Coupled electrophysiological recording and single cell transcriptome analyses revealed molecular mechanisms underlying neuronal maturation

Xiaoying Chen, Kunshan Zhang, Liqiang Zhou, Xinpei Gao, Junbang Wang, Yinan Yao, Fei He, Yuping Luo, Yongchun Yu, Siguang Li, Liming Cheng, Yi E. Sun

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Protein Cell ›› 2016, Vol. 07 ›› Issue (03) : 175-186. DOI: 10.1007/s13238-016-0247-8
RESEARCH ARTICLE
RESEARCH ARTICLE

Coupled electrophysiological recording and single cell transcriptome analyses revealed molecular mechanisms underlying neuronal maturation

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Abstract

The mammalian brain is heterogeneous, containing billions of neurons and trillions of synapses forming various neural circuitries, through which sense, movement, thought, and emotion arise. The cellular heterogeneity of the brain has made it difficult to study the molecular logic of neural circuitry wiring, pruning, activation, and plasticity, until recently, transcriptome analyses with single cell resolution makes decoding of gene regulatory networks underlying aforementioned circuitry properties possible. Here we report success in performing both electrophysiological and whole-genome transcriptome analyses on single human neurons in culture. Using Weighted Gene Coexpression Network Analyses (WGCNA), we identified gene clusters highly correlated with neuronal maturation judged by electrophysiological characteristics. A tight link between neuronal maturation and genes involved in ubiquitination and mitochondrial function was revealed. Moreover, we identified a list of candidate genes, which could potentially serve as biomarkers for neuronal maturation. Coupled electrophysiological recording and single cell transcriptome analysis will serve as powerful tools in the future to unveil molecular logics for neural circuitry functions.

Keywords

Patch-Seq / hESC/hiPSC-derived neuron / WGCNA / Biomarkers for neuronal maturation / Ubiquitination and mitochondrial function

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Xiaoying Chen, Kunshan Zhang, Liqiang Zhou, Xinpei Gao, Junbang Wang, Yinan Yao, Fei He, Yuping Luo, Yongchun Yu, Siguang Li, Liming Cheng, Yi E. Sun. Coupled electrophysiological recording and single cell transcriptome analyses revealed molecular mechanisms underlying neuronal maturation. Protein Cell, 2016, 07(03): 175‒186 https://doi.org/10.1007/s13238-016-0247-8

References

[1]
Darmanis S (2015) A survey of human brain transcriptome diversity at the single cell level .Proc Natl Acad Sci USA 112:7285–7290.
CrossRef Google scholar
[2]
Ghosh S, Chan CK (2016) Analysisof RNA-Seq data usingTopHat and Cufflinks .Methods Mol Biol 1374:339–361.
CrossRef Google scholar
[3]
Hu BY (2010) Neural differentiation of human induced pluripotent stem cells follows developmental principles but with variable potency .Proc Natl Acad Sci USA 107:4335–4340.
CrossRef Google scholar
[4]
Johnson MB (2015) Single-cell analysis reveals transcriptional heterogeneity of neural progenitors in human cortex . Nat Neurosci 18:637–646.
CrossRef Google scholar
[5]
Junker JP, van Oudenaarden A (2014) Every cell is special: genome-wide studies add a new dimension to single-cell biology .Cell 157:8–11.
CrossRef Google scholar
[6]
Kang HJ (2011) Spatio-temporal transcriptome of the human brain .Nature 478:483–489.
CrossRef Google scholar
[7]
Kim D(2013)TopHat2: accurate alignmentof transcriptomesin the presence of insertions, deletions and gene fusions . Genome Biol 14:R36.
CrossRef Google scholar
[8]
Li Y(2013) Global transcriptional and translational repressionin human-embryonic-stem-cell-derived Rett syndrome neurons .Cell Stem Cell 13:446–458.
CrossRef Google scholar
[9]
Luo Y (2015) Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells .Cell 161:1175–1186.
CrossRef Google scholar
[10]
Ma L (2012) Human embryonic stem cell-derived GABA neurons correct locomotion deficits in quinolinic acid-lesioned mice .Cell Stem Cell 10:455–464.
CrossRef Google scholar
[11]
Mariani J (2015) FOXG1-dependent dysregulation of GABA/ glutamate neuron differentiation in autism spectrum disorders . Cell 162:375–390.
CrossRef Google scholar
[12]
Miller JA (2014) Transcriptional landscape of the prenatal human brain .Nature 508:199–206.
CrossRef Google scholar
[13]
Mirnics K (2008) What is in the brain soup ? Nat Neurosci 11:1237–1238.
CrossRef Google scholar
[14]
Pollen AA (2015) Molecular identity of human outer radial glia during cortical development .Cell 163:55–67.
CrossRef Google scholar
[15]
Tang F (2009) mRNA-Seq whole-transcriptome analysis of a single cell .Nat Methods 6:377–382.
CrossRef Google scholar
[16]
Tang F (2010) RNA-Seq analysis to capture the transcriptome landscape of a single cell .Nat Protoc 5:516–535.
CrossRef Google scholar
[17]
Wu H (2007) Integrative genomic and functional analyses reveal neuronal subtype differentiation bias in human embryonic stem cell lines .Proc Natl Acad Sci USA 104:13821–13826.
CrossRef Google scholar
[18]
Zhang Y (2013) Rapid single-step induction of functional neurons from human pluripotent stem cells . Neuron 78:785–798.
CrossRef Google scholar
[19]
Zhang K, Huang K, Luo Y, Li S (2014) Identification and functional analysis of long non-coding RNAs in mouse cleavage stage embryonic development based on single cell transcriptome data .BMC Genom15:845.
CrossRef Google scholar

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2014 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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