After center fixation for 400 ms, the monkeys were presented with a central cue that was identical to the search target. The cue stayed on for 200–2500 ms randomly, after which time a search array with 20 stimuli was presented, and the center cue was replaced by the center fixation spot. Monkeys were required to hold fixation at the center of the screen before the search array onset. 3-MA supplier After the onset, monkeys had 4 s to find the target that was the same as

the central cue. No constraints were placed on their search behavior in order to allow them to conduct the search naturally. Monkeys were required to fixate the target stimulus for 700 ms continuously to receive a juice reward. The position of the target on the screen was changed randomly from trial to trial. A memory-guided saccade task was used to determine a cell’s RF and stimulus selectivity. Briefly, the trial started with the monkey fixating a central spot. A peripheral stimulus flashed for 100 ms in one of the stimulus positions used in the search array. After a random period between 500 and 1500 ms, the central spot was extinguished, and the monkey was rewarded for making a saccade to the memorized position of the peripheral stimulus. Before the offset of the fixation learn more spot, monkeys were required to maintain

center fixation. Eleven locations, including nine in the contralateral visual field and two on the vertical middle line, were used, which comprised 11 of the 20 locations used in the search array. Firing rates were compared between the prestimulus period, 200–0 ms before stimulus flash onset, and the poststimulus period, 50–250 ms after the flash onset, using the Wilcoxon rank-sum test, and stimulus locations with significant increased responses (p < 0.05)

were defined to be in the RF. Sites with RFs extending into both hemisfields were rarely found and were excluded from further analyses after a preliminary RF mapping. Multiunit spikes and local field potentials (LFPs) were recorded from the FEF and V4 simultaneously using a Multichannel Acquisition Processor system by Plexon. On a given day, up to four tungsten microelectrodes (FHC) were advanced through the dura in each area. Electrodes within an area were spaced 650 or 900 μm apart. Neural signals were filtered between 250 Hz and 8 PD184352 (CI-1040) kHz and amplified and digitized at 40 kHz to obtain spike data. The location of recordings in both the FEF and V4 was verified with MRI. In both monkeys, we electrically (<50 μA) stimulated in the FEF and elicited eye movements. Eye movements were recorded by an infrared eye tracking system (Eye Link II, SR Research) at a sampling rate of 500 Hz. Recording sites that showed a significant visual response (Wilcoxon rank-sum test, p < 0.05) were included for analysis. The intervals used for this statistical comparison were as described before. Firing rates were calculated with 10 ms nonoverlapping bins.

Based on the firing

rates of MUA versus single units, we estimate that a typical MUA contained 10 to 20 single units, thereby providing a reasonable local average. Henceforth, we refer to these unsorted MUAs with the SUA excluded as the same-site MUAs. Same-site MUA PPCs did not differ between sites delivering isolated NS versus BS units (Figure 1E) (not significant [n.s.], bootstrap test). This suggests that the overall gamma locking did not differ between the recording locations of BS and NS cells. Please note that if the MUA from a BS or NS recording site had been biased to contain more BS or NS cells, this would have created similar differences for the same-site MUA as for the respective SUA analysis, which we did this website not find. Although the same-site MUA PPC did not differ between NS and BS cells, it is conceivable that same-site MUA PPC varied across sites. In order to eliminate the variability

in PPCs across units Selleckchem VX-809 that is caused by differences in recording location, we computed, for each unit separately, the SUA-MUA PPC difference. This measure is defined as the difference between a SUA’s PPC and its corresponding same-site MUA’s PPC [PPCSUA – PPCMUA], such that a value >0 indicates stronger spike-LFP locking for the SUA than its corresponding same-site MUA. SUA-MUA gamma PPC difference was higher for NS than BS cells (p < 0.05, randomization test) and significantly different from zero only for NS cells (p < 0.05, bootstrap test) (Figure 1F). Hence, it is unlikely that the observed difference in gamma PPC between NS and BS cells (Figure 1D) was caused by differences in recording locations. In neocortex, there are more BS than NS cells (Figure 1B, Mitchell et al. (2007)). However, NS cells have higher firing rates, such that the MUA may contain approximately equal proportions of NS and BS spikes. Based on these estimates, the MUA-LFP PPC is expected to attain PPC values in between

the BS and NS cells’ PPC. In addition, Edoxaban we will demonstrate below that BS and NS cells lock on average to different gamma phases and that individual single units often lock to widely varying gamma phases. Assuming that our MUAs typically contained both BS and NS cells and individual cells that cover at least a small part of the overall intercell phase variance, this predicts that the MUA-LFP PPC is substantially smaller than the average PPC of its constituent SUAs, consistent with our observations. We found that NS cells were more gamma locked than BS cells during the sustained visual stimulation period. NS cells might also be more gamma locked than BS cells during network states in which cells receive only weak excitatory drive. Previous studies have shown that MUA-LFP gamma locking and LFP gamma power are weak in the absence of visual stimulation or in the presence of low-contrast visual stimuli in the RF (receptive field) (Fries et al.

, 2009 and Rushworth Bortezomib et al., 2011). Theoretical models predict that precommitment arises as a function of learning about one’s own self-control abilities (Kurth-Nelson and Redish, 2010, Kurth-Nelson and Redish, 2012 and Ali, 2011). In the current study,

we were able to show that between-subject differences in self-control abilities moderated precommitment-related neural activity. Future work might examine the within-subject dynamics of learning about one’s own self-control abilities and how such learning relates to precommitment. For example, one might dynamically manipulate the difficulty of resisting temptations (thus making precommitment more valuable at some times than others) and examine how activation in LFPC and its connectivity with willpower regions tracks with the expected value of precommitment on a trial-to-trial basis. The LFPC may be involved in such learning processes, given its role in self-awareness and metacognition (Fleming et al., 2010 and De Martino et al., 2013). Although the anterior prefrontal cortex (BA 10) is cytoarchitechtonically homogeneous, it may be functionally heterogeneous (Gilbert et al., 2006 and Liu et al., 2013); for instance, studies of metacognition (Fleming et al., 2010 and De Martino et al., 2013) find more have reported activations in anterior prefrontal cortex

that are situated dorsal and medial to those reported in studies of counterfactual value processing (Boorman et al., Mephenoxalone 2009 and Boorman et al., 2011). A recent study of connectivity patterns within FPC found

that the lateral FPC (FPCl) showed strongest connectivity to DLPFC, while the orbital FPC (FPCo) showed strongest connectivity to the OFC and subgenual ACC (Liu et al., 2013). Notably, the region we found to be associated with precommitment is located precisely in the transition zone between FPCl and FPCo. This region is therefore ideally situated to arbitrate between regions involved in calculating expected value (OFC, subgenual ACC) and regions involved in implementing self-control (DLPFC). Fitting with this notion, we observed that LFPC was functionally connected to DLPFC during precommitment and that the strength of this connectivity was moderated by activation in the vmPFC. Precommitment decisions in the real world often involve longer delays (in the order of weeks to months), in contrast with the shorter delays used in the current study. Future studies might examine whether the precommitment to large rewards with much longer delays engage similar neural processes as those described in the current study. Given the role of the LFPC in forward planning (Daw et al., 2006, Burgess et al., 2007, Koechlin and Hyafil, 2007, Boorman et al., 2009, Boorman et al., 2011, Rushworth et al., 2011 and Tsujimoto et al., 2011), we might expect to see even stronger effects in LFPC with longer delays than in our current design, in which the shorter delays placed relatively low demands on prospective cognition.

“
“AMPA-type glutamate receptors (AMPARs) initiate postsynaptic signaling at excitatory synapses (Traynelis et al., 2010; Trussell, 1999). Receptor desensitization can shape synaptic transmission and in turn information processing (Chen et al., 2002; Koike-Tani et al., 2008; Rozov et al.,

2001; Xu-Friedman and Regehr, 2003) as a function of the cleft glutamate transient (Cathala et al., Entinostat 2005; Jonas, 2000; Xu-Friedman and Regehr, 2003). AMPAR kinetics are tuned by the composition and alternative RNA processing of the four core subunits (GluA1–GluA4) (Geiger et al., 1995; Jonas, 2000) and by auxiliary factors (Guzman and Jonas, 2010; Jackson and Nicoll, 2011). Neurons express a variety of functionally distinct

AMPARs, which can be recruited selectively in response to different input patterns (Liu and Cull-Candy, 2000) and be targeted to specific dendritic subdomains (Bagal et al., 2005; Gardner et al., 1999; Selleck MDV3100 Tóth and McBain, 1998). However, whether assembly into distinct heteromers is modulated by activity is not known (Pozo and Goda, 2010; Turrigiano, 2008). Activity-driven remodeling of kinetically distinct receptors would permit adaptive responses to changing input patterns. The ion channel and ligand-binding domain (LBD) of the receptor feature regulatory elements at subunit interfaces introduced by alternative RNA processing (Seeburg, 1996). Q/R editing at the A2 channel pore controls Ca2+ flux and receptor tetramerization

(Greger very et al., 2003; Isaac et al., 2007), whereas the R/G editing and alternative splicing within the LBD modulate gating kinetics and subunit dimerization (Lomeli et al., 1994; Seeburg, 1996; Greger et al., 2006). Both impact on secretion of recombinant A2 from the endoplasmic reticulum (ER), where prolonging ER residence facilitates heteromeric assembly (Sukumaran et al., 2012; see also Coleman et al., 2010). Whether this mechanism contributes to the biogenesis of native AMPARs has not been addressed. Here we show that alternative splicing in the LBD is subject to regulation. Chronic reduction of activity in hippocampal slice cultures results in changes at the flip/flop (i/o) cassette. Altered RNA splicing occurs for A1 and A2 in the CA1 subfield but not in CA3, implying cell-autonomous splicing regulation. Characterization of AMPARs after activity deprivation reveals changes in pharmacology and kinetics of extrasynaptic receptors, culminating in increased response fidelity. A functional switch is also evident at CA1 synapses, which cannot be explained by a direct effect of mRNA processing (Mosbacher et al., 1994) but rather by splice variant-driven receptor remodeling.

This mechanism is robust because each of these

TRNs is effectively transformed into a functional PVD-like neuron when either ahr-1 or zag-1 is genetically eliminated ( Figures 2, 4, 5, and S3). Thus, our work has revealed the logic of alternative genetic regulatory pathways in which a single type of transcription factor (e.g., MEC-3) can specify the differentiation of two distinct classes of mechanosensory neurons ( Figure 8G). A related mechanism accounts in part for the dose-dependent effects of the homeodomain transcription factor Cut on the branching complexity of larval sensory neurons in Drosophila ( Grueber et al., 2003). The transcription factor Knot/Collier is selectively deployed in Type IV da neurons to antagonize expression of Cut targets that produce the dendritic spikes that selleck products are characteristic of Type III da neurons. In this case, however, Knot does not regulate Cut expression but functions in a parallel pathway ( Jinushi-Nakao et al., 2007). Our finding that the Zinc-finger transcription factor ZAG-1 is required to prevent the PVM touch neuron from adopting

a PVD nociceptor fate mirrors the recent observation that genetic ablation of the mammalian ZAG-1 homolog Zfhx1b (Sip1, Zeb2) results in cortical interneurons adopting the fate of striatal GABAerigic cells ( McKinsey et al., 2013). Our results are suggestive of a potentially complex regulatory mechanism in which AHR-1 and ZAG-1 inhibit expression of nociceptor genes (e.g., hpo-30) whereas MEC-3 activates transcription of these targets. Additional upstream regulators of mec-3, UNC-86, and ALR-1, are also likely involved in this pathway ( Topalidou Ivacaftor research buy et al., 2011 and Xue et al., 1992). Although transcription factors see more are well-established determinants of sensory neuron fate, the downstream pathways that they regulate are largely unknown (Jan and Jan, 2010,

Jinushi-Nakao et al., 2007, Parrish et al., 2006 and Sulkowski et al., 2011). As a solution to this problem for MEC-3, we used a cell-specific profiling strategy (Petersen et al., 2011, Spencer et al., 2011 and Von Stetina et al., 2007) to detect mec-3-regulated transcripts in the PVD neuron. We used a combination of RNAi and mutant analysis to identify the subset of targets that affect PVD branching morphogenesis ( Figure S6; Tables S3 and S5). Additional experiments with one of these hits, the claudin-like protein HPO-30, revealed a key role in the generation of PVD branches. We note that HPO-30 is expressed in the FLP neuron ( Figure S7), where it also mediates the higher order branching morphology shared by FLP and PVD ( Smith et al., 2010 and Topalidou and Chalfie, 2011) ( Figure S7). Time-lapse imaging has revealed that PVD lateral or 2° branches may adopt either of two different modes of outgrowth along the inside surface of the epidermis: (1) fasciculation with existing motor neuron commissures or (2) independent extension as noncommissural or “pioneer” dendrites ( Smith et al.

7). T. mobilensis hydrogenosomes

exhibit a flat peripheral vesicle ( Fig. 7a and c) whereas T. foetus presents a much more prominent and larger vesicle ( Fig. 7b and d). Because the T. mobilensis population was pleiomorphic, DNA analyses were performed to verify if any contamination by other tritrichomonads had occurred in T. mobilensis cultures. A molecular strategy previously described ( Kleina et al., 2004) was employed. For this purpose, the total DNA was extracted from two independent cultures and the rDNA ITS-1/5.8S/ITS-2 BMS-354825 molecular weight region was amplified. The PCR products were directly sequenced. As a result, both sequences obtained were identical to T. mobilensis isolate M776 (ATCC 50116) sequence retrieved from GenBank (U86612), thus, demonstrating that we were working with a T. mobilensis and that contamination did not take place. To compare the behavior of the different shapes of T. mobilensis, adherence assays using uncoated polystyrene microspheres were performed. Interestingly, quantitative analyses revealed that no differences were found in all parasites shapes analyzed ( Fig. 8). In addition, the binding capability of T. foetus was significantly higher than the binding capability of T.

mobilensis (P-value <0.01 by two-way ANOVA test) because approximately 40% (S.D. ± 3.42%) and 58% (S.D. ± 2.73) of the parasites from the cultured and fresh T. foetus isolates contained latex beads attached to their cell surface, respectively, whereas approximately

23% (S.D. ± 2.53%) of the parasites from both T. mobilensis isolates contained latex beads attached to their cell surface. Similar to T. foetus (data not buy Galunisertib shown), T. mobilensis presented binding capacity even during mitosis ( Fig. 8). To quantitatively assess the cytotoxicity of both species to Caco-2 cells, spectrophotometric analyses after MTT viability assays (Fig. 9) and crystal violet test (not shown) were performed. The MTT assay was carried out in the initial hours to follow the cytotoxic effects (Fig. 9). Both species were able to reduce the viability of Caco-2 Sodium butyrate cells. After 1 h of interaction, both strains from T mobilensis and T. foetus presented a similar cytotoxicity level. However, after 3 h, the cytotoxicity of both the cultured T. foetus (K strain) and T. mobilensis 4190 strain was higher than that of the fresh isolate of T. foetus (CC09-1) T. mobilensis USA:M776 strain ( Fig. 9). There are several studies on T. mobilensis concerning its pathogenicity, but only a few reports on the morphological aspects of this parasite. Therefore, it is important to have additional data based on ultrastructural studies as presented by this work. Both morphology and cytotoxicity assays of T. mobilensis were performed and compared with T. foetus in this study. To our knowledge, this is the first time that the morphological features of T. mobilensis and T. foetus have been compared. Culberson et al. (1986) showed that T.

To understand how Brm and CBP, in conjunction with EcR-B1, activate the expression of their common target gene, sox14, we examined whether the levels of the transcriptionally active chromatin mark H3K27Ac are elevated at the sox14 region in an ecdysone-dependent manner. The expression of EcR-B1, Sox14, and Mical proteins is significantly upregulated in S2 cells upon treatment with ecdysone, similar to that seen in ddaC neurons during the larval-to-pupal transition ( Kirilly et al., 2009). We used nontreated and ecdysone-treated S2 cell extracts to perform chromatin check details immunoprecipitation (ChIP)

assays with an anti-H3K27Ac antibody, examined the H3K27Ac levels at the sox14 locus using quantitative real-time polymerase chain reaction (qRT-PCR) assays

with ten sox14 genomic primer sets ( Figure 7A), and subsequently normalized them against the H3K27Ac level at the internal control actin5C. Upon ecdysone treatment, the level of H3K27Ac increased more than 3-fold at the first intron of the sox14 gene (I1-3 and I1-4; Figure 7B), as compared to those in nontreated PR-171 cells. To confirm whether ecdysone signaling facilitates the enrichment of the H3K27Ac levels at the sox14 locus, we knocked down the EcR-B1 receptor in ecdysone-treated cells using EcR-B1 dsRNA fragments ( Figure 7H) and performed ChIP assays. Although total H3K27Ac levels in EcR-B1 RNAi S2 cells remained the same ( Figure 7H), the enrichment of H3K27Ac at the sox14 locus decreased significantly, as compared to the GFP RNAi ecdysone-treated S2 cells ( Figure 7C). Hence, local enrichment of H3K27Ac at the sox14 region is drastically elevated in response to ecdysone signaling. We then investigated whether the Metalloexopeptidase enrichment of H3K27Ac at the sox14 locus is mediated by CBP, the major HAT for H3K27 acetylation in ddaC neurons. Indeed, upon CBP knockdown in ecdysone-treated S2 cells ( Figure 7G), H3K27Ac enrichment at

the sox14 locus was drastically reduced ( Figure 7D). Thus, CBP facilitates H3K27 acetylation at the sox14 locus in response to ecdysone, thereby activating Sox14 expression. Given that Brm, like CBP, is specifically required for activation of Sox14 expression during ddaC pruning, we next examined whether Brm-mediated chromatin remodeling promotes local enrichment of H3K27Ac at the sox14 gene region. Strikingly, the knockdown of Brm also resulted in strong reduction of H3K27Ac enrichment at the sox14 locus ( Figure 7E) without affecting overall H3K27Ac levels ( Figure 7H). The relative levels of H3K27Ac were reduced to a lesser extent in CBP RNAi S2 cells than in brm RNAi S2 cells because CBP RNAi, rather than brm RNAi, also led to reduction of the H3K27Ac levels in the locus of the internal control actin5C (data not shown).

More interestingly, the increases in MT stability correlate with decreases in neuronal plasticity, and both occur during aging and in some neurodegenerative

diseases. Therefore, learning about stable MT fragments, which are unique to neurons, is crucial for understanding normal axonal development and neuronal differentiation; this may also aid in identifying novel therapeutic targets for neurodegeneration and regeneration. The existence of a stable, biochemically distinct fraction of axonal tubulin was demonstrated some years ago (Brady et al., 1984; Sahenk and Brady, 1987). When preparing MTs from brain extracts, a substantial amount Dinaciclib concentration of tubulin remains in the pellet following low-temperature depolymerization. This fraction is termed cold-insoluble, or cold-stable tubulin. A more extensive differential extraction using cold and Ca2+ extractions to produce labile, cold-stable, and cold/Ca2+-stable fractions was developed (Figure 1A). The cold/Ca2+ fraction was enriched in axons. Using axonal transport to metabolically label MTs in rat optic nerve, the cold/Ca2+-stable tubulin fraction (P2) was examined by 2D-PAGE. A striking difference was found between tubulins in soluble and those in stable MTs: some tubulins in P2 exhibited a significant basic shift during isoelectric focusing (IEF) (Brady et al., 1984). This suggested that tubulins in stable MTs were biochemically distinct from those in

cold-labile MTs. Specifically, cold-stable Pexidartinib research buy MTs contained very tubulins significantly more basic than predicted from sequence or observed in cold-cycled MTs. Stability of MTs has been related to differences in MAPs, specific tubulin isotypes, and posttranslational modifications, but no factor has been identified that is sufficient to make MTs stable to depolymerization by cold or elevated Ca2+. MAPs stabilize cycled MTs in vitro (Chapin and Bulinski, 1992), but the increase in stability is modest and MAPs partition with both stable and labile MTs (Brady et al., 1984). Similarly, detyrosination and acetylation of α-tubulin correlate with MT stability in many systems (Bulinski

et al., 1988), but in vitro these modifications confer no measurable change in MT stability (Maruta et al., 1986; Webster et al., 1990) and are found in all cell types. Specific tubulin isotypes may contribute to MT stability (Falconer et al., 1994), but none partitions specifically with stable MTs, and again, differences in stability are modest. The native pI values for highly conserved tubulin isoforms all fall within a narrow range (pI = 5.5–5.6 for mouse α-tubulins, pI = 4.8–4.9 for mouse β2–6 tubulins and pI = 5.6 for β1 tubulin). MAPs do not associate with tubulin in IEF gels or change the charge on tubulins. Thus, no known tubulin isotype or modification can account for both the basic shift and exceptional stability of P2 tubulins, suggesting a novel posttranslational modification.

During development of the sensory epithelium in the cochlea, the Cdki, p27kip1 is an early marker of the part of the presumptive sensory region that will generate the hair cells and support cells ( Chen and Segil, 1999) and deletion of p27kip1 leads to an extension in the normal developmental limit in proliferation of cells in the cochlea ( Kil et al., 2011, Lee et al., 2006 and Löwenheim et al., 1999). Although these experiments demonstrated that p27kip1 is an important developmental regulator of support

cell proliferation, recently it was shown that deletion of p27kip1 in adult animals also causes support cells to enter the mitotic cell cycle ( Oesterle et al., 2011), albeit in small numbers, indicating this website that p27kip1 Bortezomib is one of the factors required in mature

mice to maintain mitotic quiescence in the support cells. Taken together, the results suggest that methods to stimulate proliferation in the mammalian inner ear epithelia might be possible through manipulation of a combination of known pathways. However, even though some support cells proliferate in the postnatal cochlea in the p27kip1 knockout mice, very few, if any, generate mature new hair cells as they would during regeneration in nonmammalian vertebrates; rather, the proliferating cells appear to generate additional support cells, or else they undergo apoptosis. This leads to the second main difference between the nonmammalian vertebrates and mammals: the support cells of the auditory sensory epithelium of nonmammalian vertebrates have the capacity to transdifferentiate into hair cells, while mammalian cochlear support cells do not. What factors enable the support cells of nonmammalian vertebrates to differentiate into hair cells after damage? Studies of the factors that control the fates of hair cells and support cells during regeneration have focused on the developmental regulators of hair cell determination/differentiation: the bHLH transcription

factor, Atoh1, and the Notch pathway (Cafaro et al., 2007, Daudet et al., 2009 and Stone and Rubel, 1999). Atoh1 is a critical transcription much factor for the specification of the hair cells during development (Figure 3), while Notch signaling has both a “prosensory” role and acts in a more conventional lateral inhibitory manner to regulate the ratios of hair and support cells. (Brooker et al., 2006, Kiernan et al., 2001, Kiernan et al., 2006, Adam et al., 1998, Brooker et al., 2006, Haddon et al., 1998, Kiernan et al., 2005 and Zine and de Ribaupierre, 2002). In the normal adult vestibular organs in birds, Atoh1 is expressed in scattered cells throughout the epithelium, suggesting a continued requirement for specification during the ongoing hair cell production in these organs (Cafaro et al., 2007). By contrast, in the normal avian adult BP, there is no Atoh1 expression; however, after hair cell damage a number of cells express Atoh1 and Notch pathway genes (Cafaro et al.

, 2012). A comparison of the neural differentiation potential of different ESC and iPSC lines revealed a large variation in differentiation efficiency, and it is likely that maturation stages and functional properties of the resulting neurons are also variable (Hu et al., 2010). The second limitation is related to the cumbersome, variable, and slow procedures needed for deriving neurons with functional properties from ESCs or iPSCs. Generating neurons by differentiation of ESCs or iPSCs requires months of tissue culture procedures and renders large-scale studies difficult (Johnson et al., 2007). Moreover, differentiation

of ESCs and iPSCs into neurons depends on specific environmental factors such as pharmacological agents and bioactive proteins that may be difficult Baf-A1 supplier to obtain with a consistent composition, injecting a further element of variation (Soldner and Jaenisch, 2012). The two major limitations of current technologies for generating human neurons

outlined above motivated us and others to develop methods for direct conversion of human fibroblasts into induced neurons, referred to as iN cells click here (Pang et al., 2011; Ambasudhan et al., 2011; Qiang et al., 2011; Pfisterer et al., 2011a, 2011b; Yoo et al., 2011; Caiazzo et al., 2011; Son et al., 2011). Although these efforts were successful and allow rapid production of human iN cells, all of the currently available protocols for generating human iN cells (as opposed to mouse iN cells) suffer from relatively low yields

and low efficiency and are further hampered by the limited availability and renewability of fibroblasts as starting materials. Moreover, the resulting iN cells often exhibited decreased competence for synapse formation. Specifically, we (Pang et al., 2011) and others (Pfisterer et al., 2011a; Son et al., 2011) found that the same three transcription factors that convert mouse fibroblasts into iN cells (Brn2, Ascl1, and MytL1; Vierbuchen et al., 2010) also transdifferentiate human fibroblasts into iN cells Mephenoxalone when combined with a fourth transcription factor (NeuroD1), a process that may be additionally facilitated by coexpression of microRNAs (Yoo et al., 2011; Ambasudhan et al., 2011). However, apart from the limited capabilities of iN cells produced by these procedures, these experiments did not clarify the minimal requirement of defined factors for transdifferentiating human nonneuronal cells into neurons and suggested that ancillary factors, such as specific culture conditions, may introduce further variability into these transdifferentiation protocols. Together, these features make analysis of disease-related phenotypes using human iN cells difficult, especially since these protocols do not generate large amounts of iN cells that are fully competent to form synapses. To address these problems, we here developed approaches that allow rapid and reproducible production of human iN cells from ESCs or iPSCs.