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Robust Generation of Person-Specific, Synchronously Active Neuronal Networks Using Purely Isogenic Human iPSC-3D Neural Aggregate Cultures

Journal article
Authors Julia Izsak
Henrik Seth
Mats Andersson
Dzeneta Vizlin-Hodzic
S. Theiss
Eric Hanse
Hans Ågren
Keiko Funa
Sebastian Illes
Published in Frontiers in Neuroscience
Volume 13
ISSN 1662-453X
Publication year 2019
Published at Institute of Neuroscience and Physiology
Sahlgrenska Cancer Center
Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Language en
Links dx.doi.org/10.3389/fnins.2019.00351
Keywords human induced pluripotent stem cells, 3D-neural model system, neuronal networks, microelectrode, pluripotent stem-cells, in-vitro, bipolar disorder, ips cell, differentiation, abnormalities, transition, maturation, responses, ontogeny, Neurosciences & Neurology
Subject categories Neurosciences

Abstract

Reproducibly generating human induced pluripotent stem cell-based functional neuronal circuits, solely obtained from single individuals, poses particular challenges to achieve personalized and patient specific functional neuronal in vitro models. A hallmark of functional neuronal assemblies, synchronous neuronal activity, can be non-invasively studied by microelectrode array (MEA) technology, reliably capturing physiological and pathophysiological aspects of human brain function. In our here presented manuscript, we demonstrate a procedure to generate 3D neural aggregates comprising astrocytes, oligodendroglial cells, and neurons obtained from the same human tissue sample. Moreover, we demonstrate the robust ability of those neurons to create a highly synchronously active neuronal network within 3 weeks in vitro, without additionally applied astrocytes. The fusion of MEA-technology with functional neuronal circuits solely obtained from one individual's cells represent isogenic person-specific human neuronal sensor chips that pave the way for specific personalized in vitro neuronal networks as well as neurological and neuropsychiatric disease modeling.

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