A quantitative framework to evaluate modeling of cortical development by neural stem cells

Jason L. Stein, Luis de la Torre-Ubieta, Yuan Tian, Neelroop N. Parikshak, Israel A. Hernández, Maria C. Marchetto, Dylan K. Baker, Daning Lu, Cassidy R. Hinman, Jennifer K. Lowe, Eric M. Wexler, Alysson R. Muotri, Fred H. Gage, Kenneth S. Kosik, Daniel H. Geschwind

Research output: Contribution to journalArticlepeer-review

137 Scopus citations

Abstract

Neural stem cells have been adopted to model a wide range of neuropsychiatric conditions invitro. However, how well such models correspond to invivo brain has not been evaluated in an unbiased, comprehensive manner. We used transcriptomic analyses to compare invitro systems to developing human fetal brain and observed strong conservation of invivo gene expression and network architecture in differentiating primary human neural progenitor cells (phNPCs). Conserved modules are enriched in genes associated with ASD, supporting the utility of phNPCs for studying neuropsychiatric disease. We also developed and validated a machine learning approach called CoNTExT that identifies the developmental maturity and regional identity of invitro models. We observed strong differences between invitro models, including hiPSC-derived neural progenitors from multiple laboratories. This work provides a systems biology framework for evaluating invitro systems and supports their value in studying the molecular mechanisms of human neurodevelopmental disease.

Original languageEnglish
Pages (from-to)69-86
Number of pages18
JournalNeuron
Volume83
Issue number1
DOIs
StatePublished - 2 Jul 2014
Externally publishedYes

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