Skip to main content
eScholarship
Open Access Publications from the University of California

UC San Diego

UC San Diego Previously Published Works bannerUC San Diego

Limitations of the human iPSC-derived neuron model for early-onset Alzheimers disease.

Abstract

Non-familial Alzheimers disease (AD) occurring before 65 years of age is commonly referred to as early-onset Alzheimers disease (EOAD) and constitutes ~ 5-6% of all AD cases (Mendez et al. in Continuum 25:34-51, 2019). While EOAD exhibits the same clinicopathological changes such as amyloid plaques, neurofibrillary tangles (NFTs), brain atrophy, and cognitive decline (Sirkis et al. in Mol Psychiatry 27:2674-88, 2022; Caldwell et al. in Mol Brain 15:83, 2022) as observed in the more prevalent late-onset AD (LOAD), EOAD patients tend to have more severe cognitive deficits, including visuospatial, language, and executive dysfunction (Sirkis et al. in Mol Psychiatry 27:2674-88, 2022). Patient-derived induced pluripotent stem cells (iPSCs) have been used to model and study penetrative, familial AD (FAD) mutations in APP, PSEN1, and PSEN2 (Valdes et al. in Research Square 1-30, 2022; Caldwell et al. in Sci Adv 6:1-16, 2020) but have been seldom used for sporadic forms of AD that display more heterogeneous disease mechanisms. In this study, we sought to characterize iPSC-derived neurons from EOAD patients via RNA sequencing. A modest difference in expression profiles between EOAD patients and non-demented control (NDC) subjects resulted in a limited number of differentially expressed genes (DEGs). Based on this analysis, we provide evidence that iPSC-derived neuron model systems, likely due to the loss of EOAD-associated epigenetic signatures arising from iPSC reprogramming, may not be ideal models for studying sporadic AD.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View