Developing Mouse Cerebellum Enhancer Atlas, where our dataset can be efficiently queried, curated and exported by the scientific community to facilitate future research efforts. Our study provides a valuable resource for studying the dynamics of gene expression regulation by enhancers in the developing cerebellum and delivers a rich dataset of novel gene-enhancer associations providing a basis for future in-depth studies in the cerebellum.We have identified active enhancers in the mouse cerebellum at embryonic and postnatal stages which provides a view of novel enhancers active during cerebellar development. The majority of cerebellar enhancers have dynamic activity between embryonic and postnatal development. Cerebellar enhancers were enriched for neural transcription factor binding sites with temporally specific expression. Putative gene targets displayed spatially restricted expression patterns, indicating cell-type specific expression regulation. Functional analysis of target genes indicated that enhancers regulate processes spanning several developmental epochs such as specification, differentiation and maturation. We use these analyses to discover one novel regulator and one novel marker of cerebellar development: Bhlhe22 and Pax3, respectively. We identified an enrichment of de novo mutations and variants associated with autism spectrum disorder in cerebellar enhancers. Furthermore, by comparing our data with relevant brain development ENCODE histone profiles and cerebellar single-cell datasets we have been able to generalize and expand on the presented analyses, respectively. We have made the results of our analyses available online in the  Neuronal development is a complex and dynamic process that involves the coordinated generation and maturation of countless cell types. For cerebellar granule cells, the most numerous neurons in the brain, neuronal differentiation consists of several steps beginning with the commitment of neural stem cells to become specified neural precursors, followed by multiple migratory stages to reach and mature at its final destination . UnderpiGene expression is typically activated when transcription factors (TFs) bind to non-coding regulatory elements and recruit the necessary components to begin transcription. Among the several classes of non-coding sequences that regulate gene expression, enhancers are the most common, with thousands predicted to coordinate transcriptional regulation during development . EnhanceSeveral molecular properties have been associated with enhancer activity, and the advancement of sequencing technology has facilitated their identification genome-wide in several developing brain structures . EnhanceThe cerebellum has been a long-standing model to study the developmental genetics of the brain. This is, in part, due to the limited number of cell types, well-defined epochs of development for these cell types and a simple trilaminar structure in which these cells are organized, making for an enhanced resolution of events in time and space . Our labWhen considering the non-coding regulatory elements, such as enhancers, that fine tune the spatial and temporal expression of these genes, there are only a few studies that have profiled these sequences genome-wide and specific to the developing cerebellum. Enhancer-associated histone marks (H3K27ac and H3K4me1) have been identified in postnatal and adult mouse cerebella . During Developing Mouse Cerebellum Enhancer Atlas which can be easily queried, curated and exported by the research community. This provides a rich resource that can be utilized to discover novel genetic regulators of cerebellar development and contribute to understanding the impact of genetic variation associated with neurodevelopmental disorders.We utilize chromatin immunoprecipitation followed by sequencing (ChIP-seq) of enhancer associated histone marks H3K4me1 and H3K27ac at 3 stages of embryonic and early postnatal cerebellar development. We identify temporally specific enhancers using a differential peak analysis comparing postnatal and embryonic timepoints. TF motif enrichment and prediction of gene targets led to the elucidation of molecular processes regulated by enhancers during these stages. As examples of the use of this data for discovery, we identify two novel regulators of cerebellar development, Pax3 and Bhlhe22: a novel marker of GABAergic progenitors and a regulator of postnatal granule cell migration, respectively. Finally, we also demonstrate how this data may be used to explore the role of enhancers in neurodevelopmental disabilities by identifying an enrichment of autism spectrum disorder (ASD) associated SNPs and de novo variants found in ASD-affected individuals in cerebellar enhancers. We have made the results of our analyses available online in the 7024 active cerebellar enhancers derived from overlapping H3K27ac and H3K4me1 signals , postnatal day 0 (P0) and postnatal day 9 (P9) . These s signals .Ascl1 and Pax6, two genes critical to cerebellar development , of cerebellar enhancers had significant differences in peak signal  throughout cerebellar development . However, none of these P0 cerebellar enhancers were differentially active when compared to both E12 and P9, indicating that enhancer activity did not spike at birth. Quality control metrics evaluating sensitivity of the H3K27ac and H3K4me1 ChIPs  robust cerebellar enhancers overlapped with CREs with open chromatin conformation. Cell-types were then assigned to robust cerebellar enhancers based on the activity of an overlapping CRE. CREs were previously aggregated into 26 clusters based on activity using an iterative clustering procedure  active in the developing cerebellum previously identified by single-nuclei ATAC-seq  cerebellar enhancers with an average Pearson correlation coefficient of 0.86 (2261 target genes. Using the Mouse Genome Informatics (MGI) database, we identified 98 target genes that when knocked out result in a cerebellar phenotype. To evaluate whether cerebellar genes were enriched in putative target genes, we conducted a permutation analysis by generating 10,000 permutations of 2261 genes randomly selected from all genes expressed in the cerebellum and assessing the number of cerebellar genes in each permutation. We found that cerebellar genes were indeed enriched in target genes . A Gene Ontology (GO) enrichment analysis revealed that each cluster is enriched for molecular processes known to be regulated by cerebellar genes within the cluster , neuron migration  and Purkinje layer development  and also contains Lhx1 and Lhx5 which are expressed in migrating Purkinje cells in cerebellar parenchyma and has previously been associated with the regulation of Purkinje cell differentiation during embryonic cerebellar development , neurotransmitter transport  and regulation of synaptic vesicle exocytosis   . Overall4 novel genes and 11 genes that have previously been implicated in postnatal granule cell development , Purb, Klf13, and Sox18. We focused on Bhlhe22 as it has previously been implicated in the differentiation of neurons in the cortex , molecular layer (ML) and in the internal granule layer (IGL) of the postnatal cerebellum . Cells iTo identify whether Bhlhe22 is expressed in differentiating granule cells, co-staining experiments were performed with Neurod1 and NeuN which mark differentiating and more mature granule cells, respectively . At P6.5We then investigated the role that Bhlhe22 plays in postnatal granule cell development using a well-established in vitro system . Three sSecond, phenotypes of the transfected cells were examined: neuritic outgrowths from the aggregate and the migration of granule cells from the aggregates, within the first 24 hr of plating . NeuritiEfnb1, Efnb2, Tag1, Cdh2, and Astn2 was observed in Bhlhe22 knockdown granule cell cultures compared to controls (Dcx) expression. Taken together, these in vitro knockdown experiments reveal a novel function for Bhlhe22, a gene that was identified by our temporal enhancer-target gene analysis and was predicted to have a critical role in postnatal granule cell development.Third, changes in the expression of cell adhesion molecules that are known to be involved in granule cell development were assessed . A signicontrols . In addiPAX6, TCF4, and ZMIZ1) are ASD risk genes according to the Simons Foundation Autism Research Initiative (SFARI) gene database (Given the emerging importance of the cerebellum in the etiology of autism spectrum disorder (ASD)  and to ddatabase .De novo mutations (DNMs) (variants present in the genome of a child but not his or her parents) have been found to play a significant role in the etiology of ASD, including those found in non-coding regions of the genome . We hypoP-value = 0.0043). We also identified de novo CNVs overlapping cerebellar enhancers. Since the number of such CNVs was too small to perform statistical enrichment tests, we selected a subset of seven of these CNVs (four deletions and three duplications) for further characterization to identify candidates for association with ASD (CDC42BPB (803 kb upstream of the TSS), which has previously been implicated in neurodevelopmental phenotypes  in cerebellar enhancers and H3K27ac peaks from E12, P0, and P9 were enriched in ASD-affected individuals, with odds ratios ranging from 1.04 to 1.10 . While twith ASD . The mosenotypes . By visuenotypes , we verienotypes . Within enotypes . Cdc42bpenotypes . Expressenotypes .In our study on the cerebellum, we performed a novel assessment of enhancer activity through genome-wide profiling of H3K4me1 and H3K27ac at three time points during embryonic and early postnatal stages. These datasets were utilized to define functional enhancer elements with temporally specific activity during these developmental ages. The biological processes under enhancer regulation were described through motif enrichment analysis and target gene prediction, identifying temporally and spatially specific regulatory functions. As a result, our study provides a novel dataset for the developmental biology and neuroscience communities, especially those interested in functionally annotating enhancers in the context of brain development. We have created an online resource that can be used to access, curate and export our dataset. In addition to genomic coordinates of our cerebellar enhancers, this resource provides the activity patterns (Early or Late) for each enhancer throughout developmental time, as well as putative target genes. Predicted cell-types in which a robust cerebellar enhancer may be active as well as activity patterns in other brain regions are also included in our resource based upon comparisons with previously generated datasets (ENCODE and single-cell chromatin accessibility). As demonstrated in our analysis of Pax3 and Bhlhe22 in the context of the cerebellum, this resource can facilitate the discovery of novel genetic regulators of cerebellar development.Identification of enriched TF binding sites and putative target genes indicated that cerebellar enhancers likely play a regulatory role in various phases of neuronal development. In agreement with our results, previous examinations of active non-coding regulatory sequences revealed that neural progenitor cells and mature neurons exhibit distinct signatures of enhancer-associated histone profiles, DNA methylation, chromatin conformation and enhancer-promoter interactions . Bonev eExpression analysis of two genes novel to cerebellar development, Pax3 and Bhlhe22, supported the notion that enhancer profiles are specific to developmental stage. TF enrichment analysis identified Pax3 preferentially enriched in Early active enhancers and robust expression of Pax3 was localized to GABAergic interneuron progenitor cells. Pax3 has previously been associated with neural tube and neural crest development ; howeverIn addition to being temporally-specific, recent evidence indicates that enhancer activity is cell type specific in the brain . In the Spatial specificity of enhancer activity is also highlighted in the identification of cerebellar enhancer target gene clusters for Early and Late active enhancers with cell specific patterns of expression  and 5. FThe results from our analyses revealed that the majority of the identified cerebellar enhancers are predicted to be active in the most abundant cell types at each developmental stage. For example, most Early active enhancers were predicted to be active in progenitors while Late active enhancers were predicted to be active in the developing granule cells and Purkinje cells. We attribute this apparent bias to our bulk tissue approach, which inherently identifies active enhancers based on signal abundance  making it more likely to capture signals specific to more abundant cell types compared to other less abundant and rare cell types. In order to assess histone marks in less abundant cell types, a more granular approach should be implemented such as isolation of certain cell types or single-cell analysis. Recent technological developments have allowed the examination of histone modifications genome-wide in single cells, which has seen preliminary success in mouse brain tissue .Drosophila, shavenbaby gene drive expression in multiple tissues and developmental stages. In one of these enhancers, the same TF binding site is used during both embryonic and pupal expression, while another enhancer utilizes different binding sites. In the context of brain development, a study that compared the binding of p300 at enhancers identified pleiotropic activity in both the embryonic mouse forebrain and midbrain at E11.5  at E12.5, P0.5 and P9.5  were decapitated for dissection of cerebella. Cerebella were dissected and collected in ice cold Dulbecco’s PBS (DPBS) without magnesium or calcium and subsequently washed 2 x for 5 minutes. Samples from each litter were pooled and trypsinized in DPBS containing 0.25% trypsin for 10, 15, and 30 min at room temperature for E12, P0, and P9, respectively. Following three washes with fresh DPBS, the tissue was triturated with three progressively smaller  bore polished and sterile pipettes in DPBS containing 250 U/ml DNase, 0.25% glucose, and 8 mg/ml BSA. The triturated cells were diluted 1:4 with cold DPBS and passed through a cell strainer (40 μm mesh) to remove large cellular debris. The cells were collected by mild centrifugation, washed in fresh DPBS and counted. The cells were split into 100,000 cell aliquots, pelleted and snap frozen using liquid nitrogen. Cell pellets were stored at –80 °C.We performed native chromatin immunoprecipitation (ChIP) using validated antibodies against H3K4me1 and H3K27ac according to previously established protocols by the International Human Epigenomics Consortium (IHEC) . Brieflyusegalaxy.org) for analyses  using input samples as a control for each replicate. bigWigs were generated and normalized by the total number of mapped reads using the BamCompare and profiles were generated from these bigWigs by calculating average coverage in 50 bp bins using Deeptools v.3.3  (http://www.enhanceratlas.org/downloadv2.php)   with hindv2.php) . For thedv2.php) .Aligned read counts (BAM file format) from our H3K27ac ChIP-seq experiments mapped to our robust active cerebellar enhancers for E12, P0, and P9 samples were used as input to the package DiffBind v1.4.2 . Read coTo determine cerebellar enhancers with embryonic-specific activity, H3K27ac signal at E12 was compared to P0 and P9. Enhancers with significantly higher signal at E12 for either contrast were considered ‘Early’ active enhancers. A region found to be enriched for both contrasts was counted as one enhancer. To determine cerebellar enhancers with postnatal-specific activity, H3K27ac signal at P9 was compared to E12 and P0. Enhancers with significantly higher signal at P9 for either contrast were considered ‘Late’ active enhancers. A region found to be enriched for both contrasts was counted as one enhancer. To determine cerebellar enhancers with activity specific to birth, H3K27ac signal at P0 was compared to P9 and E12. Enhancers with significantly higher signal at P0 in both contrasts would identify enhancers that peaked in activity at P0. We did not identify any enhancers that peaked in activity at P0 and conducted the remaining analysis for only Early and Late enhancers.https://www.encodedcc.org) using the experiment IDs listed in the supplementary information provided by the study. Cerebellar histone peaks were overlapped with hindbrain, midbrain and forebrain peaks using the intersect function from Bedtools v2.30.0  provided by the corresponding authors. The CREs were filtered for those that overlapped with Early and Late active robust cerebellar enhancers and ATAC-seq signal was plotted for progenitors, granule cells, Purkinje cells, and interneurons.Robust cerebellar enhancers were compared to CREs identified previously in the developing cerebellum by snATAC-seq . Dataset v2.30.0 . Robust  v2.30.0 . AverageTranscription factor motif enrichment was calculated using the software HOMER using the script FindMotifsGenome.pl with default parameters . Analysek-means clustering of predicted gene targets separately. Input for this analysis was gene expression captured from cerebella at 12 embryonic and postnatal time points  and Late (n=4).To identify possible gene targets of our robust cerebellar enhancers, the correlation between H3K27ac signal and mRNA expression of genes located in cis at E12, P0, and P9 was calculated. For a given enhancer, a gene located in cis was considered a possible target if it was positively correlated with H3K27ac signal throughout time. These genes were then filtered based on location using conserved topologically associating domains (TADs), which are areas of the genome that preferentially interact . These Te points . BrieflyA permutation analysis was conducted to evaluate whether cerebellar genes were enriched in robust cerebellar enhancer putative target genes. We generated 10,000 permutations of 2261  randomly selected from all expressed genes in the developing cerebellum  . The numPax3 and Bhlhe22 expression values were analyzed in scRNA-seq datasets generated in the developing mouse and human developing cerebellum . The mouEmbryos harvested between E12.5 to E15.5 were fixed by immersion in 4% paraformaldehyde in 0.1 M phosphate buffer  for 1 hr at 4 °C. Postnatal mice between P0.5 to P9.5 were perfused through the heart with a saline solution followed by 4% paraformaldehyde/0.1 M PBS. The brain tissues were isolated and further fixed in 4% paraformaldehyde in 0.1 M PB for 1 hr at room temperature. Fixed tissues were rinsed with PBS, followed by cryoprotection with 30% sucrose/PBS overnight at 4 °C before embedding in the Optimal Cutting Temperature compound (Tissue-Tek). Tissues were sectioned at 12 µm for immunofluorescence experiments and cryosections were mounted on Superfrost slides (Thermo Fisher Scientific), air dried at room temperature, and stored at −80 °C until used. Sagittal sections were cut from one side of the cerebellum to the other . In all cases, observations were based on a minimum of 3 embryos per genotype per experiment.Tissue sections were first rehydrated in PBS (3x5 minute washes) followed by a phosphate buffered saline with Triton X-100 (PBS-T) rinse. Sections were then incubated at room temperature for 1 hr with blocking solution . Following the blocking step, the slides were incubated with primary antibody in incubation buffer  at room temperature overnight in a humid chamber. Following the overnight incubation, the slides were rinsed in 3x10 min PBS-T washes. The sections were then incubated with the appropriate secondary antibody at room temperature for 1 hr, followed by three 0.1 M PB washes and one 0.01 M PB wash. Coverslips were applied to the slides using FluorSave mounting medium . The primary antibodies used were: rabbit anti-Bhlhe22 , mouse anti-Neurod1 , mouse anti-Pax3 , rabbit anti-Pax2 , mouse anti-NeuN , rabbit anti-Calbindin , rabbit anti-Foxp2 , chicken anti-Doublecortin: . For immunofluorescence, secondary antibodies (Invitrogen) labeled with fluorochrome were used to recognize the primary antibodies.Granule cells were isolated and cultured as previously described . Briefly2. Neuronal processes extend from aggregates and most form neurite bundles. After several hours, small bipolar granule cells migrate unidirectionally away from the cell clusters along these neurites and neurite bundles by extension of processes, followed by translocation of cell bodies outside of the aggregate cell cluster margin. For immunofluorescence experiments, cells were fixed in 4% paraformaldehyde for 10 min and washed with calcium and magnesium-free PBS.For aggregate cultures, aggregates were allowed to form by incubating purified granule cells for 20 hr on uncoated tissue culture dishes in DMEM containing 10% FBS, 0.45% D-glucose, Pen-strep, 2 mM L-glutamine at 4E6 cells/ml. Aggregates were then washed and cultured in Neurobasal/B27 medium on poly-d-lysine/laminin-treated chamber slides at 37 °C/5% COFor the knockdown of Bhlhe22, we purchased ON-TARGETplus SMARTPool Mouse Bhlhe22 siRNA from Horizon Discovery (Cat ID: L-063262–01). Control samples were transfected with ON-TARGETplus Non-targeting Control Pool (Cat ID: D-001810–10). siRNA molecules were electroporated into isolated postnatal cerebellar granule cells using the Nucleofector 2b Device  as previously described . BrieflyRNA was collected from cultured granule cells using the Monarch Total RNA Miniprep Kit (NEB). Then cDNA was reverse transcribed using SuperScript IV First-Strand Synthesis System (Invitrogen) using random hexamers. Quantitative PCR was conducted using the Applied Biosystems Fast SYBR Green Master Mix reagent and Applied Biosystems 7500 Real-time PCR system. PCR conditions were as follows: 95  °C for 20 seconds, 40 cycles of 95  °C for 3  s, and 60  °C for 30 s followed by 95  °C for 15  s, 60  °C for 1  min, 95  °C for 15  s and 60  °C for 15  s. Three biological replicates were analyzed for each target gene. Amplification of eGFP was used as a reference gene to normalize the relative amounts of successfully transfected cells between treated and control experiments. Gene specific primers are listed in Analysis and photomicroscopy were performed using a Zeiss Axiovert 200 M microscope with the Axiocam/Axiovision hardware-software components (Carl Zeiss) and downstream image analysis was conducted using the AxioVision software v.4.9.1 (Carl Zeiss).2 of each other. Transfected cells were identified by examining eGFP expression and for each biological replicate/experimental treatment, 20 aggregates were examined. Granule cell migration was measured by calculating the distance of migrated cells from the edge of the aggregate on captured images. Mean migration distance was calculated for each aggregate, and the average of all 20 aggregates was used for statistical analysis. The distribution of migrated cells from the aggregate was calculated for the following ranges:<25 µm, 25–50 µm, 50–75 µm, 75–100 µm, >100 µm. For each range, the average percentage was calculated for 20 aggregates per replicate. For comparisons between siRNA treated and control samples, means were compared using a two-tailed t-test. Results were expressed as the average ±SE, and p-values <0.05 were considered significant.For cerebellar granule cell aggregate cultures, aggregate size determined using the tracing tool and all aggregates analyzed were within 1000 µmFor the quantification of Pax3 and Bhlhe22 expressing cells, images captured for immunofluorescent staining were used for cell counting. Three randomly selected 100 µm square portions of each sagittal section of the developing cerebellum were analyzed at the indicated developmental stages. The percentage Pax3 and Bhlhe22 expressing cells that colocalized with cell type markers was determined by counting the number of co-stained cells dividing the total by the number of Pax3 or Bhlhe22 expressing cells in a square. Three square portions were counted for three sagittal sections for each of the three biological replicates. Each data point on the respective bar graphs represent an average calculated for each sagittal section.All plots and correlation analysis were generated in R version 3.2.3 and figures were produced using the package ggplot2. Bedtools v.2.28  was used2 ≥0.7) from 1000 G data . The probability of an overlap of either a SNP or at least one of its LD proxies with our enhancers relative to a set of matched control variants was used to evaluate significance of overlap. The enrichment p-value is the probability that the overlap of control variants with our enhancers is greater than or equal to the overlap of the GWAS variants with converted robust cerebellar enhancers.The NCBI LiftOver tool was used to convert the coordinates of cerebellar enhancers from mm9 to hg19 to hg38, and BEDTools . 6630/70De novo mutations were detected using whole-genome sequencing data from the MSSNG  and Simo This manuscript is a valuable study to understand regulatory elements in the early developing mouse cerebellum. The authors have generated important ChIP-seq datasets from embryonic and early postnatal mouse cerebellum. The authors use these data to convincingly identify enhancers in the developing cerebellum. The authors have also made their data and analyses available online in the "Developing Mouse Cerebellum Enhancer Atlas." In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:eLife. Your revised article has been evaluated by Kathryn Cheah (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Temporal analysis of enhancers during mouse brain development reveals dynamic regulatory function and identifies novel regulators of cerebellar development" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:Importantly, we think this manuscript would fit a "Resource" category better than its current status as a research article. However, even to meet that bar we believe there are substantial revisions that need to be made. The reviewers felt that while the data might constitute a valuable resource for specific researchers that the novelty and impact of the dataset overall was not sufficient for a research article. However, if the authors can integrate previously published datasets more effectively and make your dataset more easily digestible/accessible by a broader neuroscience audience, we believe this could be a valuable dataset for the community.Reviewer #1:This manuscript aims to identify and understand regulatory elements in the early developing mouse cerebellum. To this end, the authors generate datasets of H3K4me1 and H3K27ac ChIP-seq in embryonic and early postnatal mouse cerebellum. The authors use these data to identify potential enhancers and carry out a number of confirmatory experiments that overlap their data with previously published ones as well as showing expression patterns of target genes that fit with the enhancer data.The strengths of the manuscript include:1) the generation of embryonic data that have been missing in the literature.2) the integration with previously published datasets.3) the IHC/ISH experiments that correlate expression with enhancer activity.4) the functional characterization of Bhlhe22, which had not previously been described in the literature.The weaknesses of the manuscript are:1) The bulk approach that could potentially mask patterns driven by rare cell types.2) Lack of mutational testing of specific enhancers.In general, I think the manuscript is well written and easy to understand. The authors make a case for the need and novelty of their datasets. They also try to bolster their findings using a number of additional wet-bench approaches as well as integration with published datasets.The biggest weakness is the lack of cell-type resolution presented using this bulk approach. The authors do a reasonable job trying to address this with the data in hand and mention in the discussion that finer resolution is needed. I'm just not sure that given the current state of what is possible technically that the current approach provides sufficiently important data .Reviewer #2:The authors here sought to identify enhancers that are active in developing cerebellum and distinguish early (e.g. embryonic) from late  activity. To accomplish this, the authors performed new ChIP-seq experiments on two histone modifications that differentiate poised/latent from active enhancers, H3K4me1 and H3K27ac respectively, interrogating E12, P0, and P9 mouse cerebellum. The authors performed differential binding analysis and intersected these epigenomic datasets with published transcriptomic and autism human genetics datasets. While the scope is limited, the datasets and analysis are of sufficient rigor and reproducibility and the authors validate two genes with novel cerebellar function, thus the results and resulting resource of annotated enhancers should be sound.The main weakness with this study is limited novelty and impact due to studies that have generated similar datasets and general findings. Other studies have generated epigenomics datasets for developing mouse cerebellum, including recently with single cell resolution using snATAC-seq, and more detailed time courses and complete chromatin signatures. The datasets here are largely redundant with work from the mouse ENCODE project, which profiled hindbrain across eight developmental stages that overlap with the two early timepoints here, as well as adult cerebellum. Similarly, the general concept of enhancers anchoring dynamic developmental processes, including in brain and specifically cerebellum, is now firmly established. Nonetheless, focused studies such as this one have value in that the results and datasets are digestible, curated, and directed toward a specific audience that are likely to appreciate the findings and use the results as a resource. The value of the work is the focus on identification and characterization of enhancers and their target genes with differential early developmental versus post-natal activity in the cerebellum.While this focused study should have an audience, it would be stronger if the authors interfaced their data and results with other larger and more granular (e.g. single cell or more chromatin marks) datasets. If this is done effectively, this manuscript will have clear stand alone value as a complement to the larger, but not curated or cerebellum focused ENCODE data. And would link to the emergine single cell datasets that also focus on dynamic transcriptional and epigenomic changes. This would put the results from this work in the context of the larger and centralized efforts, while maintaining the useful focus.Analysis and results:1. The IHC in the manuscript lacks any quantitation and co-labeling/co-expression patterns are not clear with the lower magnification images. Figure 3 and 6 (and supp fig) analysis should have a quantitative summary of staining and of co-labeling. For example, Figure 3 is used to argue for presence or absence of co-expression/co-labeling between Pax3 and Ptf1a, Pax2, and Foxp2. I would like to see neuroanatomical annotation lines as overlay on sections for non-experts. Finally, I think higher magnification insets that clearly show co-labeling (or lack thereof) would make these figures stronger.2. The P0 enhancer set is much smaller than the E12 or P9, raising questions about technical sensitivity. The authors make the conclusion that there are no P0 enriched enhancers, but this could also simply be a lack of sensitivity. The authors could address this by presenting information regarding the signal to noise and comparing their data to published ENCODE P0 hindbrain data.3. The authors should compare their datasets to the mouse ENCODE developmental hindbrain datasets. Ideally, they would verify the patterns they found, showing that their datasets are high quality and correlated with the ENCODE data and then also review the reproducibility of the early vs. late enhancers. The authors can also use the P9 data to compare to the adult cerebellum in mouse ENCODE to see how similar these datasets are – it would be of interest if P9 is capturing novel enhancers. The authors could also look at dynamic chromatin accessibility to see if the H3K27ac + H3K4me1 signature here is more sensitive to active enhancers than accessibility alone .4. It would be of interest to see how the enhancer sets identified here map to the recently published snATAC-seq datasets from mouse cerebellum across development. This publication was referenced in the manuscript, but not described – Sarropoulos Science 2021. More specifically, can the early and late enhancers here be assigned to shared or distinct cerebellar cell types? Also as above, it would add value to this study if the H3K27ac+H4K3me1 activity signatures produces improved sampling of active enhancers vs. the snATAC.5. The authors could intersect their target early and late genes with recent snRNA-seq data from developing human cerebellum (Aldinger Nat Neuro 2021).6. Some general issues with the target gene set analsyis:. For the k-means clustering of target genes presented in Figures 4 and 5, the authors should provide rationale for using the k value selected. K-means is a great algorithm for this due to simplicity and wide use, but can require optimization and nested approaches. What other values of k were used? Did a nested approach reveal any subclusters within the four major groups at either timepoint? It is fine to use some arbitrary/post-hoc selection of best clusters, but the rationale should be described. Figure 5 should include N for genes per cluster. Finally I'm not sure what the GeneRatio measure is supposed to capture. IT would be more straightforward to use an observed vs. expected fold enrichment, as counts can skew depending on number of genes and fold enrichment is standard in GO analysis.7. Motif analysis: Motifs are often similar for families, so motif to TF matching is imprecise at best. Fine to select best guess TF as authors do, but good to also show motif logo in Figure 3A and include mention that the actual TF could be a different family member unless orthogonal evidence exists.8. Intersection with ASD variants is flimsy. The authors should use some presumed negative for non-brain and other brain GWAS to show that the ASD SNP findings are relevant e.g. have stronger signal than presumed negative or less relevant phenotypes. The overall enrichment with de novo mutations in enhancesr does not demonistrate any specific cerebellar relevance unless these enhancers are not active in other brain regions. Other studies have showed general enrichment of de novo mutations in brain enhancers. Not sure whether the rare CNV with the cerebellar gene is useful or convincing of relevance as the N is 1 and the gene expression outside the cerebellum is bot shown or discussed. All this analysis is fine, but shouldn't be treated as a major finding. I think the other parts of this study are much stronger, so de-emphasizing this would not hurt the paper.9. The argument that MGI mouse cerebellum phenotype is evidence for validity needs to be compared to some random set in order to evaluate how strong the 98/2261 finding is- e.g. via permutation analysis selecting random sets of 2261 genes to establish background.10. Figure 2B is not needed – just put numbers in legend or overlay on volcano plots in 2A.11. Figure 2C would be more informative with longitudinal data showing change over time.Text1. The authors highlight their study as the first to characterize developmentally active enhancers, both in the abstract and discussion. This is an over-statement at best and the authors would do better to both remove this language and to directly deal with some of the other datasets, as described below.2. The introduction should include brief description of the other relevant studies, particularly the mouse ENCODE data and recent snATAC-seq and explain the novelty of approach here. The discussion should devote space to comparing the results from this study and the mouse ENCOE and snATAC-seq specifically.3. The last paragraph of the introduction is essentially a detailed summary of the findings. I would prefer this be the first paragraph of the discussion to avoid redundancy and present results before making these claims. A brief general summary statement is fine.Reviewer #3:Strengths of the manuscript:The authors have carried out a stringent analysis of genomic enhancers that are dynamically active in specific cell populations over a period of cerebellar development. Their data represents a highly useful resource for the field of cerebellar research.An enrichment analysis of the enhancers for specific transcription factor binding sites identified potential transcription factors previously not well-known to regulate cerebellar development.The authors demonstrate how their dataset could be exploited to identify novel genes in cerebellar development  as well as potential disease genes and mechanisms (e.g. in autism spectrum disorder).Weaknesses:The authors select three timepoints during cerebellar development for their ChIP-seq analysis. The rational for selecting these specific timepoints is not very clear, especially as they do not cover the entire period of cerebellar development in the mouse.While the resource itself is very valuable, the presentation of example genes that the authors extract from their analyses  is rather short and lacking in detail. For example, the authors could have further strengthened their findings by utilising recent single-cell datasets, in particular the single-cell transcriptional atlas of the developing cerebellum published by Carter et al.It won't be easy for the scientific community to interact with the generated data.The authors use in silico tools to identify transcription factor binding sites enriched in the identified active enhancers. No experimental validation is provided for this analysis. This would have been particularly relevant for transcription factors that have not been previously well-described in cerebellar development including Pax3, which the authors present as a novel marker for GABAergic progenitors. Furthermore, it would have been helpful to go into further detail and to provide some predicted candidate genes that might be regulated by Pax3 in GABAergic progenitors.Figure 3: Very limited expression data is shown for Pax3. It would have been helpful to show additional immunostainings at least for P9  if not for further timepoints during cerebellar development. Moreover, it would have been really interesting to compare this to the data available on Pax3 from Carter et al. The authors mention this in their discussion; it would be helpful to include this data in the relevant figure and also to comment on how expression of Pax3 changes during the course of cerebellar development.With regards to Pax3, there are some interesting papers that link Pax3 to cerebellar development and cerebellar disorders and that the authors could comment on in their discussion; for example, Kioussi and Gruss (1994) and Jalali et al., (2008), and Zagozewski et al., (2020).I am not sure that I agree with the authors about the fact that 98 out of 2261 identified target genes result in a cerebellar phenotype when knocked out particularly demonstrates the validity of the high throughput approach (page 12). Is this a significant number?The use of CAGE-sequencing data in this manuscript is very unclear. The authors refer to a previous manuscript ; however, in this study reports transcriptomic data from only three timepoints  and not 12 timepoints as mentioned in by the authors? The authors should better describe which data they used. Also, given that the CAGE-Seq data is from bulk-sequencing, it would have been much more informative to look at gene expression of their candidate transcription factors in the Carter et al., single-cell dataset. Furthermore, this would have allowed the authors to look beyond the P9 timepoint and thus for a more complete gene expression analysis over cerebellar development. This is also true for Figures 1E,G.The approach of using the enhancer data set to identify novel target genes not previously identified in cerebellar development is unclear to me. Using their described filtering approach, the authors describe genes that differentially regulated by Atoh1. Could these not have been identified from the original dataset by Klisch et al., 2001? What does the initial identification of the enhancers add here?Figure 6: As above, the authors should include analysis of Bhlhe22 expression in the Carter et al., single-cell dataset.With regards to Bhlhe22, the authors should comment on the protein that is encoded by this gene and its potential function during cerebellar development.Figure 7 could be much more clearly presented. The font size is really small and the figures not very clear. It would be nice to see successful knockdown of Bhlhe22 by immunostaining.The authors normalize gene expression to co-transfected GFP. Can the authors demonstrate that cells that are transfected with GFP were also transfected with the siRNA construct?Can the authors comment on the evolutionary conservation of their identified enhancer elements? This is particularly relevant for their analysis of variants associated with ASD. Are the loci of interest conserved between mouse and human and thus, do enhancer activity and transcription factor occupancy occur at orthologous regions distal to the same gene promoters?For the identification of CDC42BPB, it would have been nice to link this back to expression data over time and to provide more detail on the potential transcription factors involved.Finally, given that this is a very important dataset for the scientific community, I wish it would be easier for others to interact with the data rather than just depositing data files into GEO. It would have also been really helpful to provide supplementary tables listing enhancer elements and associated transcription factors as well as nearby genes that the authors have identified. Currently, it is not transparent which lists of genes etc were used for the various analyses shown. Reviewer #1:The biggest weakness is the lack of cell-type resolution presented using this bulk approach. The authors do a reasonable job trying to address this with the data in hand and mention in the discussion that finer resolution is needed. I'm just not sure that given the current state of what is possible technically that the current approach provides sufficiently important data .The bulk tissue dataset that we present provides consensus (i.e. data from many cells) and high confidence signals that point us to coordinates that will serve as benchmarks for validation for past and future single-cell assays. We address the issue of cell-type resolution by comparing our data with recent single cell/nuclei data . The outcome is that we find a significant overlap between our data and the single cell/nuclei data. These analyses and results can be found in a new section in our manuscript entitled ‘Comparison of robust cerebellar enhancers with chromatin accessibility identified in single cells’ (pp. 11) and in the sections that report our findings Pax3 (pp.14-15) and Bhlhe22 (pp.19).Reviewer #2:The main weakness with this study is limited novelty and impact due to studies that have generated similar datasets and general findings. Other studies have generated epigenomics datasets for developing mouse cerebellum, including recently with single cell resolution using snATAC-seq, and more detailed time courses and complete chromatin signatures. The datasets here are largely redundant with work from the mouse ENCODE project, which profiled hindbrain across eight developmental stages that overlap with the two early timepoints here, as well as adult cerebellum. Similarly, the general concept of enhancers anchoring dynamic developmental processes, including in brain and specifically cerebellum, is now firmly established. Nonetheless, focused studies such as this one have value in that the results and datasets are digestible, curated, and directed toward a specific audience that are likely to appreciate the findings and use the results as a resource. The value of the work is the focus on identification and characterization of enhancers and their target genes with differential early developmental versus post-natal activity in the cerebellum.are not found in the ENCODE hindbrain data . Additionally, we identified 765 robust cerebellar enhancers active in the cerebellum and not in the hindbrain. Thus, our study has led to the discovery of novel enhancer elements, potentially important specifically for cerebellar development. These findings are presented in the Results section ‘A subset of cerebellar enhancers is active in other developing regions of the brain’ (pp. 10) in the revised manuscript. We also utilize our cerebellar histone profiles to uncover novel regulators of cerebellar development and characterize the expression and function for transcription factors Pax3 and Bhlhe22, which have not previously been described in the literature. In comparison to the ENCODE analysis previously conducted in the mouse hindbrain, our study provides results and datasets that are digestible and curated, which can be utilized to discover temporally-specific regulators of embryonic and postnatal cerebellar development.We believe that our dataset is not redundant with the chromatin profiles generated from the entire rhombencephalon as reported in the ENCODE work of Gorkin et al., (2020). The rhombencephalon develops into the pontine nuclei, the medulla and other brainstem regions in addition to the cerebellum. Three points are notable here: (1) Our analysis, is specific to chromatin profiling of the developing cerebellum which is a novel dataset. (2) In our analysis, we examined the P9 timepoint which was not examined in the ENCODE dataset, and this is a key watershed in cerebellar development; at this timepoint we find a large subset of enhancers that are specific to postnatal cerebellar development. (3) When comparing our cerebellar histone profiles to those generated in the hindbrain, we find that ~25% of our H3K27ac peaks at E12 While this focused study should have an audience, it would be stronger if the authors interfaced their data and results with other larger and more granular (e.g. single cell or more chromatin marks) datasets. If this is done effectively, this manuscript will have clear stand alone value as a complement to the larger, but not curated or cerebellum focused ENCODE data. And would link to the emergine single cell datasets that also focus on dynamic transcriptional and epigenomic changes. This would put the results from this work in the context of the larger and centralized efforts, while maintaining the useful focus.We agree with this reviewer that the integration of the previously generated data will provide our study with stand-alone value as a complement to these larger datasets. We have addressed this reviewer’s suggest to interface our data with larger and more granular datasets (see our Cover Letter). Overall, by the addition of relevant comparisons, our study provides a valuable resource of active enhancers focused on the developing cerebellum and can be used as a means to discover novel genetic regulators of cerebellar development.Analysis and results:1. The IHC in the manuscript lacks any quantitation and co-labeling/co-expression patterns are not clear with the lower magnification images. Figure 3 and 6 (and supp fig) analysis should have a quantitative summary of staining and of co-labeling. For example, Figure 3 is used to argue for presence or absence of co-expression/co-labeling between Pax3 and Ptf1a, Pax2, and Foxp2. I would like to see neuroanatomical annotation lines as overlay on sections for non-experts. Finally, I think higher magnification insets that clearly show co-labeling (or lack thereof) would make these figures stronger.We agree with this reviewer that a quantitative assessment of co-labeling will add further value to our findings and that higher magnification images of co-staining experiments will bring more clarity to our results of our co-staining experiments. To these ends, in the revised manuscript, we have now included a quantitative analysis of co-staining relative to the protein of interest. For Pax3 we quantified the percentage of Pax3 cells that colocalized with Ptf1a, Pax2, Foxp2, and Calbindin. These changes for Pax3 are detailed in Figure 3 and Figure 3 —figure supplement 1. In the revised manuscript, this analysis can be found in the section ‘Early active enhancers are enriched for Pax3 binding sites, a novel marker for GABAergic cells.’ (pp. 14).For Bhlhe22 and markers for differentiating granule cells and migrating cells, we quantified the percentage of Bhlhe22 cells that colocalized with Neurod1, NeuN and Dcx. These data for Bhlhe22 are shown in Figure 6—figure supplement 1. In the revised manuscript, this analysis can be found in the section ‘Bhlhe22 is a novel regulator of granule cell development’ (pp. 20-21).Additionally, we ensure all the images used to assess immunofluorescent co-staining with cell-type specific markers were taken at 20X magnification. We have also drawn neuroanatomical annotation lines to identify regions of interest in the developing cerebellum.2. The P0 enhancer set is much smaller than the E12 or P9, raising questions about technical sensitivity. The authors make the conclusion that there are no P0 enriched enhancers, but this could also simply be a lack of sensitivity. The authors could address this by presenting information regarding the signal to noise and comparing their data to published ENCODE P0 hindbrain data.We thank the reviewer for this suggestion and in the revised manuscript we present quality control metrics recommended by ENCODE to evaluate the success and sensitivity of our H3K27ac and H3K4me1 immunoprecipitations: fraction of reads in peaks (FRiP) and relative strand correlation (RSC) . These metrics are explained in greater detail in Landt et al., 2012 and briefly in the reviewed Methods section. All the H3K27ac and H3K4me1 samples generated in our study met ENCODE standards for a successful immunoprecipitation, with FRiP score higher than 1% and an RSC value greater than 0.8 . The P0 samples had slightly lower scores, on average, in both metrics than the E12 and P9 samples, suggesting that the lower number of P0 enriched enhancers could have been due to a lack of sensitivity compared to the other stages. Additionally, the number of peaks identified in both biological replicates for H3K27ac was greater in the P0 ENCODE hindbrain compared to our cerebellar dataset, also indicating issues in sensitivity. However, 91.7% of the H3K27ac peaks identified in the cerebellum were replicated in the hindbrain, indicating robust signals. The comparisons between our dataset and the ENCODE hindbrain data are presented in the Results section ‘Enhancer dynamics during cerebellar development’ (pp. 8).3. The authors should compare their datasets to the mouse ENCODE developmental hindbrain datasets. Ideally, they would verify the patterns they found, showing that their datasets are high quality and correlated with the ENCODE data and then also review the reproducibility of the early vs. late enhancers.We thank the reviewer for this suggestion and agree that identifying whether our findings are aligned with the ENCODE hindbrain datasets would support our enhancer predictions and provide greater confidence that our datasets are of high quality. As noted in Response #1 to Reviewer #1, our study focused on enhancer activity that is specific to the developing cerebellum while ENCODE looked at enhancer activity throughout the hindbrain. As suggested by this Reviewer (2), we compared chromatin profiles generated in our study to those in the hindbrain and identified that the majority of H3K27ac and H3K4me1 peaks overlapped between the two datasets. There is a subset of peaks specific to the cerebellum (~25% of peaks H3K27ac peaks at each stage) that highlights the identification of cerebellarspecific enhancers, bolstering the import of our analysis. These findings are detailed in a new section of the manuscript entitled “A subset of robust cerebellar enhancers is active in other developing brain regions” (pp. 10) and in Figure 2 —figure supplement 1.In the revised manuscript, we also assessed whether the activity patterns/histone ChIP-seq signal of Early and Late active enhancers could be reproduced in mouse hindbrain samples. We found that in the hindbrain, Early and Late enhancers showed similar trends as we find in the cerebellum with a slight increase and decrease in activity, respectively. These findings are detailed in the new section entitled ‘A subset of robust cerebellar enhancers is active in other developing regions of the brain’ (pp. 10) and in Figure 2—figure supplement 2 and 3.The authors can also use the P9 data to compare to the adult cerebellum in mouse ENCODE to see how similar these datasets are – it would be of interest if P9 is capturing novel enhancers.This is an interesting point and we agree that it is relevant to this study to identify whether cerebellar enhancers demonstrate postnatally specific activity compared to the adult. The postnatal day (P)9 cerebellum is far from developmentally static and previous postnatal findings by Frank et al., (2015) indicate that there may be enhancers with activity specific to postnatal development compared to adult stages. To examine this in our data, we have now included a comparison of mouse cerebellar P9 histone profiles with the ENCODE adult profiles. We find that 30-40% of peaks were unique to P9. This indicates that a subset of the enhancers identified in our study may have activity specific to postnatal development. These findings can be found in the revised Results section ‘Enhancer identification during cerebellar development’ (pp. 7).The authors could also look at dynamic chromatin accessibility to see if the H3K27ac + H3K4me1 signature here is more sensitive to active enhancers than accessibility alone .In our revised manuscript, we address this comment by looking at the overlap of our predicted enhancers with experimentally validated enhancers in the VISTA enhancer database. We find that 0.05% H3K27ac peaks overlapped with experimentally validated enhancers with activity in the hindbrain. This compares very favorably with the same overlap of cerebellar snATAC-seq and hindbrain ATAC-seq peaks, where only 0.005% and 0.006% of peaks overlapped with VISTA enhancers, respectively. This comparison is detailed in the revised Results section ‘Enhancer identification during cerebellar development’ (pp. 7) and in Figure 1—figure supplement 1. This analysis further justifies the value of our data as a resource for the identification of regulatory sequences that are likely to be active enhancers in the cerebellum.4. It would be of interest to see how the enhancer sets identified here map to the recently published snATAC-seq datasets from mouse cerebellum across development. This publication was referenced in the manuscript, but not described – Sarropoulos Science 2021. More specifically, can the early and late enhancers here be assigned to shared or distinct cerebellar cell types?We thank the reviewer for this suggestion and is aligned with this revision’s status as a resource paper.We now compare our cerebellar histone profiles to single-cell profiles of chromatin accessibility . Mapping our cerebellar enhancers with this recently published snATAC-seq dataset we find that 90.1% of robust cerebellar enhancers identified in our bulk approach overlapped with CREs identified using snATAC-seq. We then predicted the cell type in which our robust cerebellar enhancers were active based on the cell-type annotation of the overlapping CREs . Specifically, as suggested by this Reviewer, we used this dataset to quantify Early and Late active robust cerebellar enhancer accessibility in distinct cell types. The comparison of our findings with the snATAC-seq dataset provides cell-type resolution of chromatin accessibility for the majority of cerebellar enhancers. Importantly, our dataset provides novel evidence of enhancer activity by identifying enhancer-associated histone marks at thousands of CREs identified by snATAC-seq. These sequences are more likely to function as enhancers and are prime candidates for validation. The analysis and results can be found in a new Results section in our revised manuscript entitled ‘Comparison of robust cerebellar enhancers with chromatin accessibility identified in single cells’ (pp. 11).5. The authors could intersect their target early and late genes with recent snRNA-seq data from developing human cerebellum (Aldinger Nat Neuro 2021).The suggestion made by this reviewer is excellent and in the revised manuscript we now quantify the expression of Pax3 and Bhlhe22 in various cell-types in the developing human cerebellum using the snRNA-seq dataset previously generated by Aldinger et al., (2021). Pax3 and Bhlhe22 are Early and Late genes, respectively, discovered by our analysis with novel expression patterns and functions in the context of the developing cerebellum. The results of this analysis support the spatial expression patterns identified in our immunofluorescent staining for both Pax3 and Bhlhe22. These findings are presented in the revised Results sections ‘Early active enhancers are enriched for Pax3 binding sites, a novel marker for GABAergic cells’ (pp. 14-15) for Pax3 and ‘Bhlhe22 is a novel regulator of granule cell development’ (pp. 21) for Bhlhe22.6. Some general issues with the target gene set analsyis:. For the k-means clustering of target genes presented in Figures 4 and 5, the authors should provide rationale for using the k value selected.We agree that the rationale for choosing the k-value should be more clearly detailed and in our revised manuscript we emphasize the details of the analysis conducted to determine the number of clusters used for k-means clustering. To arrive at the k value (the number of clusters) for our k-means clustering analysis of Early and Late target genes we conducted an Elbow Analysis to determine the k value with minimal intra-cluster variation [total within-cluster sum of square (WSS)]. This method calculates the WSS and plots WSS as a function of the number clusters. The WSS decreases as the number of k decreases and the optimal k-value is the point at which the WSS first starts to diminish and the differences as k increases are minimal. When plotted on a line graph, this point is visible as an elbow.We determined the optimal number of clusters is 4 for both Early and Late active enhancers . These points are provided in the ‘Co-expressed putative target genes are expressed in spatially distinct areas of the developing cerebellum’ (pp. 15) section in the revised manuscript.Finally I'm not sure what the GeneRatio measure is supposed to capture. IT would be more straightforward to use an observed vs. expected fold enrichment, as counts can skew depending on number of genes and fold enrichment is standard in GO analysis.We thank the reviewer for pointing out the pitfalls of the gene ratio measure and we have changed the GO enrichment analysis to an observed vs. expected fold enrichment as suggested by the reviewer. In the revised manuscript, this was conducted for plots displaying the results of our analyses in Figure 4D and 5D. Additionally, we’ve changed the x-axis of Figure 5D to ‘-log10 adjusted p-value’ and terms were sorted based on significant enrichment . We think that this improves our findings by emphasizing the most highly enriched GO terms in each set of target genes.7. Motif analysis: Motifs are often similar for families, so motif to TF matching is imprecise at best. Fine to select best guess TF as authors do, but good to also show motif logo in Figure 3A and include mention that the actual TF could be a different family member unless orthogonal evidence exists.In agreement with these comments concerning motif families and TF matching, we have revised the manuscript to recognize that the motifs enriched in our cerebellar enhancers may represent the binding of TF families with similar motifs, not just a single best match. To address this point, we now provide a supplementary table of our results and the protein families for the TF with the best matching motif. In addition, we have revised Figure 3A to include the enriched motif logos which provides context for the TF matching. Finally, in the corresponding text of the revised manuscript, we emphasize that the TF bound to an enhancer may potentially be a member of the same protein family, as they typically have similar DNA binding sites. We now provide a Table and text in the Results section ‘Cerebellar enhancers are enriched for neural transcription factor binding sites in an age-dependent manner’ (pp. 12-13) that addresses this point.8. Intersection with ASD variants is flimsy. The authors should use some presumed negative for non-brain and other brain GWAS to show that the ASD SNP findings are relevant e.g. have stronger signal than presumed negative or less relevant phenotypes.We thank the reviewer for this recommendation and we believe that the addition of a negative control using non-brain GWAS demonstrates that our ASD SNP enrichment is relevant. As a negative control, we assessed whether our active cerebellar enhancers were enriched for SNPs associated with chronic kidney disease. We indeed find that there is no significant enrichment of chronic kidney disease SNPs, in contrast to ASD SNPs which were enriched in our cerebellar enhancers. This new addition to our analysis can be found in the section ‘Active cerebellar enhancers are enriched for common and de novo genetic variants associated with autism spectrum disorder.’ (pp. 23)The overall enrichment with de novo mutations in enhancesr does not demonistrate any specific cerebellar relevance unless these enhancers are not active in other brain regions. Other studies have showed general enrichment of de novo mutations in brain enhancers. Not sure whether the rare CNV with the cerebellar gene is useful or convincing of relevance as the N is 1 and the gene expression outside the cerebellum is bot shown or discussed. All this analysis is fine, but shouldn't be treated as a major finding. I think the other parts of this study are much stronger, so de-emphasizing this would not hurt the paper.While we agree with the reviewer that our enrichment analysis was not conducted exclusively for enhancers with cerebellar-specific activity, we believe that there is value in assessing the enrichment of de novo mutations associated with neurodevelopmental disorders such as ASD. Identifying the collective consequences of mutations in enhancers active in multiple brain regions may lead to a better understanding of the complex presentation of disorders like ASD. In accordance with the reviewer’s suggestion, we emphasize in our revised manuscript that this analysis is meant to be an example of the relevance of our dataset to human development and ASD and demonstrate how it may be utilized to functionally annotate variation associated with neurodevelopmental disorders. We believe that it would be beyond the scope of the current study to delve further into the potential impacts of the CNVs on cerebellar development, however it sets the stage for future studies.9. The argument that MGI mouse cerebellum phenotype is evidence for validity needs to be compared to some random set in order to evaluate how strong the 98/2261 finding is- e.g. via permutation analysis selecting random sets of 2261 genes to establish background.We thank the reviewer for this suggestion and we have now conducted a permutation analysis to assess whether the identification of 98 cerebellar genes within our list of 2261 target genes (2261) is evidence of statistical enrichment of cerebellar function. To do so, we generated 10,000 permutations of 2261 genes randomly selected from all genes expressed in the cerebellum and assessing the number of cerebellar genes in each permutation. We found that cerebellar genes were indeed enriched in robust cerebellar enhancer target genes, with an adjusted p-value = 0.0405. This analysis in now included in the section ‘Co-expressed putative target genes are expressed in spatially distinct areas of the developing cerebellum’ (pp. 17) and in Figure 4—figure supplement 2.Text1. The authors highlight their study as the first to characterize developmentally active enhancers, both in the abstract and discussion. This is an over-statement at best and the authors would do better to both remove this language and to directly deal with some of the other datasets, as described below.We recognize that our study is not the first to characterize developmentally active enhancers and in line with the reviewers comment we now frame our work in the context of previous studies and emphasize that our study is best viewed as a valuable contribution to the catalog of active enhancers in the cerebellum. Our study provides a novel and unique dataset previously missing in the literature and can be used as a resource for the discovery of novel regulators of cerebellar development.2. The introduction should include brief description of the other relevant studies, particularly the mouse ENCODE data and recent snATAC-seq and explain the novelty of approach here. The discussion should devote space to comparing the results from this study and the mouse ENCOE and snATAC-seq specifically.We thank the reviewer for this suggestion and we now describe the relevant studies that have conducted assessments of enhancer activity in other brain regions  and chromatin accessibility at single cell resolution . We have included a brief description of these studies in the Introduction (pp. 3-4) of the revised manuscript. This emphasizes the novelty of our approach and results. We also provide a discussion on the comparison of our findings with the ENCODE and snATAC-seq datasets.Reviewer #3:Weaknesses:The authors select three timepoints during cerebellar development for their ChIP-seq analysis. The rational for selecting these specific timepoints is not very clear, especially as they do not cover the entire period of cerebellar development in the mouse.We address the reviewer’s concern about the rationale for investigating the time periods chosen for ChIP-seq in the revised version of our manuscript by including additional text in the section ‘Enhancer identification during cerebellar development’ (pp. 5) in our results, noting the importance of these periods for the various stages of development across the principle cell types of the cerebellum. Thus, our study is a novel genome-wide assessment of enhancer-associated histone marks throughout key points in the developing cerebellum.While the resource itself is very valuable, the presentation of example genes that the authors extract from their analyses  is rather short and lacking in detail. For example, the authors could have further strengthened their findings by utilising recent single-cell datasets, in particular the single-cell transcriptional atlas of the developing cerebellum published by Carter et al.This an important point made by the reviewer, particularly in the context of this being a resource paper where the value lies not only in our initial analysis but in it’s utility when combined with other existing datasets. We have strengthened our analyses by comparing our data with previous scRNA-seq datasets quantified in the developing mouse  and human  cerebellum. Comparisons were conducted specifically for the genes with novel functions in the context of cerebellar development Pax3 and Bhlhe22, where we examine their expression in the cell-types of the cerebellum. We also take the initial steps in validating these findings through immunofluorescent staining at several stages of development and co-staining with cell-type specific markers. We demonstrate the utility of our data as a resource and provide a proper framework for investigating genes with novel functions in the cerebellum. These analyses can be found in sections ‘Early active enhancers are enriched for Pax3 binding sites, a novel marker for GABAergic cells’ (pp. 14-15) and ‘Bhlhe22 is a novel regulator of granule cell development’ pp (21).The authors use in silico tools to identify transcription factor binding sites enriched in the identified active enhancers. No experimental validation is provided for this analysis. This would have been particularly relevant for transcription factors that have not been previously well-described in cerebellar development including Pax3, which the authors present as a novel marker for GABAergic progenitors.Based on the recommendations by the Editors of this study and the fact that the corresponding data will now be available as a Resource article, we believe conducting the experimental validation of transcription factor binding is outside the scope of this revision. The goal of our motif enrichment analysis is to predict the regulators of cerebellar enhancer activity. The identification of these TFs provides further insight into the cell-types in which these enhancers may be active and may help predict the cellular processes regulated by cerebellar enhancers. We do demonstrate how our predictions can be validated through a preliminary set of staining experiments for Pax3, a TF enriched in our cerebellar enhancers with unknown function in the cerebellum. This sets the stage for further investigation of Pax3 function, as well as for other novel regulators of cerebellar development identified by our analyses.Furthermore, it would have been helpful to go into further detail and to provide some predicted candidate genes that might be regulated by Pax3 in GABAergic progenitors.We thank the reviewer for this suggestion and agree that providing further detail and characterizing the enhancers with Pax3 motifs and their putative target genes may provide a more detailed description of Pax3 function in cerebellar development. To bolster our Pax3 findings, we identified the cerebellar enhancers with a Pax3 motif and their target genes, as per the reviewer’s suggestion. We found that 923 Early active robust cerebellar enhancers with a Pax3 binding motif were highly correlated with a putative target gene. A GO enrichment analysis determined that predicted Pax3 target genes were enriched for biological processes such as ‘regionalization’, ‘pattern specification process’ and ‘neural precursor cell proliferation’ which is in line with the results of our immunofluorescent analysis.Additionally, we identified several target genes associated with the function of neural progenitors in the cerebellar VZ (ventricular zone) and the specification and differentiation of GABAergic cell types such as Ascl1, Neurog2, Sox11 and Ctnna2 . These findings are detailed in the section ‘Co-expressed putative target genes are expressed in spatially distinct areas of the developing cerebellum’ (pp. 17).Figure 3: Very limited expression data is shown for Pax3. It would have been helpful to show additional immunostainings at least for P9  if not for further timepoints during cerebellar development. Moreover, it would have been really interesting to compare this to the data available on Pax3 from Carter et al. The authors mention this in their discussion; it would be helpful to include this data in the relevant figure and also to comment on how expression of Pax3 changes during the course of cerebellar development.In agreement with the reviewer’s suggestions, we have extended the time course of Pax3 immunofluorescent staining to P3 and P9. We found that expression patterns found in P0 extend to P3 but by P9 we cannot detect Pax3 expression. These findings can be found in Figure 3 and Figure 3—figure supplement 1.I am not sure that I agree with the authors about the fact that 98 out of 2261 identified target genes result in a cerebellar phenotype when knocked out particularly demonstrates the validity of the high throughput approach (page 12). Is this a significant number?Addressed. See response to Reviewer #2 (Recommendations for the authors) Point 12.The use of CAGE-sequencing data in this manuscript is very unclear. The authors refer to a previous manuscript ; however, in this study reports transcriptomic data from only three timepoints  and not 12 timepoints as mentioned in by the authors? The authors should better describe which data they used. Also, given that the CAGE-Seq data is from bulk-sequencing, it would have been much more informative to look at gene expression of their candidate transcription factors in the Carter et al., single-cell dataset. Furthermore, this would have allowed the authors to look beyond the P9 timepoint and thus for a more complete gene expression analysis over cerebellar development. This is also true for Figures 1E,G.We thank the reviewer for bringing up the CAGE data issue as confusing. This confusion was engendered by the use of an incorrect citation . Our apologies, the correct reference is: Ha, T. J., Zhang, P. G. Y., Robert, R., Yeung, J., Swanson, D. J., Mathelier, A.,... Goldowitz, D. (2019). Identification of novel cerebellar developmental transcriptional regulators with motif activity analysis. BMC Genomics, 20(1), 718. doi:10.1186/s12864-019-6063-9. This has been corrected.The approach of using the enhancer data set to identify novel target genes not previously identified in cerebellar development is unclear to me. Using their described filtering approach, the authors describe genes that differentially regulated by Atoh1. Could these not have been identified from the original dataset by Klisch et al., 2001? What does the initial identification of the enhancers add here?The Klisch et al., (2001) dataset produced a large number of differentially expressed genes (DEGs) at P7. While ground-breaking in their analysis, it left two big gaps: (1) other time points were not assessed where many developmental events are occurring and (2) this produced about 2000 DEGs making it difficult to identify specific novel gene candidates that are critical for the postnatal differentiation of granule cells. To address these gaps, we (1) analyze enhancer activity and gene expression over embryonic and postnatal development , and (2) due to our analysis of enhancers, which are likely to be regulating genes important for specification and differentiation, this allowed us discover novel TFs not previously identified in cerebellar development. We use our enhancer putative target gene dataset in conjunction with DEGs in the Atoh1 mouse to identify Bhlhe22, a novel TF that we find is important for postnatal granule cell migration. This analysis can be found in the section of the Results entitled ‘Bhlhe22 is a regulator of postnatal granule cell development’ (pp. 18).Figure 6: As above, the authors should include analysis of Bhlhe22 expression in the Carter et al., single-cell dataset.Addressed.The authors normalize gene expression to co-transfected GFP. Can the authors demonstrate that cells that are transfected with GFP were also transfected with the siRNA construct?), where siRNA molecules targeting GFP were co-transfected with a GFP plasmid by nucleofection, which is the transfection method used in our study. Fluorescence microscopy showed that GFP expression was visibly reduced when GFP-targeting siRNA was co-transfected FACS analysis showed that an siRNA to DNA ratio of 3:1 reduced the percentage of cells expressing GFP by 71.75% when compared to controls indicating high co-transfection efficiency. This has also been demonstrated in nucleofection protocol documentation provided by Lonza, which is the is manufacturer of the Amaxa Nucleofector 2b which is used in our study for transfection (https://bioscience.lonza.com/lonza_bs/US/en/document/21126). In this example, three different cell lines are transfected with GFP plasmid and GFP targeting siRNA. A reduction in GFP expression was observed in all cell lines, indicating high co-transfection efficiency. The co-transfection of a GFP plasmid with siRNA molecules is now common practice in the field, and that cells that are successfully electroporated with GFP typically are also transfected with the siRNA molecules.We thank the reviewer for the suggestions and the aesthetic adjustments were made by increasing the font size of Figure 7 and bringing greater clarity to our results. We have provided quantitative RT-qPCR data of the reduction of Bhlhe22 in our siRNA treated cultures as evidence of a successful knockdown of Bhlhe22, and believe that immunostaining is not necessary in this regard. Co-transfection with a GFP plasmid and siRNA molecules has been previously been demonstrated as a means for identifying transfected cells in primary neuronal cultures . The co-transfection efficiency of this method has been demonstrated in human embryonic stem cells by Moore et al., 2010  were converted from the mouse genome (mm9) to the human genome (hg38). 89.6% of robust cerebellar enhancers with a putative target gene are located at orthologous regions distal to the same gene promoters indicating conservation between mouse and humans and bringing credence to our analysis. The results of this analysis can be found in Supplementary Table 4 and in the revised manuscript in the section ‘Active cerebellar enhancers are enriched for common and de novo genetic variants associated with autism spectrum disorder.’ (pp. 23)For the identification of CDC42BPB, it would have been nice to link this back to expression data over time and to provide more detail on the potential transcription factors involved.We thank the reviewer for this suggestion and agree that it is relevant to our study to examine CDC42BPB expression in the developing mouse expression time course. In our revised manuscript, we describe CDC42BPB expression throughout embryonic and postnatal development. We also profile expression in the cell-types of the cerebellum using scRNA-seq in mouse and human samples . Finally, we confirm that the enhancer predicted to regulate CDC42BPB is associated with the same gene in the mouse and located on the same chromosome and at a similar distal genomic location in the human. (pp. 25)Finally, given that this is a very important dataset for the scientific community, I wish it would be easier for others to interact with the data rather than just depositing data files into GEO. It would have also been really helpful to provide supplementary tables listing enhancer elements and associated transcription factors as well as nearby genes that the authors have identified. Currently, it is not transparent which lists of genes etc were used for the various analyses shown.We agree with the reviewer that this is a very important dataset for the scientific community and have made efforts to make our data more accessible. An online resource(https://goldowitzlab.shinyapps.io/developing_mouse_cerebellum_enhancer_atlas/) was created that can be used to browse, curate and export information regarding cerebellar enhancers and their putative target genes identified in this study. In the revised manuscript, we have also provided additional supplementary tables listing enhancer elements, their associated transcription factors and their associated target genes.


These parameters help predict membrane performance and guide secondary parameters such as membrane life, efficiency, and product purity. The model results agree with the experimental values for a selected membrane, with an average absolute relative error of 6.1%, 4.2%, and 3.2% for CO2 permeance, CH4 permeance, and CO2/CH4 selectivity, respectively. The results indicate that the model can predict values at other membrane development conditions.Membranes for carbon capture have improved significantly with various promoters such as amines and fillers that enhance their overall permeance and selectivity toward a certain particular gas. They require nominal energy input and can achieve bulk separations with lower capital investment. The results of an experiment-based membrane study can be suitably extended for techno-economic analysis and simulation studies, if its process parameters are interconnected to various membrane performance indicators such as permeance for different gases and their selectivity. The conventional modelling approaches for membranes cannot interconnect desired values into a single model. Therefore, such models can be suitably applicable to a particular parameter but would fail for another process parameter. With the help of artificial neural networks, the current study connects the concentrations of various membrane materials  and the partial pressures of carbon dioxide and methane to simultaneously correlate three desired outputs in a single model: CO Conventionally, chemical absorption was used for CO2 removal, resulting in high capital and energy costs. New technologies, such as chemical looping [Carbon dioxide –methyl ethyl acrylate as a free volume enhancer in the Pebax® matrix and graphene oxide nanosheets as the nanofiller to improve both CO2 permeability and CO2/N2 selectivity. Long-term stability over 100 days was observed without a significant decline in separation performance [Researchers are developing various MMMs by incorporating third components: polymers and fillers. Miri et al. synthesized quaternary MMMs using Pebax@1657/glycerol/polyethylene glycol 200/iron-nickel oxide. They found that 3 wt.% of nanoparticle embedding was the optimum point that offered the best performance for COectivity . Mohshimmbranes. . Shin etformance .The lab-scale analysis of the performance of MMMs could lead to the development of demonstrator/pilot units and to the later development of a commercial membrane system. For any such development, the lab-based research results must be correlated to the input (process) parameters, which can be reliably extended to industrial/end-user conditions without further experimentation . Hence, In recent years, neural network modeling of membrane processes has received enormous research interest. Feedforward neural networks are well-known instances of black-box models capable of accurately predicting the behaviors of complex surfaces and hybrid materials in a variety of chemical engineering processes ,34. Some2/CH4 gas mixtures not tested during experimentation.This study proposes an artificial neural network modeling approach for the multiparameter correlation of facilitated-transport mixed matrix membranes. The model can theoretically predict a membrane’s permeability for all gases using only the material composition of the membrane and the pressure of each gas. The experimental permeance and the selectivity of a facilitated transport mixed matrix membrane developed for carbon dioxide removal from natural gas are correlated. The model optimizes the correlation for the above three parameters simultaneously, seeking inputs from the concentrations of the materials used in membrane casting and the pressure of each gas. The modeled results show an excellent agreement with the experimental values. They indicate that the model can be confidently used for predicting membrane performance (permeance and selectivity) for other conditions and for COThe pure polymeric membranes and facilitated transport mixed matrix membranes were developed by using polyethersulfone (PES), a carbon molecular sieve (CMS) and diethanolamine (DEA). N-methyl-2-pyrrolidone (NMP) was used as a solvent. Membrane synthesis, characterization, and gas permeability were reported elsewhere ,46,47. T2 permeance, CH4 permeance, and CO2/CH4 selectivity, were included in the developed ANN. Equation (1) was used to normalize all experimental data against the maximum value for each variable to minimize the effect of numerical values on the model and to avoid asymmetry in data correlation. The experimental data were distributed randomly in training (50%), validation (15%), and testing (35%) sets:maxX is the maximum value for variable X.The ANN for the predictive membrane model was developed in a MATLAB environment. The experimental data were collected from our previous work on single gas permeation through PES-CMS-DEA facilitated transport mixed matrix membranes, as discussed in OX and TX are the model’s output and the desired target values  for each required output.The number of neurons in the output layer was fixed at a value of 3 to correlate directly with the number of desired outputs, whereas the number of neurons in the ANN’s hidden layer was optimized . The corSimilarly, the R-square values of the model correlation were compared against the number of neurons. Once the number of neurons in the hidden layer was selected, the designed neural network was executed to determine its output values. The minimization of mean square error (MSE) in the experimental data pool was the objective function . It is w4) and carbon dioxide (CO2), with respect to the pressure of each gas for a pure polymeric membrane, is shown in 4 permeance is well correlated at all pressures except 2 bar. However, the modeled CO2 permeance does exhibit small deviations from all values. At a pressure of 2 bar, the trend is slightly over-predicted to counter the under-prediction of CH4 permeance, as both values are interlinked through the selectivity. The slight overprediction of the CO2 permeance value at a pressure of 4 bar can be attributed to experimental errors. The overall correlation for CO2 permeance is in line with the generic best fit observed for the rest of the experimental values for a pure polymeric membrane.The permeance of methane , with nominal under-predictions at high-pressure conditions (10 bars). Nevertheless, the deviations seen in the model correlations are well distributed across the experimental values and are within the range of experimental error.2 permeance at pressure values higher than 4 bars. This decrease in permeance at lower pressures stems from the gas’s decrease in solubility with increasing pressure. This follows the predicted behavior of the dual-mode sorption model, which is the combination of Henry’s law associated with the dense region, and Langmuir behavior, which is related to microvoids in the polymer [2 permeance values with the addition of the amine and filler are well correlated by the model, with a decrease in AARE% values, which indicates the model’s capacity to predict CO2 and CH4 permeance at other conditions that are not tested experimentally.Interestingly, the model can chart the sudden drop in CO polymer . Further polymer . Moreove2 and CH4 permeance and selectivity versus neural network predictions are plotted in 2 = 0.9997) between experimental and predicted values was found by Dashti et al., when they predicted the performance of nanocomposite membranes for H2 separation from CO2, C3H8, and CH4 by using artificial neural networks [The experimental results for COnetworks .2 and CH4 permeances and selectivity. Compared to the conventional models, this approach can crossmatch and optimize the results, while comparing three desired outputs for the best correlation. Although neural networks are not reproducible, we have tested the approach several times with a different range of parameters and normalized values and observed that the results are somewhat similar. We suggest that any future development in the field should accommodate linear variances and avoid over-fitting to the experimental values. The primary goal of a modeling approach is not to map the experimental values but to develop a good relationship that can predict values for non-tested conditions.The model’s correlation can be further improved by adding another hidden layer that connects the first hidden layer to the output layer in the developed neural network model. However, such changes can impart a certain over-fitting to the results. Although over-fitted results are deemed good for experimental-based studies where the model’s results should show minimal errors, the results may not be viable enough to be extended beyond the range of regressed parameters. This study shows an artificial neural network-based approach for correlating COX as follows.Detecting effective variables on gas permeability is useful for designing, selecting, and managing gas separation membrane operations. To assess the strength of the link between output and input characteristics, the Cosine Amplitude Method (CAM) can be used. In this manner, all data pairings are stated in a general X-space . The datX, each of the elements (iX) is a vector of length of m.In the data array ix and jx. The calculated ijr is a pairwise comparison of two X-space items [As a result, each data pair can be demonstrated by a specific location in m-dimensional space. Equation (4) provides the strength of the relationship between the data pairs ce items .(5)rij=2 permeance increases due to the affinity of CO2 with amine. Moreover, the sensitivity analysis results showed that pressure and amine concentrations are the most influential variables on gas permeability through facilitated transport mixed matrix membranes. These results are in agreement with the literature [terature .2 permeance, 4.2% for CH4 permeance, and 3.2% for CO2/CH4 selectivity, the model not only helps to correlate experimental data but is also capable of predicting intermittent values for membrane performance parameters for a CO2/CH4 feed mixture.The current study presents a multiparameter modeling approach for simultaneously determining gas permeances and selectivity for a facilitated transport mixed matrix membrane. The model is flexible enough to accommodate any number of gaseous mixtures and their selectivity values. The inputs to the proposed model consist of the weight concentrations of the materials used for membrane casting and the gas pressure—giving the model an edge over classical modeling approaches that can only predict the permeance of a single gas at a single time—and correlated the values to it. Moreover, the model can be easily modified for different experimental data sets. The model results show an excellent correlation with the experimental values for a facilitated mixed matrix membrane designed for carbon capture. With an average absolute relative error of 6.1% for CO