Understanding host-graft crosstalk for predicting the outcome of stem cell transplantation
Mesenchymal stromal cells (MSCs) hold great promise for tissue regeneration in debilitating disorders. Despite reported improvements, the short-term outcomes of MSC transplantation, which is possibly linked to poor cell survival, demand extensive investigation. Disease-associated stress microenviron...
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Veröffentlicht in: | World journal of stem cells 2024-03, Vol.16 (3), p.232-236 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | Mesenchymal stromal cells (MSCs) hold great promise for tissue regeneration in debilitating disorders. Despite reported improvements, the short-term outcomes of MSC transplantation, which is possibly linked to poor cell survival, demand extensive investigation. Disease-associated stress microenvironments further complicate outcomes. This debate underscores the need for a deeper understanding of the phenotypes of transplanted MSCs and their environment-induced fluctuations. Additionally, questions arise about how to predict, track, and comprehend cell fate post-transplantation.
cellular imaging has emerged as a critical requirement for both short- and long-term safety and efficacy studies. However, translating preclinical imaging methods to clinical settings remains challenging. The fate and function of transplanted cells within the host environment present intricate challenges, including MSC engraftment, variability, and inconsistencies between preclinical and clinical data. The study explored the impact of high glucose concentrations on MSC survival in diabetic environments, emphasizing mitochondrial factors. Preserving these factors may enhance MSC survival, suggesting potential strategies involving genetic modification, biomaterials, and nanoparticles. Understanding stressors in diabetic patients is crucial for predicting the effects of MSC-based therapies. These multifaceted challenges call for a holistic approach involving the incorporation of large-scale data, computational disease modeling, and possibly artificial intelligence to enable deterministic insights. |
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ISSN: | 1948-0210 1948-0210 |
DOI: | 10.4252/wjsc.v16.i3.232 |