As shown in Figures 1B and 1C, these transheterozygotes slept muc

As shown in Figures 1B and 1C, these transheterozygotes slept much less than heterozygous controls

did, exhibited reduced sleep bout duration during the day and night ( Figure 1D, top and center), and displayed prolonged latency to sleep onset at the beginning of the night ( Figure 1D, bottom). Importantly, cv-cC524/cv-cMB03717 mutants showed no change in the intensity of waking locomotor activity ( Figure 1E) or the levels of arousal thresholds during sleep when compared to heterozygous controls ( Figure 1F). This suggests that the mutant’s decreased sleep time is not a consequence of hyperactivity due to heightened arousal. We further verified that the insomnia of mutants was the result of molecular BIBW2992 nmr lesions at the cv-c locus by examining sleep patterns in heteroallelic combinations of four independently generated mutant alleles. All of the tested allelic combinations exhibited decreases in total sleep time relative to heterozygous controls ( Figure 1C). Altogether, these results demonstrate that mutations in cv-c interfere with the initiation and/or maintenance of sleep. To distinguish between possible roles of cv-c

in circadian and homeostatic sleep regulation, we first tested whether the sleep phenotypes of cv-c mutants could be attributed to disruption of the circadian clock. cv-cC524/cv-cMB03717 mutants and heterozygous Metformin controls were entrained to a 12 hr light/12 hr dark cycle, and their free-running locomotor rhythms were subsequently analyzed in constant darkness. Over the course of 7 days in darkness, controls and mutants retained robust circadian rhythmicity ( Figure 2A). All genotypes exhibited similar mean circadian periods,

as measured by χ2 periodogram ( Figures 2A and 2B), indicating that cv-c mutations cause sleep disruptions through pathways that are independent of the circadian clock. To examine whether the insomnia of cv-c mutants might be associated with impaired homeostatic regulation, we mechanically deprived flies of sleep for 12 hr overnight and measured the amount of sleep that was regained over the following 24 hr. cv-cC524/cv-cMB03717 Vasopressin Receptor mutants made up for a significantly lower percentage of their lost sleep than either cv-cC524/+ or cv-cMB03717/+ controls ( Figures 2C and S2A). Although these data demonstrate that the homeostatic response to sleep deprivation is abrogated by a loss of Cv-c function, they do not distinguish between an inability to compensate for sleep loss and an overall reduced sleep requirement of mutants. To differentiate between these possibilities, we measured the ability of flies to form associative short-term memories. Sleep deprivation impairs memory formation in a variety of species, including humans ( Stickgold et al., 2001), mice ( Florian et al., 2011), and Drosophila ( Bushey et al., 2007, Li et al., 2009b and Seugnet et al., 2008).

) by optical density (periodic absorbance readings) at 620 nm in

) by optical density (periodic absorbance readings) at 620 nm in culture media Brain Heart Infusion broth (BHI, HiMedia, India). Throughout the growth curve, cell counts were determined selleck compound as log CFU/ml by serial dilution in peptone water 0.1% (w/v) and subsequent enumeration on Brain Heart Infusion agar (BHI agar, HiMedia, India) by

a spread plate methodology. C. perfringens spores were quantified during the growth curve by a Most Probable Number (MPN) method previously described by Scott et al. (2001). Dried aerial parts of winter savory spice (S. monatana L.) originating from Albania (Mediterranean climate country and mountainous region located in Southeastern Europe on the Balkan peninsula 41°21′N and 19°59′ W), were acquired from a spice store (Mr. Josef Herbs and Spices) at the local market city of São Paulo (SP, Brazil). The EO was extracted by hydrodistillation using a modified Clevenger apparatus. Dry plant material was placed with water in a 6000 ml volumetric distillation flask.

The flask was coupled to the modified Clevenger apparatus, and the extraction was performed for 3 h with the temperature maintained at 100 ± 5 °C. The obtained hydrolate (water/oil fraction) was centrifuged at 321.8 g for 10 min at 25 °C. The EO was collected with a Pasteur pipette, and the water traces selleck products were removed with anhydrous sodium sulfate (Vetec, Brazil). The oil was stored under refrigeration temperature (5 ± 2 °C) in glass flasks wrapped in aluminum foil ( Guimarães et al., 2008). Aerial parts of the winter savory (5 g) were placed with 80 ml cyclohexane (Vetec, Brazil) in a 250 ml volumetric distillation flask. The flask was coupled to a condenser with a graduated volumetric

collector and heated at 100 ± 5 °C for 2 h. After the distillation process, the volume of water in the collector was measured and expressed as the moisture content contained per 100 g sample. For the yield calculation, 350 g of dry spice was subjected to extraction by hydrodistillation, and the EO obtained was quantified. In parallel to the moisture content measurement, the EO yield for dried plants was obtained (% w/w) as the moisture free basis (MFB) (Pimentel et al., 2006). The EO chemical components were identified by gas chromatography coupled to mass spectrometry (GC–MS). Sclareol A Shimadzu gas chromatograph (model GC 17A) equipped with a mass selective detector (model QP 5000) was operated under the following conditions: fused silica capillary column (30 m × 0.25 mm) coated with a DB-5 MS stationary phase; ion source temperature of 220 °C; column temperature programmed at an initial temperature of 40 °C, and increased by 3 °C/min up to 240 °C; helium carrier gas (1 ml/min); initial column pressure of 100.2 kPa; split ratio of 1:10 and volume injected of 1 μl (1% solution in dichloromethane). The following conditions were used for the mass spectrometer (MS): impact energy of 70 eV; decomposition velocity of 1000, decomposition interval of 0.

We then generated lentiviral construct-expressing shRNA against m

We then generated lentiviral construct-expressing shRNA against mouse Ank3, and tested this in NIH 3T3 cells, which knocked down greater than 95% of endogenous Ank3 after lentiviral infection (Figure S4A). We next made lentivirus coexpressing this shRNA and GFP under control of the

1 kb human Foxj1 promoter (the same promoter used to generate the Foxj1-GFP transgene) (Ostrowski et al., 2003), and infected pRGP cultures 24 hr after plating. Lentiviral infection of pRGPs was highly efficient as more than 90% of multiciliated cells (assessed by γ-tubulin/DAPI staining) Navitoclax became GFP+ after differentiation (Figure 3C and data not shown). While control virus-infected pRGPs upregulated Ank3 in clusters as normal, we were able to knockdown this expression with the Ank3 shRNA virus (Figure 3C). As GFP expression in infected cells did not become bright enough for live imaging until 3–4 days after infection (too late for following cellular clustering in real time), we used antibody staining to quantify the ability of infected pRGPs to cluster after differentiation (Figure S4B). Counting cells

stained with GFAP, γ-tubulin, and Phalloidin, we found that Ank3 shRNA-infected pRGPs had significantly reduced numbers of clustered structures when compared to control virus-infected cultures (Figure 3D). To confirm these findings in vivo, we performed stereotactic injection of control NSC 683864 cost and Ank3 shRNA lentiviruses into P0 mice, specifically targeting pRGPs through striatal injections (Merkle et al., 2004). Ventricular whole-mount staining 5 days after lentiviral injection showed that control pRGPs were able to assemble into clustered structures, with Ank3+ ependymal cells exhibiting large apical surface areas surrounding Ank3− cells with small apical surfaces (Figure S4C). In contrast, Ank3 knocked-down pRGPs failed to organize into clusters along the ventricular surface, and retained a smaller apical surface area (by Phalloidin staining) as compared to neighboring

cells with intact Ank3 expression (Figure S4C). Furthermore, whereas the Ank3+ pRGPs Ketanserin had largely downregulated immature ependymal marker Glast (Figure 3E), Ank3 knocked-down pRGPs retained high-level Glast expression, showed disorganized patterning, and failed to differentiate into mature multiciliated ependymal cells (Figure 3E). Since striatal lentiviral injection can only target a small number of pRGPs, we would like to remove Ank3 function in vivo. One strategy is to delete its upstream regulator in pRGPs to prevent Ank3 expression. To our knowledge, transcriptional regulation of ank3 (or any of the other Ankyrins) is not known. One candidate for such control, since its expression appears before Ank3 in pRGPs ( Figure 1), is the transcription factor Foxj1. It is a well-established regulator of motile-cilia formation ( Yu et al.

As described above, the correlation between LGN inputs is necessa

As described above, the correlation between LGN inputs is necessary for this variability to appear in simple cells despite the pooling of multiple inputs at the simple cell membrane. Unlike the variability in Vm of both the model and data (Figures 5B and 5C), the variability in the modeled synaptic input from the LGN (conductance, g) is strongly orientation dependent ( Figures 7B and 7F). This dependence is a function of the elongation of the subfields,

and that larger numbers of LGN afferents are activated simultaneously by the preferred stimulus compared to the null stimulus. As discussed above, the orientation dependent variability in g is transformed into the orientation independent variability in Vm by the saturating nonlinear relationship between g and Vm; removing the nonlinearity increases the orientation dependence of Vm variability ( Figures 6G–6I). this website check details The mechanism

underlying this transformation is illustrated in Figure 7C. The variability in g at the preferred orientation (gray) is higher than at the null orientation (cyan). Because that variability is occurring around a high mean g ( Figure 7C, gray)—where the slope of the g-Vm curve is flatter—it gives rise to a comparable level of variability in Vm as does the variability in g at the null orientation, which varies around the much lower resting g ( Figure 7C, cyan). The same compressive effect occurs, to a lesser degree, at low contrast ( Figures 7F and 7G, magenta and green). As a result, the variability in Vm is less dependent on orientation ( Figures 7D and 7G) than else the variability in visually evoked conductance. Note that a more-rapidly saturating relationship between LGN activity

and Vm could potentially make the variability more equal across orientations. Historically, the feedforward model of visual cortex has been rightfully questioned for its failure to account for a large number of the response properties of simple cells: the sharpness of orientation tuning and its mismatch with receptive field maps, contrast invariance of orientation tuning and contrast-set gain control, cross-orientation suppression, contrast dependence of response phase, contrast dependence of preferred temporal frequency, and direction selectivity. All of these properties can be accounted for in models that incorporate cross-orientation inhibition or orientation-independent inhibition (Heeger, 1992, Troyer et al., 1998, Kayser et al., 2001, Lauritzen et al., 2001, Martinez et al., 2002, Lauritzen and Miller, 2003 and Hirsch et al., 2003). In gain-control models, almost all of these properties emerge from a single underlying mechanism: a large shunting inhibition that is contrast dependent and orientation independent (Heeger, 1992, Carandini and Heeger, 1994 and Carandini et al., 1997).

We confirmed that the D2 receptor antagonist sulpiride blocked HF

We confirmed that the D2 receptor antagonist sulpiride blocked HFS-LTD (103% ± 8%; p < 0.05 compared to control; Figure 4A). Interestingly, sulpiride was also able to inhibit LFS-LTD (88% ± 4%; p < 0.05 compared to control; Figure 4B), indicating that D2 receptors act on eCB-LTD at or upstream of Gq. Adenosine A2A receptors are also highly expressed in indirect-pathway MSNs, where they influence eCB signaling and act in opposition to D2 receptors (Shen et al.,

2008 and Tozzi et al., 2007). Therefore, we tested whether activation of A2A receptors BVD-523 manufacturer could block HFS- or LFS-LTD. The A2A receptor agonist CGS21680 blocked both HFS- and LFS-LTD (102% ± 7%; p < 0.05 compared to control for HFS-LTD and 90% ± 12%; p < 0.05 compared to control for LFS-LTD; Figures 4C and 4D). Thus, like D2 receptors, A2A receptors appear to be acting at or upstream of Gq to modulate both forms of eCB-LTD in indirect-pathway MSNs. We confirmed these results in two different BAC transgenic mouse strains (Drd2-EGFP, target EGFP-positive MSNs; Drd1a-Tmt, target Tmt-negative MSNs), indicating that D2/A2A regulation is robust across multiple mouse lines (Figure S2C). We next considered Carfilzomib how D2 and A2A receptors modulate eCB mobilization

and LTD. Because regulation of eCB biosynthetic pathways by cAMP/PKA signaling is not well established, we first tested whether D2 receptors act to promote eCB-LTD through a reduction in cAMP levels or PKA activation. In this and subsequent experiments, we utilized HFS-LTD to examine the mechanisms regulating eCB-LTD, because this form of LTD remains a standard in the field. To examine whether inhibition of

cAMP production alone is sufficient to enable eCB-LTD induction, even in the presence of a D2 receptor antagonist, we used a membrane-impermeable adenylyl cyclase inhibitor, ddATP, and a membrane-impermeable inhibitor of PKA, PKI, which were added to our intracellular recording solution. The membrane-impermeability Electron transport chain of these drugs limited their effects to the recorded postsynaptic MSN, which allowed us to rule out effects on cAMP/PKA-dependent processes in the presynaptic terminal or in neighboring MSNs or interneurons. With either ddATP or PKI in our intracellular recording solution, we were able to elicit LTD in the presence of sulpiride (69% ± 9% with sulpiride and ddATP; 71% ± 10% with sulpiride and PKI; both p < 0.05 compared to LTD in sulpiride alone; Figure 5A). In contrast to the action of D2 receptors, A2A receptors are Gs-coupled receptors, and we therefore hypothesized that activation of A2A receptors blocks LTD by increasing cAMP/PKA signaling. In support of this hypothesis, we found that reducing cAMP/PKA activity by including either ddATP or PKI in the intracellular recording solution allowed LTD to occur in the presence of A2A agonist CGS21680 (61% ± 4% with CGS21680 and ddATP; 65% ± 7% with CGS21680 and PKI; both p < 0.

8 ± 1 1 s/e,

8 ± 1.1 s/e, buy Bleomycin p < 0.01). Epoch frequency (including subthreshold depolarizations) was not significantly increased in fosGFP+ cells (Figure 2C; fosGFP− cells 0.035 ± 0.007 Hz; fosGFP+ cells 0.034 ± 0.007 Hz, p = 0.26), indicating that network activity can engage both cell populations. To verify that the elevated spontaneous firing activity observed in fosGFP+ neurons was not due to expression of the fosGFP transgene, a second strain of transgenic mice

expressing GFP under the control of the arc/arg3.1 promoter was analyzed (GENSAT BAC transgenic resource, Rockefeller University; Gong et al., 2003). Similar to fosGFP+ neurons, arcGFP+ neurons tended to fire more than arcGFP− neurons within a cell pair (Figure 2B and data not shown; mean overall firing rate, arcGFP− 0.23 ± 0.21 Hz versus arcGFP+ 0.32 ± 0.14 Hz; n = 9 pairs, p = 0.07). Like fosGFP+/− cell pairs, the frequency of depolarizing epochs was identical, and arcGFP+ neurons showed significantly more spikes/epoch than arcGFP− cells (Figure 2D; arcGFP− 6.4 ± 0.7 s/e, n = 83 epochs over 9 cells versus arcGFP+ 8.1 ± 0.6 s/e, n = 89 epochs over 9 cells, p = 0.003). On average, arcGFP+ cells fired 2.5-fold more than arcGFP− cells, a significant difference (p = 0.04). Although values from arcGFP+ neurons were more variable compared to fosGFP+ neurons, it is remarkable that the basic observations made

in both transgenic Carfilzomib price mice are so similar. Thus, it is unlikely that the increased firing activity characterized in fosGFP+ neurons is due to expression of the fosGFP transgene. Simultaneous recordings of fosGFP+ and fosGFP− cells enabled a direct comparison of cell engagement during an epoch of network activity. We found that fosGFP+ neurons were recruited into a depolarizing epoch significantly earlier than fosGFP−

neurons (Figures 2E first and 2F; mean onset timing for fosGFP− was 67.3 ± 27 ms after onset in fosGFP+ cells; n = 48 epochs over 9 cell pairs; p < 0.001). Thus, although spontaneous network activity engages both cell types, fosGFP+ cells are activated earlier and are more likely to fire during a depolarizing epoch. Why do fosGFP-expressing neurons display elevated spontaneous firing activity? One explanation is that these neurons show greater intrinsic excitability (i.e., depolarized resting membrane potential, action potential [AP] threshold, or input resistance). However, comparison between fosGFP+ and fosGFP− cells showed that these properties were identical between groups (Table S1). To evaluate intrinsic excitability, input-output curves were constructed, using constant current injection to elicit firing (Figure S2). FosGFP+ cells required more current to generate a single spike (mean rheobase current fosGFP− 37.12 ± 1.6 pA versus fosGFP+ 45.6 ± 2.99 pA, n = 16 for both; p = 0.02) and exhibited fewer spikes at all levels of current injection compared to fosGFP− cells (Figure S2).

Thus whisker-driven sensory

Thus whisker-driven sensory learn more experience is required for the rapid increase in stellate cell functional connectivity at P9. We analyzed the synaptic properties of the connections detected by photostimulation, calculating three parameters:

unitary amplitude (average amplitude of the synaptic response across all trials), success rate (the fraction of presynaptic action potentials producing an EPSC), and potency (amplitude of evoked EPSCs ignoring failures) (see Supplemental Experimental Procedures). Unitary amplitude of the connections was on average small; however, the distribution showed a long tail of connections with larger amplitudes (Figure 4A). This is consistent with previous work on neocortex, including that from barrel cortex, showing that there is a small proportion of connections that are strong, whereas the majority are weak (Lefort et al., 2009, Feldmeyer et al., 1999 and Song et al., 2005). The mean unitary amplitude of the connections was on average small but did show a trend INCB28060 concentration to a gradual increase in unitary amplitude during development (Figure 4B), which was associated with an increase in the reliability of transmission (success rate; Figure S6A). In contrast, the absolute size of EPSCs remained relatively constant

during this developmental period (potency; Figure S6B). In contrast to connectivity, no rapid change in any synaptic properties was observed at any one developmental time point. In whisker-trimmed animals, unitary EPSC amplitude, success rate, and potency were very similar to stellate cells in undeprived barrels at the same age (Figure 4B and Figures S6A and S6B). Thus, unlike functional connectivity, synaptic function between stellate cells appears to change Rolziracetam only

gradually during development and is regulated independent of sensory experience. An inverse relationship between connectivity probability and distance between neurons has been noted at different scales within the cortex, including local microcircuits (Braitenberg and Schüz, 1998 and Holmgren et al., 2003; but see Song et al., 2005 and Lefort et al., 2009). To assess this characteristic during development of the layer 4 local circuit, we analyzed the relationship between intersoma distance and connectivity or synaptic strength. Pconnection at P4–8 exhibited a weak inverse dependence on distance, such that cells closer together had a slightly higher probability of being connected (Figure 3E). Pconnection at P9–12, however, was strongly dependent on distance with ∼5-fold increased chance of cells 20 μm apart being connected compared to those 100 μm apart. Thus, even though the axonal and dendritic arbors of the cells are rapidly growing to span larger volumes at this developmental stage (Figures 3A and 6A), the rapid increase in connectivity at P9–12 is due to a preferential increase in connections between close neighbors.

In this section, we stand on those shoulders to speculate what th

In this section, we stand on those shoulders to speculate what the answer might look like. Retinal and LGN processing help deal with important real-world issues such as variation in luminance and contrast across each visual image (reviewed by Kohn, 2007). However, because RGC and LGN receptive CH5424802 fields are essentially point-wise spatial

sensors (Field et al., 2010), the object manifolds conveyed to primary visual cortical area V1 are nearly as tangled as the pixel representation (see Figure 2B). As V1 takes up the task, the number of output neurons, and hence the total dimensionality of the V1 representation, increases approximately 30-fold (Stevens, 2001); Figure 3B). Because V1 neuronal responses are nonlinear with respect to their inputs (from the LGN), this dimensionality expansion results in an overcomplete population re-representation (Lewicki and Sejnowski, 2000 and Olshausen and Field, 1997) in which the object manifolds are more “spread

out.” Indeed, simulations show that a V1-like representation is clearly better than retinal-ganglion-cell-like (or pixel-based) representation, but still far below human performance for real-world recognition problems (DiCarlo and Cox, 2007 and Pinto et al., 2008a). What happens as each image is processed beyond V1 via the successive stages of the ventral stream anatomical hierarchy (V2, V4, pIT, aIT; Figure 3)? Tariquidar concentration Two found overarching algorithmic frameworks have been proposed. One framework postulates that each successive visual area serially adds more processing power so as to solve increasingly complex tasks, such as the untangling of object identity manifolds (DiCarlo and Cox, 2007, Marr, 1982 and Riesenhuber

and Poggio, 1999b). A useful analogy here is a car assembly production line—a single worker can only perform a small set of operations in a limited time, but a serial assembly line of workers can efficiently build something much more complex (e.g., a car or a good object representation). A second algorithmic framework postulates the additional idea that the ventral stream hierarchy, and interactions between different levels of the hierarchy, embed important processing principles analogous to those in large hierarchical organizations, such as the U.S. Army (e.g., Lee and Mumford, 2003, Friston, 2010 and Roelfsema and Houtkamp, 2011). In this framework, feedback connections between the different cortical areas are critical to the function of the system. This view has been advocated in part because it is one way to explicitly enable inference about objects in the image from weak or noisy data (e.g., missing or occluded edges) under a hierarchical Bayesian framework (Lee and Mumford, 2003 and Rust and Stocker, 2010). For example, in the army analogy, foot soldiers (e.g., V1 neurons) pass uncertain observations (e.g., “maybe I see an edge”) to sergeants (e.g.

, 2010) By demonstrating a regulatory role of DNA demethylation

, 2010). By demonstrating a regulatory role of DNA demethylation in cognitive function (Rudenko and Tsai, 2013), these studies provide the rationale to further study the role of the Tet proteins in the nervous system. In the current work, we show that the expression of a number of genes is dysregulated in the cortex and hippocampus of Tet1 knockout (Tet1KO) mice. Interestingly, the most prominent Nintedanib cost category of downregulated genes is comprised of multiple neuronal activity-regulated genes that include Npas4, c-Fos, Arc, Egr2, and Egr4 ( Loebrich and Nedivi, 2009 and Ebert

et al., 2013). We also found that while Tet1KO mice display normal memory formation, they showed specific impairments in extinction this website learning. Moreover, we show that while hippocampal long-term potentiation was intact in Tet1KO animals, they had abnormally enhanced long-term depression compared to controls. We performed methylation analysis of a key upstream neuronal activity-regulated gene, Npas4, and found hypermethylation

of the promoter region in Tet1KO animals compared to controls, both in naive mice and after extinction training, which could lead to the reduced expression of Npas4 and its downstream targets. Our study identifies an important role for Tet1 in regulating the neuronal activity-regulated genes, hippocampal synaptic plasticity, and memory all extinction. Reports of high levels of 5hmC in the CNS genome (Kriaucionis and Heintz, 2009 and Szulwach et al., 2011) prompted a search for potential functions for the Tet1 methylcytosine dioxygenase in the mouse brain. We utilized a previously characterized Tet1 knockout (Tet1KO) mouse strain in which exon 4 of Tet1 is deleted, leading to an out-of-frame fusion of exons 3 and 5 and creating a Tet1 null allele ( Dawlaty et al., 2011). Loss of Tet1 mRNA was confirmed by real-time quantitative PCR in cortex and hippocampus ( Figure S1A available online). We also quantified

all three Tet mRNA levels in hippocampal and cortical tissues from wild-type mice and found that all three Tets are expressed in both hippocampus and cortex ( Figure S1B). The presence of all Tet proteins in the CNS may lead to potential compensatory effects caused by the loss of a single Tet family member. Since Tet proteins are responsible for the conversion of 5mC to 5hmC, we wanted to determine how Tet1 ablation affects 5mC and 5hmC levels in the brain. Global genomic 5mC and 5hmC contents in the hippocampi and cortices of 4-month-old Tet1KO and control Tet1+/+ mice were assessed by immunohistochemistry (Figure 1A) and quantified by liquid chromatography combined with tandem mass spectrometry using multiple reaction monitoring (LC/MS/MS-MRM).

Specifically, Ngn2-iN cells expressed at high levels the telencep

Specifically, Ngn2-iN cells expressed at high levels the telencephalic markers Brn-2, Cux1, and FoxG1, which are characteristic of layer 2/3 excitatory cortical neurons, but lacked other prominent forebrain transcription factors (e.g., Tbr1 and Fog2). iN cells consistently expressed AMPA-type glutamate receptors GluA1, A2, and A4, but lacked NMDA-type glutamate receptors 3 weeks after induction (Figure 3A). Moreover, nearly all iN cells expressed vGlut2, and approximately 20% of iN cells expressed vGlut1. iN cells highly expressed GABAA receptors but lacked the vesicular GABA transporter vGAT or the GABA-synthetic enzyme glutamate decarboxylase

(GAD). Ngn2 iN cells expressed all panneuronal find more markers tested, but lacked expression of markers for various glia cell types or for stem cells (Figures 3A and S3B). These measurements show that Ngn2 iN cells are relatively homogeneous and that they constitute excitatory neurons that express telencephalic markers suggestive of

cortical layers 2/3. Arguably the most important question in the production of iN cells—in fact, in the in vitro production of all human neurons—is reproducibility between lines. We therefore assessed this question for the Ngn2-based protocol in great detail. Comparison of the gene expression profiles between iN cells produced by forced differentiation of H1 ESCs and of two independent

lines of iPSCs revealed a striking concordance Bleomycin in vitro in expression patterns (Figures 3B and S3D). There was no major difference between stem cells in the expression of the genes tested. The highly similar transcriptional effects of Ngn2 indicate that forced expression of Ngn2 can override presumptive epigenetic differences between various pluripotent stem cell lines to induce differentiation of a single homogenous population of excitatory forebrain neurons. We next probed the ability of Ngn2-induced iN cells to differentiate into electrophysiologically active neurons and to form synapses. To promote synapse formation, we cocultured iN cells with mouse glial cells (Pang et al., 2011). The iN cells reliably produced robust action potentials, Farnesyltransferase and exhibited voltage-gated Na+ and K+ currents that were indistinguishable between iN cells derived from H1 ESC and different iPSC lines (Figures 4A–4C and S4A). iN cells exhibited massive spontaneous synaptic activity that was blocked by the AMPA-receptor antagonist CNQX (Figure 4D). Extracellular stimulation evoked EPSCs of large amplitudes, documenting abundant synapse formation (Figures 4E and 4F). The kinetics of evoked EPSCs were identical at −70 mV and +40 mV holding potentials, and EPSCs were blocked by CNQX at both holding potentials.