@article{6341,
  author = {Alice E. Stanton and Adele Bubnys and Emre Agbas and Benjamin James and Dong Shin Park and Alan Jiang and Rebecca L. Pinals and Liwang Liu and Nhat Truong and Anjanet Loon and Colin Staab and Oyku Cerit and Hsin-Lan Wen and David Mankus and Margaret E. Bisher and Abigail K. R. Lytton-Jean and Manolis Kellis and Joel W. Blanchard and Robert Langer and Li-Huei Tsai},
  title = {Engineered 3D immuno-glial-neurovascular human miBrain model},
  abstract = {Patient-specific, human-based cellular models integrating a biomimetic blood–brain barrier, immune, and myelinated neuron components are critically needed to enable accelerated, translationally relevant discovery of neurological disease mechanisms and interventions. To construct a human cell-based model that includes these features and all six major brain cell types needed to mimic disease and dissect pathological mechanisms, we have constructed, characterized, and utilized a multicellular integrated brain (miBrain) immuno-glial-neurovascular model by engineering a brain-inspired 3D hydrogel and identifying conditions to coculture these six brain cell types, all differentiated from patient induced pluripotent stem cells. miBrains recapitulate in vivo–like hallmarks inclusive of neuronal activity, functional connectivity, barrier function, myelin-producing oligodendrocyte engagement with neurons, multicellular interactions, and transcriptomic profiles. We implemented the model to study Alzheimer’s Disease pathologies associated with APOE4 genetic risk. APOE4 miBrains differentially exhibit amyloid aggregation, tau phosphorylation, and astrocytic glial fibrillary acidic protein. Unlike the coemergent fate specification of glia and neurons in other organoid approaches, miBrains integrate independently differentiated cell types, a feature we harnessed to identify that APOE4 in astrocytes promotes neuronal tau pathogenesis and dysregulation through crosstalk with microglia.},
  year = {2025},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {122},
  pages = {e2511596122},
  month = {2025-10-21},
  url = {https://www.pnas.org/doi/10.1073/pnas.2511596122},
  doi = {10.1073/pnas.2511596122},
}