DOT1L promotes progenitor proliferation and primes neuronal layer identity in the developing cerebral cortex

Abstract Cortical development is controlled by transcriptional programs, which are orchestrated by transcription factors. Yet, stable inheritance of spatio-temporal activity of factors influencing cell fate and localization in different layers is only partly understood. Here we find that deletion of Dot1l in the murine telencephalon leads to cortical layering defects, indicating DOT1L activity and chromatin methylation at H3K79 impact on the cell cycle, and influence transcriptional programs conferring upper layer identity in early progenitors. Specifically, DOT1L prevents premature differentiation by increasing expression of genes that regulate asymmetric cell division (Vangl2, Cenpj). Loss of DOT1L results in reduced numbers of progenitors expressing genes including SoxB1 gene family members. Loss of DOT1L also leads to altered cortical distribution of deep layer neurons that express either TBR1, CTIP2 or SOX5, and less activation of transcriptional programs that are characteristic for upper layer neurons (Satb2, Pou3f3, Cux2, SoxC family members). Data from three different mouse models suggest that DOT1L balances transcriptional programs necessary for proper neuronal composition and distribution in the six cortical layers. Furthermore, because loss of DOT1L in the pre-neurogenic phase of development impairs specifically generation of SATB2-expressing upper layer neurons, our data suggest that DOT1L primes upper layer identity in cortical progenitors.


SUPPLEMENTAL METHODS
In situ hybridization, Hematoxylin-Eosin staining and immunofluorescence ISH, HE staining and immunostainings of brain tissue and cultured cells was performed as previously described (1,2). For ISH, probes listed in supplemental table S1 were applied. Antibodies applied are listed in supplemental table 2.
RNA extraction, cDNA synthesis, quantitative real-time PCR and analysis RNA was extracted from E14.5 control (ctrl) and Foxg1-Cre Dot1l-cKO dorsal telencephalon for RNAseq. For qRTPCR, we dissected the dorsal and ventral telencephalon and used the dorsal samples if not stated otherwise. For E12.5 we isolated the entire cerebral cortex for RNA extraction. For analyses of expression changes in NPC differentiated from mESC, we harvested 3 million cells in PBS. For RNA extraction, samples were processed with QIAshredder Kit (#79654, Qiagen, Germany) and RNAeasy Mini Kit (#74104, Qiagen, Germany) including an on-column DNAse digestion (#79254, Qiagen, Germany). cDNA synthesis, qRTPCR (primers listed in supplemental table S1) and analysis were performed as described (1). Data are presented as mean ± SEM. Pairwise analyses were conducted by unpaired, two-tailed Student's t tests using GraphPad Prism 6. Significance is indicated in the figure legends.

Imaging and quantifications
Immunofluorescence images were obtained using an Axioplan M2 fluorescent microscope (Zeiss) equipped with an Apotome.2 module. Using Adobe Illustrator, a grid was placed over the image of the cortex. The grid was 200µm wide and contained 10 equally sized bins in the radial dimension (3). The total number of cells or total number of cells/bin were counted using ImageJ and expressed as number of cells in 200µm (example in Figs. S2C, D). For statistical analysis, 1 to 2 cortices of one animal were counted and pooled for at least 3 animals. In each figure legend the N number for each marker quantified specified. In the case that a different n was used for controls and Dot1l-cKO this is stated as n = n(ctrl)/n(cKO). The cell number was normalized to the height of the cortex resulting in cells/mm 2 . Means from different biological replicates were compared using unpaired Student's t-test using GraphPad Prism 6. Each bin from the ctrl condition was compared to the corresponding bin of the Dot1l-cKO. Υ-TUBULIN stained sections were scanned and imaged with a SP8 laser scanning confocal microscope (Leica). Angles of cleavage were measured using the angle tool in ImageJ as previously described by (4). To assess RFP and specific layer marker-positive cells we used the software QuPath (5).

In vivo BrdU labeling and staining
Brain fixation, embedding, BrdU labeling and staining was performed as described (supplemental table  S2, (2)).

Protein extraction and immunoblot
3 million NPCs, differentiated from mESC, were lysed using RIPA-buffer and subjected to immunoblotting as described (6). Antibodies used are listed in supplemental table S2. Images were quantified as published (6). Statistical analysis was performed using one column t-test with GraphPad Prism 6. Data are presented as mean ± SEM. Significance is indicated in the figure legends.
mESC cell culture and neuronal differentiation mESC were differentiated into neuronal lineage according to published protocols (7). For RNA-seq an automatic detection and trimming of adapters were done using TrimGalore (version 0.2.8, http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), which is based on cutadapt (8) (minimum overlap with adaptor: 5 bases). 3' end bases with Phred quality score less than 28 were excluded. We used TopHat2 (version 2.0.9(9)) to align the reads against mouse genome build mm10. We did a transcriptome guided mapping with annotations from Ensembl FTP release 79 (10) . For TopHat2, we specified --library-type fr-firststrand, --mate-inner-dist 0 and --mate-std-dev 80 options. Then using htseq-count (version 0.6.0 (8)) we did a strand-specific read counting with option --stranded reverse. Differential gene expression analysis was performed using the DESeq2 (version 1.6.1 (11)) with default settings. We considered the genes with adjusted p-value less than 0.05 as significantly differentially expressed. The whole bioinformatics analysis was carried out on the Galaxy platform (12). For analysis of the paired-end ChIP-seq libraries, reads were directly mapped with Bowtie2 version 2.2.0 (13) without trimming. The reference genome assembly was GRCm38/mm10; the reference annotation was Ensembl FTP release 79. Picard MarkDuplicates (http://broadinstitute.github.io/picard) was used to remove read duplicates before peak calling with MACS2 version 2.1.0 (14), where the 'broad' option was set for H3K79me2 only. The differential binding analysis was done with DiffBind (15). For all other in-depth ChIP-seq specific analysis deepTools2 version 2.3.5 or 2.4.1 was used (16) i.e. to generate coverage track files (bamCoverage, bamCompare, normalization 1x), estimate the ChIP performance (plotFingerprint), or compare marks between samples (computeMatrix, plotHeatmap). Results from differential expression RNA-seq analysis were also integrated into heatmaps by converting fold-change values into a plotHeatmap input file (custom script) to allow plotting them alongside to the ChIP samples. For the presented heatmaps ( Fig. 2A) the 2 groups-K-mean clustering on H3K4me3 (E14.5) and H3K79me2 (E14.5) levels was carried out for Dot1l-cKO increased and decreased gene expressions independently and then combined for an overall overview resulting in 4 different clusters. For each TSS (+/-250bp) of each differentially expressed gene the log2-ratio value was calculated by comparing ChIP to input tracks using bamCompare (deepTools 2.5.3). The values are visualized in the scatterplot and boxplot grouping them by ChIP-seq clusters combined with color-coding (up or down regulation) from RNA-seq results (log2-fold changes).