Back

Maturation of Neuronal Activity in Caudalized Human Brain Organoids

Molchanova, S. M.; Cherepkova, M.; Abdurakhmanova, S.; Kuivanen, S.; Pörsti, E. A. J.; Pöhö, P. A.; Teppo, J. S.; Saarimäki-Vire, J.; Kvist, J.; Balboa, D.; Domanskyi, A.; Gainetdinov, R. R.; Piepponen, P.; Varjosalo, M.; Kostiainen, R.; Vapalahti, O.; Taira, T. P.; Otonkoski, T.; Bespalov, M. M.

2019-09-24 neuroscience
10.1101/779355 bioRxiv
Show abstract

Human brain organoids are an emerging tool to study functional neuronal networks in health and disease. A critical challenge is the engineering of brain organoids with defined regional identity and developmental stage. Here we describe a protocol for generating hindbrain-like organoids from human pluripotent stem cells. We first generated a stable pool of caudalized stem cells that expressed hindbrain identity transcription factors and differentiated into tissue containing neurons and astrocytes. After maturation, caudalized brain organoids presented synaptically connected networks consisting of glutamate-, GABA-, and serotoninergic postmitotic neurons. These mature neurons displayed electric properties and dendritic trees resembling medulla oblongata neurons. They fired spontaneous and evoked repetitive action potentials, released serotonin and displayed excitatory and inhibitory synaptic currents, functionally resembling the activity patterns observed in normal human fetal brain. Reminiscent of infected human fetal brain, infection with Zika virus hampered organoid development, while the treatment with anticonvulsant drugs - carbamazepine and valproic acid - reduced organoid growth. Neuronal maturation also occurred in the grafted organoids in vivo. In conclusion, our approach enables efficient derivation of caudalized neuronal stem cells that differentiate into mature and functional neurons in organoids with hindbrain identity following human developmental trajectory. The organoids provide excellent model to study congenital abnormalities in brain development and for drug testing.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.