Single cell RNA sequencing and emerging transcriptomic technologies in Alzheimer's disease cerebral organoids: a mini review.

Authors

Maizatul Fazilah Abdul Razak, Francis Yew Fu Tieng

Published in

Molecular brain. Volume 19. Issue 1. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Alzheimer's disease (AD), the most common cause of dementia worldwide, presents a formidable challenge due to its complex, multifactorial etiology, and limited translational success of existing models. Recent advances in stem cell biology have enabled the generation of three-dimensional human cerebral organoids which exhibit multicellular complexity and self-organisation, recapitulating key aspects of fetal brain development in vitro. This mini review highlights the landscape of AD modelling in human cerebral organoids. While studies using single cell RNA sequencing (scRNA-seq) form the core of this review, we also discuss single-nucleus RNA sequencing, computational deconvolution, and spatial approaches as translationally relevant extensions. We categorise current organoid research into distinct model types: (1) familial AD models driven by APP, PSEN1, and PSEN2 mutations; (2) sporadic AD models incorporating genetic APOE variants and environmental risk factors (serum exposure, pollutants, and viral reactivation); (3) tauopathy models highlighting astrocyte-driven metabolic disruption; (4) models featuring glial-immune interactions; and (5) emerging models of mitochondrial dysfunction as an early disease initiator. Across these categories, single cell technologies reveal critical alterations in lipid metabolism, synaptic programs, and glial-immune crosstalk. Finally, we review methodological innovations-drawing on non-AD developmental organoid studies such as region-specific patterning, morphodynamic control, and enhanced cellular diversity, which offer a roadmap for improving the physiological fidelity of next-generation AD organoid models. Ultimately, integrating these advanced cerebral organoid platforms with single cell technologies offer a powerful toolkit to dissect early disease mechanisms and accelerate the development of personalised therapeutics.

PMID:
42902292
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.

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