Neurological Disease Modeling Using Pluripotent and Multipotent Stem Cells: A Key Step towards Understanding and Treating Mucopolysaccharidoses.
Biomedicines
; 11(4)2023 Apr 21.
Article
em En
| MEDLINE
| ID: mdl-37189853
ABSTRACT
Despite extensive research, the links between the accumulation of glycosaminoglycans (GAGs) and the clinical features seen in patients suffering from various forms of mucopolysaccharidoses (MPSs) have yet to be further elucidated. This is particularly true for the neuropathology of these disorders; the neurological symptoms are currently incurable, even in the cases where a disease-specific therapeutic approach does exist. One of the best ways to get insights on the molecular mechanisms driving that pathogenesis is the analysis of patient-derived cells. Yet, not every patient-derived cell recapitulates relevant disease features. For the neuronopathic forms of MPSs, for example, this is particularly evident because of the obvious inability to access live neurons. This scenario changed significantly with the advent of induced pluripotent stem cell (iPSC) technologies. From then on, a series of differentiation protocols to generate neurons from iPSC was developed and extensively used for disease modeling. Currently, human iPSC and iPSC-derived cell models have been generated for several MPSs and numerous lessons were learnt from their analysis. Here we review most of those studies, not only listing the currently available MPS iPSC lines and their derived models, but also summarizing how they were generated and the major information different groups have gathered from their analyses. Finally, and taking into account that iPSC generation is a laborious/expensive protocol that holds significant limitations, we also hypothesize on a tempting alternative to establish MPS patient-derived neuronal cells in a much more expedite way, by taking advantage of the existence of a population of multipotent stem cells in human dental pulp to establish mixed neuronal and glial cultures.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Guideline
Idioma:
En
Revista:
Biomedicines
Ano de publicação:
2023
Tipo de documento:
Article