Multimodal profiling of the transcriptional regulatory landscape of developing mouse cortex identifies Neurog2 as a key epigenome remodeler
Noack F., Vangelisti S., Raffl G., Carido M., Diwakar J., Chong F., Bonev B. Nature Neuroscience 2022
scRNA1-QC - Quality control and standard preprocessing of scRNA data using Seurat v3.1.5
scRNA2-UMAP - Louvain clustering + UMAP visualization of scRNA data following transformation.
scRNA3-DE - Assignment of cluster identities based DE genes (using MAST - Finak et al., 2015).
scRNA4-monocle3 - Pseudotime analysis using Monocle3. Analyses include model fitting to identify gene expression changes as a function of pseudotime.
scATAC1-QC - Quality control of scATAC data based on number of fragments and TSS enrichment.
scATAC2-aggregateBin - Final QC and code for generating count matrices based on genomic bins (for initial clustering), gene bodies and promoters.
scATAC3-normalization - Code for inital clustering based on fragments from fixed-size genome wide bins.
scATAC4-peakNormalization - Final peak calling based on intial clusters to generate high quality peak set, used for final clustering and visualization.
scATAC5-chromVar_motifs - Computing motif accessibility deviations using chromVAR (Schep et al., 2017) implemented in Signac.
scATAC6-Compute_Gene_Scores - Computing gene activity scores using Cicero, used for subsequent integration analyses (seen below).
scATAC7-cluster_unique_peaks - Identification of cluster specific peaks.
Credit for many of the functions in this part of the analysis goes to Satpathy*, Granja* et al. 2019 and Granja et al. 2019
scRNA_scATAC_Integration_01_Align_scATAC_scRNA - Integration of scRNA and scATAC data using Seurat CCA and identification of nearest neighbors (kNN).
scRNA_scATAC_Integration_02_Create_Aggregate_scATAC_scRNA - Aggregate scRNA and scATAC data using nearest neighbor information.
scRNA_scATAC_Integration_03_Compute_Peak_to_Gene_links - Identification of enhancer-gene pairs by correlating each pair of distal scATAC peak and gene promoter.
scRNA_scATAC_Integration_04_P2G_analysis - Further characterization of identified enhancer-gene pairs.
scRNA_scATAC_Integration_05_P2G_monocle - Pseudotime analysis on integrated scRNA-scATAC object using Monocle3. Analyses also include model fitting to identify changes of accessibility and motif deviations as a function of pseudotime.
scRNA_scATAC_Integration_06_chromVar - Motif analysis for integrated object using chromVar.
Figure1 - scRNA-seq analysis of mouse E14.5 cortical development. Associated Extended Data Fig. 1-2.
Figure2 - scATAC-seq identifies dynamic TF motis and variable distal regulatory elements. Associated Extended Data Fig. 3.
Figure3 - Lineage dynamics of enhancer-gene pairs and transcription factor motifs. Associated Extended Data Fig. 4-5.
Figure4 - In vivo immunoMPRA validates cell type specific activity of identified CREs and their regulation by transcription factors. Associated Extended Data Fig. 6.
Figure5 - ImmunoMethyl-HiC identifies DNA methylation-independent global changes in 3D genome architecture during cortical development. Associated Extended Data Fig. 7.
Figure6 - Dynamic enhancer-promoter loops and DNA methylation levels at regulatory regions. Associated Extended Data Fig. 8.
Figure7 - Transcription factors are associated with changes in both chromatin looping and DNA methylation levels. Associated Extended Data Fig. 9.
Figure8 - Neurog2 is sufficient to induce multilayered epigenome changes in vivo. Associated Extended Data Fig. 10.
Extended Data Fig. 1 - Quality control and further validation of the scRNA-seq data
Extended Data Fig. 2 - Pseudo-temporal analysis and comparison to human data.
Extended Data Fig. 3 - Quality control and validation of the scATAC-seq data.
Extended Data Fig. 4 - scATAC-scRNA integration metrics and identification of enhancer-gene pairs.
Extended Data Fig. 5 - Properties of the identified enhancer-gene links.
Extended Data Fig. 6 - In vivo (immune-)MPRA validates cell type specific activity of identified CREs and their regulation by transription factors.
Extended Data Fig. 7 - Global reorganization of the 3D genome in vivo during differentiation.
Extended Data Fig. 8 - Non-correlated enhancer-gene pairs are not associated with dynamic chromatin looping or changes at DNA methylation levels.
Extended Data Fig. 9 - Transcription factors associated with cell-type-specific looping based on positively correlated enhancer-gene pairs.
Extended Data Fig. 10 - No global reorganization of chromatin accessibility, DNA methylation and 3D genome architecture upon Neurog2 overexpression.