Multimodal profiling of the transcriptional regulatory landscape of the developing mouse cortex identifies Neurog2 as a key epigenome remodeler

How multiple epigenetic layers and transcription factors (TFs) interact to facilitate brain development is largely unknown. Here, to systematically map the regulatory landscape of neural differentiation in the mouse neocortex, we profiled gene expression and chromatin accessibility in single cells and integrated these data with measurements of enhancer activity, DNA methylation and three-dimensional genome architecture in purified cell populations. This allowed us to identify thousands of new enhancers, their predicted target genes and the temporal relationships between enhancer activation, epigenome remodeling and gene expression. We characterize specific neuronal transcription factors associated with extensive and frequently coordinated changes across multiple epigenetic modalities. In addition, we functionally demonstrate a new role for Neurog2 in directly mediating enhancer activity, DNA demethylation, increasing chromatin accessibility and facilitating chromatin looping in vivo. Our work provides a global view of the gene regulatory logic of lineage specification in the cerebral cortex.


Statistics
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Software and code
Policy information about availability of computer code Data collection No software was used for data collection.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability All raw and processed sequencing data are available in the Gene Expression Omnibus (GEO) repository: GSE155677. An interactive version of the single-cell and the genomics data can be visualized at https://shiny.bonevlab.com.

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Life sciences study design
All studies must disclose on these points even when the disclosure is negative.

Sample size
Sample sizes for all data types are provided in the Supplementary Data Table 2. Sample sizes for scRNA-seq/scATAC-seq were chosen based upon the ability to get representative data described based upon numerous studies in the field. Sample size for MPRA and methylHiC/HiC/ NOMe-seq to ensure replication of the results with affordable cost.
Data exclusions No samples were excluded from the analysis. For scRNA and scATAC, cells with low quality control (QC) values were excluded from the final analysis as described in the methods section.

Replication
For scRNA/scATAC/MPRA two and for methylHiC three biological replicates were used. For Neurog2 functional experiments the following biological replicates were used: 3x for Hi-C, 2x for NOMe-seq, 2x RNA-seq. Unless otherwise stated, all other experiments were performed in biological triplicates. All attempts of replications were successful.

Randomization
For all experiments involving mice, embryos from one litter were randomly assigned to the experimental group. For the scRNA, scATAC, MPRA and Methyl-HiC experiments, there was no randomization performed as they do not involve multiple study groups. For the in utero electroporation experiments animals were assigned to the control (GFP) or experimental (Neurog2) group based on the construct used during the procedure.

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The authors were not blinded to the group during the data collection and analysis.

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Laboratory animals
Time-mated pregnant C57BL/6JRj female mice (embryonic day E14). Mice were kept under standard housing conditions (room temperature 22C, humidity 55%) according to local regulations of the Regierung Oberbayern, Germany. Mouse embryos were used sex independent.

Wild animals
No wild animals were used.
Field-collected samples No field-collect samples were used.

Ethics oversight
Animal experiments were approved by the animal welfare commission of the Regierung Oberbayern, Germany under the animal license number: ROB-55.2-2532.Vet_02-19-175 Note that full information on the approval of the study protocol must also be provided in the manuscript.

Flow Cytometry
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Gating strategy
After selecting singlets using forward and side scatter, cells in G0G1 were identified by genomic content based on DAPI staining. Subsequently, these cells were divided into Tubb3 high for PN and low for progenitor cell types. The progenitor population was further subdivided into Pax6-high/Eomes-low for NSC and Eomes-high for IPC. The set gates are displayed in Extended Data Fig. 6n and 7a.
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