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Modelling Sporadic Alzheimer's Disease Using Induced Pluripotent Stem Cells
Helen A Rowland1, Nigel M Hooper1, Katherine A B Kellett2
1Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK.
Neurochemical Research
|November 3, 2018
Summary
Developing better models for sporadic Alzheimer's disease (sAD) is crucial. This review explores induced pluripotent stem cell (iPSC) models, comparing familial and sporadic forms to uncover sAD mechanisms and therapeutic targets.
Area of Science:
- Neuroscience
- Stem Cell Biology
- Genetics
Background:
- Sporadic Alzheimer's disease (sAD) modeling is difficult due to unknown triggers and slow progression.
- Human induced pluripotent stem cells (iPSCs) offer powerful tools for modeling Alzheimer's disease (AD) pathology and drug screening.
- Most current iPSC models focus on familial AD (fAD) driven by genetic mutations, limiting insights into sAD pathogenesis.
Purpose of the Study:
- To compare data from familial AD (fAD) and sporadic AD (sAD) iPSC-derived cell lines.
- To identify inconsistencies in current sAD iPSC models.
- To highlight the potential role of Aβ clearance mechanisms in sAD and discuss improvements for physiologically relevant models.
Main Methods:
- Comparative analysis of existing fAD and sAD iPSC-derived cell line data.
- Review of literature on Aβ clearance mechanisms in iPSC models.
- Discussion of advanced modeling techniques like co-culture and 3D culture with glial cells.
Main Results:
- Existing sAD iPSC models show inconsistencies, necessitating further refinement.
- Aβ clearance mechanisms represent an under-investigated but potentially crucial area for sAD research.
- Co-culture and 3D culture systems offer more physiologically relevant modeling approaches.
Conclusions:
- Developing more consistent and relevant iPSC models for sAD is essential for understanding disease mechanisms.
- Genetic stratification of iPSCs and incorporating genetic/environmental risk factors can improve sAD modeling.
- Improved sAD models hold promise for identifying novel therapeutic targets.