Many complex functions carried out by mammalian brains arise through the concerted efforts of different cell types in the neocortex. The cortex is organized during development into a laminar structure, with each layer composed of a distinct set of cell types with different morphological, electrophysiological, and connectional properties. These diverse cellular phenotypes are established and maintained by complex interactions of sequence-specific transcription factors (TFs), and they are reinforced by chromatin modifiers. Defining cell-type specific chromatin signatures will provide an understanding of the regulatory landscapes that influence transcription, as well as identification of putative cell type-specific cis-regulatory elements. Most variation associated with phenotypic differences in humans identified through genome-wide association studies is located in non-coding regions of the genome (Albert and Kruglyak, 2015; Tak and Farnham, 2015). However, the exact function of these polymorphisms and the cell types that they affect is established only in a minority of cases (Soldner et al., 2016). Assigning regulatory elements to cell types provides insight into their potential function (Corces et al., 2016). In addition, specific regulatory elements could be used to build cell-type specific genetic tools similar to those created by careful selection of well-studied regulatory regions near genes (Bou-Gharios et al., 1996; Pinkert et al., 1987) or by large genomic screens (Shima et al., 2016).

Until recently, two major hurdles have restricted access to epigenetic landscapes of specific primary cell types: selective access to those types, and the large numbers of cells required as an input for epigenomic characterization using ChIP-seq, DNAse-seq, or FAIRE-seq. Recent development of a number of Cre-recombinase transgenic lines allowed access to specific cortical cell types (Harris et al., 2014), and has enabled transcriptomic characterization and classification of cells from the visual cortex using single-cell RNA-seq (scRNA-seq) (Tasic et al., 2016). Though these transgenic lines have differing degrees of cell type heterogeneity, they provide a platform for accessing populations of related cell types from different layers of the cortex. New techniques, including ATAC-seq (Buenrostro et al., 2013) and THS-seq (Sos et al., 2016), enable measurement of chromatin accessibility from a few hundred or even single cells (Buenrostro et al., 2015; Corces et al., 2016; Cusanovich et al., 2015; Lara-Astiaso et al., 2014).

Recent studies have started to probe the correspondence between transcription and epigenetics in cortical cell types labelled by the expression of somatostatin (Sst), parvalbumin (Pvalb), and calcium/calmodulin-dependent protein kinase II alpha (Camk2a) from whole mouse cortex (Mo et al., 2015), rod and cone photoreceptor cells in retina (Mo et al., 2016), and in the developing and adult cerebellum (Frank et al., 2015). These studies have profiled the chromatin accessibility landscapes of broad cell classes, but did not examine layer-specific differences in the neocortex. Here, we take advantage of pan-GABAergic and layer-specific glutamatergic Cre-driver lines, low-input ATAC-seq, and fluorescence-activated cell sorting (FACS) to investigate chromatin accessibility landscapes in a specific cortical region, the adult mouse visual cortex (VISp).

Each layer of VISp contains distinct populations of glutamatergic cells with different transcriptional, functional, and connectional properties: layer 4 cells are the primary recipients of the visual signals from the dorsal portion of the lateral geniculate nucleus (LGd); layer 2/3 cells receive signals from L4, and communicate with L5 cells within the same cortical region and other cortical regions; layer 5 cells are highly diverse, and include cells that project to many other cortical and subcortical regions; and the layer 6 cells we examine in this study project to the thalamus (Bortone et al., 2014; Sorensen et al., 2015). Thus, within even a small region of the cortex, there is great diversity of cell types, each of which carries out a distinct transcriptional program (Tasic et al., 2016). However, the regulatory programs that produce these different transcriptional and cellular phenotypes are not known. In order to define potential regulatory elements and corresponding transcriptional regulators, we examined chromatin landscapes of these cell classes.

We found broad differences between GABAergic and glutamatergic cell types, as well as layer-specific differential chromatin accessibility in glutamatergic cell types that correlated with differential gene expression. Putative regulatory elements were identified through TF motif searches and comparisons to existing ChIP-seq datasets for each cell class. With these components, we built a putative regulatory network of TF binding sites near layer-specific TF genes that may govern layer-specific transcriptomic states. This network suggests that Cux1/2, Foxp2, Nfia, Pou3f2, and Rorb are key regulators for the maintenance of molecular identity of deep layer and upper-layer cortical cells.

In the mouse visual cortex, at least 49 transcriptomic cell types have been defined by single cell RNA-seq (Tasic et al., 2016). As a first step in uncovering layer-specific regulatory networks, including regulatory transcription factors and their targets, we performed ATAC-seq on small populations of cells derived from layer-specific transgenic lines (Figure 1). Comparisons of our data to previously published ChIP-seq (Figure 2) and scRNA-seq (Figures 4 and 5) enabled identification of potentially significant regulatory sites. By restricting our analysis to a single cortical region, the mouse visual cortex, and through the use of layer-specific Cre lines, our data provide examination of the chromatin accessibility state at the resolution of layer-specific cortical cell classes (Figures 9 and 10). Previous studies have examined the state of cells across whole brain (Yue et al., 2014), in the whole cerebellum (Frank et al., 2015), or in pooled GABAergic or glutamatergic cells across all layers in the entire mouse cortex (Mo et al., 2015). The cell class resolution of our study allowed us to identify open chromatin sites that are unique to each layer-specific cell class, and assign accessible sites from previous studies on more heterogeneous populations to more specific cell classes (Figure 3—figure supplements 3 and 4). In doing so, we were also able to build a network of transcription factor interactions that may be responsible for maintaining the identity of layer-specific cell classes (Figure 7).

Analysis of TF motif enrichment and accessible motif targets were used to identify putative regulatory TFs for cell classes from L2/3, L4, and L6 (Figure 6). However, distinct transcription factor families for L5 were not clearly identified by our analysis. This result may be due to the heterogeneity of cell types that are present in the L5 cell class defined by the Rbp4-Cre line. Rbp4-Cre labels at least nine transcriptomic cell types, including Layer 5a cells, which show some similarity to L4 cell types, and L5b types, which share some transcriptomic patterns with L6 cell types (Tasic et al., 2016). This heterogeneity may mask detection of distinct networks that regulate the cell types within L5, which could be uncovered in future studies that will require new Cre-driver lines or other labeling strategies to access L5 subtypes. We also note that L6 includes several transcriptomic cell types that were not surveyed in the current study: only corticothalamic L6a cells are labeled by the Ntsr1-Cre driver line (Tasic et al., 2016).

Development of laminar cell type identities occurs through several waves of differentiation driven by sequential changes in chromatin and transcriptional landscapes (Telley et al., 2016). In our analysis, we see largely distinct networks of interactions that define upper and lower layers of the cortex. Previous studies have shown that layer-specific populations arise at distinct times in development. Several of the TFs we predict to be key regulators of layer-specific transcription have been examined for their function in cortical development. Loss of Tbr1, which is a key lower-layer regulator expressed by lower-layer neurons, results in gross abnormalities in cortex. Curiously, in rostral cortical regions, Tbr1 knockouts appear to lack L6, while other layers appear normal, whereas in caudal regions, Tbr1 knockouts present a generally disorganized cortical structure (Hevner et al., 2001). These findings suggest that independent networks may be involved in upper versus lower laminar development in these regions. Double knockout of Pou3f2 and Pou3f1, which we predict are key regulators in upper cortical layers, results in incorrect migration of upper cortical neurons (layers 2/3 and 4), but not lower layer neurons (McEvilly et al., 2002; Sugitani et al., 2002). Loss of key TFs can also affect layer-specific cortical projection patterns: knockdown of Cux1 ablates ipsilateral cortico-cortical projections of upper-layer neurons (Rodríguez-Tornos et al., 2016), and loss of Tbr1 results in truncated corticothalamic projections by L6 neurons (Hevner et al., 2001).

While these broad phenotypes show the importance of these factors in the development of cortical structure and function, specific interactions previously studied in development are frequently not found in our accessibility data from adult cortex. For example, a previously described regulatory interaction between RORB and its biding site near Pou3f2, which results in down-regulation of Pou3f2 during laminar development of L2/3 and L4 (Oishi et al., 2016), is not part of our putative regulatory network (Figure 7). In single-cell RNA-seq of adult visual cortex, we often see simultaneous expression of Rorb and Pou3f2 in Layer 2/3 and Layer 4 cells (Tasic et al., 2016, Figure 6—figure supplement 2), suggesting that this interaction may not be present in the adult visual cortex. We investigated this discrepancy between adult and developmental states by examining the RORB target sites that were previously described near the Pou3f2 gene (Figure 11). We found very little chromatin accessibility at or around these RORB-binding sites, which suggests that the chromatin state in adult cells prevents this interaction, thus allowing coexpression of Rorb and Pou3f2 after differentiation (Figure 6—figure supplement 2). Our network also lacks a previously reported interaction between POU3F2 and Foxp2 through a highly conserved POU3F2-binding site in the Foxp2 gene that is mutated in human compared to most other species (Maricic et al., 2013). In reporter assays, the conserved POU3F2 motif, which is present in mouse, acts as a cis-regulatory enhancer element (Maricic et al., 2013). In adult visual cortex, the chromatin is inaccessible near this POU3F2 motif (Figure 11), suggesting that POU3F2 does not bind to this site to regulate Foxp2 expression. This is consistent with gene expression data from adult cortex, where we see no expression of Foxp2 in cell classes that express Pou3f2 (layers 2–5, Figure 6—figure supplement 2). Further chromatin accessibility experiments would be needed to determine if this regulatory element is accessible during development or other cell types in visual cortex, or in other brain regions.

Figure 11

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A recent publication has shown that FOXP2 directly suppresses Mef2c expression in the striatum, where knockout of Foxp2 allows higher Mef2c expression, which, in turn, suppresses synaptogenesis (Chen et al., 2016). Our network analysis suggests that FOXP2 binding may also repress Mef2c in lower layers of the adult visual cortex (Figure 7). We examined the FOXP motifs near Mef2c exon three that were identified in this recent study (Figure 12B). In adult cortex, we observe only a modest and statistically insignificant decrease in their accessibility in lower layers. Thus, we expanded our search to all FOXP motif-containing peaks near Mef2c in the cortex (Figure 12A). We found two peaks upstream of the Mef2c TSS that are less accessible in L6 than in upper layers. Therefore, FOXP2 may regulate Mef2c by binding different sites in these different tissues: sites upstream of TSS in the cortex, and sites near exon three in the striatum. This hypothesis could be tested by future experiments that measure the chromatin accessibility landscape of the striatum, and genome-wide interaction sites of FOXP2 by ChIP-seq in striatum and cortex, as well as the effect of decreasing FOXP2 levels through cell-type specific knockout or knockdown on these chromatin landscapes.

Figure 12

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These comparisons emphasize the importance of developmental, anatomical, and cell type context in studies of chromatin accessibility and transcriptomic regulation. We note that several transcription factors identified in our network are known to be expressed in development. Some of their target sites in development may be retained in adulthood, while others may be different due to changes in accessibility. Future studies performed on layer-specific cell classes in development will be needed to determine how changes to the epigenetic landscape affect transcription during development.

Our sampling strategy utilized only Cre lines that yielded at least 500 tdTomato-positive FACS-isolated cells per animal. This limited our genetic access to relatively abundant cell classes in the visual cortex, all of which label multiple cell types that can be distinguished by single-cell RNA-seq (Figure 1C). Because GABAergic cell types are much less abundant in the neocortex than glutamatergic cells, we sampled only a single, pan-GABAergic cell line, Gad2-IRES-Cre. A previous study was able to examine Pvalb+ and Vip+ (but not Sst+ or Ndnf+) GABAergic cell type classes using the whole mouse cortex (Mo et al., 2015) by utilizing the INTACT nuclear labelling technique. The higher spatial resolution of our glutamatergic data, but lower cell-type resolution among GABAergic types makes our study complementary to this work. Further advances in low-input chromatin accessibility assays, low input ChIP-seq methods, and improvements in Cre-driver specificity will increase our ability to analyze the epigenomic state of specific neural cell types.

Our integrated analysis of chromatin accessibility, gene expression, and TF motif enrichment provides an unprecedented look at the key interactions that underlie layer-specific transcriptomes. We were able to identify layer-wide patterns of TF accessibility from analysis of the entire set of peaks from each layer, and found interactions that may drive expression of key transcriptomic regulators Nfia and Cux1 by looking at coordinated TF expression and motif accessibility. Further research into the roles that these and other TFs play in development and maintenance of layer-specific cell classes will enhance our understanding of the functional consequences of these transcriptomic programs. Conditional loss-of-function experiments in specific cell classes at specific times in development or adulthood would help to disentangle the specific roles from more global roles of these TFs. For instance, constitutive, homozygous loss-of-function of Foxp2 is lethal by postnatal day 21, and causes developmental delays and motor dysfunction that may interfere with studies of adult brain function (French et al., 2007), whereas heterozygous Foxp2 knockout mice display few differences in cortical structure (Fujita et al., 2008). Our network suggests that Foxp2 could be a key regulator of L6-specific transcriptional program in VISp. Investigation of this role of Foxp2 would require generation of a conditional knockout in specific cortical cell populations, and perhaps at specific times in development or adulthood (for example, through conditional alleles combined with Cre/CreER lines). Another area that would greatly benefit from further study is assessing the correspondence between chromatin modifications and chromatin accessibility. New techniques for ChIP-seq enable enrichment of modified histones from as few as 500 cells (Lara-Astiaso et al., 2014), which may allow the correspondence between accessibility and chromatin modifications in specific cell classes to be studied directly. The synthesis of these data modalities will further our understading of the mechanisms of cell type-specific transcriptional regulation. This new knowledge will be useful for building new cell-type specific genetic tools, and for testing the reprogramming potential of layer-specific cell classes (De la Rossa et al., 2013).

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Author details

1. Lucas T Gray

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   LTG, Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-8814-6818

2. Zizhen Yao

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   ZY, Software, Formal analysis, Visualization, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1210-4195

3. Thuc Nghi Nguyen

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   TNN, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6466-5883

4. Tae Kyung Kim

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   TKK, Investigation, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-9646-5969

5. Hongkui Zeng

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   HZ, Supervision, Funding acquisition, Project administration, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0326-5878

6. Bosiljka Tasic

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   BT, Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   bosiljkat@alleninstitute.org

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6861-4506

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