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Cis-regulatory basis of sister cell type divergence in the vertebrate retina

  1. Daniel P Murphy
  2. Andrew EO Hughes
  3. Karen A Lawrence
  4. Connie A Myers
  5. Joseph C Corbo  Is a corresponding author
  1. Washington University School of Medicine, United States
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Cite this article as: eLife 2019;8:e48216 doi: 10.7554/eLife.48216

Abstract

Multicellular organisms evolved via repeated functional divergence of transcriptionally related sister cell types, but the mechanisms underlying sister cell type divergence are not well understood. Here, we study a canonical pair of sister cell types, retinal photoreceptors and bipolar cells, to identify the key cis-regulatory features that distinguish them. By comparing open chromatin maps and transcriptomic profiles, we found that while photoreceptor and bipolar cells have divergent transcriptomes, they share remarkably similar cis-regulatory grammars, marked by enrichment of K50 homeodomain binding sites. However, cell class-specific enhancers are distinguished by enrichment of E-box motifs in bipolar cells, and Q50 homeodomain motifs in photoreceptors. We show that converting K50 motifs to Q50 motifs represses reporter expression in bipolar cells, while photoreceptor expression is maintained. These findings suggest that partitioning of Q50 motifs within cell type-specific cis-regulatory elements was a critical step in the evolutionary divergence of the bipolar transcriptome from that of photoreceptors.

https://doi.org/10.7554/eLife.48216.001

eLife digest

Humans see the world through a light-sensitive tissue at the back of the eye called the retina, which is made up of three layers that each contain specific cell types. The layers form a circuit, with light-sensing photoreceptor cells in the outermost layer connected to bipolar cells in the middle layer, which connect to the brain via specialized cells in the innermost layer. Photoreceptors and bipolar cells share similar characteristics and are thought to be ‘sister cells’ which evolved from a common ancestral cell type. However, it is not well understood how these two cells types diverged during evolution.

Every cell type has a specific role, which is largely determined by the set of genes that it switches on or off. Specialized regions of DNA, called enhancers, determine whether a gene is turned on or off in a particular cell type. In each cell, DNA strands are bundled together with proteins into a coiled structure known as chromatin. In some cells, a particular enhancer may be ‘shut down’ and rendered inactive on account of being tightly packed within chromatin. Whilst in other cells, the same enhancer may be ‘open’ and ready for action. For a given cell type, which genes are turned on is determined, in part, by which enhancers are open.

One way to distinguish between cells is by examining how their chromatin is packaged to see which enhancers are open. Researchers have previously characterized the chromatin structure of photoreceptor cells, but the structure of chromatin in bipolar cells, and how it compares to that of photoreceptors, remained unknown. Now, Murphy et al. have examined the chromatin profile of bipolar cells from the mouse retina in order to gain a better understanding of how these two cell types may be evolutionarily related.

The analysis revealed that although bipolar and photoreceptor cells switch on different sets of genes, the enhancers open in each cell type are very similar. Despite this similarity, Murphy et al. were able to detect subtle differences in short sequences of DNA, known as motifs, present in bipolar and photoreceptor enhancers. Further experiments showed that one of these motifs may be responsible for turning photoreceptor genes off in bipolar cells. This motif therefore appears to play a critical role in distinguishing photoreceptors from bipolar cells.

This comparison of photoreceptor and bipolar cells has provided a possible mechanism whereby photoreceptor and bipolar cells diverged in evolution from a single common ancestral cell type. This insight may help explain how complex organisms with many cell types may have evolved from a single-cell ancestor long ago.

https://doi.org/10.7554/eLife.48216.002

Introduction

Complex tissues require the coordinated activity of a wide array of specialized cell types. It has been proposed that cellular diversity arises in the course of evolution through a ‘division of labor’ process, in which a multifunctional ancestral cell type gives rise to descendant cell types with divergent and novel functions (Arendt et al., 2009; Brunet and King, 2017). Such descendants are often referred to as ‘sister’ cell types, and typically share a range of morphological, functional, and transcriptional features while at the same time displaying key differences (Arendt, 2003; Arendt et al., 2016). A canonical example of sister cell types are mammalian retinal photoreceptors and bipolar cells (BCs) (Arendt, 2008; Lamb, 2013). In a typical vertebrate retina, photoreceptors synapse onto bipolar cells, which, in turn, synapse onto retinal ganglion cells that send their axons to the brain. Bipolar cells therefore constitute the central interneuronal cell class in the vertebrate retina. In mice, an array of 15 distinct bipolar cell types, broadly categorized as ON and OFF based on their response to light onset/offset, serve as a scaffold upon which the complex information-processing circuitry of the retina is built (Euler et al., 2014). In this paper, we refer to photoreceptors and bipolar cells as cell ‘classes’, since they each comprise multiple distinct cell types.

During retinal development, photoreceptors and bipolar cells arise from the same population of OTX2-expressing progenitor cells (Koike et al., 2007; Wang et al., 2014; Emerson et al., 2013), share a similar elongate morphology (Lamb, 2013), and possess the molecular machinery required for ribbon synapse formation, a structure not found in any other retinal cell class (tom Dieck and Brandstätter, 2006). In some vertebrate species, a subset of bipolar cells exhibit additional photoreceptor-like features, including localization of their cell bodies in the outer nuclear layer and the presence of an inner segment-like structure known as Landolt’s club, which extends from the dendrite to the outer limiting membrane and contains a 9+0 cilium (Tauchi, 1990; Hendrickson, 1966; Locket, 1970; Quesada and Génis-Gálvez, 1985). These ‘transitional’ cell types point to the evolutionary origin of bipolar cells from photoreceptors (Arendt, 2008; Lamb, 2013).

Both shared and divergent features of sister cell types are mediated by the transcriptional regulatory networks that govern gene expression in each cell type. In vertebrates, photoreceptors and bipolar cells express the paired-type homeodomain (HD) TFs CRX and OTX2, which are master regulators of gene expression in both cell classes (Omori et al., 2011; Nishida et al., 2003; Hennig et al., 2008; Corbo et al., 2010). A third paired-type HD TF, VSX2, is expressed specifically in bipolar cells and is required for bipolar fate (Livne-Bar et al., 2006; Liu et al., 1994). Paired-type homeodomains recognize a core ‘TAAT’ motif, with additional specificity conferred by amino acids in positions 47, 50, and 54 of the homeodomain (Treisman et al., 1989; Noyes et al., 2008; Berger et al., 2008) In particular, a lysine at position 50 (K50, as found in CRX and OTX2) favors recognition of TAATCC, whereas a glutamine (Q50, as found in VSX2) favors recognition of TAATTA/G. Thus, substitution of a single amino acid in the HD toggles the TF’s binding preference for the nucleotides 3’ of the TAAT core (Treisman et al., 1989). Various bHLH TFs, which recognize E-box motifs (CANNTG), also play important roles in photoreceptor and bipolar cell gene expression programs (Tomita et al., 2000; Akagi et al., 2004). For example, NEUROD1 is required for photoreceptor survival, and BHLHE22 and BHLHE23 are required for development of OFF cone bipolar cells and rod bipolar cells, respectively (Bramblett et al., 2004; Feng et al., 2006; Huang et al., 2014; Pennesi et al., 2003).

Our lab has previously shown that the cis-regulatory elements (CREs; i.e., enhancers and promoters) of mouse rods and cones are strongly enriched for K50 HD motifs as well as moderately enriched for Q50 HD and E-box motifs (Hughes et al., 2017). In addition, we recently used a massively parallel reporter assay to analyze the activity of thousands of photoreceptor enhancers identified by CRX ChIP-seq and found that both K50 HD and E-box motifs are positively correlated with enhancer activity in photoreceptors while Q50 HD motifs have a weakly negative correlation with enhancer activity (Hughes et al., 2018). In contrast, studies of individual reporters have shown that Q50 HD motifs mediate weak activation of expression via RAX, and that RAX can either enhance or suppress the transactivation activity of CRX, depending upon RAX expression levels (Irie et al., 2015; Kimura et al., 2000). Thus, Q50 HD motifs appear to have both positive and negative effects of photoreceptor enhancer activity, depending on context. In contrast, Q50 motifs in bipolar cells appear to be strongly repressive when bound by VSX2, which has been proposed to inhibit the expression of photoreceptor genes in bipolar cells (Livne-Bar et al., 2006; Dorval et al., 2006; Clark et al., 2008). The opposing effects on transcriptional activity mediated by K50 and Q50 motifs suggest that even subtle changes in HD binding sites may mediate major differences in gene expression. Indeed, a recent study in Drosophila showed that single base pair substitutions that interconvert Q50 and K50 half-sites within dimeric motifs are sufficient to switch the specificity of opsin expression within photoreceptor sub-types (Rister et al., 2015).

Photoreceptors and bipolar cells offer an attractive model system in which to examine the mechanisms of cis-regulatory divergence in evolution and development, but the cis-regulatory landscape of bipolar cells is currently unknown. To elucidate the cis-regulatory grammar of bipolar cells we isolated specific bipolar cell populations from mouse retina and obtained profiles of open chromatin and gene expression. By comparing these datasets to matching data from mouse rod and cone photoreceptors we identified differential enrichment of Q50 motifs in photoreceptor-specific enhancers and a corresponding enrichment of E-boxes in bipolar-specific enhancers. We propose that the differential partitioning of Q50 motifs in photoreceptor and bipolar enhancers was a key evolutionary innovation contributing to transcriptomic divergence between the two cell classes.

Results

Photoreceptors and bipolar cells exhibit divergent transcriptional profiles

To obtain cell class-specific transcriptome profiles of mouse bipolar cells, we used fluorescence-activated cell sorting (FACS) to purify bipolar cell populations from adult mice. We first isolated all bipolar cells using Otx2-GFP mice. This line harbors a GFP cassette knocked into the endogenous Otx2 locus (Fossat et al., 2007). Adult Otx2-GFP mice display high-level GFP in bipolar cells and low-level expression in photoreceptors (Figure 1B). To purify ON and OFF bipolar cells separately, we crossed Otx2-GFP mice with a Grm6-YFP line, in which YFP is driven by the Grm6 promoter and expressed only in ON bipolar cells (Morgan et al., 2011). In the double transgenic mice (Otx2-GFP+; Grm6-YFP+), ON bipolar cells co-express GFP and YFP and can be separated from OFF bipolar cells based on fluorescence intensity (Figure 1B). We subjected OFF bipolar cells to a second round of sorting to maximize purity from the adjacent weakly fluorescent photoreceptor population. Purity of bipolar cell populations was confirmed by RT-qPCR which showed enrichment of ON- and OFF-specific genes in the appropriate populations and depletion of markers for other retinal cell classes as compared to whole retina (Figure 1C and Figure 1—figure supplement 2). We then used RNA-seq to profile gene expression in purified populations of bipolar cells, obtaining highly reproducible profiles across biological replicates (Figure 1—figure supplement 1).

Figure 1 with 2 supplements see all
Isolation of bipolar cell populations from adult mouse retina.

(A) Left, Histological section of adult mouse retina stained with 4′,6-diamidino-2-phenylindole (DAPI), to highlight nuclei. ONL = outer nuclear layer, INL = inner nuclear layer, GCL = ganglion cell layer. Right, Schematic depiction of the location and relative abundance of photoreceptor and bipolar cell types. Percentage representation for each cell population in the mouse retina is shown, based on Jeon et al. (1998). (B) Left, Histologic sections of retina from transgenic mice expressing fluorescent marker proteins. In Otx2-GFP mice, GFP is strongly expressed in all bipolar cells, and weakly in photoreceptors. In Grm6-YFP mice, YFP is expressed exclusively in ON bipolar cells. Bipolar cell populations in the INL are linked to their position on FACS scatterplots with arrows (FSC = Forward Scatter). (Top). All bipolar cells from Otx2-GFP+ mice are boxed in green. Bottom: in Otx2-GFP+;Grm6-YFP+ mice, ON bipolar cells (purple box) are separable from OFF bipolar cells (gold box) based on intensity of fluorescence. (C) RT-qPCR analysis of retinal cell class markers (Macosko et al., 2015) in sorted ON (purple) and OFF (gold) bipolar cells normalized to expression in whole retina (blue). Vsx2 = bipolar cells; Gnat1 = rod photoreceptors; Lhx1 = horizontal cells; Pax6 = amacrine, ganglion, horizontal cells; Rlbp1 = Müller glia; Slc17a6 = ganglion cells; Grm6 = ON bipolar cells; Tacr3 = OFF bipolar cells (Types 1A, 1B, and 2).

https://doi.org/10.7554/eLife.48216.003

To define the transcriptional differences between photoreceptor and bipolar cells we compared RNA-seq data from bipolar cells to similar data from wild-type rods and Nrl-/- photoreceptors previously generated in our lab (Hughes et al., 2017). We used Nrl-/- photoreceptors as a surrogate for blue cones (i.e., Opn1sw-expressing cones) since mouse photoreceptors lacking Nrl transdifferentiate into blue cones during development (Daniele et al., 2005; Nikonov et al., 2005). We identified a total of 5259 genes with at least a two-fold difference in expression between bipolar cells and either rods or blue cones (FDR < 0.05) (Supplementary file 5). Despite the large number of differentially expressed genes, published single-cell RNA-seq profiles indicate that the bipolar cell transcriptome is more similar to that of photoreceptors than to that of any other retinal cell class (Macosko et al., 2015).

To evaluate the functional differences between photoreceptor and bipolar cell gene expression programs we compared the top ~ 30% most differentially expressed genes in each cell class (832 bipolar cell, 818 photoreceptor) using the gene ontology (GO) analysis tool, PANTHER (Thomas et al., 2003). Top bipolar-enriched GO terms were typical of many neuronal cell types and related to aspects of synaptic transmission, while photoreceptor-enriched GO terms mainly related to light-sensing (Supplementary file 6). Next, we compared the list of differentially expressed genes to a database of mouse TFs (AnimalTFDB3.0; Hu et al., 2019), which revealed that 394 of the differentially expressed genes encode putative transcriptional regulators (Supplementary file 5). These include TFs known to be responsible for controlling gene expression in rods (Nrl, Nr2e3, Neurod1), cones (Thrb), bipolar cells (Vsx2, Neurod4), or both cell classes (Crx). Nearly one-third (176) of differentially expressed TFs are members of the zinc finger (ZF) family, many of which are more highly expressed in rods compared to bipolar cells but not in blue cone compared to bipolar cells. Conversely, of the top 10% differentially expressed TFs, the majority (25 of 35) are more highly expressed in bipolar cells compared to either rods or cones, and of these, most (16 of 25) are classified as HD, ZF or bHLH. Thus, the transcriptomes of bipolar cells and photoreceptors are significantly divergent, despite various functional and morphological similarities between the two cell classes.

In contrast, comparison of the transcriptomes of ON and OFF bipolar cells identified only 680 genes that were differentially expressed by at least two-fold (317 ON- and 363 OFF-enriched at FDR < 0.05; Supplementary file 5). This figure is less than half of the number of differentially expressed genes identified between rods and blue cones (1,471), indicating that the transcriptomes of the two categories of bipolar cells are quite similar.

Of note, a recent study by Shekhar et al. (2016), described single-cell expression profiles for bipolar cell types using Drop-seq. To compare our results with this study, we pooled data from Shekhar et al. across inferred cell types to generate ‘pseudo-bulk’ ON, OFF, and pan bipolar cell gene expression profiles. We found that expression estimates from bulk and pooled pseudo-bulk single-cell RNA-seq are well correlated (Pearson correlation coefficients range from 0.82 to 0.85, Figure 2—figure supplement 1A–C). Similarly, pairwise comparisons showed that bulk ON, OFF, and pan bipolar cell populations were most strongly correlated with their pseudo-bulk counterparts, suggesting that the two approaches yield similar cell type-specific expression profiles (Figure 2—figure supplement 1D).

Shekhar et al. also identified an ON bipolar cell type (BC5D) that expresses low levels of Grm6 and could thus have been incorrectly sorted into our OFF population. To evaluate this possibility, we examined RNA-seq data for Lrrtm1, a gene specific to BC5D bipolar cells (Shekhar et al., 2016). We observed low read counts for Lrrtm1 in both ON and OFF bipolar populations, suggesting that BC5D cells were not sorted into either group. To investigate further, we repeated the FACS experiments, this time also collecting cells exhibiting fluorescence levels in between those of the ON and OFF populations (termed YFP-low). We found that the YFP-low population expressed both the ON BC marker Isl1 and the BC5D marker Lrrtm1, while the ON and OFF populations did not express Lrrtm1 (Figure 1—figure supplement 2). These results indicate that low levels of Grm6-YFP expression caused BC5D cells to be excluded from both the ON and OFF bipolar populations. Given the rarity of BC5D cells, which constituted 2.3% of cells identified as bipolar cells by Shekhar et al., we believe their absence does not materially impact our analysis.

To gain insight into gene expression among individual bipolar cell types, we compared the list of genes differentially expressed between ON and OFF bipolar cells with the data of Shekhar et al. Overall, we found a strong correlation between the results of bulk ON and OFF bipolar cell expression profiling and single-cell transcriptome analysis (Figure 2; Figure 2—figure supplement 1E and Figure 2—source data 1). Additionally, 50 of the 680 differentially expressed genes found in our bulk analysis were not present in the Drop-seq data (Supplementary file 5). These 50 genes are generally expressed at low levels, even in the cell population in which they are enriched. This finding suggests that bulk RNA-seq of purified populations can provide information complementary to that obtained by single-cell profiling.

Figure 2 with 1 supplement see all
Gene expression in ON and OFF bipolar cells.

Left, heatmap displaying 680 genes identified by bulk RNA-seq as differentially expressed between FACS-purified ON and OFF bipolar cell populations (current study) mapped onto single cell expression profiles for bipolar cell types identified by Drop-seq (Shekhar et al., 2016). Overall, genes identified as ON- or OFF-specific by bulk RNA-seq showed corresponding differential expression between ON and OFF bipolar types identified by Drop-seq. Right, Expression of the top 25 most differentially enriched genes (ranked by p-value) in OFF (top) and ON (bottom) bipolar populations presented as dot plots as in Shekhar et al. nTrans = mean number of transcripts expressed per cell in each cluster identified as a bipolar cell type; PercExpr = percentage of cells within each cluster found to express the indicated gene. Dot plots for all 680 differentially expressed genes are presented in Figure 2—source data 1.

https://doi.org/10.7554/eLife.48216.006

Taken together, these data indicate that despite their sister cell type relationship, photoreceptor and bipolar cells have markedly distinct transcriptional profiles, while ON and OFF bipolar cells are more similar at the transcriptome level than rods and cones.

Bipolar cells have a more accessible chromatin landscape than either rods or cones

To compare chromatin accessibility between photoreceptor and bipolar cells, we used ATAC-seq (Assay for Transposase-Accessible Chromatin by sequencing) to generate open chromatin profiles from FACS-purified bipolar cells (Buenrostro et al., 2013). Similar to our RNA-seq data, ATAC-seq generated highly reproducible profiles across biological replicates (Pearson correlation 0.95–1.00, Figure 1—figure supplement 1). For the purpose of our bioinformatic analyses, we define ‘promoters’ as those ATAC-seq peaks that occur between 1000 bp upstream and 100 bp downstream of a transcription start site (TSS); we refer to ATAC-seq peaks outside of this range as ‘enhancers’ or ‘TSS-distal’ elements. To compare chromatin accessibility across tissues, we combined ATAC-seq peaks from bipolar cells with previously generated ATAC-seq data from purified mouse rods, blue cones, and ‘green’ cones (i.e., Opn1mw-expressing cones) as well as DNase-seq data from whole retina, brain, heart, and liver to obtain a list of > 345,000 open chromatin regions (Hughes et al., 2017; ENCODE Project Consortium, 2012). Hierarchical clustering of chromatin accessibility profiles at enhancers showed that photoreceptors, bipolar cells, and whole retina cluster separately from other tissues (Figure 3A). Thus, the sister cell type relationship between photoreceptors and bipolar cells is reflected by the similarity of genome-wide patterns of enhancer chromatin accessibility.

Genome-wide open chromatin profiles of ON and OFF bipolar cells.

(A) Heatmap showing pairwise correlation between ATAC-seq data from photoreceptor and bipolar cell populations as well as DNase-seq data from whole retina, brain, heart and liver. Multiple biological replicates are shown for most tissues. Peaks from each sample were combined to generate a set of 302,518 enhancer peaks, and replicates were clustered based on read counts at each peak. Bipolar cells and photoreceptors form separate clusters. Whole retina DNase-seq clusters with bipolar cells. Photoreceptors, bipolar cells and whole retina cluster separately from other tissues. (B) Genome-wide profiles of chromatin accessibility in isolated photoreceptor and bipolar cell ATAC-seq datasets as well as DNase-seq datasets from additional control tissues. Rows show accessibility as indicated by read depth in 3 kb windows centered on peak summits sampled from photoreceptor, bipolar, and whole retina datasets (10,000 peaks randomly sampled from a total of 99,684 enhancer peaks are shown). Hierarchical clustering reveals peak sets enriched in photoreceptors (red box) or bipolar cells (green box). (C–F) Screenshots of UCSC genome browser tracks show regions of accessible chromatin in bipolar and photoreceptor populations at loci that exhibit shared or cell class-specific expression patterns. Black arrow in panel C indicates a known enhancer of Vsx2 (Kim et al., 2008). There is an imperfect correlation between chromatin accessibility and gene expression. Bar graphs aligned with browser tracks indicate mean RNA-seq counts of each gene for the indicated populations.

https://doi.org/10.7554/eLife.48216.009

We found that whole retina DNase-seq clustered with bipolar cell samples (Figure 3A). This finding was unexpected given that rod photoreceptors constitute ~ 80% of all cells in the mouse retina (Figure 1A) (Jeon et al., 1998) and indicates that the open chromatin profile of a complex tissue is not necessarily dominated by the most abundant cell type. This is in line with our previous comparison of genome-wide patterns of chromatin accessibility in rods, cones and whole retina, which showed that more than half of the whole retina peaks did not overlap with photoreceptor peaks, suggesting that they derive from non-photoreceptor retinal cell types (Hughes et al., 2017). This finding may be a consequence of the distinctive pattern of global chromatin closure that we previously described in rod photoreceptors (Hughes et al., 2017).

We next set out to examine patterns of chromatin accessibility within the retinal cell types in greater detail. We combined TSS-distal ATAC-seq peaks from bipolar cells and photoreceptors with DNase-seq peaks from whole retina to create a list of 99,684 retinal open chromatin regions. Clustering these regions based on chromatin accessibility across retinal and non-retinal cell types offers a broad view of cell class- and cell type-specific regions of open chromatin (Figure 3B). We found a large subset of bipolar-enriched peaks, many of which were also accessible in whole retina and brain (Figure 3B, green box). Conversely, a smaller subset of peaks showed selective accessibility in photoreceptors, with lower levels of accessibility in bipolar cells, and even less in other cell types (Figure 3B, red box). While pan BCs and ON-BCs showed nearly identical open chromatin profiles, OFF-BC open chromatin patterns were somewhat divergent, with slightly more accessibility in the photoreceptor-enriched subset (red box) and less in the bipolar-enriched subset (green box) compared to ON-BC. Overall, the sum of photoreceptor and bipolar cell ATAC-seq peaks accounted for 83 percent of whole-retina DNase-seq peaks, with the remainder presumably deriving from other inner retinal cell classes. These data suggest that relatively few regions of open chromatin are truly photoreceptor-specific, and that regions enriched in bipolar cells are more likely to be accessible in other tissues, particularly in brain. For instance, compared to photoreceptors, a greater proportion of bipolar cell peaks corresponded to DNase-seq peaks in brain (47% vs 37%). Finally, direct comparison of ATAC-seq peaks from photoreceptor and bipolar cells identified 55,402 differentially accessible regions (FDR < 0.05), 75% of which are more accessible in bipolar cells (Supplementary file 7).

While we observed significant differences in global chromatin accessibility between photoreceptors and bipolar cells, the open chromatin profiles of ON and OFF bipolar cells were largely similar, consistent with the high degree of similarity between their transcriptomes. Specifically, only 4263 peaks were differentially accessible between ON and OFF bipolar cells. Of note, 79% (3359) of these differential peaks were more accessible in ON bipolar cells. When examining loci surrounding genes expressed in both ON and OFF bipolar cells, we found that ON and OFF subclasses typically had similar open chromatin profiles (e.g., Vsx2, Figure 3C). In contrast, some ON- or OFF-specific genes exhibit cell subclass-specific patterns of chromatin accessibility (e.g., Isl1 and Esam, Figure 3D–E). We found an overall correlation between accessibility and associated gene expression (Figure 5—figure supplement 1) but did not see this pattern at all gene loci (e.g., Grik1, Figure 3F).

Photoreceptor and bipolar cells employ closely related yet distinct cis-regulatory grammars

Having compared global accessibility between photoreceptor and bipolar cells, we next sought to compare them in terms of ‘cis-regulatory grammar’, which we define as the number, affinity, spacing and orientation of TF binding sites within the open chromatin regions of a given cell type or class. To begin, we assessed all 319 TF binding site motifs from the HOMER database for enrichment within bipolar cell open chromatin regions (Heinz et al., 2010). The most highly enriched motifs within enhancers corresponded to CTCF, K50 HD, E-box, nuclear receptor, and MADS box motifs (Supplementary file 8). All of these motifs were previously shown to be among the most enriched motifs in photoreceptor ATAC-seq peaks as well (Hughes et al., 2017). The similarity in the patterns of TF binding site enrichment between photoreceptors and bipolar cells can be better understood in the context of known patterns of TF expression in these cell classes. Specifically, the K50 HD TFs OTX2 and CRX are master regulators of gene expression programs in photoreceptor and bipolar cells. Likewise, bHLH TFs (which recognize E-box motifs) play roles in fate specification and maintenance of both photoreceptor and bipolar cell gene expression programs, as described in the Introduction. Finally, enrichment of ZF motifs recognized by CTCF in both cell classes is in line with reports that CTCF motifs often lie in ubiquitously accessible chromatin regions, where CTCF recruitment is thought to mediate interactions between promoters and enhancers, among other functions (Splinter et al., 2006; Rao et al., 2014; Song et al., 2011).

Despite the overall similarity between the cis-regulatory grammars of bipolar cells and photoreceptors, there are notable quantitative differences in motif enrichment between the two cell classes. To systematically identify these differences, we compared the proportion of peaks containing each of the 319 motifs between pan-bipolar cells and each photoreceptor cell type (Supplementary file 8). The most differentially enriched motifs corresponded to those with the highest enrichment in each cell class and are summarized in Figure 4. Although both photoreceptors and bipolar cells showed marked enrichment for K50 HD motifs, these motifs were more enriched in photoreceptors (Figure 4). Conversely, E-box motifs were more enriched in bipolar cells than photoreceptors. The most striking difference in TF binding site enrichment between the two cell classes was the enrichment of both monomeric and dimeric Q50 HD motifs in photoreceptor open chromatin regions and their lack of enrichment in bipolar regions (Figure 4). The most well-characterized Q50 HD TF expressed in photoreceptors is RAX, which is required for cone gene expression and survival (Irie et al., 2015). In contrast, bipolar cells express multiple Q50 HD TFs (VSX2, VSX1, ISL1, LXH3, LHX4, AND SEBOX) (Shekhar et al., 2016). VSX2 is required for bipolar cell development and is expressed in all mouse bipolar cell types throughout development and into adulthood (Livne-Bar et al., 2006; Liu et al., 1994). The paradoxical absence of Q50 HD motif enrichment in bipolar open chromatin regions despite the presence of multiple Q50 HD TFs in this cell class may be explained by the observation that VSX2 acts as a repressor of photoreceptor CREs (Dorval et al., 2006). The lack of Q50 motif enrichment in bipolar cells could be due to selective repression of photoreceptor-specific open chromatin regions by VSX2, which, in turn, prevents ectopic expression of photoreceptor genes in bipolar cells, a possibility that we will return to in the final section of the Results.

Figure 4 with 2 supplements see all
Patterns of TF binding site enrichment across retinal cell classes.

Motif enrichment patterns identified in bipolar and photoreceptor ATAC-seq datasets as well as in DNase-seq datasets from adult mouse whole retina, brain, heart, and liver. This analysis included all enhancer (i.e., TSS-distal) open chromatin peaks from each cell class or tissue. Each panel is centered on a 1 kb window around peaks from the indicated dataset. Motif density (motifs per base pair per 1000 peaks) is shown on the Y-axis. Consensus sequences for each motif class and example TFs (in parentheses) expressed in photoreceptors and bipolar cells are shown on the right. Photoreceptor and bipolar cell populations share enrichment for K50 HD motifs, while only photoreceptors show enrichment for Q50 HD motifs.

https://doi.org/10.7554/eLife.48216.010

To further compare the cis-regulatory grammars of bipolar cells and photoreceptors, we examined TF binding site co-occurrence and spacing within each cell class. For this analysis we compared a combined list of enhancer regions from rod and cones to that of bipolar cells. As expected, motif pairs enriched in specific peak sets tended to include motifs that showed the highest individual enrichment in the same cell type. Likewise, differentially enriched motif pairs included one or more differentially enriched motifs, such as K50 and Q50 HD motifs in photoreceptors and bHLH motifs in bipolar cells (Figure 4—figure supplement 1A–B). Of note, while we identified specific motif pairs enriched in each cell type, the frequencies of these pairs are consistent with an independent model (i.e., the number of occurrences of motif pairs is approximately what would be expected given the number of occurrences of each individual motif and assuming a random distribution of motifs across peaks). As with chromatin accessibility, enrichment of motif pairs is highly similar between ON and OFF bipolar cells, with nominally differentially enriched pairs showing similar proportions (Figure 4—figure supplement 1C). Finally, to investigate preferences in spacing and orientation between pairs of motifs, we plotted the density of highly enriched motifs (those depicted in Figure 4) centered on regions flanking K50 and Q50 HD motifs in each peak set. As described previously for photoreceptors, spacing and orientation preferences in bipolar open chromatin regions were minimal (Figure 4—figure supplement 2) (Hughes et al., 2017). Thus, the primary differences in the cis-regulatory grammar of the two cell classes appears to be the degree of HD and E-box motif enrichment.

Photoreceptor- and bipolar-specific open chromatin regions are positively correlated with cell class-specific gene expression

We next sought to determine the extent to which photoreceptor- and bipolar-enriched open chromatin regions correlate with cell type-specific gene expression. To this end, we assigned each of the 55,402 regions identified as differentially accessible between photoreceptor and bipolar cells to a candidate target gene based on proximity to the nearest transcription start site and compared mean RNA-seq expression values for the assigned genes. As described in previous studies, we observed a modest correlation between enhancer accessibility and gene expression, and a more robust correlation between promoter accessibility and gene expression (Figure 5—figure supplement 1) (Hughes et al., 2017; Pastor et al., 2014; Ampuja et al., 2017; de la Torre-Ubieta et al., 2018).

Our analysis of global chromatin accessibility suggested that many of the differentially accessible peaks were also open in other tissues, especially those enriched in bipolar cells compared to photoreceptors (Figure 3E). To gain a better understanding of the cell type-specific open chromatin regions that drive gene expression differences between these two cell classes, we refined our analysis to exclude peaks shared with non-retinal cell types. We identified 8435 enhancer regions which are accessible either in photoreceptors or bipolar cells, but not accessible in brain, liver or splenic B cells (Figure 5A). This set includes 1291 regions that are open in both photoreceptors and bipolar cells, and 7144 regions that are differentially accessible between the two cell classes (Figure 5A). We found that ~ 46% (3,270) of the differentially accessible peaks were more open in bipolar cells. Thus, most of the bipolar cell-enriched regions identified in the previous section were also accessible in one or more non-retinal tissues. As with the enhancer regions from the unfiltered list, assigning genes to this more retina-specific set of differentially accessible regions also shows a correlation between accessibility and gene expression (Figure 5B). To visualize this association and identify the peaks that underly it, we plotted all 8435 peaks according to fold-change differences in accessibility and gene expression between bipolar cells and rods (Figure 5C) and between bipolar cells and blue cones (Figure 5—figure supplement 1D). We then selected for further analysis those peaks that exhibited correlated accessibility and gene expression in photoreceptors (highlighted in red or blue in Figure 5C and Figure 5—figure supplement 1D; n = 901) or bipolar cells (highlighted in green in Figure 5C and Figure 5—figure supplement 1D; n = 833). These differentially enriched peaks represent strong candidates for CREs that mediate the gene expression differences between the two cell classes. Indeed, the photoreceptor peak set contains known enhancers responsible for driving cell type-specific expression of Rhodopsin (Rho) and components of the rod-specific phototransduction cascade (Corbo et al., 2010; Nie et al., 1996), while the bipolar peak set contains a known enhancer that drives Vsx2 expression in bipolar cells (Kim et al., 2008). To gain insight into the possible biological functions of these peaks we used GREAT to assign biological annotations based on nearby genes (McLean et al., 2010). As was found with the unfiltered datasets, photoreceptor peaks are linked with genes associated with light sensation, whereas bipolar peaks are linked to genes involved in more generic neuronal functions (Figure 5—figure supplement 2).

Figure 5 with 2 supplements see all
The cis-regulatory grammar of genomic regions associated with differential chromatin accessibility and gene expression in bipolar cells and photoreceptors.

(A) Heatmap depicting 8,435 genomic regions determined by pairwise comparison to be differentially accessible in photoreceptors, bipolar cells, or both cell classes, compared to adult mouse B cells, brain and liver. Photoreceptor-enriched (n = 3,874), bipolar cell-enriched (n = 3,270), shared (n = 1,291). (B) Expression of genes to which classes of peaks defined in A were assigned by proximity to nearest TSS. There is a moderate correlation between chromatin accessibility and gene expression in each cell class. (C) Peaks identified in panel A are plotted according to chromatin accessibility (x-axis) and associated gene expression (y-axis) in bipolar cells versus rods. Peaks with four-fold greater chromatin accessibility and associated gene expression in bipolar cells are shown in green (FDR < 0.05 for both accessibility and expression), while those peaks with greater accessibility and associated gene expression in rods are shown in red. Shared peaks and those associated with modest (less than four-fold) differences in expression are in gray. Peaks with discordant chromatin accessibility and associated gene expression are shown in black. Peaks assigned to genes expressed specifically in photoreceptors (Rho, Abca4) or bipolar cells (Vsx2, Prkca) are indicated. (D) Motif enrichment within peaks displaying correlated chromatin accessibility and associated gene expression in bipolar cells and photoreceptors as well as within peaks displaying shared accessibility, including those shown in panel A (shared-retina) and shared peaks which were not filtered to remove those accessible in non-retinal tissues (shared-all). Both photoreceptor and bipolar cell peaks show enrichment for K50 HD motifs. Bipolar cell peaks (n = 833) are also highly enriched for E-box motifs but lack enrichment of Q50 HD motifs. In contrast, photoreceptor peaks (n = 901) show enrichment for Q50 HD motifs but almost entirely lack E-box enrichment. Shared retina-specific peaks (n = 1,291) show a hybrid pattern of motif enrichment. Only the shared-all peak set exhibits enrichment for CTCF motifs, underscoring a key difference between cell-class specific open chromatin regions (which show no enrichment of CTCF motif) and ubiquitously open chromatin regions, which show strong enrichment.

https://doi.org/10.7554/eLife.48216.013

Next, we asked whether the patterns of TF binding site enrichment observed with aggregate sets of ATAC-seq peaks from each cell class would be preserved within the retina-specific peak sets associated with correlated gene expression. We compared photoreceptor-enriched regions, bipolar-enriched regions, and regions that share accessibility between the two cell classes which were either specific to the retina (Figure 5A, ‘shared retina’ n = 1,291), or unfiltered (‘shared all’, n = 47,550). We found that K50 HD motifs were enriched in both shared and cell class-selective regions, but to a lesser extent in regions specifically enriched in bipolar cells. The bipolar-selective regions were markedly enriched for E-box motifs but completely lacked enrichment for Q50 HD motifs (Figure 5D). Conversely, photoreceptor-selective regions were enriched for Q50 HD motifs, but lacked E-box motif enrichment. Peaks that were shared between the two cell classes showed an intermediate pattern of motif enrichment, highlighting the roles of both HD and bHLH TFs in regulating the gene expression programs of each class. Of note, CTCF enrichment was absent in all but the unfiltered peak set, suggesting that the CTCF enrichment observed in Figure 4 is attributable to ubiquitously accessible peaks. Taken together, these findings suggest that differential enrichment of Q50 HD and E-box motifs are the key features that distinguish the cis-regulatory grammars of photoreceptors and bipolar cells.

K50 motifs in the Gnb3 promoter are required for both photoreceptor and bipolar expression, but addition of Q50 motifs selectively represses expression in bipolar cells

Given the critical roles of HD TFs in the regulation of photoreceptor and bipolar gene expression, we further investigated the role of K50 and Q50 motifs in the cis-regulatory region upstream of Gnb3. Gnb3 encodes the β subunit of a heterotrimeric G-protein required for cone phototransduction as well as ON bipolar cell function (Dhingra et al., 2012). Gnb3 is expressed in rods, cones, and bipolar cells during early postnatal retinal development in the mouse. Selective repression of Gnb3 in rods by the nuclear receptor TF NR2E3 results in a cone + bipolar pattern after postnatal day 10 (Haider et al., 2009; Corbo and Cepko, 2005). We focused on an 820 bp region around the TSS of Gnb3 which drives robust expression in rods, cones, and bipolar cells when electroporated into early postnatal mouse retina. This region lacks Q50 motifs but contains five K50 HD motifs of varying affinity which occur in two clusters, one immediately upstream of the TSS (−65 bp) and the other more distally (−350 bp) (Figure 6A). To evaluate the role of these five K50 motifs in mediating photoreceptor and bipolar expression, we engineered reporter constructs in which each of the five motifs was individually inactivated by mutating the TAAT core to TGGT. We then introduced wild-type and mutant reporters into mouse retinal explants via electroporation and compared expression levels after 8 days. Mutations in K50 motifs 2, 4 and 5 resulted in coordinate loss of expression in both photoreceptor and bipolar cells, indicating that these motifs are required for reporter expression in both cell classes (Figure 6B,D). Conversely, mutations in site 1 or 3 had no effect on expression in either cell class (Figure 6B,D). Binding site affinity did not correlate with expression, as site 3 has a higher predicted affinity than sites 4 or 5. Thus, the Gnb3 promoter region contains both essential and nonessential K50 motifs, underscoring the critical role for these shared motifs in both photoreceptors and bipolar cells.

K50 motifs are required for expression in both photoreceptors and bipolar cells, while Q50 motifs mediate repression of reporter expression specifically in bipolar cells.

(A) Schematic of reporter construct containing 820 bp from the promoter region and 5’ untranslated region (UTR) of mouse Gnb3. This region contains two phylogenetically conserved blocks harboring a total of five K50 motifs (black ellipses). (B) Left, Schematics of reporter pairs containing wild-type (WT; black) and inactivated (crossed out) K50 motifs. Right, Each pair of reporters was electroporated into explanted newborn mouse retina, which were subsequently harvested after eight days in culture and photographed in a flat mount preparation. While K50 sites 2, 4 and 5 are required for reporter expression, sites 1 and 3 are dispensable. (C) Left, Pairs of reporters containing WT, inactivated K50, or novel Q50 (TAATTA; yellow) motifs were electroporated into newborn mouse retina in vivo. After 20 days, the retinas of electroporated mice were harvested and photographed in vertical cross-sections. (C) Right, Representative cross-sections of retinas injected with the indicated pair of reporters. Loss of both K50 motifs 1 and 3 has no effect on expression in either photoreceptors or bipolar cells, while converting these same sites to Q50 motifs abrogates expression specifically in bipolar cells (n = 2–7 depending on reporter pair, Figure 6—source data 1). White arrowheads indicate bipolar cells expressing the WT reporter, but not the mutated one. (D) Table summarizing the results of reporter analysis presented in (B) and (C).

https://doi.org/10.7554/eLife.48216.016

Next, we sought to determine the effect of introducing Q50 motifs into the Gnb3 promoter region. For these experiments, reporters were introduced into newborn mouse retinas via in vivo electroporation and harvested for histologic analysis after 20 days. First, we electroporated identical wild-type sequences driving both DsRed and GFP to confirm that essentially all photoreceptors and bipolar cells received both constructs (Figure 6C). Next, we compared the expression of a Gnb3 promoter containing mutations in K50 motifs 1 and 3 to that of a wild-type promoter, confirming that elimination of both of these sites has no effect on expression in either photoreceptors or bipolar cells (Figure 6C,D and Figure 6—source data 1). To test the effect of introducing Q50 motifs into the Gnb3 promoter region, we replaced K50 motifs 1 and 3 with a Q50 motif (TAATTA), both individually and in combination. Whereas introduction of a Q50 motif into site one had no apparent effect, replacement of site three caused a selective decrease in bipolar expression with no change in photoreceptor expression. When we introduced Q50 motifs into both sites, reporter expression was markedly reduced in bipolar cells, with no effect on photoreceptor expression (Figure 6C,D and Figure 6—source data 1). These data, along with previous reports of VSX2-mediated repression of photoreceptor-specific promoters and enhancers, suggest that Q50 motifs play an important role in mediating repression of photoreceptor genes in bipolar cells.

Discussion

In this study we generated open chromatin maps and transcriptome profiles of mouse bipolar cells, including FACS-purified ON and OFF bipolar cell populations, and compared them to analogous data from rod and cone photoreceptors. We found that photoreceptors and bipolar cells differ in the expression of thousands of genes and yet have very similar cis-regulatory grammars. The key cis-regulatory differences that distinguish the two cell classes are the preferential enrichment of Q50 HD motifs in open chromatin regions associated with photoreceptor-specific gene expression and a corresponding enrichment of E-box motifs in chromatin associated with bipolar-specific expression. The cellular features and transcriptional mechanisms shared by photoreceptors and bipolar cells have prompted speculation that these two sister cell types arose from a single ancestral photoreceptor cell type via a process of progressive cellular divergence (Arendt, 2008; Lamb, 2013). We propose that the elimination of Q50 motifs from bipolar-specific CREs likely played a key role in differentiating the bipolar transcriptome from that of photoreceptors during early stages of vertebrate retinal evolution. Alternatively, given the multiplicity of K50 HD binding sites within both photoreceptor and bipolar cell regulatory elements, a subset of photoreceptor-specific elements may have emerged through the simple conversion of K50 (TAATCC) to Q50 (TAATTA/G) sites. Prior studies of individual photoreceptor CREs showed a role for the Q50 HD TF, VSX2, in selectively repressing photoreceptor genes in bipolar cells (Livne-Bar et al., 2006; Dorval et al., 2006). Our results generalize this conclusion, suggesting that VSX2 plays a genome-wide role in silencing photoreceptor gene expression in bipolar cells. A similar role for VSX2 has recently been described in the spinal cord, where closely related progenitor cells give rise to either motor neurons or V2a interneurons (Clovis et al., 2016). VSX2 promotes V2a identity by directly repressing the motor neuron gene expression program and by competing for Q50 sites at motor neuron enhancers. Thus, in both retina and spinal cord, expression of VSX2 promotes interneuron fate at the expense of the alternative neuronal (photoreceptor or motor neuron) cell type. These parallels suggest that transcriptional repression by cell type-specific TFs such as VSX2 represent a common mechanism for differentiating the gene expression programs of two closely related cell types.

In addition to differential enrichment of Q50 sites, we also observed enrichment of E-box motifs in regions associated with bipolar-specific expression (Figure 5C). The lack of corresponding enrichment in regions associated with photoreceptor-specific expression suggests that bHLH TFs also play an important role in distinguishing the gene expression programs of photoreceptor and bipolar cells. It is important to note that this finding does not contradict known roles for bHLH TFs in photoreceptors, as E-box motifs are also enriched within the complete set of photoreceptor ATAC-seq peaks (Figure 4), as well as in the subset that exhibits shared accessibility with bipolar cells (Figure 5C). The role of bHLH TFs in establishing the cellular identity of both classes is further demonstrated by multiple loss-of-function studies (Tomita et al., 2000; Bramblett et al., 2004; Feng et al., 2006; Huang et al., 2014; Pennesi et al., 2003).

In contrast to the differences between photoreceptors and bipolar cells, ON and OFF bipolar cell populations displayed striking similarities in their cis-regulatory landscape and gene expression profiles. In a paper that appeared after our profiling studies had been performed, Shekhar et al. (2016) documented the single-cell expression profiles of individual bipolar cell types and showed that the transcriptomes of ON and OFF cone bipolar cells are more similar to each other than to that of rod bipolar cells (RBCs). This finding implies that the ON bipolar population analyzed in the present study represents a grouping of distinct bipolar cell types (RBC and cone ON BC), which should be separated for a more informative comparison. Despite this drawback, our analysis indicates that there are relatively few differences in chromatin accessibility between bipolar cell types. We estimate that RBCs constitute nearly 60% of the cells in our ON population (RBCs compose 56% of ON bipolar cells identified in Shekhar et al., 2016, and ~ 57% of those identified as ON BCs by Wässle et al., 2009). Thus, much of the signal in our ON bipolar RNA-seq and ATAC-seq data likely derives from RBCs. It remains to be determined whether open chromatin profiling of individual bipolar subtypes will reveal additional differences in their epigenomic landscapes beyond those reported here.

We found an imperfect correlation between chromatin accessibility and gene expression in bipolar cells. For example, ATAC-seq peaks upstream of Grik1 are open in ON bipolar cells despite an absence of Grik1 expression in this cell type (Figure 3F). We observed similar instances of discordance between chromatin accessibility and gene expression in our previous analysis of rod and cone photoreceptors (Hughes et al., 2017), and other groups have documented this discrepancy in other cell types (de la Torre-Ubieta et al., 2018; Lara-Astiaso et al., 2014; Starks et al., 2019). Presumably, transcriptional activity in these instances requires expression of additional cell type-specific factors (Heinz et al., 2015), perhaps most clearly demonstrated in developmental contexts, wherein accessibility is frequently established prior to the onset of transciption (Lara-Astiaso et al., 2014).

Support for the idea that bipolar cells diverged from photoreceptors via progressive partitioning of cellular function is provided by the existence of cell types in the retinas of non-mammalian vertebrates with features intermediate between those of mammalian photoreceptors and bipolar cells. In some turtle species ~ 30% of the cell bodies in the photoreceptor layer (the outer nuclear layer) belong to bipolar cells, not photoreceptors (Tauchi, 1990). These so-called ‘displaced bipolar cells’ possess an inner segment-like process that extends to the outer limiting membrane and contains abundant mitochondria and even a sensory-type (‘9 + 0’) cilium (Figure 7A,B). Thus, displaced bipolar cells closely resemble typical photoreceptors except that they lack an outer segment, possess dendrites in the outer plexiform layer, and synapse directly onto retinal ganglion cells. Another intermediate type of bipolar cell occurs in nearly all non-mammalian vertebrate classes and even in some mammalian species (Hendrickson, 1966; Locket, 1970; Quesada and Génis-Gálvez, 1985; Young and Vaney, 1990). This bipolar type has a nucleus localized to the inner nuclear layer, but retains an inner segment-like structure (Landolt’s club), which extends from the cell’s dendritic arbor to the outer limiting membrane and contains abundant mitochondria and a sensory-type cilium (Figure 7A) (Hendrickson, 1966; Locket, 1970; Quesada and Génis-Gálvez, 1985). We suggest that displaced bipolar cells and those with a Landolt’s club represent ‘transitional forms’ on the evolutionary path from photoreceptor to typical bipolar cell. The existence of these transitional forms suggests that bipolar cells may have evolved via the stepwise repression of discrete gene modules required for the development of individual cellular features, or ‘apomeres’, that are specific to photoreceptors (Arendt et al., 2016).

Evolutionary model for the divergence of bipolar cells from photoreceptors.

(A) Schematic depiction of photoreceptors (1), conventional bipolar cells (4), and two ‘transitional’ cell types with cellular features intermediate between those of photoreceptors and conventional bipolar cells: displaced bipolar cells (2) and bipolar cells with Landolt’s club (3). (B) Table of individual cellular features (referred to in Arendt et al. as ‘apomeres’) possessed by photoreceptors, bipolar cells, or both cell classes. (C) Evolutionary model for the divergence of present-day photoreceptors and bipolar cells from a common ancestral photoreceptor type. We propose that the ancestral photoreceptor (possibly present in a hagfish-like ancestor) expressed cell type-specific genes via both K50 HD TFs (CRX and OTX2) and a possibly weakly activating Q50 HD TF (RAX). The emergent expression of a strongly repressive Q50 HD TF (VSX2) in bipolar cells then permitted silencing of selected photoreceptor gene modules underlying the formation of defined photoreceptor apomeres (e.g., outer segment). Selective expression of activating Q50 HD TFs in ‘transitional’ bipolar cell types may have allowed the derepression of specific photoreceptor apomeres (e.g., cilium formation, mitochondrial aggregates). Novel bipolar-specific apomeres (e.g., dendrites in the outer plexiform layer [OPL]) may have evolved via co-option of other gene expression programs.

https://doi.org/10.7554/eLife.48216.018

If this evolutionary model is correct, then how can we account for the co-existence of ‘transitional’ bipolar cell types and ‘conventional’ bipolar cells in a single retina? One testable hypothesis is that VSX2 may be expressed at lower levels in transitional bipolar cell types, thereby permitting expression of additional photoreceptor gene modules and their corresponding apomeres. Alternatively, it is possible that additional activating Q50 HD TFs are expressed in transitional bipolar types, and these TFs can overcome VSX2-mediated repression of selected photoreceptor gene modules. Indeed, we and others have found that multiple Q50 HD TFs are expressed in subsets of mouse bipolar cells (Shekhar et al., 2016). In addition, there is evidence that transitional bipolar cell types with Landolt’s club may exist in the mouse (Rowan and Cepko, 2004). Thus, individual bipolar cell types may control the number of photoreceptor apomeres they express by modulating the balance of activating and repressing Q50 HD TFs in their nuclei.

The evolutionary divergence of bipolar cells from photoreceptors likely required coordinated changes in both cis-regulatory grammar and HD TF expression. The Q50 HD TF, RAX, is expressed in developing vertebrate rods and cones and is required for normal activation of photoreceptor gene expression in mice (Irie et al., 2015; Rodgers et al., 2018). The expression of a RAX homolog in the photoreceptors of the tadpole larva of the protochordate, Ciona intestinalis, suggests a primordial role for this Q50 HD TF in activating photoreceptor gene expression in chordates (Cao et al., 2019). These data suggest that both K50 and Q50 motifs were present in the CREs of the ancestral vertebrate photoreceptor prior to the evolutionary emergence of bipolar cells, and that both K50 (OTX2 and CRX) and Q50 (RAX) HD TFs were required for gene activation in that ancestral cell type (Figure 7C). In this context, the emergent expression of a repressive Q50 HD TF (VSX2) in a primordial bipolar cell would have permitted selective repression of CREs containing Q50 motifs, allowing the cis-regulatory landscape of the two nascent cell types to begin to diverge. Maintaining expression of selected ‘photoreceptor’ genes in bipolar cells (e.g., Gnb3) would then have required the elimination of Q50 motifs from the cis-regulatory regions of those genes.

These evolutionary considerations suggest that the modern vertebrate retina arose from an ancestral retina in which photoreceptors directly synapse onto projection neurons (i.e., ganglion cells) without an intervening layer of interneurons (left side of Figure 7C). Two lines of evidence suggest that such a retina may have existed. First, the retina of the hagfish, the most primitive extant vertebrate, reportedly has photoreceptors that directly synapse onto projection neurons (i.e., ganglion cells) (Lamb, 2013; Locket and Jørgensen, 1998). Second, some vertebrate species (including reptiles, amphibians, and larval lamprey) have an unpaired, median ‘parietal eye’ developmentally related to the pineal gland, which contains photoreceptors that directly synapse onto ganglion cells (Eakin, 1973; Dodt, 1973; Mano and Fukada, 2007; Solessio and Engbretson, 1993). It is possible that the parietal eye evolved from the midline ‘eye’ of a protochordate ancestor, akin to the present-day ascidian larva. The simple lateral eyes of a hagfish-like vertebrate ancestor may then have emerged via co-option of the gene networks required for parietal eye development. Subsequently, bipolar cells may have arisen in the lateral eyes of early vertebrates via subtle changes in cis-regulatory grammar and TF expression, paving the way for the emergence of the sophisticated interneuronal circuitry found in present-day vertebrate retinas.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Gene (Mus musculus)Vsx2Ensembl:
ENSMUSG00000021239
Strain, strain background (Mus musculus. male and female)CD-1Charles RiverStrain code 022
Genetic reagent (Mus musculus)Otx2-GFPFossat et al., 2007
Genetic reagent (Mus musculus)Grm6-YFPMorgan et al., 2011
Genetic reagent (Mus musculus)Otx2-GFP;Grm6-YFPThis paperCross between Otx2-GFP and Grm6-YFP
Recombinant DNA reagentpCAGGSHsiau et al., 2007Plasmid used to create Gnb3 reporters.
Recombinant DNA reagentGnb3-WT-DsRedThis paperGnb3 promoter region driving DsRed
Recombinant DNA reagentGnb3-WT-EGFPThis paperGnb3 promoter region driving EGFP
Recombinant DNA reagentGnb3- K50 #1-null-DsRed-This paper
Recombinant DNA reagentGnb3- K50 #2-null-DsRed-This paper
Recombinant DNA reagentGnb3- K50 #3-null-DsRed-This paper
Recombinant DNA reagentGnb3- K50 #4-null-DsRed-This paper
Recombinant DNA reagentGnb3- K50 #5-null-DsRed-This paper
Recombinant DNA reagentGnb3- K50 #3 and #5-null-DsRed-This paper
Recombinant DNA reagentGnb3- Q50 #1-EGFPThis paper
Recombinant DNA reagentGnb3- Q50 #3-EGFPThis paper
Recombinant DNA reagentGnb3- Q50 #1 and #3-EGFPThis paper
Commercial assay or kitQiagen Mini-elute PCR Purification kitQiagenCat no. 28004
Commercial assay or kitKAPA Library Quantification KitRocheCat no. 07960140001
Commercial assay or kitSMARTer Ultra Low RNA kit for Illumina Sequencing-HVClonetechCat. Nos. 634820, 634823, 634826, 634828 and 634830)Utilized by Washington University Genome Technology Access Core (GTAC)
Sequence-based reagentPrimersIDTListed in Supplementary file 2
Software, algorithmRhttp://rstudio.comR programming language
Software, algorithmDESeq2https://bioconductor.org/packages/release/bioc/html/DESeq2.htmlDifferential gene expression analysis based on the negative binomial distribution
Software, algorithmHOMERhttp://homer.ucsd.edu/homer/index.htmlHypergeometric Optimization of Motif EnRichment
Software, algorithmSAMtoolshttp://www.htslib.orgSamtools
Software, algorithmBowtie2http://bowtie-bio.sourceforge.net/bowtie2/index.shtmlBowtie2
Software, algorithmPicardhttps://broadinstitute.github.io/picard/Picard
Software, algorithmMACS2https://pypi.org/project/MACS2/2.1.1.20160309/Model-based Analysis of ChIP-Seq
Software, algorithmSTARhttps://github.com/alexdobin/STARSpliced Transcripts Alignment to a Reference
Software, algorithmHTSeqhttps://pypi.org/project/HTSeq/HTSeq 

Mouse models

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All animal experiments were carried out in accordance with the regulations of the IACUC at Washington University in St. Louis. Retinal dissociation and FACS were carried out using Otx2-GFP or Otx2-GFP; Grm6-YFP mice. Otx2-GFP mice were heterozygous for a GFP cassette inserted at the C-terminus of the endogenous Otx2 locus (Fossat et al., 2007). The Grm6-YFP line harbors a YFP transgene driven by the Grm6 promoter (Morgan et al., 2011). All electroporation experiments were carried out in CD-1 mice.

Retinal dissociation and FACS

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Following dissection, retinas of mice aged 6–8 weeks, or 3 months (for one biological replicate of Figure 1—figure supplement 2) were dissociated with papain as described previously (Trimarchi et al., 2007). Briefly, two retinas were incubated in 400 µl of calcium/magnesium free Hanks’ Balanced Salt Solution (HBSS) (Thermo Fisher) containing 0.65 mg papain (Worthington Biochem) for 10 min at 37°C. Cells were then washed in a DMEM (Thermo Fisher) solution containing 100 units DNase1 (Roche) and incubated an additional 5 min at 37°C. Cells were then resuspended in 600 µl of sorting buffer (2.5 mM EDTA, 25 mM HEPES, 1% BSA in HBSS) and used directly for sorting. Cells were sorted on a FACS Aria-II (BD biosciences) with gates based on forward scatter, side scatter, and GFP fluorescence. OFF bipolar cell populations (Otx2-GFP+;Grm6-YFP-) were immediately sorted a second time to increase purity.

Generation of reporter constructs

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An 820 bp region encompassing part of the Gnb3 5’ UTR and upstream sequence was amplified from mouse genomic DNA. Site-directed mutagenesis by overlap extension was used to modify K50 sites (Ho et al., 1989). The resultant PCR products were digested and ligated into GFP or DsRed reporter vectors derived from pCAGGS (Hsiau et al., 2007). After verification by sequencing, plasmid DNA was resuspended in PBS at a concentration of ~ 6–7 µg/µl prior to injection. All primers are listed in Supplementary file 2.

In vivo and explant electroporation

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In vivo subretinal injection and electroporation of newborn CD1 mice was performed as previously described (Matsuda and Cepko, 2004). Briefly, mice were first anesthetized on ice. A 30-gauge needle was then used to incise the eyelid and puncture the sclera, and a Hamilton syringe with a 33-gauge blunt-tipped needle was used to inject the DNA into the subretinal space. Tweezer electrodes placed across the head were then used to electroporate with five square pulses of 80 volts and 50 millisecond duration at 950 millisecond intervals. Explant electroporation was carried out as described previously (Hsiau et al., 2007), except that the electroporation chamber contained a solution of 0.5 µg/µl DNA in PBS.

Retinal tissue sectioning and imaging

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Eyes were removed at P21, punctured with a 26-gauge needle, and incubated in 4% paraformaldehyde for 5 min before dissection to remove the cornea. The lens was removed, and eyes were then incubated for an additional 45 min in 4% paraformaldehyde. Eye cups were next washed in PBS and incubated overnight at 4°C in 30% sucrose-PBS. The following day, eye cups were incubated in a 1:1 mixture of OCT compound (Sakura) and sucrose-PBS before being flash frozen in OCT and stored at −80°C. Retinal sections of 14 µm were cut using a cryostat (Leica CryoCut 1800), mounted on Superfrost Plus slides (Fisher), and stored at −20°C. Prior to placement of cover slips, slides were washed with PBS to remove OCT. The sections were then stained with DAPI, and coverslips were mounted using Vectashield mounting medium (Vectorlabs). Retinal sections were imaged on a Zeiss 880 laser-scanning confocal microscopy in the Washington University Center for Cellular Imaging (WUCCI) Core.

ATAC-seq

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Transposition and library preparation from sorted cell populations were performed as previously described (Buenrostro et al., 2015). Briefly, 30,000–100,000 sorted cells were pelleted at 500 G and washed twice in ice-cold PBS before lysis. Transposition reactions were incubated at 37°C for 30 min and purified using a Qiagen MiniElute PCR Purification kit. Libraries were amplified with Phusion High-Fidelity DNA Polymerase (NEB). Cycle number was calibrated by a parallel qPCR reaction. Gel electrophoresis was used to assess library quality, and final libraries were quantified using KAPA Library Quantification Kit (KAPA Biosystems). Equimolar concentrations of each library were pooled and run on an Illumina HiSeq2500 to obtain 50 bp paired-end reads.

ATAC-seq, DNase-seq, and RNA-seq data processing

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ATAC-seq and RNA-seq reads from bipolar cell populations were processed in an identical manner to those previously obtained from rod and cone photoreceptor cells (Hughes et al., 2017). ATAC-seq reads were aligned to the GRCm38/mm10 mouse genome assembly using Bowtie2 (v2.3.5) with a max fragment size of 2000 (Langmead and Salzberg, 2012). Alignments were filtered using SAMtools (v1.9) (Li et al., 2009), PCR duplicates were removed using Picard (v2.19.0) (https://broadinstitute.github.io/picard/), and nucleosome-free reads were selected by removing alignments with an insertion size greater than 150 bp. Peaks were called using MACS2 (v2.1.1) (Zhang et al., 2008) and annotated with HOMER (v4.8) (Heinz et al., 2010). DNase-seq datasets generated by ENCODE (ENCODE Project Consortium, 2012) were downloaded as FASTQ files from https://www.encodeproject.org/ and processed in the same manner as ATAC-seq data. RNA-seq reads were aligned to the GRCm38/mm10 using STAR (v2.7.0d) (Dobin et al., 2013), with an index prepared for 50 base-pair reads and the RefSeq gene model, and read counts were calculated using HTSeq (v1.9) (Anders et al., 2015). All datasets are listed in Supplementary file 3.

Processing of single-cell RNA-seq data

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Single-cell data from Shekhar et al. were downloaded from the Gene Expression Omnibus (GSE81904) and processed as described by the authors to yield a digital expression matrix of normalized counts for each gene for each cell as well as cluster assignments for each cell. ‘Pseudo-bulk’ expression estimates were then generated by taking the weighted average of counts for each gene across cells belonging to clusters that constituted bulk populations of interest (RBC, BC5A, BC5C, BC5D, BC6, BC7, and BC8/BC9 for ON BCs; BC1A, BC1B, BC2, BC3A, BC3B, BC4 for OFF BCs; and these clusters combined for pan BCs). Finally, pseudo-bulk expression estimates were re-scaled to match bulk expression estimates.

Transcription factor binding site motif analysis

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Motif enrichment, co-enrichment, and spacing analyses for ATAC-seq and DNase-seq datasets were performed as described previously using HOMER (v4.8) (Hughes et al., 2017; Heinz et al., 2010). Differential motif enrichment was determined using a test of equal proportions (R stats v3.5.3) to compare each motif between pan BC and rods, blue cones or green cones. The top motifs across the three comparisons were manually filtered for redundancy and are shown in Figure 4. Motif co-occurrence analysis was performed using a list of 66 non-redundant motifs (Hughes et al., 2017) to which the motif for LHX3 (representing a Q50 HD motif) was manually added. For purposes of the analysis, peaks from rod, green cones and blue cones were merged to obtain a ‘photoreceptor’ peak list, while those from pan-, ON- and OFF-bipolar cells were merged to create a ‘bipolar cell’ peak list. Enrichment for co-occurrence was calculated by taking the log2(observed pairs +1/expected pairs+1). Expected frequency of individual pairs was estimated from the counts for each motif within the pair (motif one count × motif two count ÷ number of total peaks). Differential enrichment between tissues was calculated with a Fisher’s exact test. Motif spacing was analyzed for the top enriched peaks shown in Figure 4. The same set of peaks for co-occurrence were centered on individual K50 or Q50 motifs, and the density of flanking secondary motifs was plotted on either strand.

Identification of differentially accessible peaks and differentially expressed genes

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DESeq2 (v1.14.1) (Love et al., 2014) was used to test for differential expression or differential accessibility using a log2 fold-change threshold of 1 and an FDR of 0.05. For comparison of ATAC-seq data with DNase-seq data from non-retinal tissues (Figure 5), photoreceptors were collapsed into a single level. Differentially expressed genes are listed in Supplementary file 5, and differentially accessible regions are listed in Supplementary file 7. For each comparison (i.e. ON versus OFF, pan BC versus rod), gene expression stemming from low-level contamination of bipolar cell populations with either rod or cone photoreceptors was filtered out. When comparing pan BC to photoreceptor populations, potential contaminating genes from the alternate photoreceptor type (i.e. rod genes identified as enriched in pan BC versus blue cone) were identified as those highly expressed (>16 fold) and specific to rod compared to blue cone, and also more highly expressed in rod compared to pan BC (at least four-fold). In comparing ON and OFF bipolar cells, genes enriched in each bipolar cell population were filtered for those which were also identified as highly specific to either photoreceptor population compared to the enriched bipolar cell type. For example, genes increased in OFF- compared to ON-bipolar cells were filtered for genes that were also highly enriched (>16 fold) in rods and blue cones compared to OFF bipolar cells. In total, 38 genes were filtered from those identified as enriched in pan BC compared to photoreceptors, and 39 genes were filtered from the ON versus OFF comparison (12 from the ON-enriched, 27 from the OFF-enriched). These genes include those known to be expressed at very high levels in either rod or cone photoreceptors (Corbo et al., 2007).

RNA isolation, qPCR and RNA-seq

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Sorted bipolar cell populations were resuspended in 500 µl TRIzol reagent (Invitrogen), and RNA was isolated according to manufacturer’s instructions. Prior to sequencing, RNA quality was analyzed using an Agilent Bioanalyzer. cDNA was prepared using the SMARTer Ultra Low RNA kit for Illumina Sequencing-HV (Clontech) per manufacturer’s instructions. cDNA was fragmented using a Covaris E210 sonicator using duty cycle 10, intensity 5, cycles/burst 200, time 180 s. cDNA was blunt-ended, an ‘A’ base added to the 3′ ends, and Illumina sequencing adapters were ligated to the ends of the cDNAs. Ligated fragments were amplified for 12 cycles using primers incorporating unique index tags. Replicate libraries from each bipolar cell population were pooled in equimolar ratios and sequenced on an Illumina HiSeq 3000 (single-end 50 bp reads). For qPCR, RNA samples were treated with TURBO DNase (Invitrogen) and cDNA was synthesized with SuperScript IV (Invitrogen) and oligo(dT) primers according to manufacturer’s instructions. For Figure 1C, expression was normalized to the average of reference genes Gapdh, Sdha, Hprt, and Pgk. For Figure 1—figure supplement 2, expression was normalized to Gapdh alone. Primers for Grm6, Gnat1, Lhx1, Pax6, Rlbp1, Slc17a6, Vsx2, Grik1, Tacr3, Isl1, and Lrrtm1 are listed in Supplementary file 2.

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Decision letter

  1. Claude Desplan
    Reviewing Editor; New York University, United States
  2. Patricia J Wittkopp
    Senior Editor; University of Michigan, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Cis-regulatory basis of sister cell type divergence in the vertebrate retina" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Patricia Wittkopp as the Senior Editor. The reviewers have opted to remain anonymous.

The paper has been reviewed by three excellent reviewers who have entered into an intense discussion about its strength and limitations.

As you will be able to see below, the reviewers find the comparison in transcription factor binding site usage between photoreceptors and bipolar cells to be of significant interest and worth publishing. However, the reviewers were also concerned about potential problems with the profiling of ON vs. OFF bipolar cells and the impact of the paper might depend on how well you can separate the ON and OFF bipolar cells. Only the Grm6-GFP, but not the YFP version that you have used, labels the ON BCs. If the data indeed are based on mixed ON/OFF populations, then this needs to be clearly stated in the manuscript that currently seems to implicate that the separation is perfect.

One suggestion would be to remove the entire ON vs. OFF portion of the manuscript, but in this case, the paper would be significantly weakened unless the comparison of photoreceptor to bipolar could be enhanced. The other alternative might be to combine the Shekhar et al. single cell data into ON and OFF pools and then compare them against your RNA-seq data to see if they are well correlated. If this were to be feasible, and the data were to support the separation, this would significantly strengthen the model. As for now, the comparison in Figure 2 is neither quantitative nor visually clear.

Reviewer #1:

In order to compare the cis-regulatory features of vertebrate photoreceptors and bipolar cells, Murphy et al. analyze the transcriptomes and open chromatin landscapes of these sister cell types. The authors find that the transcriptomes are very different, while there are similarities in the usage of cis-regulatory motifs, especially K50 motifs. Interestingly, bipolar cell-specific enhancers exhibit an enrichment of E-box motifs that are bound by bHLH TFs, while photoreceptor-specific enhancers exhibit an enrichment of Q50 motifs that are bound by Q50 homeodomain TFs. Murphy et al. demonstrate that a reporter, which labels both photoreceptors and bipolar cells, can be made photoreceptor-specific by mutating its K50 to Q50 motifs. They propose that such conversion and partitioning of cis-regulatory motifs, together with the emergence of transcriptional repressors (such as VSX2 that binds to Q50 motifs), were critical steps in the evolutionary divergence of photoreceptors and bipolar cells as separate sister cell types.

This rigorous and well-written manuscript should be of interest for a broad readership, as it proposes an exciting model for how closely related cell classes are distinguished during evolution and provides a great resource for the retina community.

- Is there anything special concerning the expression and/or function subset of genes that use dimeric Q50 motifs (Figure 4)? Do they have anything else in common?

- In the first paragraph of the Discussion section: “Thus, in both retina and spinal cord, expression of VSX2 promotes interneuron fate at the expense of the alternative 'effector' neuron (photoreceptor or motor neuron) cell type.” The wording is confusing, as a photoreceptor is not an effector neuron.

Reviewer #2:

In their manuscript, Murphy et al. describe a study which combines expression (with RNA-seq) and open chromatin (with ATAC-seq) profiling of the ON-bipolar (BC) and OFF-BC cells in the mouse retina. The aim is to identify TF binding motifs that function to differentiate the bipolar cells and the photoreceptors (PRs) and then ON-BCs from OFF-BCs. I have very mixed feelings about this paper for reasons outlined below.

Authors performed RNA-seq and ATAC-seq on cells FACS-sorted based on Otx2-GFP and Grm6-YFP to obtain BCs and to subdivide them into ON and OFF subclasses (Otx2-GFP/Grm6-YFP +/+ and +/- respectively, that can be discriminated based on brightness). However, according to Schubert et al. (2008), which describes the Grm6-YFP mice, "YFP is expressed in a subset of on-bipolar cells", i.e. sparsely, which allows them to do electrophysiology on these cells. If I read this correctly, this means that Otx2-GFP+/Grm6-YFP- population should contain a significant number of ON-BCs which did not express Grm6-YFP. Why Grm6 RT-PCR is clean is not clear, but other markers should also be used to rule out that ON is contaminating OFF population. Next, Shekhar et al. (2016) also found that one of the ON-BC subtypes (BC5D) expresses Grm6 at low levels if at all, probably resulting in little or no YFP expression and thus these cells would sort with the OFF population. Grm6 is expressed in the Rod BCs (RBCs) which authors here group with ON-BCs for their analysis. Yet, according to Shekhar et al. transcriptomicly ON- and OFF-BCs are more closely related to each other than ON-BCs are related to RBCs. This is pretty obvious from Figure 2, left panel. All of these issues have a strong potential to muddle the ON vs. OFF comparisons and should be addressed by the authors.

Indeed, perhaps the above issues are the reason why authors find that the relationship between chromatin accessibility and gene expression in the ON- and OFF-BCs is "complex". Yet in Figure 5—figure supplement 1C these are pretty clearly negatively correlated, a puzzling result. It would be nice to see this shown as in Figure 5C. Furthermore, this negative correlation would be interesting to explore for both, the BC vs. PRs as well as ON vs. OFF BCs. Thus, to me it seems that the strategy used in the second to last section of the Results (subsection “Photoreceptor- and bipolar-specific open chromatin regions are positively correlated with cell class-specific gene expression”) should be also applied to the ON vs. OFF, and also perhaps to be modified for the anti-correlated genes.

It is not clear to me why the authors did not use the single cell expression data from Shekhar et al. instead of their own RNA-seq data for comparison with the accessible chromatin regions. (By the way, authors' point that their pooled RNA-seq is somewhat more sensitive is a bit hollow given Shekhar et al. effort to optimize the tradeoff between the sequencing depth and the sequenced cell number.) Would it be possible to classify genes based on their expression in a particular cell type (or cell type pool) and to use the accessibility information simply to know where to look for the TF binding sites (i.e. without worrying about the enrichment of the accessibility)? If this were possible, authors could try to go deeper down the branches of the transcriptional relatedness tree from Shekhar et al., then just the first ON vs. OFF branch. A related question to this is whether (and how frequently) authors found different accessible chromatin regions for the same gene that were enriched in different cell classes?

I would remove Figure 2 (at least the right side) and Figure 2—figure supplement 2. They seem to be just reprinted Shekhar et al. data reordered according to RNA-seq "lowest adjusted p-value" of the present study. If authors want to compare the gene expression from the two studies, they need to find a way to pool Shekhar et al. data into PR, ON and OFF classes.

Regarding the electroporation experiments: While it is a nice idea to replace useless Q50 sites with K50 and show transcriptional repression, this experiment does not address the hypothesis posed in the second paragraph of the subsection “Photoreceptor and bipolar cells employ closely related yet distinct cis-regulatory grammars”, i.e. it does not show whether the presence of K50 makes chromatin inaccessible. Also, in the rest of the paper a distinction is made between enhancers and promoters, and the analysis of TF binding sites was done with enhancers, yet the electroporation experiment was done with a promoter (by their definition of <1000bp from a transcription start site) and in fact raises doubt whether the enhancer/promoter partition used by the authors in their analysis was the biologically relevant one. Finally, this experiment relies on pairing of two different TF binding sites, while sequence analysis showed that there is no enrichment of specific pairs in PR vs. BC comparison. In fact, it is not obvious to me why the authors did not find differential enrichment of the TF binding site pairs where one site is a differentially enriched TF binding site (Q50 and E-box).

In the Discussion, the evolutionary hypothesis is a nice touch, however, given that there are no experiments actually addressing it, does it deserve such a prominent role? Meanwhile, the many interesting observations (e.g. why there is no enrichment of TF binding pairs) remain unaddressed.

Finally, a major concern is that the paper is difficult to follow and was not written with a broad readership in mind. The authors reasoning is often not clear (e.g. subsection “Bipolar cells have a more accessible chromatin landscape than either rods or cones”, first paragraph), the definitions of the terms used are hard to find (promoter vs. enhancer is defined in the legend of a figure supplement, the fact that they use "TSS-distal" and "enhancer" as synonyms is also hidden in figure legends), figure legends are not clear (e.g. Figure 4—figure supplement 1) and sometimes it is just not clear what authors mean (what are "replicates" in the first sentence of Figure 3A legend). Finally, the entire paper needs some re-organization (e.g. the way PR vs. BC and ON vs. OFF comparisons are written up should parallel each other).

To sum up, while I am not against the publication of at least part of this data in eLife, the paper would need a major overhaul. There are seemingly large issues to address and the analysis feels superficial.

Reviewer #3:

In this manuscript, Murphy and colleagues examine the regulatory logic governing photoreceptor- and bipolar cell-specific gene expression. They generate a considerable amount of new data to describe the transcriptome and the open chromatin regions within bipolar cells. Murphy and colleagues then compare their results with prior data from photoreceptors. Interestingly, this revealed that photoreceptor and bipolar cell genes were enriched for different types of transcription factor binding sites. In one example, they show that subtly changing the type of binding site will restrict a broader expression pattern to be only made by photoreceptors. From their data, they then extrapolate a model to explain how bipolar cells evolved from a photoreceptor ancestral state. The experiments and data analysis in this manuscript are rigorous and compelling, but would be improved by some minor modifications. While the discussion about the evolution of photoreceptors is of high interest, the manuscript would be strengthened by additional discussion of the cis-regulatory findings. Specific recommendations are listed below:

1) A couple modifications to the data analysis and figures would improve the manuscript.

a) The authors mention that zinc finger transcription factor sites are more common in photoreceptors. However, this binding site preference data is not shown in Figure 4 (except for CTCF). Showing a zinc finger example in Figure 4 would improve the clarity of the manuscript.

b) In Figure 5C (and Figure 5—figure supplement 1D), it would help the reader to label some of the dots in these plots. For example, showing a few examples of known photoreceptor and bipolar genes. Highlighting a few discordant (black dots, e.g., Grik1) genes would help the reader as well.

c) In Figure 6D, the categorical format provides a nice summary of the data. However, a plot or table showing quantitative data and statistics would make a much stronger case that the type of binding site affects gene expression.

2) While the discussion about the evolution of photoreceptors and bipolar cells and Figure 7 are done well, it may be hard for readers to link the cis-regulatory data to the evolutionary model. In addition, there are some regulatory findings that are not discussed. The manuscript would benefit from a brief expansion of the Discussion section to address the cis-regulatory grammar findings in more detail. Addressing the following items would strengthen the manuscript:

a) Why is overall chromatin accessibility different between photoreceptors and bipolars? Does it impact the evolutionary model?

b) If bHLH factors are important for photoreceptor formation or maintenance, why are these cites depleted from mature photoreceptors? Are there differences between developmental and homeostatic/mature cis-regulatory networks? Are the cis-regulatory changes expected to be the same in a developmental context?

c) Why are bZIP sites (e.g., Nirl) strong in green cones and modest in rods (Figure 4)?

d) Why do some genes (e.g., Grik1) behave in a discordant fashion?

https://doi.org/10.7554/eLife.48216.043

Author response

As you will be able to see below, the reviewers find the comparison in transcription factor binding site usage between photoreceptors and bipolar cells to be of significant interest and worth publishing. However, the reviewers were also concerned about potential problems with the profiling of ON vs. OFF bipolar cells and the impact of the paper might depend on how well you can separate the ON and OFF bipolar cells. Only the Grm6-GFP, but not the YFP version that you have used, labels the ON BCs. If the data indeed are based on mixed ON/OFF populations, then this needs to be clearly stated in the manuscript that currently seems to implicate that the separation is perfect.

See below, reviewer #2, first comment.

One suggestion would be to remove the entire ON vs. OFF portion of the manuscript, but in this case, the paper would be significantly weakened unless the comparison of photoreceptor to bipolar could be enhanced. The other alternative might be to combine the Shekhar et al. single cell data into ON and OFF pools and then compare them against your RNA-seq data to see if they are well correlated. If this were to be feasible, and the data were to support the separation, this would significantly strengthen the model. As for now, the comparison in Figure 2 is neither quantitative nor visually clear.

See below reviewer #2, tenth comment.

Reviewer #1:

[…]

- Is there anything special concerning the expression and/or function subset of genes that use dimeric Q50 motifs (Figure 4)? Do they have anything else in common?

We assume that reviewer #1 is inquiring about differences between genes which are in proximity to dimeric Q50 motifs, as opposed to those associated with monomeric Q50 motifs. To address this question, we compared lists of genes associated with peaks identified in Figure 5A (open in photoreceptor or bipolar cells, but not other tissues) which harbored either monomeric or dimeric Q50 sites, using the Gene Ontology (GO) tool PANTHER. The list of genes associated with monomeric Q50 motifs (n = 1,370) was enriched for GO biological processes relating to neuronal development, including axon guidance and dopaminergic neuronal differentiation. The list of genes associated with dimeric motifs (n = 423) was similar, but associated with fewer and more generic GO terms, including amino acid import, cell-cell adhesion, and generation of neurons. This limited analysis does not suggest that dimeric and monomeric motifs distinguish functionally distinct gene expression programs. Whether or not dimeric and monomeric Q50 sites function differently as cis-regulatory elements would require a systematic comparison of their activity, which is beyond the scope of the present study. Previous research in our lab indicates that dimeric K50 sites act as stronger activators than monomeric sites in photoreceptors (Hughes et al., 2018). Thus, it could be possible that, in bipolar cells, dimeric and monomeric Q50 sites have similar function, with dimeric motifs representing stronger repressive elements.

- In the first paragraph of the Discussion section: “Thus, in both retina and spinal cord, expression of VSX2 promotes interneuron fate at the expense of the alternative 'effector' neuron (photoreceptor or motor neuron) cell type.” The wording is confusing, as a photoreceptor is not an effector neuron.

We have removed the expression “effector neuron”.

Reviewer #2:

In their manuscript, Murphy et al. describe a study which combines expression (with RNA-seq) and open chromatin (with ATAC-seq) profiling of the ON-bipolar (BC) and OFF-BC cells in the mouse retina. The aim is to identify TF binding motifs that function to differentiate the bipolar cells and the photoreceptors (PRs) and then ON-BCs from OFF-BCs. I have very mixed feelings about this paper for reasons outlined below.

Authors performed RNA-seq and ATAC-seq on cells FACS-sorted based on Otx2-GFP and Grm6-YFP to obtain BCs and to subdivide them into ON and OFF subclasses (Otx2-GFP/Grm6-YFP +/+ and +/- respectively, that can be discriminated based on brightness). However, according to Schubert et al. (2008), which describes the Grm6-YFP mice, "YFP is expressed in a subset of on-bipolar cells", i.e. sparsely, which allows them to do electrophysiology on these cells. If I read this correctly, this means that Otx2-GFP+/Grm6-YFP- population should contain a significant number of ON-BCs which did not express Grm6-YFP. Why Grm6 RT-PCR is clean is not clear, but other markers should also be used to rule out that ON is contaminating OFF population.

The reviewer’s reasoning is sound but is based on a citation error on our part. The correct citation for the source of the Grm6-YFP mouse line used in our study is Morgan et al. (2011). The line was referred to as ‘Grm6-YFP’ in that study and was also used in a subsequent study (Johnson et al., 2017, Nat. Commun, 8: 1220) where it was referred to as ‘Grm6L-YFP-DTAcon’. This mouse line expresses YFP in the vast majority of ON bipolar cells and not in OFF bipolar cells. We have corrected the citation in the text and address the purity of our ON and OFF bipolar cell populations in the following points:

1) Co-labeling of Grm6-YFP positive cells with a marker for photoreceptor and bipolar cell axon terminals (VGLUT1) shows that Grm6-YFP positive cells terminate exclusively in the inner half of the inner plexiform layer (S3-S5), as expected of ON bipolar cells (Figure 3A, Johnson et al. 2017 Nat. Commun, 8: 1220). Grm6-YFP positive cells in the innermost sublamina of the IPL are also labelled with PKCα, a marker for rod bipolar cells.

2) RNA-seq read counts for pan-ON BC markers (e.g., Grm6 and Isl1) are very high in the ON bipolar population and very low in the OFF bipolar population.

3) Similarly, qPCR for Grm6 (Figure 1C) and Isl1 (new RT-qPCR data, presented in Figure 1—figure supplement 2) show high expression in the ON bipolar population and little or no expression in the OFF bipolar population.

4) In a new analysis (presented in Figure 2—figure supplement 1) we combined single-cell RNA-seq data from Shekhar et al. to create ‘pseudo-bulk’ ON and OFF bipolar profiles for comparison to our data. We found that our RNA-seq data for ON and OFF populations correlates very well with pooled pseudo-bulk data for ON and OFF bipolar cells from Shekhar et al. In addition, we found a strong correlation between estimates of fold-change in pseudo-bulk single-cell RNA-seq data and fold-changes identified in our ON vs. OFF comparison (See response to the tenth comment below).

5) Additional analysis (see next point) indicates that a rare ON BC subtype, BC5D, is absent from both our ON and OFF populations.

Next, Shekhar et al. (2016) also found that one of the ON-BC subtypes (BC5D) expresses Grm6 at low levels if at all, probably resulting in little or no YFP expression and thus these cells would sort with the OFF population.

We thank reviewer #2 for highlighting the problem of low Grm6 expression in BC5D cells. To determine whether BC5D cells were sorted into ON or OFF populations, we first examined the expression of the BC5D-specific marker, Lrrtm1 (identified in Shekhar et al. 2016) in our RNA-seq data. We found that there were very few Lrrtm1 sequence reads (<100) in either ON or OFF bipolar cells, suggesting that BC5D cells were absent from both populations. Given the low-level expression of Grm6 in BC5D cells, we reasoned that these cells may have fallen between the fluorescence gates used in our original FACS analysis. To evaluate this possibility, we repeated our FACS analysis, but this time we also collected cells with intermediate levels of green fluorescence which fell between the previously used ON and OFF population gates (this intermediate population is termed ‘YFP-low’). We found that the YFP-low population expressed both the ON BC marker Isl1 and the BC5D marker Lrrtm1, while the newly sorted ON and OFF populations again failed to express Lrrtm1. These results (included in Figure 1—figure supplement 2) indicate that BC5D cells were likely excluded from both ON and OFF populations in our original analysis.

We believe that the absence of BC5D cells does not in any way alter the conclusions of our study. Published estimates of the prevalence of BC5D cells suggest that this cell type comprises only a small percentage of bipolar cells in the mouse retina. Shekhar et al. found that BC5D made up only 2.3% of bipolar cells analyzed by Drop-seq. Furthermore, the authors note that BC5D cells resemble the bipolar cell type identified by electron microscopy as “XBC” by Helmstaedter et al. (2013, Nature, 500:168–174). In that publication, XBC cells comprised only ~1.5% (7 out of 459 cells) of bipolar cells analyzed. We attempted to estimate the proportion of BC5D among ON bipolar cells by quantifying the number of cells in Shekhar et al. Supplementary Figure 7F, which shows a retinal whole-mount preparation labelled with Lrrtm1 and Grm6 by double FISH. Using ImageJ to identify cells by particle analysis, we counted a total of 258 Grm6 positive cells in Figure 7F, indicating that the seven Lrrtm1 positive cells constitute about 2.7% of ON bipolar cells. While quantifying a single field of view is by no means definitive, this number correlates well with the overall estimates of BC5D representing 1.5-2.3% of all bipolar cells. We have added a discussion of these issues to the main text (subsection “Photoreceptors and bipolar cells exhibit divergent transcriptional profiles”, sixth paragraph).

Grm6 is expressed in the Rod BCs (RBCs) which authors here group with ON-BCs for their analysis. Yet, according to Shekhar et al. transcriptomicly ON- and OFF-BCs are more closely related to each other than ON-BCs are related to RBCs. This is pretty obvious from Figure 2, left panel. All of these issues have a strong potential to muddle the ON vs. OFF comparisons and should be addressed by the authors.

The grouping of RBCs and cone ON bipolar cells is a drawback of our sorting approach. Our analysis nonetheless suggests that there are very few differences in chromatin accessibility between individual BC types. We estimate that RBCs constitute nearly 60% of the cells in our ON population (RBCs compose 56% of ON bipolar cells identified in Shekhar et al. and ~57% of those identified as ON BCs by Wässle et al., 2009). Thus, we predict that RNA-seq and ATAC-seq data from isolated rod bipolar cells will likely resemble our ON population data quite closely. We have addressed these issues in a new paragraph in the Discussion.

Indeed, perhaps the above issues are the reason why authors find that the relationship between chromatin accessibility and gene expression in the ON- and OFF-BCs is "complex". Yet in the Figure 5—figure supplement 1C these are pretty clearly negatively correlated, a puzzling result. It would be nice to see this shown as in Figure 5C. Furthermore, this negative correlation would be interesting to explore for both, the BC vs. PRs as well as ON vs. OFF BCs.

We thank the reviewer for pointing this out. We inadvertently transposed the X-axis labels of Figure 5—figure supplement 1 in the original submission. We have corrected this error. In addition, we now include a comparison between ON and OFF bipolar cells as suggested by the reviewer (see Figure 5—figure supplement 1).

Thus, to me it seems that the strategy used in the second to last section of the Results (subsection “Photoreceptor- and bipolar-specific open chromatin regions are positively correlated with cell class-specific gene expression”) should be also applied to the ON vs. OFF, and also perhaps to be modified for the anti-correlated genes.

This is a great suggestion. We attempted to remove peaks open in other tissues from the ON vs. OFF comparison, as was done for the photoreceptor/bipolar cell comparison. However, this yielded a very small number of remaining peaks which are more accessible in OFF bipolar cells than in ON (n = 25). We think this is largely a function of the already small number of regions more accessible in OFF bipolar cells and highlights the high degree of similarity between the two datasets.

It is not clear to me why the authors did not use the single cell expression data from Shekhar et al. instead of their own RNA-seq data for comparison with the accessible chromatin regions.

Our goal was to compare chromatin accessibility and transcriptomic data from identical bipolar cell populations. We therefore performed both analyses on the same populations isolated in an identical manner.

(By the way, authors' point that their pooled RNA-seq is somewhat more sensitive is a bit hollow given Shekhar et al. effort to optimize the tradeoff between the sequencing depth and the sequenced cell number.)

As described by Shekhar et al., the authors optimized their experimental approach for cost-effective discovery and classification of BC subtypes. Single-cell RNA-seq proved to be a powerful approach for unsupervised discovery of BC subtypes, characterization of BC subtype transcriptomes, and identification of BC subtype marker genes. Consistent with our results, previous comparisons of single-cell and bulk RNA-sequencing have suggested that bulk sequencing has higher sensitivity to detect (and precision to quantify) the transcription of genes with low expression. Therefore, we believe bulk RNA-seq from purified populations of BCs provides a complementary resource for understanding BC gene expression over a broad dynamic range. In response to this comment, we have modified the relevant section of the text (subsection “Photoreceptors and bipolar cells exhibit divergent transcriptional profiles”, seventh paragraph).

Would it be possible to classify genes based on their expression in a particular cell type (or cell type pool) and to use the accessibility information simply to know where to look for the TF binding sites (i.e. without worrying about the enrichment of the accessibility)? If this were possible, authors could try to go deeper down the branches of the transcriptional relatedness tree from Shekhar et al., then just the first ON vs. OFF branch.

This is an excellent suggestion, and we have performed the analysis. Unfortunately, we did not identify convincing differences in motif content between peak sets associated with different subtypes of bipolar cells. We evaluated several different strategies for associating genes identified as cluster-enriched by Shekharet al. with ATAC-seq peaks that we identified by profiling bulk populations of bipolar cells, including using peaks within 5, 10, 25, or 50 kb of the transcription start sites corresponding to cluster-enriched genes, and considering promoter and enhancer peaks separately as well as jointly. We then performed motif enrichment analysis on the resulting peak sets. We found that none of these approaches revealed strong differences in the motifs enriched among ATAC-seq peaks associated with genes from different clusters. There are several possible explanations for this result. First, the approach may not adequately enrich for cluster-specific ATAC-seq peaks (high background of shared peaks). Second, ATAC-seq from larger populations may fail to identify cluster-specific peaks in less common bipolar cell subtypes (low signal). And third, the cis-regulatory grammars of bipolar cell subtypes may be too similar to identify differences with this approach. Ultimately, ATAC-seq from purified bipolar cell subtypes and/or single-cell ATAC-seq will be required to differentiate among these possibilities.

A related question to this is whether (and how frequently) authors found different accessible chromatin regions for the same gene that were enriched in different cell classes?

A very interesting question. We certainly do find peaks near the same gene which exhibit discordant accessibility patterns. For example, using the list of 7,144 peaks identified in Figure 5A, which are either specific to photoreceptors or to bipolar cells, there are a total of 6 peaks which were associated with Prkca. Despite bipolar-specific expression, four of these peaks (one of which is highlighted in Figure 5C) are more highly accessible in photoreceptor cells, while two are more highly accessible in bipolar cells. Overall, about 15% (496 bipolar cell-enriched, and 574 photoreceptor enriched) of the 7,144 peaks are associated with genes that were also assigned to peaks enriched in the alternate cell type. Perhaps this percentage reflects the high relatedness of photoreceptor and bipolar cells. We hypothesize that this percentage would be somewhat lower when comparing elements that are specific to more distantly related cell types. Of course, these analyses depend on our ability to accurately match ATAC-seq peaks with genes, which is imperfect.

I would remove Figure 2 (at least the right side) and Figure 2—figure supplement 2 They seem to be just reprinted Shekhar et al. data reordered according to RNA-seq "lowest adjusted p-value" of the present study. If authors want to compare the gene expression from the two studies, they need to find a way to pool Shekhar et al. data into PR, ON and OFF classes.

We believe that it is important to highlight the correlation between our data and those of Shekhar et al. For this reason, we have chosen not to remove Figure 2 or Figure 2—figure supplement 2.

We appreciate the suggestion to pool data from Shekhar et al. for comparison with our data. Accordingly, we reprocessed the single-cell data from Shekharet al., aggregating gene-level expression estimates for pan, ON, and OFF bipolar cells by taking the weighted average of normalized counts over all cells belonging to each class (determined by their assigned cluster). We then rescaled these values to directly compare expression estimates from bulk and ‘pseudo-bulk’ RNA-seq. This analysis showed that expression estimates from bulk and single-cell RNA-seq are largely consistent (Pearson correlation coefficients ranged from 0.82 to 0.85). As expected, fewer genes were detected by single-cell RNA-seq, and these tended to have relatively low expression as estimated by bulk RNA-seq. Furthermore, this analysis suggested that our sorting strategy enriched for the desired bipolar cell populations. That is, bulk pan bipolar cell data was more strongly correlated with single-cell RNA-seq from pan bipolar cells compared to rods or cones, and bulk ON bipolar cell data was more strongly correlated with single-cell RNA-seq from ON bipolar cells compared to OFF bipolar cells (and vice versa for OFF vs. ON bipolar cells). Finally, this analysis allowed us to compare the ON vs. OFF bipolar cell differential expression analysis between bulk and single-cell RNA-seq. We found that the resulting fold-change estimates were strongly correlated (R2 = 0.78), again supporting the interpretation that our sorting strategy was effective in isolating enriched populations of ON and OFF bipolar cells. We now present this analysis in detail in the Results (subsection “Photoreceptors and bipolar cells exhibit divergent transcriptional profiles”, fifth paragraph), Materials and methods section, and in Figure 2—figure supplement 1.

Regarding the electroporation experiments: While it is a nice idea to replace useless Q50 sites with K50 and show transcriptional repression, this experiment does not address the hypothesis posed in the second paragraph of the subsection “Photoreceptor and bipolar cells employ closely related yet distinct cis-regulatory grammars”, i.e. it does not show whether the presence of K50 makes chromatin inaccessible.

This is a valid point. The text (subsection “Photoreceptor and bipolar cells employ closely related yet distinct cis-regulatory grammars”, second paragraph) has been adjusted accordingly.

Also, in the rest of the paper a distinction is made between enhancers and promoters, and the analysis of TF binding sites was done with enhancers, yet the electroporation experiment was done with a promoter (by their definition of <1000bp from a transcription start site) and in fact raises doubt whether the enhancer/promoter partition used by the authors in their analysis was the biologically relevant one.

This comment identifies an important contrast between our analysis of candidate regulatory regions identified by ATAC-seq and our functional studies characterizing bipolar cell regulatory activity by electroporation. Regarding the biological relevance of promoter and enhancer designations, there are a variety of well-documented differences between enhancers and promoters, including GC content (Natarajan et al., 2012,Genome Res.,9:1711-22 and Wang et al., 2012, Genome Res., 9:1798-812) patterns of transcription factor binding site enrichment (Zheng et al., 2012, Nucleic AcidsRes., 43:74-83), and cell type-specificity (Corces et al., 2016, Nat. Genet., 48:1193–203). While the -1000 bp to +100 bp definition of ‘promoter’ used for data analysis is largely consistent with the size and location of depleted nucleosomes overlapping gene promoters (see Supplementary Figure 6A in Hughes et al., 2017), its main purpose is to exclude promoter sequences from the ‘enhancer’ set. This distinction is not meant to imply that enhancers cannot occur within 1 kb of the transcription start site. We believe the mouse Gnb3 ‘promoter region’ contains bona fide enhancer sequences.

Finally, this experiment relies on pairing of two different TF binding sites, while sequence analysis showed that there is no enrichment of specific pairs in PR vs. BC comparison. In fact, it is not obvious to me why the authors did not find differential enrichment of the TF binding site pairs where one site is a differentially enriched TF binding site (Q50 and E-box).

The reviewer is correct in expecting that individually enriched TF sites would lead to pairs which are differentially enriched between the two cell populations. This is in fact the case. Our original analysis was structured to identify combinations of TF binding sites that were enriched given the baseline frequencies of each motif in each peak set. We took this approach reasoning that it was more likely to identify pairs of motifs that function cooperatively (and that the enrichment of pairs of motifs among one peak set vs. another would be trivial if one or both had already been shown to be enriched). We have now reanalyzed motif co-occurrence without normalizing for baseline enrichment. As expected, we find that motifs which are highly enriched in each tissue (e.g. E-box in bipolar cells and homeodomain in photoreceptors) appear in motif pairs which are also enriched in the respective cell population. We have updated our supplementary figure accordingly (see Figure 4—figure supplement 1). In addition, we state the result of the original analysis: although specific pairs are enriched in one peak set vs. another, the frequencies of peaks with one or both motifs are consistent with an independent model. Please see the last paragraph of the subsection “Photoreceptor and bipolar cells employ closely related yet distinct cis-regulatory grammars”.

In the Discussion, the evolutionary hypothesis is a nice touch, however, given that there are no experiments actually addressing it, does it deserve such a prominent role? Meanwhile, the many interesting observations (e.g. why there is no enrichment of TF binding pairs) remain unaddressed.

We now address multiple additional points in the Discussion. Please see the above comment in regard to motif co-enrichment.

Finally, a major concern is that the paper is difficult to follow and was not written with a broad readership in mind. The authors reasoning is often not clear (e.g. subsection “Bipolar cells have a more accessible chromatin landscape than either rods or cones”, first paragraph), the definitions of the terms used are hard to find (promoter vs. enhancer is defined in the legend of a figure supplement, the fact that they use "TSS-distal" and "enhancer" as synonyms is also hidden in figure legends), figure legends are not clear (e.g. Figure 4—figure supplement 1) and sometimes it is just not clear what authors mean (what are "replicates" in the first sentence of Figure 3A legend). Finally, the entire paper needs some re-organization (e.g. the way PR vs. BC and ON vs. OFF comparisons are written up should parallel each other).

Thank you for bringing up these issues. We have made the following efforts to clarify the text:

- The second paragraph of the subsection “Bipolar cells have a more accessible chromatin landscape than either rods or cones” has been re-written;

- We have added a definition of ‘promoter’ and ‘enhancer’ to the main text (subsection “Bipolar cells have a more accessible chromatin landscape than either rods or cones”, first paragraph);

- We have updated the legend for Figure 4—figure supplement 1;- The wording in the legend of Figure 3 has been modified to improve clarity.

Reviewer #3:

[…]

1) A couple modifications to the data analysis and figures would improve the manuscript.

a) The authors mention that zinc finger transcription factor sites are more common in photoreceptors. However, this binding site preference data is not shown in Figure 4 (except for CTCF). Showing a zinc finger example in Figure 4 would improve the clarity of the manuscript.

We believe the reviewer is referring to the enrichment of zincfinger TFs themselves, especially in rods compared to bipolar cells. To clarify this point, we have changed ‘enrichment’ to ‘expression’. Otherwise, zincfinger motifs are in fact highly enriched in both tissues, specifically those corresponding to CTCF and BORIS (a homolog of CTCF with a similar binding site preference). The CTCF motif is actually more highly enriched in bipolar cells than in either rods or cones. Other zincfinger motifs are also differentially enriched, such as PRDM1 and PRDM9 (higher in bipolar cells) and ZNF711, ZNF467 (higher in photoreceptors). However, these motifs are not highly prevalent in the enhancers of either cell type, making density plots as in Figure 4 relatively uninformative.

b) In Figure 5C (and Figure 5—figure supplement 1D), it would help the reader to label some of the dots in these plots. For example, showing a few examples of known photoreceptor and bipolar genes. Highlighting a few discordant (black dots, e.g., Grik1) genes would help the reader as well.

Done.

c) In Figure 6D, the categorical format provides a nice summary of the data. However, a plot or table showing quantitative data and statistics would make a much stronger case that the type of binding site affects gene expression.

Unfortunately, we do not have quantitative data for this section. To demonstrate the reproducibility of our findings, we have compiled a new supplementary figure (Figure 6—figure supplement 1) with images of additional retinas electroporated with the various Gnb3 reporter constructs.

2) While the discussion about the evolution of photoreceptors and bipolar cells and Figure 7 are done well, it may be hard for readers to link the cis-regulatory data to the evolutionary model. In addition, there are some regulatory findings that are not discussed. The manuscript would benefit from a brief expansion of the Discussion section to address the cis-regulatory grammar findings in more detail. Addressing the following items would strengthen the manuscript:

a) Why is overall chromatin accessibility different between photoreceptors and bipolars? Does it impact the evolutionary model?

This finding may partly be attributable to the uniquely closed architecture of rod chromatin documented in a prior study (Hughes et al., 2017). This topic is addressed throughout the Discussion.

b) If bHLH factors are important for photoreceptor formation or maintenance, why are these cites depleted from mature photoreceptors? Are there differences between developmental and homeostatic/mature cis-regulatory networks? Are the cis-regulatory changes expected to be the same in a developmental context?

This question highlights an important distinction between the motif density data presented in Figure 4 and that presented in Figure 5. Specifically, the data from Figure 5 is derived from peak sets that are specific to either photoreceptor or bipolar cells. When all regions of accessibility are considered (as in Figure 4), both photoreceptor and bipolar cells show an overall enrichment of E-box motifs, albeit to a different degree. As such, the lack of E-box motif enrichment in the peak set specific to photoreceptors does not necessarily mean that E-box motifs are depleted from the cis-regulatory regions of mature photoreceptors. Rather, this shows that within the specific subset of peaks which are accessible in photoreceptors but not in bipolar cells or other cell types, E-box motifs are not observed in high density. The importance of E-box motifs in photoreceptor cis-regulation is indicated by the high density of E-box motifs within the set of peaks that are shared by photoreceptors and bipolar cells, but not other cell types (Figure 5C, second panel from the right). We expect that there is variation in cis-regulatory networks throughout development, and that these changes are likely to vary from cell type to cell type. It would be interesting to profile and compare the cis-regulatory landscape of developing photoreceptor and bipolar cells in future studies. We now address these points in the Results and Discussion.

c) Why are bZIP sites (e.g., Nrl) strong in green cones and modest in rods (Figure 4)?

The significance of the difference in bZIP motif enrichment in rod and cone open chromatin is an interesting and unanswered question. One possibility is that these motifs are bound by NRL (a MAF-type bZIP TF) in rods and by another currently unknown bZIP TF in cones. For example, we previously found that MAF (a MAF-type bZIP TF) is expressed in adult mouse cones (at low levels) but not in rods (see Supplementary Table 6 in Hughes et al., 2017). Additional work is needed to evaluate this idea.

d) Why do some genes (e.g., Grik1) behave in a discordant fashion?

This is an important question. Our observation that there is a correlation between accessibility and gene expression is consistent with previous reports in photoreceptors (Hughes et al., 2017) and other cell types (de la Torre-Ubieta et al., 2018 and Starks et al., 2019). However, the correlation is not perfect (i.e., accessibility is not sufficient for expression or enhancer activity). For example, the mouse LCR, an enhancer which drives expression of red/green cone opsin (Opn1mw), is open in both rods and cones, despite its cone-specific expression (Hughes et al., 2017). Similarly, in developmental contexts, regions that are ‘poised’ exhibit accessibility yet remain inactive until later developmental timepoints (Lara-Astiaso et al., 2014 and Schulz et al., 2015, Genome Res., 25:1715-26). Presumably, transcriptional activity in these instances requires expression of additional cell type-specific factors. We now address this in the Discussion.

https://doi.org/10.7554/eLife.48216.044

Article and author information

Author details

  1. Daniel P Murphy

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4846-4608
  2. Andrew EO Hughes

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  3. Karen A Lawrence

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Connie A Myers

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Formal analysis, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  5. Joseph C Corbo

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    jcorbo@wustl.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9323-7140

Funding

National Eye Institute (F32EY029571)

  • Daniel P Murphy

National Eye Institute (T32EY013360)

  • Daniel P Murphy

National Eye Institute (R01EY025196)

  • Joseph C Corbo

National Eye Institute (R01EY026672)

  • Joseph C Corbo

National Eye Institute (R01EY024958)

  • Joseph C Corbo

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors would like to thank Leo Volkov and Yohey Ogawa for critical reading of the manuscript. The Otx2-GFP mouse line was a generous gift from Dr. Thomas Lamonerie (Université Côte d'Azur), and the Grm6-YFP line was a generous gift from Dr. Daniel Kerschensteiner (Washington University). We also credit the ENCODE consortium for the DNase-seq datasets, the Genome Technology Access Core (GTAC) in the Department of Genetics at Washington University in St. Louis for next-generation sequencing, and the Flow Cytometry Core in the Department of Pathology and Immunology at Washington University in Saint Louis for FACS services. This work was supported by the National Institute of Health (EY025196, EY026672, and EY024958 to JCC and T32EY013360 and F32EY029571 to DPM)

Ethics

Animal experimentation: All animal experiments were carried out in accordance with the regulations of the Institutional Animal Care and Use Committee (IACUC) at Washington University in St. Louis (protocol 20170054).

Senior Editor

  1. Patricia J Wittkopp, University of Michigan, United States

Reviewing Editor

  1. Claude Desplan, New York University, United States

Publication history

  1. Received: May 5, 2019
  2. Accepted: September 19, 2019
  3. Version of Record published: October 21, 2019 (version 1)

Copyright

© 2019, Murphy et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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