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Cortical RORβ is required for layer 4 transcriptional identity and barrel integrity

  1. Erin A Clark  Is a corresponding author
  2. Michael Rutlin
  3. Lucia Capano
  4. Samuel Aviles
  5. Jordan R Saadon
  6. Praveen Taneja
  7. Qiyu Zhang
  8. James B Bullis
  9. Timothy Lauer
  10. Emma Myers
  11. Anton Schulmann
  12. Douglas Forrest
  13. Sacha B Nelson  Is a corresponding author
  1. Department of Biology and Program in Neuroscience, Brandeis University, United States
  2. Janelia Research Campus, United States
  3. Laboratory of Endocrinology and Receptor Biology, National Institutes of Health, NIDDK, United States
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Cite this article as: eLife 2020;9:e52370 doi: 10.7554/eLife.52370

Abstract

Retinoic acid-related orphan receptor beta (RORβ) is a transcription factor (TF) and marker of layer 4 (L4) neurons, which are distinctive both in transcriptional identity and the ability to form aggregates such as barrels in rodent somatosensory cortex. However, the relationship between transcriptional identity and L4 cytoarchitecture is largely unknown. We find RORβ is required in the cortex for L4 aggregation into barrels and thalamocortical afferent (TCA) segregation. Interestingly, barrel organization also degrades with age in wildtype mice. Loss of RORβ delays excitatory input and disrupts gene expression and chromatin accessibility, with down-regulation of L4 and up-regulation of L5 genes, suggesting a disruption in cellular specification. Expression and binding site accessibility change for many other TFs, including closure of neurodevelopmental TF binding sites and increased expression and binding capacity of activity-regulated TFs. Lastly, a putative target of RORβ, Thsd7a, is down-regulated without RORβ, and Thsd7a knock-out alone disrupts TCA organization in adult barrels.

Introduction

Localization of function is a fundamental principle organizing mammalian brain circuitry. Structure to function mapping is particularly striking in sensory input to L4 of the neocortex (Woolsey and Van der Loos, 1970; Catania and Kaas, 1995). L4 neurons are distinctive in their propensity to form cellular aggregates, or modules, that receive segregated thalamic inputs and represent features of the sensory periphery. Whisker barrels in the rodent somatosensory cortex are a prototypical example, but other somatosensory modules within L4 are also present in the cortices of insectivores, carnivores and primates (Krubitzer and Seelke, 2012), and columns receiving segregated input are present in the visual cortices of carnivores and primates, and in other cortical regions (Mountcastle, 1997). At the same time, gene expression studies in mouse and human show that L4 neurons also have a distinctive transcriptional identity that includes expression of RORβ (Zeng et al., 2012). Despite these two striking features, little is known about the relationships between transcriptional identity, the mechanisms that establish and regulate that identity, and features of L4 cytoarchitecture.

Researchers have long used the rodent whisker pathway to study cytoarchitecture development (Hand and Strick, 1982; Fox, 1992; Yang et al., 2018). The whisker map is organized into microcolumnar units called barrels located in primary somatosensory cortex (S1). In mice, L4 cortical neurons assemble into columns that form barrel walls and input is relayed via thalamocortical afferents (TCAs), which cluster in the center of barrel hollows. Each whisker is projected through corollary maps in the brainstem and ventrobasal thalamus (Van Der Loos, 1976) before reaching S1.

Many proteins and pathways are required for presynaptic organization of TCAs and/or postsynaptic organization in L4 (Li and Crair, 2011; Wu et al., 2011; Erzurumlu and Gaspar, 2012). Much of what we know has focused on the requirement of input activity and intact signaling pathways. Genetic disruption of synaptic transmission via glutamate (Iwasato et al., 1997; Iwasato et al., 2000; Hannan et al., 2001; Datwani et al., 2002; Li et al., 2013; Ballester-Rosado et al., 2016), or serotonin pathways (Cases et al., 1995; Salichon et al., 2001) perturb some aspect of barrel organization. Several related signal transduction pathways are also required (Abdel-Majid et al., 1998; Barnett et al., 2006; Inan et al., 2006; Watson et al., 2006; Lush et al., 2008).

Barrel formation is also regulated transcriptionally. Transcription factors (TFs) such as Bhlhe22/Bhlhb5 and Eomes are involved in the early stages of cortical arealization and barrel development (Arnold et al., 2008; Joshi et al., 2008; Elsen et al., 2013). Downstream of these early developmental processes activity-dependent TFs, including Lmo4, NeuroD2, and Btbd3 regulate aspects of barrel organization in response to TCA inputs (Ince-Dunn et al., 2006; Kashani et al., 2006; Huang et al., 2009; Matsui et al., 2013). In addition, the TFs retinoic acid-related orphan receptor alpha (RORα) and beta (RORβ), are also implicated in barrel formation. RORα and RORβ are expressed in regions of the somatosensory barrel map, with RORα expressed in brainstem, thalamus and cortex, and RORβ in thalamus and cortex (Nakagawa and O'Leary, 2003). Recently, RORα was shown to be required in the thalamus and cortex for proper TCA segregation and barrel wall formation (Vitalis et al., 2018). Mis-expression of RORβ in neocortex is sufficient to drive cortical neuron clustering and TCA recruitment to ectopic barrel-like structures (Jabaudon et al., 2012). Together these studies have identified multiple TFs with major roles in early barrel development that likely set the stage for more downstream terminal differentiation TFs and activity-regulated TFs to hone the network. Early cortical development, TCA pathfinding, and activity dependent gene regulation are prolific areas of research. However, the later stages of neuronal specification and the molecular mechanisms of TFs involved in barrel development are currently underexplored. TFs such as Bhlh5 and Eomes have broad roles and are widely expressed in the cortex while the more narrowly expressed TFs such as Btbd3 are downstream of activity input leaving a gap in our understanding of the intermediate steps that connect cortical development to activity driven processes. Given the restricted layer-specific expression of RORβ and its up-regulation concomitant with the final stages of barrel formation and the onset of input activity, we hypothesized it would be a good candidate to study transcriptional mechanisms connecting cellular specification in L4 with cytoarchitecture and network development.

We show that in addition to being sufficient, RORβ is also required for both pre- and postsynaptic barrel organization. Without RORβ in the cortex, L4 neurons fail to migrate tangentially and do not organize into barrel wall structures. This also reduced TCA segregation shortly after barrel formation would have normally occurred. Interestingly, TCA segregation also declined as animals aged. Without RORβ, L4 gene expression and chromatin accessibility were disrupted, with L4-specific genes down-regulated and L5-specific genes up-regulated suggesting a disruption in terminal cellular identity. This involved complex changes in the expression and/or chromatin accessibility at binding motifs for many TFs in addition to RORβ, including developmental regulators and activity-regulated TFs. L4 neurons also received delayed excitatory input, a key step in barrel development. Lastly, we identify a putative direct gene target of RORβ, Thsd7a, that is down-regulated without RORβ and is required for maintained TCA organization in adulthood. Together these data characterize the role of RORβ across multiple levels to connect molecular and transcriptional mechanisms to cortical organization and place RORβ as a key regulator of a complex developmental transition orchestrating terminal L4 specification and initiating activity responsiveness.

Results

Cortical barrels in mice are complex structures. Cell-sparse barrel hollows are where thalamic projections are concentrated. Barrel walls are formed by cortical cell aggregates that surround the TCAs. Barrel septa consist of the intermediate spaces between barrel walls (Woolsey and Van der Loos, 1970). To assess the impact of RORβ loss on barrel organization we used two staining methods. Barrel walls were visualized by Nissl staining (Van der Loos and Woolsey, 1973) and barrel hollows were visualized by vesicular glutamate transporter 2 (VGLUT2), which is strongly expressed in TCAs (Fremeau et al., 2001; Liguz-Lecznar and Skangiel-Kramska, 2007), or as clusters of reporter expressing afferents from VPM neurons. This strategy allowed clear identification of changes in either structure independently. Cytochrome oxidase (CO) staining was also used in some conditions, but the presence of CO signal in both barrel walls and TCAs made it less useful.

RORβ is required for postnatal barrel wall formation and influences segregation of thalamocortical afferents (TCAs)

To begin exploring RORβ function in barrel organization, we used a global, constitutive knock-out (KO), which contains a GFP expression cassette knocked-in to the Rorb locus. RorbGFP/+ mice express GFP in RORβ expressing cells allowing identification of barrel cortex without significant disruption to barrel structures or neuronal function (Liu et al., 2013). RorbGFP/+ mice were used as controls (Ctl), while RorbGFP/GFP mice disrupt both copies of Rorb to generate a KO. Controls showed no detectable disruption to barrel organization compared to WT animals (Figures 1A and 2A).

RORβ is required for postnatal barrel wall formation.

Nissl staining on tangential sections of flattened cortices after global, constitutive knock-out shows barrel wall organization requires RORβ. (A) Nissl staining (Left) in whisker barrel field as identified by strong GFP expression (Right). Control (Ctl) and Rorb knock-out (KO) animals were age matched at P7, P30, and P60. (B) Quantification of barrel hollow to barrel walls/septa contrast (Barrel-Septa Contrast) from Nissl staining. N = 4 age-matched animals for each genotype (Ctl or KO). Two tissue sections containing the largest portions of whisker barrel field identified by GFP signal were averaged per animal. Whisker plots show the median per animal ± standard deviation. Gray points show mean contrast for each animal. P-value by independent sample t-test, between Ctl and KO at each timepoint.

Figure 2 with 1 supplement see all
Rorb KO reduces thalamocortical afferent (TCA) segregation.

(A) VGLUT2 staining of excitatory thalamic axon terminals in cortical whisker barrels shows normal initial TCA patterning at P7 but with reduced barrel-septa contrast in Rorb KO, and further reductions in contrast with age in both KO and Ctl. Ctl and Rorb KO animals were age matched. (B) Quantification of barrel hollow to barrel walls/septa contrast (Barrel-Septa Contrast) in VGLUT2. N = 4–6 age-matched animals for each genotype (Ctl or KO; each section shown is from a separate animal). Two tissue sections containing the largest portions of whisker barrel field identified by GFP signal were averaged per animal. Whisker plots show median contrast per animal ± standard deviation. Gray points show mean contrast for each animal. P-value by independent sample t-test, between Ctl and KO at each timepoint.

Barrels form around postnatal day 5 (Rice and Van der Loos, 1977). Nissl staining of barrel walls at P7, P30, and P60 showed that RORβ is required for barrel wall formation. Representative images of Nissl and GFP are shown in Figure 1A where the lack of barrel wall organization is clearly visible at P7 and remains disrupted at P30. Figure 1B quantifies this effect as the contrast between barrel hollows and barrel wall/septa fluorescence intensity. Contrast was calculated as (barrel - septa) / (barrel + septa) where septa includes barrel walls (see methods for details). Quantification demonstrated a near complete lack of contrast in KO barrel cortex supporting a lack of cortical organization.

While TCAs have been shown to instruct cortical cell organization we hypothesized the lack of barrel walls might reciprocally affect TCA organization. TCAs visualized by VGLUT2 staining showed an intact pattern of barrel hollows at P7 in KO animals, Figure 2A. However, careful quantification of the VGLUT2 contrast between hollows and septa showed a significant decrease in the KO suggesting loss of RORβ and/or the lack of barrel walls had a mild but measurable effect on TCA segregation. Interestingly, as animals aged into adulthood TCA segregation also declined in control as well as Rorb KO animals. Disorganization in the Rorb KO was characterized by both loss of quantifiable VGLUT2 contrast as well as the qualitative barrel patterning most obvious at P60 between Ctl and KO in Figure 2A. Both genotype and age significantly affected VGLUT2 contrast (genotype p=4.5e-07 and age p=2.6e-06 by two-way ANOVA) but did not interact significantly. Comparing pairwise across ages we find a significant decline in TCA organization between P7 and P20 controls, with no significant change from P20 to P60. This suggests that while both age and loss of RORβ significantly reduced contrast, loss of RORβ did not significantly change the time course of TCA desegregation.

To examine whether loss of VGLUT2 contrast could be due to late arrival of VGLUT2+ inputs from outside the VPM we injected AAV expressing mCherry under the hSyn promoter specifically into the VPM (Figure 2—figure supplement 1A). The VGLUT2 barrel-septa contrast was comparable to the barrel-septa contrast in the VPM-specific mCherry filled afferents at P30 strongly suggesting loss of VGLUT2 contrast with age is due to loss of TCA organization (Figure 2—figure supplement 1B-C). Together these data show that RORβ is critical for normal whisker barrel formation and, loss of TCA segregation into adulthood suggests that time/age continues to affect cytoarchitecture.

RORβ is required in the cortex but not the thalamus for barrel organization

In addition to L4 excitatory neurons, RORβ is expressed in the thalamic neurons that project to barrel hollows. To assess whether the disruption of barrels is dependent on RORβ expression in thalamus and/or locally in cortex we used a floxed allele of Rorb (Rorbf/f) crossed to Cre driver lines generating tissue-specific disruption of RORβ as diagrammed in Figure 3A. A knock-in line expressing Cre from the serotonin transporter gene, Sert (Slc6a4 or 5-HTT) locus was used to knock-out Rorb in the thalamus. The SertCre line alone showed a mild disruption to TCA organization without disrupting barrel walls, suggesting the Cre knock-in might be hypomorphic (Figure 3B–C). However, thalamic KO of Rorb (SertCre Rorbf/f) showed no additional disruption to TCAs or barrel walls. This is consistent with the observation that Rorb KO also did not disrupt barreloid organization (Figure 3—figure supplement 1A). Thus, loss of RORβ in thalamic neurons was not responsible for the loss of cortical wall organization or the majority of TCA disorganization observed in the global RorbGFP/GFP KO.

Figure 3 with 1 supplement see all
RORβ is required in the cortex but not the thalamus for barrel organization.

(A) Diagram and timeline of Cre driver line tissue-specific expression in cortex versus thalamus and timing relative to barrel formation and consolidation. Color indicates expression in cortex (red) or thalamus (purple). (B) VGLUT2 and Nissl staining of whisker barrel cortex at P30 from floxed Rorb control without Cre (Rorbf/f Ctl), SertCre control (Sert Ctl) without floxed Rorb and the cross (Rorbf/f SertCre), which knocks out Rorb specifically in thalamus during embryonic development. Whisker plots as described for Figure 1B. (C) Quantification of VGLUT2 Barrel-Septa Contrast in genetic lines from B. N = 3–5 P30 animals. Quantification and plotting as described in Figure 2B. P-value by ANOVA. (D) VGLUT2 and Nissl staining of whisker barrel cortex from Emx1Cre control (Emx1 Ctl) without floxed Rorb, and the cross (Rorbf/f Emx1Cre) from P7 and P30 animals, and a P60 animal from floxed Rorb crossed to a CamK2aCre driver line. Emx1Cre knocks out Rorb specifically in forebrain during embryonic development, and CamK2aCre knocks out Rorb in forebrain neurons at postnatal weeks 2–3. (E) Quantification of VGLUT2 Barrel-Septa Contrast in genetic lines from D. N = 3–5 animals per age group. Quantification and plotting as described in Figure 2B. P-values by independent sample t-test, between Ctl and KO at each time point. CamK2aCre showed no difference from Rorbf/f Ctl. Whisker plots as described for Figure 1B.

A knock-in line expressing Cre from the Emx1 locus removed RORβ specifically in forebrain structures. Emx1Cre alone showed no significant disruption to barrel organization (Figure 3D–E). However, barrel organization was significantly disrupted by cortical KO of Rorb (Emx1Cre Rorbf/f). In addition, a CamK2aCre diver line that removes RORβ in the cortex after barrel formation, showed no effect. CamK2aCre activated expression of a tdTomato reporter from the Rosa26 locus in only a subset of GFP+ L4 neurons (Figure 3—figure supplement 1B), therefore it is not clear whether late expression of RORβ is expendable or whether expression in a subset of L4 neurons is sufficient for barrel organization. Together these data demonstrate that RORβ is required in the cortex prior to barrel formation. Loss of RORβ in the thalamus does not disrupt barrel architecture, suggesting RORβ drives barrel wall organization through cell-intrinsic mechanisms within layer 4.

RORβ is required for expression of a layer four gene profile and repression of layer five genes

Because RORβ is a transcription factor we hypothesized loss of function would change gene expression in L4 neurons. To test this, RNA-seq was performed on sorted GFP+ cells from micro-dissected L4 S1. We were careful in this dissection to exclude a small population of GFP+ L5 neurons. Differential expression analysis between RorbGFP/+ and RorbGFP/GFP cells identified many dysregulated genes (fold change ≥2, adjusted p-value<0.01). At postnatal day 2 (P2) and prior to barrel formation, 246 genes were significantly disrupted with 51% down-regulated in the KO. At P7, just after barrel formation, 433 genes were disrupted with 36% down-regulated. At P30, 286 genes were disrupted with 37% down-regulated. Examining the overlap between ages we find very few genes significantly disrupted in the same direction across time points, suggesting highly dynamic and complex regulation, Figure 4A, B.

Rorb KO disrupts the layer four expression profile including up-regulating many deep layer genes.

(A) Heatmaps showing marker genes or genes strongly enriched, as identified in the Allen Brain Atlas, for each layer of the neocortex differentially expressed between control and Rorb KO. Log-transformed transcripts per million (TPM) are color scaled in red and blue for each of the four RNA-seq replicates in the left most heatmap and the mean for each time point and genotype in the middle heatmaps. Log fold change (LFC) between control (Ctl) and Rorb KO is color-scaled in orange and purple in the right most heatmaps. (B) Numbers of differentially expressed genes (DEGs) for the three ages examined. (C) Line plots showing LFC for the same genes. The solid black line indicates no change. Negative LFC indicates decreased expression in Rorb KO, and LFC >0 indicates increased expression in Rorb KO. Each colored line is a layer-specific DEG and the dashed black line plots the mean across the group of genes. (D) RNA-seq expression of layer 5 TFs. Lines plot the mean ± SE. P by moderated t-test adjusted for multiple comparisons (Benjamini-Hochberg). (E) Additional L4 and L5 genes were identified using the Allen Brain Atlas differential search contrasting L4 SSp structures to L5 SSp. Genes with >1.5 fold change and expression threshold >1.6 were selected. Genes already shown in A-C were removed. Hence each gene shown does not meet statistical criteria for differential expression in Ctl/KO by RNA-seq. Line plots show RNA-seq LFC for each layer-specific gene. The solid black line is the mean across genes and the solid gray line indicates no change. Negative LFC indicates decreased expression in Rorb KO, and LFC >0 indicates increased expression in Rorb KO. (F) Overall (first bar), 1% of genes were downregulated (blue) and 2% were upregulated (red). Downregulated genes were overrepresented (26%) among the 102 L4-specific genes (middle bar), while upregulated genes were overrepresented (19%) among the 240 L5-specific genes. Both overrepresentations were significant (p<2.2e-16) by fisher exact test.

RORβ expression is a key feature distinguishing L4 neurons (Lein et al., 2007). To examine the effect of RORβ loss on layer-specific transcriptional identity we assessed the layer specificity of genes differentially expressed between control and Rorb KO (DEGs). The Allen Brain Atlas was used to manually screen all DEGs for layer-specific expression in the neocortex. Genes were considered layer-specific if the in-situ hybridization (ISH) signal appeared at least three-fold higher in one layer (considering layers 2 and 3 together). Many genes had complex specificities showing enrichment in two or more layers. These were not included for simplicity. Grouping DEGs based on the layer they are normally expressed within, we see that DEGs which should be expressed in upper layers were generally down-regulated and DEGs that should be expressed in deep layers were generally up-regulated in the Rorb KO, Figure 4A–D. The strongest effects were loss of many L4 genes and increased expression of many L5 genes. While many L4 and L5 genes were affected, this was not a global identity switch. Many L4 and L5 genes identified from the Allen Brain Atlas were not differentially expressed. In order to assess the statistical significance of the down-regulation of L4 genes and up-regulation of L5 genes we used the Allen Atlas differential search function to contrast L4 to L5 of primary somatosensory cortex (SSp) and included all genes with >1.5 fold change and expression threshold >1.6 (Figure 4E–F). Of the 102 L4-specific genes 26% were down-regulated in the KO, a single gene was up-regulated, and the remainder were unchanged. Conversely, up-regulated genes were overrepresented (19%) among the 240 L5-specific genes, and a fisher exact test revealed that these overrepresentations were highly significant (p<2.2e-16). Thus, although only a portion of the L4 gene expression profile is altered by loss of Rorb, it is disproportionately weighted towards down-regulation of L4 genes and upregulation of L5 genes.

Several L5 genes are worth noting. Bcl11B/Ctip2, is a marker of thick-tufted L5B-type neurons and significantly up-regulated at P2 in the KO, but silenced at P7 and P30 similar to control (Figure 4D). Fezf2, another L5B marker and regulator of Bcl11B (Chen et al., 2005), was similarly silenced over barrel development, but was overexpressed at P30 in the KO. Foxo1 is mainly expressed in L5 at younger ages (Allen Developing Mouse Brain Atlas) declining over barrel development, but in the KO was significantly overexpressed at P7. Etv1, also a L5A marker (Doyle et al., 2008), was upregulated in the KO at both P2 and P30. Lastly, Egr4 was up-regulated at P30 in the KO, and has been associated with Etv1 expressing neurons (Doyle et al., 2008). RNAscope (Wang et al., 2012) in situ analysis against two L5 genes confirmed up-regulation in L4 (Figure 5, Figure 5—figure supplement 1A). Together these data support a disorganized partial shift in layer identity with many different factors implicated at distinct time points.

Figure 5 with 1 supplement see all
Confirmation of upregulated L5 genes in L4 neurons of Rorb KO.

(A) RNAscope in situ hybridization of Fezf2 in control (Ctl) and Rorb KO tissue. (B) Quantification of Fezf2 and Tox RNA puncta per cell in either layer 4 or layer 5 of control and Rorb KO tissue. Tox images are shown in a Figure 5—figure supplement 1. N = 4 P30 animals for each genotype (Ctl or KO). Two regions containing S1 were averaged per animal. Whisker plots show the median per animal ± standard deviation. Gray points show mean number of puncta per cell for each layer in each animal. P-value by independent sample t-test. (C) RNA-seq changes in Fezf2 and Tox expression at P30 replotted from heatmap of Figure 4. Gray points show values for individual replicates. Whisker plots show the mean ± standard deviation (N = 4).

Rorb KO disrupts transcription factor binding sites near DEGs

RORβ, Bcl11b, Foxo1, Etv1, and Egr4 are TFs that often regulate gene expression by binding to distal regulatory sites such as enhancers. There are many chromatin features of enhancers, one of which is that they are open and accessible to enzymatic fragmentation in assays such as the Assay for Transposase Accessible Chromatin (ATAC) (Buenrostro et al., 2015). To begin examining mechanisms involved in changing gene expression, we performed ATAC-seq on sorted GFP+ L4 neurons from control and Rorb KO animals at P30 (Figure 6A). High confidence ATAC-seq peaks were assessed for differential accessibility between control and KO samples. We identified 5210 peaks with ≥2 fold change in accessibility (FDR < 0.02). Nearly 4-times as many regions lost accessibility (N = 4123 closed) than increased (N = 1087 opened), (Figure 6—figure supplement 1A). Differential ATAC peaks were primarily located in introns and intergenic regions Figure 6—figure supplement 1B suggesting loss of RORβ function resulted in closure of many more regulatory regions than opening.

Figure 6 with 1 supplement see all
Rorb KO disrupts transcription factor binding sites near DEGs.

(A) ATAC-seq normalized reads per million (RPM) for biological replicates, y-axis scaled 0–2. Samples collected from GFP+ S1 L4 RorbGFP/+ neurons (Ctl, blue) and GFP+ S1 L4 RorbGFP/GFPneurons (KO, red). Arrows indicate differential peaks (fold change ≥2, FDR < 0.02). Open arrows indicate differential peaks with transcription factor motif sequences as in (B). (B) Cross-validated motifs with significant enrichment in ATAC peaks with differential accessibility. Closed; regions with significantly reduced access, Opened; regions with significantly increased access in the Rorb KO. Motif instances were cross-validated between MEME and HOMER algorithms. Odds ratio and p-value calculated comparing to motif frequency in control regions. (C–D) Cross-validated motif enrichment in ATAC peaks near the TSSs of (C) up-regulated or down-regulated DEGs and (D) L4- or 5-specific genes. Bars plot odds ratio over control regions. Asterisk indicates significant motif enrichment (p<0.03 by Fisher exact test) in nearby ATAC peaks compared to control regions and separately significant enrichment (p<0.03 by Fisher exact test) of DEGs with a nearby motif compared to an independent group of control genes.

We hypothesized that many of the closed regions might contain a RORβ binding motif while regions that opened may have binding potential for other TFs. To assess this possibility, two software algorithms (MEME and HOMER) were used to identify de novo enriched motifs from the DNA sequences of differential ATAC peaks separating closed and opened regions. This unbiased analysis also identifies which enriched sequences match known TF binding motifs. RORβ was the top motif from closed regions, Figure 6—figure supplement 1C. Considering only expressed TFs, the potent neurogenic factors NeuroD1 and Ascl1 were also among the top motifs in closed regions. In regions that opened, the top motifs from expressed TFs were Nfil3, Hlf, Jun, Fos, Trps1, Mef2a/c/d and Irf2. Similar analysis was performed on ATAC peaks near up or down-regulated DEGs as well as L4 and L5 DEGs. To confirm enrichment and identify motif locations we used MEME FIMO and HOMER to scan for instances of a given set of motifs. This was done for all expressed TFs either enriched in the de novo motif analysis or differentially expressed, for which high quality motif models existed. Motif instances were cross-validated by retaining only those found by both MEME and HOMER. Figure 6B plots the odds ratio of motifs significantly enriched compared to control regions. Many of the motifs found by de novo analysis were confirmed, including RORβ in regions that closed.

To assess which TFs might play a significant role in up or down-regulation of DEGs we varied a distance window around the transcription start site (TSS) to identify nearby ATAC or control regions containing a DNA motif. We tested for enrichment of motifs in ATAC regions near DEGs compared to motifs in control regions. We also tested whether DEGs with a nearby motif were significantly enriched compared to a control group of genes that did not change expression in the Rorb KO. In essence, we tested whether motifs were enriched around certain DEGs and whether a significant portion of those DEGs had a nearby motif. To reduce false positives, only motifs with significant enrichment in both tests are shown in Figure 6C–D.

Genes down-regulated at P30 showed significant enrichment of nearby RORβ motifs suggesting RORβ is important for gene activation (Figure 6C). Motifs for Nr4a1 and Nfil3 were enriched near up-regulated DEGs at P2 and P7 respectively consistent with an early role for these TFs in activating expression. Foxo1 motifs were enriched near genes down-regulated at P2 and P7. Consistent with a role in early gene regulation, Foxo1 was highly expressed at P2 and declined with age in control neurons (Figure 4D). However, in the KO, Foxo1 remained significantly elevated at P7 eventually decreasing to levels comparable to control at P30. The close proximity of Foxo1 binding sites to down-regulated genes and its elevated expression at younger ages suggests it may act as a repressor that is normally silenced just after barrel formation to allow proper gene induction in L4 neurons. Without RORβ, silencing of Foxo1 is delayed allowing it to aberrantly repress targets at younger ages.

Interestingly, we did not find RORβ motifs enriched near L4 genes suggesting the shift in layer-specific gene expression is a downstream effect of RORβ loss. While RORβ does not appear to directly regulate layer-specific genes, Zfp281 motifs were enriched near L4 genes in the de novo motif search and confirmed by specific mapping (Figure 6—figure supplement 1C and Figure 6D). Zfp281 was highly expressed in both samples, at all ages, and unchanged by Rorb KO (Figure 6—figure supplement 1C). Zfp281 motifs were also enriched in regions that closed in the Rorb KO suggesting it might be a novel activator of L4-specific genes and dependent on some other factor to maintain accessible chromatin at its binding sites.

Nfe2l and NeuroD1 motifs were enriched near L5 genes. NeuroD1 motifs were also enriched in regions that closed suggesting it might act as an inhibitor of L5-specific genes as these genes increased expression when NeuroD1 sites closed. Nfe2l consists of a family of TFs that share a binding motif. Nfe2l1 was expressed at younger ages and increased in the adult while Nfe2l3 was highly expressed at P2 and silenced by P7 (Figure 6—figure supplement 1D). Rorb KO did not significantly disrupt expression of either, but the motif was enriched in regions that opened suggesting Nfe2l1 and/or three may be novel activators of L5-specific genes.

The TF motifs enriched near up-regulated DEGs were noteworthy for possible relationships with neuronal activity. Nr4a1 is an activity induced TF that regulates the density and distribution of excitatory synapses (Chen et al., 2014). Nfil3 and Hlf bind and compete for similar DNA motifs (Mitsui et al., 2001), and may also be involved in activity-regulated transcription. Nfil3 is up-regulated in human brain tissue following seizures (Beaumont et al., 2012), and mutations in Hlf are linked to spontaneous seizures (Gachon et al., 2004; Hawkins and Kearney, 2016). In addition, motifs for the classic immediate early genes, Jun and Fos, were enriched in regions that opened. These observations led us to examine the expression of other activity-regulated TFs. Many were significantly up-regulated at P30 while Lmo4 and its binding partner Lbd2 were up-regulated at P7 (Figure 6—figure supplement 1E; Matsui et al., 2013). Lmo4 expression is induced by calcium signaling and is required for TCA patterning in barrel cortex (Kashani et al., 2006; Huang et al., 2009). Another activity-regulated TF, Btbd3, which drives L4 neurons to orient their dendrites into barrel hollows, was significantly down-regulated (Figure 6—figure supplement 1E). Lmo4 and Btbd3 are the only genes previously shown to disrupt barrels that were also dysregulated in the Rorb KO (Figure 6—figure supplement 1F). In the Rorb KO Lmo4 was up-regulated, but Lmo4 KO disrupts barrels, suggesting that Rorb KO disrupts barrels through a divergent mechanism from what has been previously described.

Interestingly, the protein product of S100A10, p11, is involved in serotonin signaling via binding to the serotonin receptors Htr1b, Htr1d, and Htr4 (Warner-Schmidt et al., 2009). S100A10 was down-regulated at P7 and P30 (Figure 6—figure supplement 1G). Htr1b was the only of the three serotonin receptor subunits known to interact with p11 expressed in our samples and was also significantly down-regulated at P7 and P30. These data suggest that in addition to altered layer identity, Rorb KO may also disrupt serotonergic signaling, an important pathway in TCA communication with cortex (Kawasaki, 2015). Together with up-regulation of activity-regulated TFs, L4 neurons in the Rorb KO likely have significantly altered responses to activity.

These analyses paint a complex picture where gene expression in L4 Rorb KO neurons is disrupted by multiple mechanisms. Loss of RORβ results in closure of many RORβ binding sites which are also enriched near genes with reduced expression in adults consistent with an activator role for RORβ. Other regulatory changes involve complex combinations of altered TF expression and/or altered binding potential at sites that opened or closed in the KO likely due to downstream effects of RORβ loss. These changes impact both known neurodevelopmental regulators as well as activity-regulated TFs.

Rorb KO delays excitatory input to barrel cortex

To examine whether RORβ loss impacts network activity, we examined inhibitory and excitatory synaptic properties of L4 neurons. We found no change in inhibitory innervation at P14 or P24 as measured by miniature inhibitory postsynaptic currents (mIPSCs), Figure 7—figure supplement 1A-B. However, synaptic function as measured by miniature excitatory postsynaptic currents (mEPSCs) revealed a significant delay in excitatory input, Figure 7A–C. At P5, the frequency of mEPSCs was low and comparable in control and KO, Figure 7B–C. At P7, around the time when recurrent cortical synapses begin to sharply increase (Ashby and Isaac, 2011) and LTP has just ended (Crair and Malenka, 1995), controls showed increased mEPSC frequency. However, Rorb KO animals had a significantly lower mEPSC frequency at P7 (Figure 7A–C), suggesting decreased functional synaptic input. At P10, Rorb KO neurons increased mEPSC frequency to levels comparable with controls. This suggests synaptic connections were delayed by Rorb KO mostly likely affecting recurrent excitatory connections. At P10, this defect in frequency is mostly corrected, but Rorb KO also showed significantly increased mEPSC amplitude at P10, possibly compensating for the delay at P7. By P19, both frequency and amplitude of mEPSCs were similar between control and KO (Figure 7B). These data support a subtle functional disruption to the barrel circuit in Rorb KO animals that is consistent with the transcriptional changes.

Figure 7 with 1 supplement see all
Rorb KO delays excitatory input to barrel cortex.

(A) Example of miniature excitatory postsynaptic currents (mEPSCs) from L4 barrel cortex at P7. (B) Average mEPSC frequency and amplitude from Ctl and Rorb KO L4 barrel cortex at P5, P7, P10 and P19. Bars plot mean + SE, number of cells in parentheses. P values by 2-way ANOVA adjusted for multiple comparisons. (C) Cumulative histogram of inter-event intervals for control and Rorb KO L4 barrel cortex at P5, P7, and P10.

The putative RORβ target, Thsd7a, is required for adult TCA, but not barrel wall organization

To begin exploring the relationship between disrupted gene expression in the Rorb KO and barrel organization, we examined known functions of genes differentially expressed at multiple developmental time points. Two candidates were identified with potential roles in cell migration and synaptogenesis. PlexinD1 (Plxnd1) is a cell signaling molecule known to play a role in pathfinding and synaptogenesis (Chauvet et al., 2007; Wang et al., 2015). Thrombospondin 7a (Thsd7a) regulates endothelial cell migration (Wang et al., 2010), but its role in the brain is unknown. In controls, expression of both genes followed a similar developmental trajectory as Rorb, peaking around P7 (Figure 8A). In the Rorb KO, Plxnd1 was significantly lower at P2 and P7 while Thsd7a was significantly lower at all three time points. In addition, we identified several differential ATAC peaks near Thsd7a with reduced accessibility (Figure 8B). This included a peak containing a strong RORβ motif just downstream of the transcription start site, suggesting Thsd7a might be a direct target of RORβ regulation.

Figure 8 with 1 supplement see all
Thsd7a is required for TCA but not barrel wall organization.

(A) Line plots of transcripts per million (TPM) measured by RNA-seq for three genes (Rorb, Thsd7a, and Plxnd1) from Ctl (blue) or Rorb KO (red) S1 layer IV barrel cortex. Lines plot the mean ± SE. (B) ATAC-seq around the Thsd7a gene (as in Figure 6A), y-axis scaled 0–3. (C) VGLUT2 and Nissl staining of barrel cortex at P7 and P30 from wild-type (Wt), Plxnd1 KO, or Thsd7a KO. (D) Quantification of VGLUT2 Barrel-Septa Contrast from genetic lines in C. N = 2–5 animals. Whisker plots as described for Figure 1B. Statistical analysis summarized in Figure 8—figure supplement 1A. (E) Background normalized quantification of VGLUT2 contrast in barrel hollows. Two tissue sections containing the largest portions of whisker barrel field were averaged per animal. N = 5, P30 animals per genotype. Whisker plots as described for Figure 1B. (F) Background normalized quantification of VGLUT2 contrast in septa. Two tissue sections containing the largest portions of whisker barrel field were averaged per animal. N = 5, P30 animals per genotype. Whisker plots as described for Figure 1B. (G) VGLUT2 staining imaged at high magnification (63X) in P30 Wt or Thsd7a KO whisker barrel cortex. Barrels are labeled ‘b’.

There was no detectable disruption to barrel organization in Plxnd1 conditional KO mice (PlexinD1flox crossed to Emx1cre, Figure 8C–D). A Thsd7a constitutive KO also showed no disruption to barrel wall organization at P7 or P30. Interestingly, Thsd7a KO did show decreased VGLUT2 contrast between barrels and septa at P30 but not P7, suggesting Thsd7a is important for maintenance of TCA organization in adulthood (Figure 8C–D). The barrel phenotype of Thsd7a KO was qualitatively different from Rorb KO barrels. Specifically, the overall barrel pattern remained more intact in the Thsd7a KO despite the quantitative decrease in VGLUT2 contrast. As before, desegregation of VPM afferents was confirmed by VPM injection of AAV-hSyn-mCherry (Figure 8—figure supplement 1). Thsd7a KO may maintain sharper barrel borders than Rorb KO due to intact barrel walls. Reduction in VGLUT2 contrast in the Thsd7a KO could be due to increased TCA localization in the septa and/or decreased TCA localization in the barrels. To distinguish these two possibilities, three regions of low VGLUT2 staining adjacent to the barrel field were quantified and used for within tissue slice normalization of barrel and septa intensities. Thsd7a KO resulted in a 24% decrease in barrel hollow VGLUT2 signal and a 56% increase in the septa (Figure 8E–F). High resolution imaging showed a clear increase in VGLUT2 puncta located in the septa (Figure 8G). Thus, loss of Thsd7a after Rorb KO likely contributes to the decrease in TCA segregation in adulthood.

Discussion

While somatotopic maps were one of the earliest and most obvious forms of cytoarchitecture, our understanding of the role neuronal identity plays in module formation is largely unknown. Studies have long approached the question of what drives cortical organization from the perspective of network activity and, in the case of barrel cortex, from the perspective of key structures and pathways needed to relay sensory input. More recent studies characterizing transcription factors required for barrel organization point to the importance of molecular mechanisms regulating transcriptional programs. However, many of these TFs are part of the pathways that carry sensory input or are fundamental regulators of broad developmental programs. It was unclear whether a TF such as RORβ, a highly restricted marker of L4 identity, could influence macro-scale processes such as module formation. Indeed, we show that while RORβ is clearly regulating only a fraction of the phenotypic and transcriptional properties of L4 neurons, it is necessary for terminal specification of L4 identity and proper organization of L4 cytoarchitecture.

Specifically, RORβ is required in the cortex for barrel wall formation and full TCA segregation. This differs from earlier work focusing on the role of TCA patterning and activity as instructive for barrel wall formation. Instead, we find that loss of RORβ specifically in the cortex affects TCA segregation shortly after barrel walls should have formed, suggesting that bidirectional signaling between L4 neurons and TCAs is involved in establishing proper organization. That such signaling occurs was first suggested by cortex-specific knockout of NMDA receptor subunits (Iwasato et al., 2000; Lee et al., 2005). A second study highlighting this role of cortical influence on TCA organization knocked out the metabotropic glutamate receptor Grm5 (Ballester-Rosado et al., 2016) in cortical neurons. In contrast, cortex-specific knockout of another member of the ROR family of transcription factors, Rora (Vitalis et al., 2018) disrupts the cellular organization of cortical barrels, but appears to leave TCA segregation intact.

While loss of RORβ function affected TCA segregation from the time of formation we note that loss of the putative RORβ gene target, Thsd7a, primarily affected TCA segregation in adults despite maximal expression at P7. While additional studies are needed, we speculate one possible explanation could be that Thsd7a functions around the time of barrel formation to establish long lasting TCA structures that only manifest aberrant phenotypes later in life. Alternatively, the moderate expression level of Thsd7a at P30 may be sufficient for a role in adult maintenance. In either case, a role for Thsd7a in the nervous system has not been described previously. In endothelial cells, Thsd7a localizes to the membrane of the leading edge of migrating cells where it functions to slow or inhibit migration (Wang et al., 2010). Perhaps in somatosensory cortex it inhibits movement of nearby projections such as dendrites or axons allowing cortical neurons to ‘corral’ TCAs in barrel hollows. Thsd7a is not the only potential downstream target of RORβ worthy of further investigation. Pcdh20 has a role in L4 identity in regulating appropriate laminar positioning of L4 cells. Without Pcdh20, cells migrate to L2/3 instead (Oishi et al., 2016). In RORβ KO cells, Pcdh20 is down-regulated but cells still migrate to L4 suggesting a possible novel role for Pcdh20 downstream of RORβ function.

Our observation that barrel organization declined with age in wildtype animals is very interesting and possibly the first description of this phenomenon in mice (Rice, 1985). It suggests continued plasticity or degradation of maintenance mechanisms over time. Few studies have examined plasticity within this structure in adulthood. This is in part because studies have shown a decline in the capacity to rewire sensory input to the cerebral cortex with age in certain systems. In the visual system, loss of sensory input has been shown to alter TCAs during a critical postnatal period (Antonini and Stryker, 1993; Erzurumlu and Gaspar, 2012). It is thought that once this critical period closes, TCA organization is fixed. Thus, developmental processes in the visual and somatosensory systems are assumed to stabilize TCAs and restrict learning and memory related changes to plasticity among cortical connections (Fox, 2002; Feldman and Brecht, 2005; De Paola et al., 2006; Karmarkar and Dan, 2006). However, there is some evidence to support a shift in this model of adult plasticity in both the visual and somatosensory cortex (Khibnik et al., 2010; Wimmer et al., 2010). In particular, Oberlaender et al. showed that a mild form of sensory deprivation induced by whisker trimming in 3 month old rats substantially altered TCAs in barrel cortex (Oberlaender et al., 2012). However, because adult TCA plasticity has garnered limited attention, we currently lack genetic studies examining the molecular mechanisms behind these processes. The natural decline in barrel organization and the mechanism of Thsd7a influence on TCA segregation merit further investigation as exciting new contexts to study both the functional roles of cortical organization and the impact of age.

Recent studies are revealing that neuronal identity in certain structures remains plastic during early postnatal periods. For example, mistargeted L4 neurons that migrate to layer 2/3 take on characteristics of their surroundings (Oishi et al., 2016) and misexpression of some TF can alter the identity of postnatal neurons (Rouaux and Arlotta, 2010; Rouaux and Arlotta, 2013). We find that loss of RORβ disrupts the transcriptional identity of L4 neurons, which lose expression of many L4 genes and aberrantly express many L5 genes. While this shift to a more L5-like transcriptional profile is not a global identity switch, it suggests L4 identity continues to be refined relative to deeper layer profiles late into postnatal development.

The complex expression changes observed likely occur through a multi-tiered reorchestration of gene regulation. Up-regulation of known L5 TFs such as Bcl11b/Ctip and Etv1 at P2 may help drive an early diversion down an L5-like trajectory. Regulatory signatures detected in adult neurons such as closure of binding sites for Zfp281 enriched near L4 genes and opening of Nfe2l1/3 motifs enriched near L5 genes may represent the tip of the developmental iceberg. In addition, our stringent motif analysis aimed to keep false positives low may also miss relevant regulators with more minor roles. While we detect changes in binding capacity for many TFs, including RORβ, the complexity of dysregulation spread out across early postnatal development means there are certainly additional mechanisms driving this shift in cellular identity to be discovered. Here we combine the power of genetic knock-out strategies with multiple molecular profiling assays to interrogate the transcriptional network influenced by RORβ. We found RNA-seq paired with ATAC-seq provided a rich picture of the transcriptional changes occurring in Rorb KO neurons and insight into both developmental and adult functioning. Changes to the transcriptional network involved both differentially expressed TFs and TFs whose only perturbation was increased or decreased access to binding sites. Without these complementary perspectives, proteins such as Zfp281 and Nfe2l1/3 TFs might have been overlooked.

We identify several other TFs worthy of further investigation for their role in cortical development. Ascl1 and NeuroD1 are potent TFs that can induce transdifferentiation of mouse embryonic perinatal fibroblasts or microglia into neurons (Vierbuchen et al., 2010; Matsuda et al., 2019). NeuroD1 binds a different motif than NeuroD2, which is known to regulate barrel formation (Ince-Dunn et al., 2006), suggesting a distinct role. In addition, Trps1 was strongly up-regulated by RORβ loss at P7 and P30, and it was enriched in regions that opened. Its role in neurons is not clear, but it has been characterized as a transcriptional repressor that inhibits cell migration making it a tempting target to explore the lack of L4 neuron migration necessary to form barrel walls (Wang et al., 2018).

In addition to disrupted layer identity, we also detect a significant disruption in the potential for Rorb KO cells to transcriptionally respond to activity connecting cellular identity, module formation and molecular responsiveness to input. In the adult Rorb KO, many activity-regulated TFs were up-regulated, with the exception of Btbd3, and their DNA motifs showed increased accessibility. Around P7, when activity is critical for instructing cortical reorganization, we see reduced mEPSC frequency in L4 Rorb KO neurons, which is rectified by P10. Some of the transcriptional changes in the Rorb KO may be a form of compensation for the lack of input at P7. Failed up-regulation of Htr1b and down-regulation of S100a10/p11 may also be an attempt to increase activity in KO neurons. More is known about the role of Htr1b in TCAs where it is transiently expressed and, when stimulated, inhibits thalamic neuronal firing (Bennett-Clarke et al., 1993; Rhoades et al., 1994) and disrupts barrel formation (Young-Davies et al., 2000). TCA inhibition is thought to be the mechanism by which excess 5-HT disrupts barrels. While it is difficult to infer the role of Htr1b and p11 without characterizing cellular localization in S1 L4 neurons, down-regulation of p11 resulting in less Htr1b localizing to the membrane coupled with reduced Htr1b expression could relieve inhibition in L4 Rorb KO neurons. Barrel formation and the ability to respond to activity inputs corresponds with increased RORβ expression and this increase is attenuated when TCA inputs are eliminated (Pouchelon et al., 2014). Together this suggests terminal differentiation and migration of neurons within L4 to form barrel walls are closely synchronized to excitatory input and require RORβ for proper establishment.

Although few other studies have examined the transcriptional targets and molecular mechanisms of TFs that regulate barrel formation, our study suggests RORβ is likely involved in the later stages of cellular specification and implicates several new TFs. RORβ also appears to function by distinct mechanisms from TFs previously characterized to regulate barrel formation. Loss of Bhlhe22 disrupts both barrel wall formation and TCA segregation but results in down-regulation of Lmo4 (Joshi et al., 2008) unlike Rorb KO, which increased Lmo4. Interestingly, Eomes is required for barrel wall organization but does not appear to affect TCA segregation (Elsen et al., 2013). Lhx2 and RORα are more broadly expressed than RORβ. Lhx2 KO results in moderate down regulation of RORβ suggesting it is also likely upstream of RORβ in barrel development (Wang et al., 2017). Loss of Lhx2 greatly reduced TCA branching producing smaller barrels and barrel field. This phenotype is very similar to Rora KO barrels (Vitalis et al., 2018) suggesting RORα’s mechanism may be more similar to earlier developmental TFs than to RORβ. Disruption of barreloid development in Rora KO thalamus is also consistent with a role in earlier stages of development (Vitalis et al., 2018). However, Rora was down-regulated in our Rorb KO dataset suggesting it may also have a role downstream of RORβ. Several additional TFs appear to be downstream of RORβ. For example, Btbd3 is important for dendritic orientation and is down-regulated in the Rorb KO. It may be that dendritic orientation occurs after L4 cells have migrated to form barrel walls and provide an organized reference point for orientation. Thus, we have characterized in depth the molecular and transcriptional mechanism of RORβ as it orchestrates a critical juncture in barrel development where terminal differentiation and activity inputs are integrated to drive cellular organization in the cortex.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Genetic reagent (M. musculus)RorbGFP (Rorb1g)PMID:23652001Dr. Douglas Forrest (Laboratory of Endocrinology and Receptor Biology, National Institutes of Health)
Genetic reagent (M. musculus)Rorbf/f (Rorbflox/flox)PMID:29224725Dr. Douglas Forrest (Laboratory of Endocrinology and Receptor Biology, National Institutes of Health)
Genetic reagent (M. musculus)Rosa26tdTomatoJackson LaboratoriesRRID:IMSR_JAX:007909
Genetic reagent (M. musculus)plexinD1floxJackson LaboratoriesRRID:IMSR_JAX:018319
Genetic reagent (M. musculus)Thsd7a KOJackson LaboratoriesRRID:MGI:6263683
Genetic reagent (M. musculus)Emx1creJackson LaboratoriesRRID:IMSR_JAX:005628
Genetic reagent (M. musculus)SertCreJackson LaboratoriesRRID:IMSR_JAX:014554
Genetic reagent (M. musculus)CamK2acreJackson LaboratoriesRRID:IMSR_JAX:005359
AntibodyGuinea pig anti-VGLUT2Millipore AB2251RRID:AB_26654541:500-1:1000
Antibodyrabbit anti-VGLUT2Synaptic Systems 135 403RRID:AB_8878831:250
Antibodychicken anti-GFPAves labs GFP-1020RRID:AB_100002401:500-1:1000
AntibodyGoat Anti-Rabbit Alexa Fluor 564Invitrogen A-11037RRID:AB_25340951:500
AntibodyGoat Anti-Chicken Alexa Fluor 488Invitrogen A-11039RRID:AB_25340961:500
AntibodyGoat Anti-Rabbit Alexa Fluor 633Invitrogen A-21070RRID:AB_25357311:500
AntibodyGoat Anti-Guinea Pig Alexa Fluor 647Invitrogen A-21450RRID:AB_27350911:500
StainNisslInvitrogen N214791:250
OtherAAV-hSyn-mCherryAddgene 114472-AAV8RRID:Addgene_114472Undiluted
Commercial assay or kitRNAscope Fluorescent Multiplex kitAdvanced Cell Diagnostics, 320850

Animals

All animals were bred, housed, and cared for in Foster Biomedical Research Laboratory at Brandeis University (Waltham, MA, USA). Animals were provided with food and water ad libitum and kept on a 12 hr:12 hr light:dark cycle. Cages were enriched with huts, chew sticks, and tubes. All experiments were approved by the Institutional Animal Care and Use Committee of Brandeis University, Waltham, MA, USA.

RorbGFP (Rorb1g) and Rorbf/f (Rorbflox/flox) mice were obtained from Dr. Douglas Forrest (Liu et al., 2013; Koch et al., 2017; Byun et al., 2019). RorbGFP mutation deletes the RORβ1 isoform, the predominant isoform in brain, and not the RORβ2 isoform (Liu et al., 2013). The Rorbf/f allele deletes both isoforms. The following mice were obtained from Jackson Laboratories: Rosa26tdTomato (stock 007909, RRID:IMSR_JAX:007909); plexinD1flox (stock 018319, RRID:IMSR_JAX:018319); Thrombospondin7a KO (Thsd7a) (stock 027218, RRID:MGI:6263683); Emx1cre (stock 005628, RRID:IMSR_JAX:005628); SertCre (Slc6a4) (stock 014554, RRID:IMSR_JAX:014554). CamK2acre (stock 005359, RRID:IMSR_JAX:005359).

Perfusion

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Animals were fatally anesthetized and transcardially perfused with 15 mL 1x PBS (Fisher, SH3001304) then 15 mL 4% PFA (Sigma Aldrich P6148-500G). Brains were fixed overnight in tangential orientation. After removing the whole brain from the skull, the cerebellum and olfactory bulbs were removed. The brain was split into two hemispheres along the longitudinal fissure and the midbrain was gently excised. The remaining cortex was placed in a shallow well made from a cryostat mold, filled with 4% PFA and a glass slide set on top for flattening. Brains were removed from PFA after 24–48 hr and stored in 30% sucrose/PBS solution at 4°C.

Immunohistochemistry

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50 µm slices were made on a freezing Microtome (Leica SM 2010R). Controls and KOs were stained together in batches. Slices were permeabilized overnight at 4°C in 0.3% Triton-X100 (Sigma Aldrich, T8787) and 3% Bovine Serum Albumin (Sigma B4287-25G) in PBS. Slices were then incubated for 24 hr in primary antibody solution containing 0.3% Triton-X100% and 3% Bovine Serum Albumin (BSA) in PBS at 4°C. Primary antibody dilutions were as follows: Guinea pig anti-VGLUT2 (Millipore AB2251, RRID:AB_2665454) 1:500-1:1000, rabbit anti-VGLUT2 (Synaptic Systems 135 403, RRID:AB_887883) 1:250, chicken anti-GFP (Aves labs GFP-1020, RRID:AB_10000240) 1:500-1:1000. Slices were washed three times in PBS for 10 min each at room temp and then moved to secondary antibody solution containing 0.3% Triton-X100, 3% Bovine Serum Albumin, 10% normal goat serum. All secondaries were used at 1:500; Goat Anti-Rabbit Alexa Fluor 564 (Invitrogen A-11037, RRID:AB_2534095), Goat Anti-Chicken Alexa Fluor 488 (Invitrogen A-11039, RRID:AB_2534096), Goat Anti-Rabbit Alexa Fluor 633 (Invitrogen A-21070, RRID:AB_2535731), Goat Anti-Guinea Pig Alexa Fluor 647 (Invitrogen A-21450, RRID:AB_2735091). Slices were stained using Nissl (Invitrogen N21479) at 1:250 in PBS for 2 hr at room temperature, washed in PBS as before, and mounted in VECTASHIELD HardSet Mounting Medium (Vector Laboratories, H-1500, RRID:AB_2336787). Slides were stored at −20C and imaged within 1 week.

Imaging and fluorescence quantification

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Tissue was imaged on a Leica DMI 6000B Inverted Widefield Imaging Fluorescence Microscope or a Zeiss LSM 880 confocal microscope. All genotypes and age groups contained roughly even numbers of males and females. A minimum of two slices containing at least five intact barrels between rows B-D were quantified per animal. Experimenters were blinded to age and genotype during imaging and quantification. Regions of interest (ROIs) were drawn manually by a blinded researcher around 5–6 intact barrels from rows B, C, or D using Fiji (Schindelin et al., 2012). An ROI including the total space around selected barrels up to the edges of adjacent barrels was drawn to be used for calculating septa intensity (Figure 9). For Thsd7a KO and controls, three additional ROIs were drawn in the region adjacent to barrel cortex with low VGLUT2 signal to be used as background to normalize barrel and septa intensity. Custom MATLAB code was used to quantify the average fluorescence in ROIs. Septa intensity was calculated as septa total ROI intensity - sum(barrel ROIs). Contrast = (barrel - septa) / (barrel + septa). For absolute barrel or septa intensity, measurements were normalized to background regions (Figure 9) within each tissue section. This was not necessary for contrast calculations because contrast is a ratio. Contrast and normalized barrel and septa intensity were averaged for two slices per animal. Two-way ANOVA was used to test for a significant effect of genotype and/or age as well as for an interaction between the two variables. Independent sample t-test was used to test for significant differences between genotypes at each age. No power analysis was performed and numbers of replicates performed were the minimum needed to demonstrate reproducibility, consistent with practices in similar published studies.

Example of quantification method.

Regions of interest (ROIs) were drawn in Fiji by a researcher blinded to genotype and age.

AAV injection into VPM

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50 nl of AAV-hSyn-mCherry (Addgene 114472-AAV8, RRID:Addgene_114472) was delivered by stereotactic injection to the dorsal VPM of P18-20 animals. Mice were euthanized by cardiac perfusion of 4% paraformaldehyde solution at P30. Cortex was removed and flattened for tangential sectioning of barrel field into 50 µm slices on a freezing Microtome (Leica SM 2010R). Subcortical structures were embedded in agarose and sectioned into 50 µm coronal slices on a vibratome (Leica VT1000S), counterstained with DAPI, and imaged (Keyence BZ-X700). Barrel cortex was stained, imaged and contrast calculated as described above. We required two slices with a minimum of two mCherrry saturated barrels and no mCherry outside of the barrel field. Saturated barrels were defined as adjacent barrels surrounded by barrels with mCherry signal. Only saturated barrels were quantified and the same ROIs were used to quantify mCherry and VGLUT2.

Multiplex fluorescent RNA in situ hybridization (RNAscope) with immunohistochemistry

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Mice were euthanized by cardiac perfusion of 4% paraformaldehyde solution at P30. Brain tissue was pretreated according to the RNAscope Sample Preparation and Pretreatment Guide for Fresh Frozen Tissue (Manual RNAscope assay; Advanced Cell Diagnostics). Tissue was sectioned at 12 μm and subsequent staining performed according to the manufacturer’s instruction for the RNAscope Fluorescent Multiplex kit (Advanced Cell Diagnostics, 320850) with two protocol modifications. Antigen retrieval was carried out in an autoclave set to a 5 min ‘fast’ cycle, 121°C, 15 psi. After protease III digestion, probe solutions containing 313301-C2 (Fezf2) or 484781 (Tox) also contained 10% NGS and 3% BSA to allow the probe binding step to also serve as the IHC blocking step. After developing the fluorescent in situ signal, slides were protected from light and stained overnight at room temperature with 1:250 chicken anti-GFP (Aves labs GFP-1020, RRID:AB_10000240) diluted in 1X Tris-borate-EDTA (TBE) buffer containing 10% NGS and 3% BSA. Slides were washed four times in 1X TBE for 2–5 min and incubated for two hours at room temperature with 1:500 Goat Anti-Chicken Alexa Fluor 488 (Invitrogen A-11039, RRID:AB_2534096). Slides were washed four times in 1X TBE for 2–5 min, counterstained with DAPI and coverslips mounted according to the instructions for the RNAscope Fluorescent Multiplex kit. Batches of staining were balanced to contain equal numbers of control and Rorb KO samples per batch.

Stained tissue was imaged on a Zeiss LSM 880 confocal microscope. Two regions of neocortex containing S1 were imaged for each animal with automated image stitching so that layers 2 through six were contained in a single image. Images of RNA signal were background subtracted in ImageJ (Fiji) using a rolling ball radius of 5 pixels. GFP signal was used to draw ROIs within L4 and L5. A custom CellProfiler (Lamprecht et al., 2007) pipeline identified cells by identifying nuclei from DAPI images and expanding ROIs, and identified RNA puncta. RNA puncta were associated with the nearest cell in R using X,Y coordinates output from CellProfiler. RNA puncta were tallied per cell and the mean calculated per image then per animal and plotted. P-values were calculated by independent sample t-test between Ctl and KO L4.

Electrophysiology

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RorbGFP/GFP (KO) and RorbGFP/+ (control; Ctl) mice were anesthetized with isoflurane and decapitated. Coronal slices (300 µm) containing the primary somatosensory cortex were cut on a Leica (VT1000S) vibratome and incubated at room temperature in ACSF containing (mM) 126 NaCl, 25 NaHCO3, 2.5 KCl, 1.2 NaHPO4, 2 CaCl2, 1 MgCl2 and 32.6 dextrose adjusted to 326 mOsm, pH 7.4 and saturated with 95%/5% O2/CO2. Submerged, whole cell recordings were performed at 32 ± 1° on an upright microscope (Olympus BX50) equipped with epifluorescence. Pipettes with resistance 4–6 Mohm were filled with internal solution containing (mM) 100 K-gluconate, 20 KCl, 10 HEPES, 4 Mg-ATP, 0.3 Na-GTP, 10 Na-phosphocreatine and 0.2% biocytin adjusted to 300 mOsm, pH 7.35. For mIPSC recordings, the internal included 133 mM KCl and gluconate was omitted to bring ECl to 0 mV. Recordings were made using an Axoclamp 700A amplifier, and were digitized at 10–20 kHz and analyzed using custom software running under Igor 6.03 (Wavemetrics). Miniature synaptic events were recorded in voltage clamp at −70 mV in the presence of PTX (mEPSCs) or DNQX+APV (mIPSCs) respectively.

Spiny stellate neurons were recognized based on their compact, GFP+ cell bodies within the GFP+ cell-dense layer 4. Input resistance was measured every 10–20 s with a small hyperpolarizing pulse and data were discarded if input or series resistance changed by >20%. P-values were calculated by 2-way ANOVA and adjusted for multiple comparisons by Tukey post hoc correction.

RNA-seq

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RNA-seq was performed as described previously (Sugino et al., 2019). Briefly, 1000–1500 GFP+ cells were isolated by FACS (BD FACSAria Flow Cytometer) from micro dissected L4 S1 live tissue (N = 4 biological replicates per age and genotype). Figure 10 shows examples of the region micro dissected out to exclude L5. The four independent biological samples were collected from a pool generated by combining tissue from one male and one female mouse for a total of 8 animals used per time point. Cells were sorted directly into extraction buffer and RNA stored at −80C for <three weeks. All libraries were prepared and sequenced in a single batch to prevent batch effects. Total RNA was purified (Arcturus PicoPure RNA Isolation kit, KIT0204) according to manufacturer's specifications. Libraries were prepared using Ovation Trio RNA-Seq library preparation kit with mouse rRNA depletion (0507–32) according to manufacturer's specifications and sequenced on a NextSeq Illumina platform (NextSeq 500/550 High Output (1 × 75 cycles)) obtaining 27 ± 2 million reads (mean ± SE). Reads were mapped by STAR with 90 ± 0.3% unique mapping (mean ± SE) and quantified with featureCounts (Liao et al., 2014). Differentially expressed genes were identified by Limma (Ritchie et al., 2015) using a fold change cutoff of 2 and padj <0.01 from a moderated t-test adjusted for multiple comparisons using FDR (Benjamini-Hochberg).

Example of micro dissected region for L4 S1 from coronal slices.

Yellow dashed line indicates the tissue retained for FACS. Layer five is labeled for reference.

ATAC-seq

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ATAC-seq was performed as described previously (Clark et al., 2019; Sugino et al., 2019). Briefly, 30,000–50,000 GFP+ cells were isolated by FACS from microdissected L4 live tissue (N = 2 biological replicates per age and genotype). The two independent samples were collected from a pool generated by combining tissue from two male and two female mice for a total of 8 animals used. Nuclei were transposed for 30 min and libraries amplified according to published methods (Corces et al., 2017). Tagmented nuclei were stored at −20C for <two weeks. All ATAC libraries were purified, amplified, and sequenced as a single batch. Libraries were sequenced on a NextSeq Illumina platform (high output 300 cycles (2 × 150 bp)) producing 105 ± 24 (mean ± SE) million reads per replicate. Reads were mapped using Bowtie2 and filtered producing 24 ± 2 (mean ± SE) million unique non-mitochondrial reads per replicate. TSS enrichment calculated per replicate according to the ENCODE quality metric (Corces et al., 2017) (https://github.com/ENCODE-DCC/atac-seq-pipeline) was 34 ± 3 (mean ± SE). Peaks were identified permissively using HOMER (-style dnase –fdr 0.5 -minDist 150 -tbp 0 -size 75 -regionRes 0.75 -region) (Heinz et al., 2010) and IDR (threshold = 0.01, pooled_threshold = 0.01) was used to identify reproducible peaks (Li et al., 2011). Differential ATAC peaks were identified using DiffBind with an FDR threshold = 0.02 and log2 fold change in normalized read coverage threshold ≥1 (Ross-Innes et al., 2012).

Data access

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Raw and processed RNA-seq and ATAC-seq files are available at GEO accession GSE138001.

Motif analysis

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Motifs identified de novo from the sequences underlying ATAC peaks was carried out using MEME AME with shuffled input sequences as control and default settings (Fraction of maximum log-odds = 0.25, E-value threshold ≤10) (McLeay and Bailey, 2010), and HOMER findMotifsGenome.pl function masking repeats and -size given (Heinz et al., 2010). Scanning for specific motif matches in the sequences underlying ATAC peaks was carried out using MEME FIMO used the default threshold of p-value<1e-4 (Grant et al., 2011) and HOMER findMotifsGenome.pl -find function. When possible 2–3 PWMs were obtained from Jaspar (Khan et al., 2018) and Cis-BP (Weirauch et al., 2014) prioritizing PWMs from direct data sources such as ChIP-seq. The R package GenomicRanges (Lawrence et al., 2013) was used to identify overlapping motifs between the two algorithms for cross validation. The overlap criteria allowed a 1 bp difference in the start or end position of the motif to accommodate ambiguity among motif models. Fisher Exact tests were calculated in R to test for enrichment of motifs in ATAC regions compared to control regions and to test for enrichment of genes with a nearby motif from a DEG group compared to a control group of genes. The set of control regions was generated by shuffling ATAC peaks throughout the genome excluding sequence gaps using BedTools (Quinlan and Hall, 2010) and the control group of genes were defined as expressed above 5 TPM but unchanged by age or Rorb KO.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
    Plasticity of the rat cortical barrel system
    1. PJ Hand
    2. PL Strick
    (1982)
    Changing Concepts of the Nervous System Proceedings of the First Institute of Neurological Sciences Symposium in Neurobiology (Morrison A). pp. 49–68.
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67
  68. 68
  69. 69
  70. 70
  71. 71
  72. 72
  73. 73
  74. 74
  75. 75
  76. 76
  77. 77
  78. 78
  79. 79
  80. 80
  81. 81
  82. 82
  83. 83
  84. 84
  85. 85
  86. 86
  87. 87
  88. 88
  89. 89
  90. 90
  91. 91
  92. 92
  93. 93
  94. 94
  95. 95
  96. 96

Decision letter

  1. Catherine Dulac
    Senior Editor; Harvard University, United States
  2. Anne E West
    Reviewing Editor; Duke University School of Medicine, United States
  3. Nenad Sestan
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This study provides insights into the molecular mechanisms underlying RORβ's involvement in barrel formation and thus provides a tantalizing link across gene expression, chromatin accessibility, excitatory activity, and cellular organization in L4. The data highlight Thsd7a as a novel gene involved in barrel organization and also implicate several other genes as novel regulators of laminar identity. The observation that barrel organization declines with age in wildtype mice is an interesting one that provides new avenues of research. Thus, overall, this work represents a significant contribution to our understanding of a complex cortical developmental process.

Decision letter after peer review:

Thank you for sending your article entitled "Cortical RORβ is required for layer 4 transcriptional identity and barrel integrity" for peer review at eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Catherine Dulac as the Senior Editor.

The three reviewers were split on the significance of the current study as written but all felt there were two findings in particular that, if they could be strengthened, would be of broad interest. These points were 1) the layer 4 to layer 5 "identity switch" of the RORb knockout neurons and 2) the degeneration of the organization of thalamocortical axons with age and/or Thsd7 knockout. The reviewers raised concerns about interpretation of the data supporting each of these findings, and felt that additional experiments would be required to support either claim. These concerns are most clearly articulated in the comments of reviewer #3 below but were voiced by all three reviewers during the discussion period.

Essential revisions:

1) Alternative methods in addition to FACS purification by GFP and sequencing are needed to confirm that the layer 5 gene expression programs are indeed occurring in what would have been layer 4 RORb+ neurons.

2) Complementary methods in addition to vGLut2 staining are needed to characterize the arborization of TC axons in aging and in the Thsd7 knockout mice to confirm that this organization are disrupted.

Reviewer #1:

This manuscript addresses the circuit functions of the layer 4 neuron marker RORb. The premise of the study is that layer 4 neurons are somewhat unique in their morphological arrangement into sensory input defined cellular aggregates and the authors presuppose that there is a transcriptional basis for this. Prior studies support this idea and already a number of transcription factors are known that link thalamocortical inputs with the organization of sensory barrels. Here the authors add RORb to this list, knocking it out and showing disruption of barrel formation. Global knockout of RORb was shown to reduce barrel formation (though barrels are still visible). Conditional knockout showed that RORb was required in the cortex (Emx1-cre) prior to barrel formation to have this effect. In both cases the effects get worse with age, though RORb knockout by CamKII-cre, after the barrels have formed, appeared to have no effect. The authors took advantage of the fact that the constitutive knockout allele expresses GFP to purify cells from S1. They compared RNA-seq data against Allen Brain in situ data for layer 4 markers and saw an apparently transformation of the cell fate in the KO neurons, with a down regulation of layer 4 markers and upregulation of layer 5. Using ATAC-seq to find the transcriptional mechanisms of these changes the authors found RORb motifs in many differentially-accessible regions comparing WT and KO, though not near the layer 4 genes. Many other sites contained binding sites for activity-sensing TFs, consistent with the known role of sensory input in barrel development. Finally the authors explore one potential target, Thsd7a, which also seems to disrupt some aspects of barrels.

The work is well done, the studies are rigorous, and the manuscript is well written. However it is not clear that the story is highly novel or significant. Several TFs (as the authors cite) are already known to couple barrel development with sensory input. The authors find some interesting information about RORb's specific functions in this process, but it is not clear that fundamental new insights emerge from the data. Overall I see the manuscript as being of great interest to specialists, but of limited interest to the broader audience.

Reviewer #2:

This study provides insights into the molecular mechanisms underlying RORβ's involvement in barrel formation and thus provides a tantalizing link across gene expression, chromatin accessibility, excitatory activity, and cellular organization in L4. The data highlight Thsd7a as a novel gene involved in barrel organization and also implicate several other genes as novel regulators of laminar identity. The observation that barrel organization declines with age in wildtype mice is an interesting one that provides new avenues of research. Thus, overall, this work represents a significant contribution to our understanding of a complex cortical developmental process. The body of the manuscript is well written, though I would urge the authors to carefully review the figure legends which contain errors and require some editing. In general, the conclusions drawn by the authors are supported by the data. The tests chosen for statistical analysis seem appropriate and sufficiently rigorous.

1) It would have been nice to see the progression from the molecular to the cellular level extended to the functional level to complete the whole picture, for instance by using a behavioral test of whisker function to see whether these molecular and cellular changes translate to a meaningful functional phenotype.

2) The mini EPSC frequency appears to stabilize in Rorb KO mice by P10, yet at P30 the authors observed changes in the expression of many activity-dependent TFs. To strengthen the claim that the changes in excitatory activity are consistent with the observed transcriptional changes, it would seem appropriate to perform the mini EPSC experiment at the P30 time point when most of the activity-dependent TFs show up-regulated expression.

3) It is unclear why normalization was only used when quantifying changes in barrel-septa contrast in the Thsd7a experiment (Figure 7). It seems prudent to use this approach in all the other experiments (Figures 1-3).

4) Figure 3D lacks an appropriate negative control for comparison at P60 (i.e. CamK2a-cre alone). This is especially important since the authors demonstrate an age-related desegregation of TCA.

Reviewer #3:

Using conditional KO strategies authors show that the transcription factor RORb is required in the cortex but not in thalamus for barrel formation; then, based on extensive genomic analyses of RORb+ neurons from layer 4 (L4) neurons , they find that RORb+ neurons loose some L4 molecular characteristics while acquiring new L5 characteristics. Finally they analyse 2 target genes of RORb looking for morphological change in barrel organisation.

Overall these data are interesting and well analysed; on one hand they confirm the role of RORb in L4 barrel formation ; this has had been proposed based on expression data and on gain of function experiments (Jabaudon et al., 2012). On the other hand, they bring an interesting controversy on the role of RORb in acquiring L4 molecular identity. L4 is generally considered to be part of the upper cortical plate neurons, and to share developmental origin and molecular identity with L2/L3 neurons (Oishi et al., 2016 ).

The following weak points need however to be addressed.

1) The most original claim of the study is that L4 neurons acquire L5 identity in Rorb-KO. However, this based solely on changes in gene expression, which are not really compelling, because effects are not consistent and vary strongly with age. To support their conclusion, authors need to provide further evidence on the laminar distribution of L4 and L5 /L6 molecular markers (e.g. Cux1, Brn2b, ctip2,...;). Additionally it would be important to know whether L4 neurons acquire new morphological characteristics, of L5 neurons such as pyramidal shape and sub-cerebral projections. Because GFP is expressed in the RORb deficient neurons these could be easily traced and analyzed.

2) The changes in gene expression in RORb KO vary most between P2 and P7: L5 markers (e.g. ctip2 and Fez1) are up regulated only at P2. Could there be some contamination of the P2 samples with L5 (in which RORb is also expressed), despite efforts in the dissection?

3) Another strong claim is that segregation of TCAs degrades with age. However, this is based exclusively on Vglut2 immunostaining with low resolution. This is questionable as the cortex matures, since Vglut2 staining becomes much more diffuse, possibly because of the arrival of other VGluT2+ cortical inputs than TCAs. In fact the difference between WT and RORb- KO becomes is less clear as animals age (Figure 2B) and authors note "loss of RORb did not significantly change the time course of TCA desegregation”. Thus without complementary approaches it is hard to make such strong conclusions. Previous studies showing desegregation of TCAs in the barrel cortex, secondary to cortex-specific deletions have used complementary methods such as tracing reconstructions of TCA axon terminals in the cortex (Ballester-Rosado, 2000, Lee et al., 2005).

4) For similar reasons as above, the phenotype of the barrel phenotype of Thsd7-KO is not really convincing. Although some higher resolution images are shown, these are not confocal, and would not allow rigorous measures of VGluT2+ terminals.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your article "Cortical Rorb is required for layer 4 transcriptional identity and barrel integrity" for consideration by eLife. Your revised article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Catherine Dulac as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Patricia Gaspar (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

This study provides insights into the molecular mechanisms underlying RORβ's involvement in barrel formation and thus provides a tantalizing link across gene expression, chromatin accessibility, excitatory activity, and cellular organization in L4. The data highlight Thsd7a as a novel gene involved in barrel organization and also implicate several other genes as novel regulators of laminar identity. The observation that barrel organization declines with age in wildtype mice is an interesting one that provides new avenues of research. Thus, overall, this work represents a significant contribution to our understanding of a complex cortical developmental process.

Essential revisions:

This is a revised manuscript, and in a previous round of review the reviewers requested a plan from the authors outlining how they would address what were seen as two major concerns. These were the claim from RNA-seq data that Layer 4 neurons were expressing Layer 5 gene programs, and the use of vGlut2 labeling to assess inputs to barrel cortex. The authors proposed to offer new data to address each of these points, using quantitative in situ to validate the RNA-seq and a viral tracing method to complement the vGLut2 staining evaluation of the organization of inputs in barrel cortex.

The authors did provide these data, however two of the reviewers felt that these new figures needed more clarification. In particular the way the authors presented and interpreted the viral input labeling experiment was a source of significant confusion. Comments from the reviewers on these two new datasets are below and these need to be addressed with text/figure revisions and/or tempering of claims.

Barrel cortex viral input labeling:

– The fact that the TC barrelless phenotype increases with age is not supported by the additional evidence provided. Furthermore, the quantification in Figure 2B actually shows the opposite trend: using measures of VMAT2 intensity in barrel hollows/septae the Ctrl/KO difference is most marked at P7 and P20, it seems less marked at P30 and it is no longer significant at P60. Therefore I would suggest not highlighting this in the Abstract and Discussion without better evidence.

– Figure 2—figure supplement 1 and Figure 7—figure supplement 1, compare TC tracing (very nice) with Vglut2 to show this is similar, but they do not illustrate/compare data from the ctrl and KO on the same or graph. The mean control values (n = ?) are represented by the dashed line; but then the SEM and stats need to be added. These 2 figures could be combined into one clearer figure.

– I do not understand the authors' interpretation of Figure 2—figure supplement 1C and Figure 7—figure supplement 1. First, what is the y-axis in Figure 2—figure supplement 1C and Figure 7—figure supplement 1 – barrel septal contrast of what? VGlut2? Comparing the absolute contrast levels of two methods of labeling inputs at a single time point or in a single genotype would not seem to mean much. I was assuming when they suggested this method that the authors would compare the Cherry signal in WT and KO to show it reproduced the lower signaling in the KO relative to WT like they saw with vGLut 2. I thought they would do a similar experiment and look at cherry labeling over time in the WT to show it decreases similar to the vGlut2 labeling. Those comparisons would seem to have been needed to address the concerns that were raised by the reviewers about the vGlut2 signal.

RNAscope in situ quantification:

– What is the time point for the in situ in Figure 5A? Why is the difference in Fezf2 expression so large in this sample whereas it is negligible at all time points in Supplementary Figure 4B? What did Tox look like in the sequencing data – it would be good to include it in Supplementary Figure 4B. The authors should also show the images for Tox in the supplementary figure because this is important to the validation. Finally, I do not understand what Figure 4E is trying to show or what the authors are concluding. There is no quantification so it is not possible to judge whatever conclusion was intended.

https://doi.org/10.7554/eLife.52370.sa1

Author response

Essential revisions:

1) Alternative methods in addition to FACS purification by GFP and sequencing are needed to confirm that the layer 5 gene expression programs are indeed occurring in what would have been layer 4 RORb+ neurons.

2) Complementary methods in addition to vGLut2 staining are needed to characterize the arborization of TC axons in aging and in the Thsd7 knockout mice to confirm that this organization are disrupted.

Reviewer #1:

This manuscript addresses the circuit functions of the layer 4 neuron marker Rorb. The premise of the study is that layer 4 neurons are somewhat unique in their morphological arrangement into sensory input defined cellular aggregates and the authors presuppose that there is a transcriptional basis for this. Prior studies support this idea and already a number of transcription factors are known that link thalamocortical inputs with the organization of sensory barrels. Here the authors add RORb to this list, knocking it out and showing disruption of barrel formation. Global knockout of RORb was shown to reduce barrel formation (though barrels are still visible). Conditional knockout showed that RORb was required in the cortex (Emx1-cre) prior to barrel formation to have this effect. In both cases the effects get worse with age, though RORb knockout by CamKII-cre, after the barrels have formed, appeared to have no effect. The authors took advantage of the fact that the constitutive knockout allele expresses GFP to purify cells from S1. They compared RNA-seq data against Allen Brain in situ data for layer 4 markers and saw an apparently transformation of the cell fate in the KO neurons, with a down regulation of layer 4 markers and upregulation of layer 5. Using ATAC-seq to find the transcriptional mechanisms of these changes the authors found RORb motifs in many differentially-accessible regions comparing WT and KO, though not near the layer 4 genes. Many other sites contained binding sites for activity-sensing TFs, consistent with the known role of sensory input in barrel development. Finally the authors explore one potential target, Thsd7a, which also seems to disrupt some aspects of barrels.

The work is well done, the studies are rigorous, and the manuscript is well written. However it is not clear that the story is highly novel or significant. Several TFs (as the authors cite) are already known to couple barrel development with sensory input. The authors find some interesting information about RORb's specific functions in this process, but it is not clear that fundamental new insights emerge from the data. Overall I see the manuscript as being of great interest to specialists, but of limited interest to the broader audience.

Several features of our study make it of broad interest. Prior studies identified transcription factors required for barrel development and laminar fate but stopped short of revealing the underlying transcriptional networks. Here, we begin to open the black box by which loss of a single transcription factor, through a cascade of changes in gene expression and chromatin accessibility, can shape activity-dependent cortical development. Our study dives deeper into the transcriptional regulation of barrel development than previous studies. The mechanisms we highlight have broader implications for understanding the complexity of transcriptional networks governing cellular identity and the diversity of transcriptional mechanisms altered by a single TF. Additionally, our description of TCA desegregation in adulthood and the increase in adult desegregation after Thsd7a KO, two findings further supported by additional VPM-specific experiments, opens up the barrel cortex to study mechanisms involved in age-related plasticity and cytoarchitecture maintenance.

Reviewer #2:

This study provides insights into the molecular mechanisms underlying RORB's involvement in barrel formation and thus provides a tantalizing link across gene expression, chromatin accessibility, excitatory activity, and cellular organization in L4. The data highlight Thsd7a as a novel gene involved in barrel organization and also implicate several other genes as novel regulators of laminar identity. The observation that barrel organization declines with age in wildtype mice is an interesting one that provides new avenues of research. Thus, overall, this work represents a significant contribution to our understanding of a complex cortical developmental process. The body of the manuscript is well written, though I would urge the authors to carefully review the figure legends which contain errors and require some editing. In general, the conclusions drawn by the authors are supported by the data. The tests chosen for statistical analysis seem appropriate and sufficiently rigorous.

1) It would have been nice to see the progression from the molecular to the cellular level extended to the functional level to complete the whole picture, for instance by using a behavioral test of whisker function to see whether these molecular and cellular changes translate to a meaningful functional phenotype.

While we agree extending the findings into behavioral readouts would be very interesting, those experiments are very time and resource intensive. We feel they are beyond the scope of this paper which focuses on molecular transcriptional mechanisms, and their roles in anatomical and physiological development.

2) The mini EPSC frequency appears to stabilize in Rorb KO mice by P10, yet at P30 the authors observed changes in the expression of many activity-dependent TFs. To strengthen the claim that the changes in excitatory activity are consistent with the observed transcriptional changes, it would seem appropriate to perform the mini EPSC experiment at the P30 time point when most of the activity-dependent TFs show up-regulated expression.

We previously performed mEPSC recordings at P19 and found that both frequency and amplitude returned to normal levels. We have added this data to Figure 6B.

3) It is unclear why normalization was only used when quantifying changes in barrel-septa contrast in the Thsd7a experiment (Figure 7). It seems prudent to use this approach in all the other experiments (Figures 1-3).

Normalization was not used in Figure 7D in the barrel-septa contrast calculation. It was used to measure absolute intensity within barrels or septa separately in Figure 7E-F. Because the contrast value is a ratio, it does not need to be normalized as both the barrels and septa would be corrected by the same measurement. We have added more detail regarding this aspect to the Materials and methods (subsection “Imaging and fluorescence quantification”).

4) Figure 3D lacks an appropriate negative control for comparison at P60 (i.e. CamK2a-cre alone). This is especially important since the authors demonstrate an age-related desegregation of TCA.

We are not able to detect a significant difference in barrel contrast between P30 and P60 in our control data (Figure 2B). There is a slight downward trend but the variance is too high and our N is too small to determine whether this is significant at the thresholds we’ve set. We do detect a significant change in TCA segregation comparing P7 to P20. We have added a more detailed description to the Results to make this distinction clearer (subsection “RORβ is required for postnatal barrel wall formation and influences segregation of thalamocortical afferents (TCAs)”).

Given that the Rorbf/f CamK2a-cre does not affect TCA organization, it is very unlikely that the CamK2a-cre alone will have an effect. We do not think excluding this control changes the interpretation of the negative result.

Reviewer #3:

Using conditional KO strategies authors show that the transcription factor RORb is required in the cortex but not in thalamus for barrel formation; then, based on extensive genomic analyses of RORb+ neurons from layer 4 (L4) neurons , they find that RORb+ neurons loose some L4 molecular characteristics while acquiring new L5 characteristics. Finally they analyse 2 target genes of RORb looking for morphological change in barrel organisation.

Overall these data are interesting and well analysed; on one hand they confirm the role of RORb in L4 barrel formation ; this has had been proposed based on expression data and on gain of function experiments (Jabaudon et al., 2012). On the other hand, they bring an interesting controversy on the role of RORb in acquiring L4 molecular identity. L4 is generally considered to be part of the upper cortical plate neurons, and to share developmental origin and molecular identity with L2/L3 neurons (Oishi et al., 2016 ).

The following weak points need however to be addressed.

1) The most original claim of the study is that L4 neurons acquire L5 identity in RORb-KO. However, this based solely on changes in gene expression, which are not really compelling, because effects are not consistent and vary strongly with age. To support their conclusion, authors need to provide further evidence on the laminar distribution of L4 and L5 /L6 molecular markers (e.g. Cux1, Brn2b, ctip2,...;). Additionally it would be important to know whether L4 neurons acquire new morphological characteristics, of L5 neurons such as pyramidal shape and sub-cerebral projections. Because GFP is expressed in the RORb deficient neurons these could be easily traced and analyzed.

We note that we are not claiming that loss of RORβ leads to a fate-switch in which all aspects of L4 identity are lost and all aspects of L5 identity are gained. Instead, we believe the data argue for a more nuanced view of “identity” comprised of multiple transcriptional circuits, only some of which are disrupted by loss of RORβ. This issue is likely in part due to a lack of clarity in our description. Our use of the phrase “shift in identity” understandably, but also unintentionally, evokes the concept of fate switching. We have edited the manuscript to make it clear (subsection “RORβ is required for expression of a layer 4 gene profile and repression of layer 5 genes”, Discussion) that we don’t think loss of RORb causes L4 neurons to take on a L5 identity. We have also added an analysis of additional layer-specific genes identified from the Allen Brain Atlas demonstrating that while many L4 and L5 genes are dysregulated, many are not (Figure 4—figure supplement 1E). Additionally, we don’t find any strong evidence that the L4 neurons have acquired L5 morphology. Instead, we propose that loss of RORb disrupts L4 specification, a process which appears to involve repression of L5 genes. In fact, we think RORb’s role is more likely in fine-tuning L4 identity after fate selection, and upregulation of deeper layer genes is a symptom of dysregulated L4 specification rather than a large-scale identity switch. We apologize for our lack of thoughtfulness in word choice and phrasing while describing these transcriptional changes.

2) The changes in gene expression in RORb KO vary most between P2 and P7: L5 markers (e.g. ctip2 and Fez1) are up regulated only at P2. Could there be some contamination of the P2 samples with L5 (in which RORb is also expressed), despite efforts in the dissection?

While we agree with reviewer #3 that age is a factor in upregulation of L5 genes, we do not agree with the assessment that P2 and P7 showed the most change between control and KO. The red line graphs of Figure 4B show that the younger ages show more variation in L5 genes changes with several genes down regulated at younger ages but ultimately upregulated at P30. The P30 adult time point shows the most consistent upregulation with the bulk of the L5 DEGs increased, and for several genes, also with the largest fold increase. We have added a line showing the mean LFC across the group of genes in Figure 4B to make this conclusion easier to assess. While Ctip2 does show the strongest upregulation at P2, FezF2 (Fez1 is not a DEG in our dataset) is only upregulated at P30.

The reviewer is insightful to suggest possible difficulty in the P2 microdissection. While we think the most compelling RNA-seq evidence is at P7 and P30 we performed RNAscope in situ hybridization to confirm upregulation of two L5 genes, FezF2 and Tox (Figure 4—figure supplement 1C-D). As an additional control, we analyzed expression changes in all of the genes differentially expressed between layers 4 and 5 of somatosensory cortex according to the Allen Brain Atlas (analysis and criteria in Figure 4—figure supplement 1). Most genes (74 L4 and 190 L5) did not, on average show a change between WT and Rorb KO. This would be unexpected if the changes seen in the genes shown in Figure 4 were due to contamination.

3) Another strong claim is that segregation of TCAs degrades with age. However, this is based exclusively on Vglut2 immunostaining with low resolution. This is questionable as the cortex matures, since Vglut2 staining becomes much more diffuse, possibly because of the arrival of other VGluT2+ cortical inputs than TCAs. In fact the difference between WT and RORb- KO becomes is less clear as animals age (Figure 2B) and authors note "loss of RORb did not significantly change the time course of TCA desegregation”. Thus without complementary approaches it is hard to make such strong conclusions. Previous studies showing desegregation of TCAs in the barrel cortex, secondary to cortex-specific deletions have used complementary methods such as tracing reconstructions of TCA axon terminals in the cortex (Ballester-Rosado, 2000, Lee et al., 2005).

4) For similar reasons as above, the phenotype of the barrel phenotype of Thsd7-KO is not really convincing. Although some higher resolution images are shown, these are not confocal, and would not allow rigorous measures of VGluT2+ terminals.

To complement our VGLUT2 staining quantification and to address comments 3 and 4, we injected AAV carrying an mCherry reporter gene driven by the hsyn promoter (AAV-hSyn-mCherry) into VPM to anatomically label TCAs. We use VLGUT2 staining to define barrels and compared the barrel-septa contrast calculated from mCherry expression in VPM axons to that calculated from VGLUT2. Figure 2—figure supplement 1A-C and Figure 7—figure supplement 1 show that the contrast calculated from VPM-specific mCherry expressing axons was similar to the contrast calculated from VGLUT2 staining. This supports our interpretation that VPM TCA organization degrades with age and without functional Thsd7a, rather than as a result of the arrival of other VGLUT2+ cortical inputs as the cortex matures.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Essential revisions:

This is a revised manuscript, and in a previous round of review the reviewers requested a plan from the authors outlining how they would address what were seen as two major concerns. These were the claim from RNA-seq data that Layer 4 neurons were expressing Layer 5 gene programs, and the use of vGlut2 labeling to assess inputs to barrel cortex. The authors proposed to offer new data to address each of these points, using quantitative in situ to validate the RNA-seq and a viral tracing method to complement the vGLut2 staining evaluation of the organization of inputs in barrel cortex.

The authors did provide these data, however two of the reviewers felt that these new figures needed more clarification. In particular the way the authors presented and interpreted the viral input labeling experiment was a source of significant confusion. Comments from the reviewers on these two new datasets are below and these need to be addressed with text/figure revisions and/or tempering of claims.

Barrel cortex viral input labeling:

– The fact that the TC barrelless phenotype increases with age is not supported by the additional evidence provided. Furthermore, the quantification in Figure 2B actually shows the opposite trend: using measures of VMAT2 intensity in barrel hollows/septae the Ctrl/KO difference is most marked at P7 and P20, it seems less marked at P30 and it is no longer significant at P60. Therefore I would suggest not highlighting this in the Abstract and Discussion without better evidence.

The paper does not argue that the effect of Rorb KO increases with age. Instead, we find that segregation of afferents declines with age and this effect is separate from the more profound loss of segregation seen in the knockout. This was highlighted by reviewer #2 in the prior set of reviews: “The observation that barrel organization declines with age in wildtype mice is an interesting one that provides new avenues of research. Thus, overall, this work represents a significant contribution to our understanding of a complex cortical developmental process.” To try to make this point clearer, we have altered the sentence in the Abstract which previously read “Interestingly, barrel organization also degrades with age.” to read: “Interestingly, barrel organization also degrades with age in wildtype mice.” We have also clarified this portion of the Discussion by changing: “Our observation that barrel organization declined with age is very interesting and possibly the first description of this phenomenon in mice (Rice, 1985)” to “Our observation that barrel organization declined with age in wildtype animals is very interesting and possibly the first description of this phenomenon in mice (Rice, 1985).” Please also note that we are careful to state in the Results that the change with age is common to both control and KO animals and that “while both age and loss of RORβ significantly reduced contrast, loss of RORβ did not significantly change the time course of TCA desegregation”.

– Figure 2—figure supplement 1 and Figure 7—figure supplement 1, compare TC tracing (very nice) with Vglut2 to show this is similar, but they do not illustrate/compare data from the ctrl and KO on the same or graph. The mean control values (n = ?) are represented by the dashed line; but then the SEM and stats need to be added. These 2 figures could be combined into one clearer figure.

The prior round of reviews included the following request from reviewer #3:

“3) Another strong claim is that segregation of TCAs degrades with age. However, this is based exclusively on Vglut2 immunostaining with low resolution. This is questionable as the cortex matures, since Vglut2 staining becomes much more diffuse, possibly because of the arrival of other VGluT2+ cortical inputs than TCAs…4) For similar reasons as above, the phenotype of the barrel phenotype of Thsd7-KO is not really convincing.”

I realize now that the reviewer may have thought that we were claiming that the Rorb KO phenotype increases with age (see point immediately above) but at the time we and the editors clearly interpreted the reviewer’s comments as referring solely to the aging phenotype, which is also present in wildtype mice, and the Thsd7-KO. Specifically, in the prior round of reviews Essential revision #2 reads “Complementary methods in addition to vGLut2 staining are needed to characterize the arborization of TC axons in aging and in the Thsd7 knockout mice to confirm that this organization are disrupted.”

Based on our understanding of these requests we submitted a revision plan that included the a response as follows:

To complement our vGLUT2 staining quantification we propose to inject anterograde tracers into the VPM of adult wild-type and Ths7a KO animals. We will then quantify the barrel-septa contrast both of vGLUT2 staining and the axonal label. We will also use high resolution confocal microscopy to document the presence of axonal terminals in the septa. If the loss of vGLUT2 contrast in the older adults and Ths7a KO is primarily due to loss of TCA localization to barrel hollows we should see a comparable loss of contrast in the VPM anterograde tracer. On the other hand, if the loss of vGLUT2 contrast is due to ingrowth of other vGluT2+ afferents, the TCA and vGLUT2 contrast will be mismatched and axonal labeling in the septa will be absent.”

We did carry out these experiments and illustrate them in Figure 2—figure supplement 1 and Figure 8—figure supplement 1. The number of animals for these experiments (4 for both figures) is stated in the legends. We did not propose to directly compare Rorb KO animals to wildtype animals or to produce a complete time course. While we did not produce a full time course for Figure 2—figure supplement 1, we replot the Vglut2 contrasts obtained for P7 for reference and for P30 to show that both measures of Vglut2 contrast are similar despite the experiments having been carried out many months apart. We felt it was important to show that the new experiments involving surgical and viral methods did not significantly alter Vglut2 contrast. The key result shown by these experiments is that TCAs specifically originating in the VPM do not show a difference in contrast compared to total Vglut2 staining. This data rules out the alternative interpretation of non-thalamic afferents raised by the reviewer in the first round of comments. If loss of Vglut2 contrast was due to the arrival of other VGluT2+ inputs we would not see such close alignment of Vlglut2 contrast and mCherry contrast at P30.

As the reviewer suggests, we have restated the N’s for the control data in the legends and have given the statistics in the two legends as well. We have also added the SD to the figures as requested.

We do not believe it would be clearer to combine the two figures into one since they come at very different points within the manuscript and address different questions. Figure 2—figure supplement 1 addresses the decline of barrel segregation with age in normal mice, while Figure 8—figure supplement 1 addresses the loss of barrel-septa contrast in the Ths7a KO.

– I do not understand the authors' interpretation of Figure 2—figure supplement 1C and Figure 7—figure supplement 1. First, what is the y-axis in Figure 2—figure supplement 1C and Figure 7—figure supplement 1 – barrel septal contrast of what? VGlut2? Comparing the absolute contrast levels of two methods of labeling inputs at a single time point or in a single genotype would not seem to mean much. I was assuming when they suggested this method that the authors would compare the Cherry signal in WT and KO to show it reproduced the lower signaling in the KO relative to WT like they saw with vGLut 2. I thought they would do a similar experiment and look at cherry labeling over time in the WT to show it decreases similar to the vGlut2 labeling. Those comparisons would seem to have been needed to address the concerns that were raised by the reviewers about the vGlut2 signal.

The y-axis in both figures compares the contrast of VGlut2 to the contrast of mCherry contained within VPM axons. Unlike intensity, contrast is not an absolute measure, but a relative one. It measures the fractional difference between labeling within the barrel compartment relative to that within the septal compartment as a measure of the segregation of axons. A direct comparison of the spatial contrast (segregation) between Vglut2 labelled axons and mCherry-labelled axons in the same tissue was precisely the point of this experiment. What this figure shows clearly is that both methods of labeling axons produce a nearly identical measure of contrast. In other words, the original concern that “Vglut2 staining becomes much more diffuse, possibly because of the arrival of other VGluT2+ cortical inputs than TCAs” can be ruled out.

RNAscope in situ quantification:

– What is the time point for the in situ in Figure 5A?

The in situ was performed at P30 which is stated in the Materials and methods. We have now added this to the legend for clarity.

Why is the difference in Fezf2 expression so large in this sample whereas it is negligible at all time points in Supplementary Figure 4B?

The scale in Supplementary Figure 4B made the difference appear negligible because Fezf2 levels decline greatly by P30. We have replotted the data from Supplementary Figure 4B so that the close agreement between the sequencing and in situ can be better appreciated. We have also included this panel in a new main figure (Figure 5) as suggested below.

What did Tox look like in the sequencing data – it would be good to include it in Supplementary Figure 4B. The authors should also show the images for Tox in the supplementary figure because this is important to the validation.

We have now included both images for Tox and sequencing data for Tox to allow appreciation of the correspondence. This now comprises a new Figure 5—figure supplement 1.

Finally, I do not understand what Supplementary Figure 4E is trying to show or what the authors are concluding. There is no quantification so it is not possible to judge whatever conclusion was intended.

The former Supplementary Figure 4E supports the point that not all L4-specific and L5-specific genes are affected by the knockout, which one would expect if the change we observe were simply due to L5 contamination (a concern raised by reviewers in the first round of comments). This panel is now moved to Main Figure 4E. We have added a statistical analysis (Fisher exact test) to show that although only a subset of L4 and L5 genes are affected, downregulation of L4 genes and upregulation of L5 genes are highly overrepresented (Figure 4F). As explained in the legend and the text, these include all of the genes with 1.5 L4:L5 fold-change and expression > 1.6 as identified by the Allen Brain Atlas differential search. Since these genes exclude the 31 L4 genes and 50 L5 differentially expressed genes shown in Figure 4A-C, they are by definition, not statistically altered by the knockout, as shown by the similarity of the mean fold-change (black line) to the gray line indicating no change. Therefore, this new analysis aims to make two points: (1) the data are not consistent with simple contamination of Rorb KO samples by L5 cells, and (2) provide additional statistical support for the conclusion that L4 and L5 genes are significantly altered. We have added clarification of these results to the legend and to the Results subsection “RORβ is required for expression of a layer 4 gene profile and repression of layer 5 genes”.

https://doi.org/10.7554/eLife.52370.sa2

Article and author information

Author details

  1. Erin A Clark

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Project administration
    For correspondence
    eaclark@brandeis.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4013-325X
  2. Michael Rutlin

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Validation, Investigation, Visualization, Project administration
    Competing interests
    No competing interests declared
  3. Lucia Capano

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3470-9360
  4. Samuel Aviles

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Data curation, Software, Formal analysis, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
  5. Jordan R Saadon

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Data curation, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
  6. Praveen Taneja

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  7. Qiyu Zhang

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7141-4046
  8. James B Bullis

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  9. Timothy Lauer

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Emma Myers

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  11. Anton Schulmann

    Janelia Research Campus, Ashburn, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  12. Douglas Forrest

    Laboratory of Endocrinology and Receptor Biology, National Institutes of Health, NIDDK, Bethesda, United States
    Contribution
    Resources
    Competing interests
    No competing interests declared
  13. Sacha B Nelson

    Department of Biology and Program in Neuroscience, Brandeis University, Waltham, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration
    For correspondence
    nelson@brandeis.edu
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0108-8599

Funding

National Institute of Neurological Disorders and Stroke (NS109916)

  • Erin A Clark
  • Michael Rutlin
  • Lucia Capano
  • Samuel Aviles
  • Jordan R Saadon
  • Praveen Taneja
  • Qiyu Zhang
  • James B Bullis
  • Timothy Lauer
  • Emma Myers
  • Anton Schulmann

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

Acknowledgements

We thank Dr. Roland Schüle for agreeing to share the Rorbf/f line, and Dr. Matthew Eaton for friendly bioinformatic advice. Supported in part by the intramural research program at NIDDK at the National Institutes of Health (DF).

Ethics

Animal experimentation: All experiments were conducted in accordance with the requirements of the Institutional Animal Care and Use Committees at Brandeis University (protocol #17001).

Senior Editor

  1. Catherine Dulac, Harvard University, United States

Reviewing Editor

  1. Anne E West, Duke University School of Medicine, United States

Reviewer

  1. Nenad Sestan

Publication history

  1. Received: October 2, 2019
  2. Accepted: August 26, 2020
  3. Accepted Manuscript published: August 27, 2020 (version 1)
  4. Version of Record published: September 15, 2020 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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