (AD), Down syndrome (DS), and Parkinson's disease (PD). This review summarizes the evidence of the shared neuropathological events in these neurodegenerative diseases and the implication of cellular senescence in their onset or aggravation. Understanding the role that cellular senescence plays in them could help to develop new therapeutic strategies.

Project description:The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD) of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct comparison between CAD procedures is not possible. Furthermore, the sample size is often small for developing accurate machine learning methods. Multi-center initiatives are currently a very useful, although limited, tool in the recruitment of large populations and standardization of CAD evaluation. Conversely, we propose a brain image synthesis procedure intended to generate a new image set that share characteristics with an original one. Our system focuses on nuclear imaging modalities such as PET or SPECT brain images. We analyze the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF) estimator. Once the model has been built, we can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space. The system has been evaluated on different functional neuroimaging datasets assessing the: resemblance of the synthetic images with the original ones, the differences between them, their generalization ability and the independence of the synthetic dataset with respect to the original. The synthetic images maintain the differences between groups found at the original dataset, with no significant differences when comparing them to real-world samples. Furthermore, they featured a similar performance and generalization capability to that of the original dataset. These results prove that these images are suitable for standardizing the evaluation of CAD pipelines, and providing data augmentation in machine learning systems -e.g. in deep learning-, or even to train future professionals at medical school.

Dataset Information

Application of Urine-Derived Stem Cells to Cellular Modeling in Neuromuscular and Neurodegenerative Diseases.

Publications

Application of Urine-Derived Stem Cells to Cellular Modeling in Neuromuscular and Neurodegenerative Diseases.

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