Genetic and epigenetic coordination of cortical interneuron development

Allaway, Kathryn; Gabitto, Mariano I.; Wapinski, Orly L.; Saldi, Giuseppe-Antonio; Wang, Chen Yu; Bandler, Rachel C.; Wu, Sherry Jingjing; Bonneau, Richard; Fishell, Gord

doi:10.1038/s41586-021-03933-1

Cited by 76 publications

(64 citation statements)

References 44 publications

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“…Because a large number of cognate motif sites can exist in the genome for each individual TF, selective chromatin accessibility controlled by collaborative postmitotic reorganizers may serve to ensure that neuron-type-specific effector genes are selectively activated only in the presence of multiple positive regulatory inputs. Other TFs may also perform similar function in other neuron types ( Allaway et al, 2021 ). The mechanism by which Pet1 and Lmx1b program neuronal chromatin is not understood, but could involve the recruitment of chromatin remodelers or direct cooperative displacement of histones ( Spitz and Furlong, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Reorganization of postmitotic neuronal chromatin accessibility for maturation of serotonergic identity

Zhang

Spencer

Tabuchi

et al. 2022

eLife

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Assembly of transcriptomes encoding unique neuronal identities requires selective accessibility of transcription factors to cis-regulatory sequences in nucleosome-embedded postmitotic chromatin. Yet, the mechanisms controlling postmitotic neuronal chromatin accessibility are poorly understood. Here, we show that unique distal enhancers define the Pet1 neuron lineage that generates serotonin (5-HT) neurons in mice. Heterogeneous single-cell chromatin landscapes are established early in postmitotic Pet1 neurons and reveal the putative regulatory programs driving Pet1 neuron subtype identities. Distal enhancer accessibility is highly dynamic as Pet1 neurons mature, suggesting the existence of regulatory factors that reorganize postmitotic neuronal chromatin. We find that Pet1 and Lmx1b control chromatin accessibility to select Pet1-lineage-specific enhancers for 5-HT neurotransmission. Additionally, these factors are required to maintain chromatin accessibility during early maturation suggesting that postmitotic neuronal open chromatin is unstable and requires continuous regulatory input. Together, our findings reveal postmitotic transcription factors that reorganize accessible chromatin for neuron specialization.

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Section: Discussionmentioning

confidence: 99%

Reorganization of postmitotic neuronal chromatin accessibility for maturation of serotonergic identity

Zhang

Spencer

Tabuchi

et al. 2022

eLife

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“…31,[50][51][52] , our dataset represents the most comprehensive analysis of the chromatin landscape in the developing brain to date. With this combinatorial approach, we characterized the variation and dynamic…”

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confidence: 99%

An epigenome atlas of neural progenitors within the embryonic mouse forebrain

Mitra

et al. 2021

Preprint

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While epigenetic modifications are critical for cell state changes throughout development, a detailed characterization of chromatin accessibility during neurogenesis has not been explored. We collected single-cell chromatin accessibility profiles from four distinct neurogenic regions of the embryonic mouse forebrain using single nuclei ATAC-Seq (snATAC-Seq). We identified thousands of differentially accessible peaks, many restricted to distinct progenitor cell types or brain regions. Integrating snATAC-Seq and transcriptome data, we characterized changes of chromatin accessibility at enhancers and promoters that were tightly coupled to transcript abundance during neurodevelopment. Integrating chromatin accessibility profiles from embryonic and adult interneurons with the iPSYCH2012 dataset revealed several disease-associated polymorphisms overlapped with accessible regions in embryonic cells. These findings highlight a diverse chromatin landscape in embryonic neural progenitors, extensive coordination between chromatin accessibility and gene expression during neuron fate determination, and open the door for future studies to define critical enhancer-promoter interactions that direct cell fate decisions.

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“…Benchmarking using model organism data should be incorporated in all single-cell method development, as it mitigates cherry-picking from complex network results and can prevent use of flawed performance metrics which are the only option when no reliable gold standard exists. In organisms without a reliable gold standard, network inference predictions should not be assumed correct and must be validated experimentally (Allaway et al ., 2021).…”

Section: Discussionmentioning

confidence: 99%

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

Gibbs

Jackson

Saldi

et al. 2021

Preprint

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Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator 3.0 reliably learns informative networks from the model organisms Bacillus subtilis and Saccharomyces cerevisiae. We demonstrate its capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression data set with paired single-cell chromatin accessibility data.

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Genetic and epigenetic coordination of cortical interneuron development

Cited by 76 publications

References 44 publications

Reorganization of postmitotic neuronal chromatin accessibility for maturation of serotonergic identity

Reorganization of postmitotic neuronal chromatin accessibility for maturation of serotonergic identity

An epigenome atlas of neural progenitors within the embryonic mouse forebrain

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

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