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.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (20)
Alice E. Stanton
Koch Institute, Massachusetts Institute of Technology
Adele Bubnys
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Emre Agbas
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Benjamin James
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Dong Shin Park
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Alan Jiang
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Rebecca L. Pinals
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Liwang Liu
Nhat Truong
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Anjanet Loon
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Colin Staab
Oyku Cerit
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Hsin-Lan Wen
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
David Mankus
Koch Institute, Massachusetts Institute of Technology
Margaret E. Bisher
Koch Institute, Massachusetts Institute of Technology
Abigail K. R. Lytton-Jean
Koch Institute, Massachusetts Institute of Technology
Manolis Kellis
Joel W. Blanchard
Picower Institute for Learning and Memory, Massachusetts Institute of Technology
Robert Langer
Li-Huei Tsai