Mapping the technology-demand interface: how brain organoid evolution is reshaping the translational landscape of stroke
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Caohang Qiu1, Ning Sun2,3, Jun Yang1
1Department of Cerebrovascular Diseases, Luzhou People's Hospital, Luzhou, China.
Abstract:
Stroke research faces a dual modeling barrier: interspecies divergence and oversimplified 2D cultures have contributed to the failure of many neuroprotective trials. Brain organoids, which provide human genetic backgrounds and 3D neural networks, offer a promising but incomplete solution. Existing reviews often address isolated technological aspects without linking them to stroke-specific demands. This narrative review establishes a "technological iteration-clinical demand" mapping matrix (Figure 1). We trace organoid evolution from static oxygen-glucose deprivation models to vascularized, assembloid, and microfluidic chip-integrated systems, and toward multi-organ coupling. We highlight how these advances may uncover human-specific neuroinflammation, proposed PANoptosis-like spatiotemporal cascades, and synaptic deficits mediated by human-specific genes such as SRGAP2C. We argue that the inability of animal models to resolve certain human-specific targets is driving organoids from static endpoint models toward dynamic platforms with perfusable vasculature and immune integration, although current systems remain at an early stage. We critically evaluate the preclinical role of organoids, defining scenarios where they may partially replace animal testing and where they cannot. Major bottlenecks include fetal-like organoid maturity (transcriptome approximately equivalent to gestational weeks 12-20) versus the average stroke onset age of about 70 years, and the absence of peripheral immune infiltration, which mediates a large fraction of secondary injury. We outline potential solutions including multi-omics integration, inducible aging, gene editing, and multi-organ chip coupling, and emphasize that standardized evaluation frameworks are needed to translate organoid findings into personalized stroke therapy.