, 2006). In the current issue of Neuron, Woloszyn and Sheinberg (2012) shed new light on the plasticity of ITC shape

representations and help reconcile the disparate selleckchem results of the prior studies mentioned above. They examined ITC activity while monkeys viewed visual stimuli that were either novel or highly familiar ( Figure 1). They classified their ITC population into putative excitatory and inhibitory cells by virtue of the width of neurons’ spike waveforms and examined whether these distinct neuronal populations exhibited different patterns of selectivity and learning effects. Narrow spiking neurons usually correspond to inhibitory interneurons while broad spikes are typically generated by excitatory pyramidal neurons ( McCormick et al., 1985). Recent studies in V4, posterior parietal cortex, and prefrontal cortex found that these two neuron classes showed distinct patterns of effects during attention ( Mitchell et al., 2007), discrimination ( Hussar and Pasternak, 2009), and numerical categorization ( Diester and Nieder, 2008). Woloszyn and Sheinberg (2012) show that in ITC, putative excitatory and inhibitory neurons exhibit very different effects of experience—excitatory neurons typically showed experience-dependent increases in activity that were

specific to their preferred stimuli (i.e., the images in the Epigenetic inhibitor manufacturer stimulus set that elicited the strongest responses). In contrast, inhibitory neurons showed global decreases to familiar compared Bay 11-7085 to novel stimuli (including the most preferred stimuli in the tested sets). Notably, putative excitatory neurons also showed widespread decreases in firing rate to nonpreferred familiar stimuli. These results suggest that the net effect of experience on putative excitatory neurons

is to boost responses to neurons’ preferred stimuli, potentially leading to sparser representations with a higher signal-to-noise ratio. These stronger and sharper representations of familiar stimuli could have a greater impact on downstream neurons, potentially enhancing the read out information from ITC. Long-range connections between cortical areas originate predominantly from excitatory pyramidal neurons; thus, the stronger and sharper representations of familiar stimuli would support more efficient read-out of object identity from excitatory ITC neurons. These results help to reconcile the conflicting findings from earlier studies. As the authors point out, previous studies which reported stronger responses to familiar stimuli tended to use large and diverse stimulus sets and/or screened neurons to identify their preferred stimuli. Thus, these studies were more likely to test neurons with preferred stimuli that would drive strong responses.

Surprisingly, we did not see changes in the levels of chaperones at P10. This discrepancy is likely due to the fact that steady-state levels reflect both protein synthesis and degradation, and under conditions of stress, heat shock proteins are induced. For select CSPα interactome members, we determined that their mRNA levels were unchanged in wild-type and CSPα KO brains, indicating that the observed decreases in protein levels occurred posttranscriptionally (Figures S2D and S2E). The 15 proteins whose levels we validated in the CSPα KO can either be direct clients of the CSPα chaperone complex or indirectly decreased due to secondary changes. To determine

which of these proteins are direct clients, we tested if they bind either CSPα or Hsc70 in a nucleotide-dependent selleck chemicals manner. In the presence of ATP, clients typically bind the Hsc70-DnaJ cochaperone complex with low affinity, while ADP promotes a high-affinity interaction (Kampinga and Craig, 2010). We expressed CSPα and Hsc70 as GST fusions and carried out GST pull-downs with wild-type mouse brain MDV3100 mouse homogenates (Figure 3A), using the Hsc70 binding protein Stip

1/HOP as a positive control. Our results revealed that dynamin 1 binds CSPα, suggesting that it is a client of this chaperone complex. Importantly, dynamin 1 behaves like a prototypical Hsc70-DnaJ chaperone client in that it binds CSPα, and not Hsc70, and its binding is ADP dependent. Consistent very with previously published work, SNAP-25 binds both CSPα and Hsc70 (Figure 3A) (Chandra et al., 2005 and Sharma et al., 2011). Additionally, we could show that BASP1 is an Hsc70 binding protein and rule out that complexin I, NSF, and synucleins are direct CSPα clients. Based on our proteomic and biochemical analysis, we narrowed our analysis

of CSPα clients to dynamin 1 and SNAP-25. We confirmed the interactions of both dynamin 1 and SNAP-25 with the CSPα chaperone complex in vivo by immunoprecipitating dynamin 1 and SNAP-25 with CSPα from brain homogenates in the presence of nucleotides (Figure 3B). Again, the binding of dynamin 1, but not of SNAP-25, to CSPα is promoted by addition of ADP. We also showed these interactions with proteins heterologously expressed in HEK293T cells (Figure 3C). These results indicate that dynamin 1 and SNAP-25 are both direct clients of the Hsc70-CSPα chaperone complex but probably have different sites of interaction. We therefore tested binding of dynamin 1 and SNAP-25 to CSPα and Hsc70 with purified proteins. As seen in Figure 3D, dynamin 1 is recruited to this complex via CSPα binding, while SNAP-25 is recruited via Hsc70 (Sharma et al., 2011). Previous work has shown that binding of purified SNAP-25 to Hsc70 is stabilized in the presence of ADP-β-S (Sharma et al., 2011). Based on these findings, we predicted that the two clients may have different effects on the nucleotide binding domain of Hsc70 and therein its ATPase activity.

While overexpression of the phosphomimetic Smurf1 decreased Par6 ubiquitination and increased RhoA ubiquitination, the overexpression of the nonphosphorylatable Smurf1 caused the opposite effects. This switch of substrate specificity was due to a higher binding affinity INCB018424 nmr of phosphorylated Smurf1 to RhoA than to Par6. Therefore, PKA-dependent phosphorylation of Smurf1 switches its substrate preference from Par6 to

RhoA causing the stabilization of Par6 and proteasomal degradation of RhoA. How does this switch determine axon specification? The local exposure of BDNF to one neurite led to a localized accumulation of phosphorylated Smurf1 in the neurite tip. Consistent with the fact that such a local exposure of BDNF can induce axon growth, increased phosphorylated Smurf1 levels were also detected in the future axons of spontaneously polarizing neurons. Indeed, overexpressing the phosphomimetic Smurf1 mutant increased the formation of multiple axons, while Smurf1 knockdown by shRNA or overexpression of nonphosphorylatable Smurf1 inhibited axon formation. Together, with the observation that RhoA was reduced in the growth cone of future axons and the rescue of the Smurf1 knockdown with Par6 overexpression, these results indicate that increasing the Par6/RhoA ratio is necessary and sufficient for axon formation. Why is the Par6/RhoA

ratio so important for axon specification? find more Par6 and its binding partner Par3 localize

specifically to the nascent axon (Shi et al., 2003), where they modulate the small GTPases Cdc42 and Rac1. Cdc42 and Rac1 are known to promote axon growth (Garvalov et al., 2007 and Tahirovic et al., 2010), and thus, increasing Phosphoprotein phosphatase the Par6 levels in the future axon could trigger axon formation. Simultaneous RhoA degradation would be also beneficial for axon specification, as RhoA is known to inhibit axon growth by modulating the actin cytoskeleton via Rho kinase (ROCK) (Da Silva et al., 2003). Indeed, local ROCK inhibition transformed a neurite into an axon and a constitutively active form of RhoA abolished neurite formation completely, indicating that RhoA inhibits axonal growth in the minor neurites. In addition, a Smurf1-resistant, nondegradable mutant of RhoA inhibited spontaneous as well as BDNF-induced axon growth. Therefore, these data suggest that both BDNF-induced and spontaneous axon formation are based on the degradation of RhoA via the UPS. Loss of RhoA in turn causes reduced ROCK activity and may change the actin cytoskeleton in the axonal growth cone into a growth permissive state. Interestingly, the Smurf1 knockout mouse has no distinct neuronal phenotype and only the double knockout of Smurf1 and Smurf2 leads to very severe defects in neuronal development (Narimatsu et al., 2009).

This transport occurs via the NaHCO3 cotransporter (NBC, SLC4a4) (Bevensee et al., 2000; Boyarsky et al., 1993; Pappas and

Ransom, 1994; Schmitt et al., 2000), a protein that is highly expressed in astrocytes (Cahoy et al., 2008). In addition, astrocytes also express other HCO3−-relevant enzymes such as carbonic anhydrase (Cahoy et al., 2008). We reasoned that HCO3−-sensitive sAC, if present in astrocytes, could provide an important link for coupling neuronal activity to the metabolic protection provided by the breakdown of glycogen and subsequent release of lactate from astrocytes. Here we show that in the brain, HCO3−-sensitive sAC is highly expressed in astrocytes. HCO3− activation of this enzyme, by either high [K+]ext or aglycemia, increases intracellular cAMP, which leads AZD6738 to glycogen breakdown and the delivery of lactate to neurons for use as an energy substrate. We used several approaches to determine whether HCO3−-sensitive sAC is expressed in the brain and, if so, in which cell types it resides. Immunohistochemical staining showed that GFAP-labeled astrocyte somata and major processes, including endfeet, expressed sAC (using R21, anti-sAC monoclonal antibody) (Figure 1A,

top), whereas MAP-2-labeled neuronal somata and dendrites revealed no specific sAC staining (Figure 1B). As a control for the specificity of labeling, DAPT concentration immunohistochemical staining using R21 in the presence of a sAC blocking peptide that corresponds to the epitope identified by R21 (Hallows et al., 2009) showed no sAC labeling in rat brain slices (Figure 1A, bottom). Western

blotting (with R21 antibody) results confirmed that sAC protein was expressed in both rat brain slices and cultured astrocytes (Figure 1C) and, in the presence of sAC-blocking peptide, antigen-antibody interaction was disrupted (Figure 1C). RT-PCR results confirmed that sAC mRNA was expressed in both rat brain slices and cultured astrocytes (Figure 1D). Several splice variants of sAC have been reported in different Calpain tissues (Farrell et al., 2008). Using further RT-PCR experiments with cultured astrocytes, we demonstrated that astrocytes expressed all the different reported splice variants of sAC. These include sAC, which is encoded by exons 1–5 (see Figure S1A available online), sACsomatic, which has a unique start site upstream of exons 5–13 (Farrell et al., 2008) (Figure S1B), sACfl, which is encoded by all 32 of the known exons (Buck et al., 1999; Jaiswal and Conti, 2001) (indicated by the top band in Figure S1C), and sACt, which is encoded by exons 9–13 but skips exon 12, resulting in an early stop codon (indicated by the bottom band in Figure S1C). Finally, we used immunoelectron microscopy to examine the distribution of sAC in the hippocampus region of wild-type and Sacytm1Lex/Sacytm1Lex (genetic deletion of the exon 2 through exon 4 catalytic domain, sAC-C1 knockout [KO]) mice (Esposito et al., 2004; Hess et al.

“
“Activity-dependent check details plasticity at synapses formed by Schaffer collaterals (SCs) onto CA1 pyramidal neurons in the hippocampus represents the most studied and best-understood cellular model for learning and memory to date. This has been driven in part by the simplicity and accessibility of the trisynaptic excitatory pathway through the hippocampus and in part by the relevance of the hippocampus in that it is essential for encoding new declarative memories. Two forms of synaptic plasticity

that have received a great deal of attention are long-term potentiation (LTP) and long-term depression (LTD). These have been analyzed at the molecular level and have been shown to depend on glutamatergic input through postsynaptic NMDA receptors, calcium influx, and downstream signaling pathways in the postsynaptic neuron (Malenka, 2003 and Collingridge et al., 2010). Cholinergic transmission, employing the transmitter acetylcholine (ACh) to activate ligand-gated ion channels (nicotinic ACh receptors, nAChRs) and G protein-coupled muscarinic receptors (mAChRs), is known Capmatinib chemical structure to be critical for cognitive function (Reis et al., 2009). Cholinergic deficits contribute to a number of cognitive diseases, including Alzheimer’s and Parkinson’s diseases, as well as schizophrenia (Kenney and Gould, 2008). Cholinergic input to the hippocampus comes primarily from the septum and is thought to be important for modulating synaptic

plasticity. Numerous studies have shown that nicotine or ACh applied acutely to the CA1 can promote synaptic plasticity. This usually results from presynaptic nAChRs enhancing glutamate or GABA release, but can also be mediated by postsynaptic nAChRs and muscarinic receptors acting through other mechanisms (Ji et al., 2001, Ge and Dani, 2005 and Buchanan et al., 2010). A limitation of many studies on synaptic plasticity, however,

is that they usually employ high-frequency stimulation of synaptic inputs to induce LTP or LTD and then assess the effects of modulatory compounds such as nicotine. Tetanic stimulation of this kind may not represent a good synaptic model for learning. Metalloexopeptidase It is now clear that the exact timing of an individual presynaptic action potential relative to postsynaptic depolarization is critical for determining the long-lasting outcome (Dan and Poo, 2004). How endogenous cholinergic input might modulate this spike timing-dependent plasticity is unknown. Gu and Yakel (2011) in this issue of Neuron report an elegant series of experiments in which they analyze the timing required for cholinergic modulation of synaptic plasticity. They use single pulses of stimulation to activate SCs and elicit postsynaptic currents (PSCs) in CA1 pyramidal neurons while at the same time stimulating the stratum oriens (SO) with single pulses to activate cholinergic input from the septum to the CA1. By varying the timing of SC and SO stimulation, Gu and Yakel obtain qualitatively different outcomes.

By employing a within-subjects design for the Control and Other tasks, the present study provides, to our knowledge, the first direct evidence that vmPFC is the area in which representations of reward prediction error are shared between the self and the simulated-other. Subjects used the sRPE to learn the other’s hidden variable and the vmPFC was the only brain region with BOLD signals that were significantly modulated by both the subject’s reward prediction error in the Control task and the subject’s sRPE

in the Other task. Moreover, our findings also provide direct evidence that the same vmPFC region is critical for the subject’s decisions, whether or not the other’s process was simulated. In both tasks, vmPFC signals were significantly modulated by the subject’s decision variable check details (the subject’s reward probability) at the time their decisions were made. Mentalizing by direct recruitment requires the same neural circuitry for shared representations between the self and the simulated-other. Even apart from direct recruitment, shared representations between the self and the other are considered to play an important role in other forms of social cognition, such as empathy. Our

findings, with specific roles described for making and learning value-based decisions, indicate that vmPFC belongs to areas for shared representations in various cognitive domains (Decety and Sommerville, 2003, Keysers and Gazzola, 2007, Mobbs et al., 2009, Rizzolatti and Sinigaglia, 2010 and Singer et al., Rolziracetam 2004). For encoding learning signals, the vmPFC is likely more adaptive than the ventral striatum. In contrast learn more to the vmPFC signals, signals in the ventral striatum were significantly modulated only by

the subject’s own reward prediction error in the Control task (Figure S3; Table 2). The vmPFC was preferentially recruited to simulate the other’s process in this study, concordant with the general notion that the vmPFC may encode signals related to reward prediction error when internal models are involved (O’Doherty et al., 2007). The vmPFC may be more sensitive to task demands. During the Other task, no area was significantly modulated by the subject’s own reward prediction error. This might be simply due to a limitation in the task design, as the fixed reward size for subjects might have limited detection of reward prediction error. Another aspect, however, is that the subject’s own reward prediction error was not as useful as the sRPE for learning to predict the other’s choices in this task. Also, the vmPFC may be specifically recruited when subjects used the other’s outcomes for learning, as in the Other task, rather than when they vicariously appreciated the other’s outcomes. The activity in the ventral striatum might be evoked only when the other’s outcomes are more “personal” to subjects (Moll et al., 2006), e.g.

Over time, the compensatory mechanisms fail, cellular damage

accumulates, and FTD pathology and symptoms evolve. The compensatory mechanisms that keep the disease “in check” for half a century are poorly understood, and it is not known if this compensation is mediated through a Wnt-dependent signaling pathway. However, it is very likely that this part of the protective-adaptational response will involve additional, non-Wnt-dependent processes ( Kumar-Singh, 2011), potentially including growth factor-related signaling cascades for endogenous neuroprotection ( Saragovi et al., 2009). Current FTD drug find more discovery approaches are targeting pathways of TDP-43 and tau, with a rationale that the new drugs should either prevent formation or increase clearance of these protein aggregates ( Trojanowski et al., 2008). So, could modulation

of the Wnt signaling pathway achieve this goal? Regardless of the enticing findings of the current study, there is no clear-cut answer to this question, and one can only be cautiously optimistic. Wnt/β-catenin signaling is widespread in the whole body (from www.selleckchem.com/products/VX-809.html brain to bone and muscle), and it is conceivable that systemic modulation of the Wnt pathway might result in numerous and potentially serious side effects ( Takahashi-Yanaga and Sasaguri, 2007). Dipeptidyl peptidase In addition, the in vitro cell line and in vivo mouse models might not fully recapitulate the critical features of the human disease. Finally, the most beneficial effect of Wnt pathway modulation would be expected during the latent phase of the disease: any beneficial effect of Wnt modulation could be diminished by the time that the diagnosis is made and/or the inflammatory and degenerative changes arise. Given that disease pathophysiology encompasses both neuronal and glial changes, what is

the relationship between these two deficits? Previous studies indicated that GRN-deficient macrophages and microglia were cytotoxic to hippocampal cells in vitro, and that GRN-deficient hippocampal slices were hypersusceptible to deprivation of oxygen and glucose (Yin et al., 2010). Thus, while the present results by Rosen et al. (2011) argue for a strong neuronal pathology in response to reduced GRN levels, early contribution of glial dysfunction to the FTD pathology cannot be excluded. Both glia and neurons express GRN from early development (Ahmed et al., 2007), and microglia lacking GRN may become activated, triggering neuronal-glial interactions that can further accelerate neuronal degeneration and cell death.

An observer blinded to genotype quantified the frequency and duration of seizures. The Tsc1ΔE12/ΔE12 mice averaged 3.7 seizures/hr (CI95: 2.0–6.9 seizures/hr), while control littermates

never exhibited seizures ( Figure 7H). Ninety-one percent of the Tsc1ΔE12/ΔE12 mice (10/11) that were analyzed experienced convulsive seizures as described above during the observation periods. While the remaining mouse did Tanespimycin molecular weight not have overt seizures, it did display abnormal behavior in that it remained in a motionless, sleep-like state for minutes at a time, which may have been absence seizures. In contrast, Tsc1ΔE18/ΔE18 mice did not exhibit seizures at 2 months of age. However, by 8 months of age, four of the 17 Tsc1ΔE18/ΔE18 mice had experienced a seizure ( Figure 7H, Movie Alpelisib molecular weight S2), but these rare seizure events only occurred upon

handling. Thus, we conclude that 100% of Tsc1ΔE12/ΔE12 mice and 24% of Tsc1ΔE18/ΔE18 mice displayed abnormal behavior, with some variation in form and severity. Notably, the severity of the grooming and the seizure phenotypes was not correlated within individuals. Because Gbx2CreER mediates recombination in the spinal cord at E12.5 ( Luu et al., 2011), we tested peripheral sensory and motor function ( Figure S6). We did not detect a significant difference in tactile sensitivity (von Frey filament test, p = 0.315) or motor function (wire hang assay, p = 0.134) between control and Tsc1ΔE12/ΔE12 animals. We also showed that thermal pain sensitivity was unaffected in Tsc1ΔE12/ΔE12 mutants (hot plate test, p = 0.188). Because Gbx2CreER is no longer expressed in the spinal cord after E14.5 ( John et al., 2005), we did not perform similar tests on Tsc1ΔE18/ΔE18 animals. Taken together, our collective analysis of thalamocortical circuitry, neuronal physiology, and neocortical local field potentials strongly suggest that the primary drive almost of these Tsc1ΔE12/ΔE12 or Tsc1ΔE18/ΔE18 phenotypes is mTOR dysregulation in the thalamus. TS is a developmental mosaic genetic disorder caused by disrupting the TSC/mTOR pathway. In this study, we tested the hypothesis that disrupting

the mTOR pathway elicits different phenotypes depending on the identity and developmental state of cells in which Tsc1 is deleted and mTOR is dysregulated. Genetic circuit tracing showed that Tsc1ΔE12/ΔE12 thalamic projections are disorganized and have excessive processes that innervate layer IV septal regions of the somatosensory barrel cortex. This phenotype may result from the lack of activity-dependent pruning or excess axonal ramifications filling intrabarrel spaces. Our observations are consistent with previous reports describing abnormal axonal targeting of retinal projections in both the Drosophila and mouse brain, in which Tsc1 mutant axons overshoot their target and have branches that terminate outside the normal target regions ( Knox et al., 2007; Nie et al., 2010).

85, p = 0.203, one-tailed;

if anything, there was greater activation for the low forgetters). Consequently, the relationship between DLPFC recruitment and forgetting trended to be stronger CX-5461 datasheet for the direct suppression group than it was for the thought substitution group (interaction group × forgetting: F(1,32) = 3.85, p = 0.058). These findings are consistent with a greater involvement of DLPFC in direct suppression than in thought substitution. It should be noted, however, that exploratory brain analysis (with an uncorrected threshold of p < 0.001 and at least five contiguous voxels) also revealed an effect for the thought substitution group in a more caudal DLPFC region, although this effect did not survive whole-brain or small-volume FWE correction (in contrast to the effect for the direct suppression group, which remained significant; Tables S1–S4). Second, the right hippocampal ROI also showed the expected effects. Activation in the HC was decreased during suppress compared

with recall events for the direct suppression (Figure 2B; t(17) = 3.53, p < 0.005) but not for the thought substitution group (Figure 2B; t(17) = 0.81, p = find more 0.429). Moreover, the activation difference for the suppress versus recall conditions indeed differed between the two groups (t(34) = −1.78, p < 0.05, one-tailed). (A similar significant effect emerged for the left hippocampus; Supplemental

Information and Figure S1.) Thus, only the task likely to engage the direct suppression mechanism was associated with increased DLPFC and decreased HC activation. These findings support the hypothesis that attempts to prevent retrieval are supported by a neural circuit that achieves retrieval inhibition. By contrast, attempts to suppress awareness of an unwanted memory through thought substitution were associated with significant engagement of the two hypothesized left prefrontal regions. The thought substitution group exhibited greater cPFC activation second for suppress than recall events (Figure 2C; t(17) = 3.48, p < 0.005). This effect was not present during direct suppression (Figure 2C; t(17) = 0.59, p = 0.566), and the group difference was significant (t(34) = −2.43, p < 0.05, one-tailed). As predicted, a similar pattern emerged for the mid-VLPFC ROI, with an effect of suppress versus recall for the thought substitution (Figure 2D; t(17) = 2.78, p < 0.05) but not the direct suppression group (Figure 2D; t(17) = 1.38, p = 0.185), though the group difference was not significant (t(34) = 0.82, p = 0.21, one-tailed). Thus, the two memory suppression tasks were indeed associated with BOLD signal changes in those brain structures hypothesized to support the two opposite mechanisms of voluntary memory control. Moreover, the involvement of most areas differed between the groups.

Upon Helt downregulation and before Sox14 induction, this population also activates expression Tal1 ( Figures 1C, 1E, and 1F). Colabeling of the embryonic day (E) 12.5 diencephalon with Dlx2, Sox14, and Gad1 indicates that GABA-synthesizing neurons arise from either the Dlx2-positive population or Sox14-positive population ( Figures 1D and 1G). We therefore conclude that all Dlx2-negative GABAergic neurons in the diencephalon arise from the Helt-, Tal1-, and Sox14-positive population and that Dlx2 expression or lack of it defines two alternative GABAergic subtypes. The onset of Sox14 expression correlates with cell-cycle exit in cells that have already initiated

transcription of the Gad1 gene ( Figures 1D–1F). Sox14 expression is maintained during embryogenesis but is progressively lost within the first 3 weeks after birth PF-02341066 cell line (data not shown). By contrast, Helt is only Alectinib supplier transiently expressed from the onset of neurogenesis up to E14.5 and

Tal1 is expressed in intermediate progenitors but not in the most differentiated stages ( Figures 1A–1C and 1F). Therefore, to further study the development and function of this diencephalic neuronal population, we took advantage of a knockout (KO) mouse in which the Sox14 coding sequence is replaced by the cDNA for eGfp by homologous recombination ( Crone et al., 2008). The heterozygote Sox14gfp/+ is virtually a wild-type (WT) Carnitine dehydrogenase animal and is therefore a useful tool to study Sox14-expressing neurons during their normal development. From the onset of neurogenesis, green fluorescent protein (GFP)-expressing cells are visible in two stripes extending transversely across the diencephalon, coinciding with the r-Th and the caudal pretectum ( Figures 2A and 2B).

In the hypothalamic region, GFP is visible in the future ventromedial hypothalamus (VMH) and in the medial preoptic area (MPO) ( Figures 2B and 2C and data not shown). Several differences in marker expression between the r-Th/pretectal domain and the hypothalamic domain of Sox14 expression, including the Helt and Tal1 transcription factors and the neurotransmitter markers Gad1 and Vglut2, suggest that the hypothalamic Sox14-positive domain follows an altogether different developmental program and was not considered further. To assess the fate of pretectal and thalamic Sox14-positive cells, we followed their location during nucleogenesis from stage E14.5 to postnatal day (P) 2. By E16.5, Sox14 cells form well-defined clusters in the pretectum, thalamus, and prethalamus. The most rostrodorsal cluster of Sox14 cells is located next to the lateral habenula (LHa, labeled by Prokr2 expression) ( Figures 2C and 2D and see Figure S1 available online). This cluster extends in a caudoventral direction along the thalamus-pretectum border to form the nucleus posterior limitans (PLi).