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NGN2 induces diverse neuron types from human pluripotency.

ABSTRACT: Human neurons engineered from induced pluripotent stem cells (iPSCs) through neurogenin 2 (NGN2) overexpression are widely used to study neuronal differentiation mechanisms and to model neurological diseases. However, the differentiation paths and heterogeneity of emerged neurons have not been fully explored. Here, we used single-cell transcriptomics to dissect the cell states that emerge during NGN2 overexpression across a time course from pluripotency to neuron functional maturation. We find a substantial molecular heterogeneity in the neuron types generated, with at least two populations that express genes associated with neurons of the peripheral nervous system. Neuron heterogeneity is observed across multiple iPSC clones and lines from different individuals. We find that neuron fate acquisition is sensitive to NGN2 expression level and the duration of NGN2-forced expression. Our data reveal that NGN2 dosage can regulate neuron fate acquisition, and that NGN2-iN heterogeneity can confound results that are sensitive to neuron type.

SUBMITTER: Lin HC

PROVIDER: S-EPMC8452516 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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NGN2 induces diverse neuron types from human pluripotency.

Lin Hsiu-Chuan HC He Zhisong Z Ebert Sebastian S Schörnig Maria M Santel Malgorzata M Nikolova Marina T MT Weigert Anne A Hevers Wulf W Kasri Nael Nadif NN Taverna Elena E Camp J Gray JG Treutlein Barbara B

Stem cell reports 20210805 9

Human neurons engineered from induced pluripotent stem cells (iPSCs) through neurogenin 2 (NGN2) overexpression are widely used to study neuronal differentiation mechanisms and to model neurological diseases. However, the differentiation paths and heterogeneity of emerged neurons have not been fully explored. Here, we used single-cell transcriptomics to dissect the cell states that emerge during NGN2 overexpression across a time course from pluripotency to neuron functional maturation. We find a ...[more]

PMID: 34358451

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Project description:The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy. The computationally efficient Izhikevich model can reproduce a wide range of neuronal behaviors qualitatively. Previous studies using optimization techniques, however, were less successful in quantitatively matching experimentally recorded voltage traces. In this article, we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. Importantly, by leveraging the parameter-exploration capabilities of evolutionary algorithms, and by representing qualitative spike pattern class definitions in the error landscape, our approach creates several suitable models for each neuron type, exhibiting appropriate feature variabilities among neurons. Moreover, we demonstrate the flexibility of our methodology by creating multi-compartment Izhikevich models for each neuron type in addition to single-point versions. Although the results presented here focus on hippocampal neuron types, the same strategy is broadly applicable to any neural systems.

Dataset Information

NGN2 induces diverse neuron types from human pluripotency.

Publications

NGN2 induces diverse neuron types from human pluripotency.

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