Which site or sites of action are relevant for activity-induced new spine growth? We observed that expression of Rpt6-S120A in individual neurons inhibited activity-induced spine outgrowth. Because this genetic manipulation was carried

out in sparsely transfected RO4929097 datasheet neurons, and thus any nearby presynaptic neurons were untransfected, our results demonstrate that postsynaptic proteasomal function is necessary to facilitate new spine growth. In addition, because global pharmacological inhibition was not more effective at reducing spine outgrowth than overexpression of Rpt6-S120A in individual postsynaptic cells, our data also suggest BIBW2992 mw that independent presynaptic

and circuit-wide effects do not contribute significantly to the observed reduction in new spine growth. Finally, uncaging-induced spine outgrowth, which is independent of presynaptic activity, was also significantly reduced by blocking the proteasome, emphasizing the role of localized postsynaptic signaling. Our results strongly support a postsynaptic site of action for the proteasome in activity-induced new spine outgrowth. How might synaptic activity and the proteasome act together to facilitate new spine growth? One possibility is that synaptic activity enhances proteasome function to cause the emergence of new spines. Alternatively, the synaptic stimulus could be the primary cause of spine outgrowth, and normal steady-state levels of proteasomal degradation are required for activity-induced new spine growth. We think that the latter possibility is unlikely

because expression of Rpt6-S120A for 4 days does not produce any noticeable effects on cell health compared to untransfected neurons, suggesting that general proteasomal function Idoxuridine is not significantly disrupted. In addition, the Rpt6-S120A mutation does not interfere with normal steady-state levels of proteasome-mediated protein degradation in heterologous cells (Djakovic et al., 2012). Instead, because the Rpt6-S120A mutation blocks CaMKII-mediated enhancement of proteasomal degradation (Djakovic et al., 2012), our data suggest that locally enhanced proteasomal degradation, probably through CaMKII phosphorylation of Rpt6 at S120, is required for activity-induced new spine growth. How might neural activity translate to enhanced local proteasomal degradation? Changes in neuronal activity have been shown to alter both proteasome activity (Bingol and Schuman, 2006 and Djakovic et al., 2009) and localization (Bingol and Schuman, 2006 and Bingol et al., 2010).

, 2002) and which is present close to olivary Connexin 36 plaques in the inferior olive (Alev et al., 2008). We therefore repeated the plasticity experiments with KN62, a selective CaMKII inhibitor (Tokumitsu et al., 1990), in the pipette solution (10 μM). No significant plasticity was observed under these conditions (coupling after induction 89% ± 29% of baseline; n = 4 pairs, p = 0.70; Figures S4C and S4D). Since complex GS-7340 clinical trial spike synchrony depends on both synaptic input and electrotonic coupling between olivary neurons (Marshall et al., 2007 and Wise et al., 2010), it is important to know how the strength

of chemical synapses is affected by protocols that modify gap junctional coupling. To test this, we assessed the chemical synaptic response (i.e., the evoked EPSP) along with electrical coupling strength. During the baseline and monitoring periods, we used a stimulus strength that would allow direct monitoring of the EPSP (without occlusion of the EPSP by olivary bursts). The induction protocol consisted of a steady-state depolarization 3-Methyladenine nmr (0–500 pA), with 100 synaptic stimuli at 1 Hz. The synaptic stimuli were paired with short depolarizing pulses (20 ms, 800 pA) to ensure reliable induction of burst spiking (Chorev et al.,

2007 and Khosrovani et al., 2007). As before, we found that the electrical coupling was depressed after induction (coupling reduced by 37% ± 7.5% of baseline, p < 0.01; n = 11 pairs; Figure 4). Since the EPSP was occasionally occluded by the low-threshold calcium spike or a rebound spike, our analysis was restricted to subthreshold synaptic responses (mean baseline EPSP size 6.4 ± 0.3 mV; n = 14 cells). We found that the strength of the chemical synapse in these cells did not vary significantly after induction (107% ± 7.8% of baseline; mean EPSP size after induction 6.1 ±

0.8 mV, p = 0.39, n = 17 cells). This indicates that plasticity induction is specific to electrical synapses Thalidomide and does not affect chemical synapses in the same cell, even though the chemical synapses have been used to induce the plasticity. We have demonstrated that physiological activation of glutamatergic synapses triggers long-term depression of electrical coupling between inferior olive neurons while maintaining the strength of the chemical synapse. This provides a direct functional role for the precise anatomical arrangement of glutamatergic synaptic input and gap junction plaques in the synapse at the glomerulus that links multiple dendritic spines. The fact that chemical-electrical synapses have been shown to coexist throughout the mammalian nervous system, and the demonstration of similar intersynaptic plasticity mechanisms in the Mauthner cell of the goldfish (Yang et al., 1990, Pereda and Faber, 1996, Pereda et al.

The third mutation does not shake under ether anesthesia and complements Shaker. This mutation, insomniac (inc), causes a severe reduction of sleep to an average of 317 min per day, over four standard deviations from the mean of all screened lines ( Figure 1A) and a > 65% reduction from that of wild-type CS control animals, which average 927 min of sleep per day ( Figure 1B). Individual mutant animals exhibit strikingly reduced sleep during both day and night ( Figure 1C), but do not display other obvious behavioral (including geotaxis and

phototaxis) or morphological abnormalities. We mapped insomniac to a region of 250 kb–1 Mb near the tip of the X chromosome (see Figure S1A available online and Experimental Procedures), and further analysis identified a deficiency, IDO inhibitor removing 190 kb and nine annotated genes, that failed to complement insomniac ( Figure S1B). Coding regions of these candidate genes, as well as some introns and intergenic regions, were amplified from insomniac and CS animals and buy GSK1349572 sequenced. A single mutation was identified, a 257 bp deletion between two divergently transcribed genes, CG14795 and CG32810 ( Figures 2A–2C). This deletion is not present in other wild-type strains, and complete sequencing of CG32810 and CG14795 revealed no other alterations in either gene (data

not shown). To map the 5′ termini of CG32810 and CG14795 with respect to the deletion, we performed 5′ RACE. This analysis indicated that the deletion removes the transcriptional initiation site of CG32810 and 50 bp of its 5′UTR, but leaves intact the 5′ terminus of CG14795 and a small amount of upstream sequence ( Figure 2B). In insomniac animals, discrete 5′ RACE products were reduced however or absent for both CG32810 and CG14795, indicating that the transcription of both genes is affected by the deletion ( Figure 2D). To further assess whether disruption of one or both genes causes the insomniac phenotype, we obtained a transposon insertion located in the 5′UTR

of CG32810 (CG32810f00285; Figures 2A and 2B). As described below, molecular and behavioral analysis of these mutations indicates that CG32810 corresponds to insomniac, and we therefore refer to the 257 bp deletion as inc1 and the transposon insertion as inc2. To quantitatively compare the effects of these mutant alleles on transcript levels of CG32810 and CG14795, we prepared RNA from whole animals, as well as from isolated heads and bodies, and performed northern blotting analysis using probes specific for each transcript and for rp49, a control gene. The inc1 deletion is associated with a severe (>90%) decrease in CG32810 transcript levels and a substantial (∼60%) reduction in those of CG14795 ( Figure 2E and data not shown), consistent with the reduced transcript levels observed in 5′ RACE analysis ( Figure 2D).

Stochasticity also appears to be a feature of cell fate assignment. We therefore speculate that all vertebrate retinas, though vastly different in size and the proportional composition of different cell types, may follow similar stochastic rules but tune their proliferative and cell fate probabilities

to arrive at appropriate species-specific retinal sizes and cellular compositions. Zebrafish lines were maintained and bred at 26.5°C. Embryos were raised at 28.5°C and staged in hours postfertilization (hpf). Embryos were treated with 0.003% phenylthiourea (PTU, Sigma) at 8 hpf to delay pigmentation and were anaesthetised by 0.04% MS-222 (Sigma) prior to live imaging. All animal work was approved by Local Ethical Review Committee at the University of Cambridge and performed according to www.selleckchem.com/products/BAY-73-4506.html the

selleck compound protocols of UK Home Office license PPL 80/2198. Geminin-GFP (Tg(EF1α:mAG-zGem(1/100))rw0410h), UAS-Kaede, and MAZe transgenic lines have been described previously (Collins et al., 2010; Scott and Baier, 2009; Sugiyama et al., 2009). H2B-GFP transgenic line was generated by injection of the actin promoter-driven H2B-GFP DNA construct. The cell number of entire retinas or individual cell types was formulated by multiplying the cell density by the volume of retinas (or individual cell layers). To measure the volume, CYTH4 we acquired the confocal z stacks of entire retinas at distinct stages (24, 32, 48, 52, and 72 hpf) on the inverted confocal microscope (Olympus FV1000) equipped with 40× oil objective (NA = 1.3). The surfaces of retinas were created based on retinal confocal stacks using the contouring adaptive tools in Imaris 7.3 (Bitplane). To distinguish different cell layers, we crossed the H2B-GPF line with the Ptf1a-DsRed line, in which all layers were separated in space by the Ptf1a-DsRed labeling and the surface of individual layers therefore could be reliably generated. The resultant surface was further used to calculate the volume using the

statistics tool in Imaris 7.3. Cell density was estimated by counting the number of cells in given 1 μm sagittal section acquired using the confocal microscope (Olympus FV1000), at a depth in which all the cell layers were present, followed by a necessary correction using the protocol outlined in Figure S7. Cell number in retina sections (or individual cell layers) was counted manually using ImageJ or Photoshop CS5 (Adobe), and the corresponding areas were measured using the contouring adaptive and statistics tools in Imaris 7.3. Twenty-four hour postfertilization embryos embedded in 1% low-melting agarose (type IV, Sigma) were prepared in the Steinberg’s solution (100× stock: 0.5 g KCl, 0.8 g Ca(NO3)2 × 4H2O, 2.1g MgSO4 × 7H2O, 34 g NaCl, 119 g HEPES, to 1 l dd H2O [pH 7.

Cerebellar cortex samples from the same subjects served as controls. Brain tissue was frozen and stored at −80°C until further analysis. Tissue samples were thawed and Dounce homogenized in 10 ml lysis buffer (0.32 M sucrose, 5 mM CaCl2, 3 mM magnesium

acetate, 0.1 mM EDTA, 10 mM Tris-HCl [pH 8.0], 0.1% Triton X-100, and 1 mM DTT). Homogenized samples were suspended in 20 ml of sucrose solution (1.7 M sucrose, 3 mM magnesium acetate, 1 mM DTT, and 10 mM Tris-HCl [pH 8.0]), layered onto a cushion of 10 ml sucrose solution, and centrifuged at 36,500 × g for 2.4 hr at 4°C. The isolated nuclei were resuspended in nuclei storage buffer (NSB) (10 mM Tris [pH 7.2], 2 mM MgCl2, 70 mM KCl, and 15% sucrose) AZD2281 solubility dmso for consecutive immunostaining and flow cytometry analysis. Isolated nuclei were stained with mouse NeuN (A-60) (Millipore, 1:1,000), rabbit Fox3 (Atlas Antibody, 1:300), mouse HuD (E-1) (Santa Cruz, 1:100), mouse HuD/HuC 16A11-biotin (Invitrogen, 1:300), or goat Sox10 (R&D, 1:300). NeuN (A-60) antibody was directly conjugated to Alexa 647 (Invitrogen Antibody Labeling Kit Alexa 647). All other primary antibodies were visualized with appropriate secondary antibodies conjugated to Alexa 488 (1:500), Ibrutinib ic50 Alexa 647

(1:500) (Invitrogen), or R-phycoerythrin (PE) (Santa Cruz, 1:100). Flow cytometry sorting was performed with a BD FACS Diva and flow cytometry analysis was performed with a BD FACS Aria instrument. Olfactory bulbs were fixed in 4% formaldehyde buffered in PBS for 24 hr and embedded in low-melting paraffin (52°C–54°C), according to standard

procedures. Olfactory bulbs were sectioned (5 μm) longitudinally and orthogonally according to their long axis. For immunohistochemistry, sections were deparaffinized in xylene and rehydrated in a descending ethanol series. Antigen retrieval was performed in citraconic acid solution (pH = 7.4; 0.05% citraconic acid) for 20 min in a domestic steamer (Namimatsu et al., 2005). The sections were allowed to cool down for 20 min before immunostaining was started. Sections were incubated with the respective primary antibody overnight at 4°C: mouse NeuN (Millipore A-60 clone; 1:100), rabbit Fox3 (Atlas Antibody, 1:300), goat Sox10 (R&D, 1:100), rabbit calbindin (Abcam, 1:200), chicken MAP-2 (Abcam, 1:1,000), rabbit calretinin (Abcam, found 1:200), rabbit parvalbumin (Abcam, 1:1,000), rabbit tyrosine hydroxylase (TH) (Millipore, 1:1,000), rabbit GAD65/67 (Millipore, 1:500), rabbit CNPase (Atlas Antibody, 1:400), mouse GFAP (Sigma Aldrich, 1:1,000), rabbit Iba1 (Wako, 1:1,000), mouse HuD (E-1) (Santa Cruz, 1:100), and mouse HuD/HuC 16A11-biotin (Invitrogen, 1:100), and visualized with the matching secondary antibody and streptavidin conjugated to Alexa 488, 546, or 647 (1:1,000, Invitrogen). All experiments were carried out in a clean room (ISO8) to prevent any carbon contamination of the samples. All glassware was prebaked at 450°C for 4 hr.

Most models involving digital reconstructions are constrained and validated by measurements from experiments. For this purpose, the goal of real-scale simulations shifts the

demand to massive experimental data sets, not only to ensure sufficient statistical power for adequate estimation of all model parameters, but also to capture the natural diversity of neuron types (Hill et al., 2012). The amount of necessary experimental data requires fully automated digital tracing. Yet a century after Cajal’s drawings, the majority of publicly available morphological data is still being reconstructed manually (Halavi et al., 2012), because the extensive heuristic expertise of humans has not yet been matched by computer algorithms (Donohue and Ascoli, 2011). As recent developments pull within reach of full automation (e.g., Chiang et al., Epacadostat cell line 2011), the emphasis OTX015 mouse will move to

generalization of high quality results to all routine laboratory preparations. An important lesson taught by the DIADEM Challenge is that success hinges not only on independent advancements in imaging technology and algorithm design, but also on specifically tailoring the experimental details to the computational goal. As large volumes of reconstructions become attainable by high-throughput pipelines, quality control will still require human validation, which will probably become the ultimate bottleneck. In this review, we highlighted from the research designs and digital resources that fuel the thriving scientific progress of neuromorphology reconstruction in so many areas of neuroscience. Applications abound in morphometric and stereological analyses, biophysically realistic simulations of neuronal activity, computational models of developmental growth and migration, and stochastic generation of synaptically connected networks. Real-scale, four-dimensional reconstructions of entire plastic circuits at the single-neuron level promise to make the next decade the most exciting yet. This work was supported in part by grants R01-NS39600 from

the National Institutes of Health and MURI-N00014-10-1-0198 from the Office of Naval Research. We are grateful to Dr. Michele Ferrante for Figure 4C and to Dr. Maryam Halavi for Figure 5A. We thank Mr. Todd Gillette, Dr. Kerry Brown, and Dr. Michele Ferrante for feedback on an earlier version of this manuscript. “
“The Drosophila neuromuscular junction (NMJ) is a powerful system to investigate mechanisms underlying retrograde signaling ( Keshishian and Kim, 2004). Spaced stimulation of Drosophila larval and embryonic NMJs results in potentiation of spontaneous (quantal) release ( Ataman et al., 2008; Yoshihara et al., 2005) through a retrograde signaling mechanism requiring postsynaptic function of the vesicle protein Synaptotagmin 4 (Syt4) ( Barber et al., 2009; Yoshihara et al., 2005).

These data therefore provide cell biological support for previous studies of Dscam1 in the control of contact-mediated recognition and repulsion, and reveal an important role for substrate interactions in promoting self-avoidance. da neurons, and class IV neurons in particular, have become a model for studies of dendritic self-avoidance

and tiling mechanisms. Separating two causes of crossing in these cells should enable the identification of key molecules that regulate repulsive signaling between dendrites, as well as mechanisms that establish relationships between dendrites and other surrounding cell types that impact dendrite development and, perhaps also, function. Alleles used were mys1 (Bloomington Stock Center), and mysXG43 (linked to markers y, w, f), mewM6 ifk27e (linked to markers y, f), and ifk27e Selleck PARP inhibitor (linked to marker f) on FRT19A, and rheatendrils13-8 on FRT2A (all provided by Dr. M. Krasnow, Stanford University) ( Levi selleck chemicals et al., 2006), and Dscam123 on FRT42D ( Matthews et al., 2007). GFP protein trap lines were provided by Drs. L. Cooley (Princeton University) and B. Ohlstein (Columbia University). RNAi lines were obtained from the Vienna RNAi Collection ( Dietzl et al., 2007). UAS-αPS2 (if), UAS-βPS (mys) flies were provided

by Dr. K. Broadie (Vanderbilt University). 221-Gal4, ppk-Gal4, and clh201-Gal4 lines have been described previously ( Grueber et al., 2003, Grueber et al., 2007 and Hughes and Thomas, 2007). MARCM experiments were performed as described (Grueber et al., 2002 and Lee and Luo, 1999) by crossing FRT lines to either

hsFLP, C155-Gal4, UAS-mCD8::GFP; enough FRT2A tubPGal80 or hsFLP, tubPGal80, FRT19A; 109(2)80-Gal4, UAS-mCD8::GFP. For time-lapse analysis of MARCM clones, we examined mid-stage second instar larvae for the presence of dorsal cluster clones. Selected animals were imaged live under halocarbon oil (Sigma, St. Louis, MO) and a coverslip, recovered to yeasted grape plates, raised to late third instar at 25°C, then dissected and labeled with anti-HRP, anti-GFP, and anti-Coracle. Larvae were processed for immunohistochemistry largely as described (Grueber et al., 2002). Antibodies and dilutions used were CF.6G11 (anti-βPS, 1:10; developed by D. Brower), DK.1A4 (anti-αPS1, 1:10; developed by D. Brower), CF.2C7 (anti-αPS2, 1:10; developed by D. Brower), c556.9 and c615.16 (anti-Coracle, 1:20; developed by R. Fehon), 4F3 (anti-discs large, 1:10; developed by C. Goodman). These antibodies were obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by the University of Iowa, Department of Biology. Other primary antibodies were chicken anti-GFP (Abcam; 1:1,000) and goat anti-HRP (Sigma; 1:200). Species-specific fluorophore-conjugated secondary antibodies (Jackson Immunoresearch) were used at 1:200 in PBS with 0.3% Triton X-100 (PBS-TX).

A.K. performed volumetric analysis of AD/bvFTD/aged patients and extracted the healthy-brain structural

network. She also helped fine-tune the manuscript. M.W. provided guidance, clinical input, and interpretation, MRI data for volumetric analysis, and improvement of the manuscript. A.R. would like to thank Dr. Norman Relkin for valuable advice. “
“Neurodegenerative diseases have long been linked to neural networks by the clinical and anatomical progression observed in patients (Braak and Braak, 1991, Pearson et al., 1985, Saper et al., 1987 and Weintraub and Mesulam, 1996). Emerging network-sensitive neuroimaging techniques selleck chemicals have allowed researchers to demonstrate that the spatial patterning of each disease relates closely to a distinct functional intrinsic connectivity network (ICN), mapped in the healthy brain with task-free or “resting-state” fMRI (Buckner et al.,

2005 and Seeley et al., 2009). Collectively, these findings raise mechanistic questions about whether and how connectivity in health predicts regional neurodegeneration severity in disease. In Alzheimer’s disease, increasing evidence suggests that pathology may begin within key vulnerable “hubs,” defined as central nodes within the target network’s architecture (Buckner et al., 2009). Still, open questions remain with regard to why each disease adopts a network-related spatial pattern. At least four disease-general hypotheses Linsitinib purchase have been offered and can be summarized as (1) “nodal stress,” in which regions subject to heavy network traffic (i.e., “hubs”) undergo activity-related “wear and tear” that gives rise to or worsens disease (Buckner et al., 2009 and Saxena and Caroni, 2011); (2) “transneuronal spread,” in which some toxic agent propagates along network connections, perhaps through “prion-like” templated conformational

change (Baker et al., 1994, Frost and Diamond, 2010, Frost et al., 2009, Jucker and Walker, 2011, Lee et al., 2010, Prusiner, 1984, Ridley et al., 2006 and Walker et al., Non-specific serine/threonine protein kinase 2006); (3) “trophic failure,” in which network connectivity disruption undermines inter-nodal trophic factor support, accelerating disease within nodes lacking collateral trophic sources (Appel, 1981 and Salehi et al., 2006); and (4) “shared vulnerability,” in which networked regions feature a common gene or protein expression signature that confers disease-specific susceptibility evenly distributed throughout the network. Although these hypothesized network degeneration mechanisms need not be considered mutually exclusive, they make competing predictions with regard to how healthy network architecture should influence disease-associated regional vulnerability (Figure 1).

, 2008). Additionally, lateral ventricular injection of sAPPα increased the proliferation of NPCs in the subventricular zone, another neurogenic niche

in mouse brain ( Caillé et al., 2004). All ADAM family proteins contain an N-terminal prodomain, which acts to chaperone and ensure the proper Metabolism inhibitor folding of this family of metalloproteases. Our results show that the cleaved ADAM10 prodomain appears to be quickly degraded in the brain and that cellular trafficking of ADAM10 is not affected by the prodomain mutations. Interestingly, while the prodomain may be degraded, it may still have the ability to affect the mature enzyme via its intramolecular chaperone function. This phenomenon, dubbed “protein memory,” was reported in mutant subtilysin, a serine protease harboring a mutation in its prodomain (Shinde et al., 1997). The point mutation yielded mature subtilysin that had a different structure and activity via “structural imprinting” during protein folding (Shinde et al., Selleckchem CT99021 1997). Improperly chaperoned, mis-folded proteases can be restructured and become active

by ectopic expression of WT prodomain (Cao et al., 2000). In our mammalian cell-based studies, WT but not mutant ADAM10 prodomain rescued the α-secretase activity of inactive ADAM10 expressed from a prodomain-deleted cDNA construct. This indicates impairment of the chaperone function of the prodomain by the LOAD mutations (Figure 8C). In further support of this conclusion, secondary structure predictions showed that the only α-helix in the prodomain can be terminated by the R181G mutation (McGuffin

et al., 2000). To date, a few pathogenic amino acid substitutions, which are Farnesyltransferase not present in the mature forms, have been associated with diseases. But to our knowledge, the two LOAD ADAM10 mutations are the first to be associated with the etiology of any disease by impairing the intramolecular chaperone function of a prodomain. Increased ADAM10 α-secretase activity could potentially be achieved by multiple different mechanisms, including the activation of ADAM10 gene transcription by retinoic acid, the inhibition of natural ADAM10 inhibitors (e.g., TIMPs, tissue inhibitor of metalloproteases), and the modulation of ADAM10 cellular trafficking (Lichtenthaler, 2011). While dozens of proteins have been reported as ADAM10 substrates (Pruessmeyer and Ludwig, 2009), only a handful are related to brain and neuronal function. Moreover, in contrast to the ADAM10 knockdown, our data and a previous report by Postina et al. (2004) support that modest elevation of ADAM10 is relatively well tolerated and does not affect Notch1 signaling in adult brain.

, 2008). This process also occurs in birds to a limited extent (Fischer and Reh, 2000). The continued production of sensory receptor cells in these epithelia requires a mitotic cell population that can act like the stem cells in nonneural epithelia. In the case of the olfactory epithelium, there are at least two types of mitotic cells: the globose basal cells (GBCs) and the horizontal basal cells (HBCs). The GBCs are mitotically active in the normal, undamaged epithelium and selleck products act as a multipotent progenitor

to generate all of the other types of olfactory cells, including the sensory receptors (Caggiano et al., 1994, Chen et al., 2004 and Huard et al., 1998). The more slowly cycling (or even quiescent) HBCs are more like “stem cells” serving both to replenish the more actively proliferating GBCs (Iwai et al., 2008) and as a reserve pool after more extensive damage to the receptors (Leung et al., 2007). The model of a slow-cycling stem cell (HBC) with a more rapidly cycling, transit-amplifying progenitor cell (GBC) has similarities with nonneural epithelia, like the epidermis (Watt et al., 2006).The situation in the vestibular system of nonmammals is somewhat different, in that there does not appear

learn more to be a committed hair cell progenitor. Rather, it appears that some or all of the support cells remain capable of mitotic division and divide at a low rate to produce both additional hair cells and support cells as the epithelium grows. In the retina, the source of the new rods in the fish is a group of cells

called the rod precursors (Johns and Fernald, 1981), which typically generate only rod photoreceptors under normal conditions and are likely derived from the Müller glia (more on this later). The different progenitors/precursors in these systems also share some common molecular expression patterns that are similar to those expressed during initial development (see Figure 3 and further discussion below). In the olfactory epithelium, for example, at least some of the GBCs express Ascl1, Org 27569 Neurog1, Sox2, and Pax6, genes critical during olfactory epithelial development (Guo et al., 2010 and Manglapus et al., 2004). NeuroD1 is expressed at a slightly later stage, in the cells that will differentiate into the olfactory receptor neurons. In the inner ear, the support cells also express Sox2 (Oesterle et al., 2008), and many of the support cells that are in the S-phase or M-phase of the cell cycle, as well as the newly generated postmitotic daughters, express Atoh1 (Cafaro et al., 2007).