NDEL1 is another

DISC1 interacting protein that regulates neuronal development in vivo (Duan et al., 2007, Sasaki et al., 2005 and Shu et al., 2004). Consistent with our previous findings (Duan et al., 2007), expression of a specific shRNA against mouse ndel1 (shRNA-N1) led to developmental defects of newborn dentate granule cells, mostly in the appearance of ectopic dendrites and aberrant positioning ( Figure 4). Thus, FEZ1 and NDEL1 appear to mediate DISC1 signaling in a complementary set of neuronal developmental processes. To determine whether FEZ1 and NDEL1 also functionally interact to regulate development of Vandetanib newborn neurons, we performed double knockdown experiments in vivo. The effect of coexpressing shRNA-F1 and shRNA-N1 on dendritic growth and soma size of newborn neurons was very similar to those expressing

shRNA-F1 alone ( Figures 4A–4C), whereas the effect on ectopic dendrites and neuronal positioning was similar to those expressing shRNA-N1 alone ( Figures 4D and 4E). Thus, concomitant suppression of NDEL1 and FEZ1 only leads to Adriamycin manufacturer additive effects of individual knockdown, instead of a synergistic action. These results further support the notion that FEZ1 and NDEL1 differentially regulate distinct aspects of new neuron development in the adult brain. KIAA1212/Girdin is also a DISC1 binding partner that regulates development of newborn dentate granule cells in the hippocampus (Enomoto et al., 2009 and Kim et al., 2009). We next examined whether KIAA1212 interacts with FEZ1 or NDEL1 in regulating neuronal development. Consistent with previous findings, DISC1 was co-IPed with each of also the three proteins, NDEL1, FEZ1, or KIAA1212, when each pair was coexpressed in the heterologous system (Figure S4A).

Furthermore, these four proteins could be co-IPed together with DISC1 when all were coexpressed (Figure S4A). Also consistent with the previous finding (Kim et al., 2009), overexpression of KIAA1212 led to increased total dendritic length, number of primary dendrites, and soma size in newborn neurons in the adult dentate gyrus (Figures S4B–S4D). Compared with KIAA1212 overexpression or FEZ1 knockdown alone, comanipulation exacerbated phenotypes of increased dendritic length and soma size, but not the number of primary dendrites and positioning of newborn neurons (Figures S4B–S4E). On the other hand, simultaneous KIAA1212 overexpression and NDEL1 knockdown exhibited phenotypes very similar to those of NDEL1 knockdown alone (Figures S4B–S4E). Taken together, these results support a model that DISC1 interacts with FEZ1 and KIAA1212 mainly to regulate dendritic growth and soma size of newborn neurons during adult neurogenesis, whereas DISC1 interacts with NDEL1 mainly to regulate positioning of newborn neurons (Table 1).

It has a physiology indistinguishable—to standard testing—from the classic Y/parasol cell, nonlinearly summing its inputs so that it is particularly sensitive to stimuli that flash or move. And yet it is clearly a different cell: (1) the smooth cell is instantly distinguishable from parasol cells in dendritic morphology, (2) it has twice the dendritic field diameter of a parasol cell, and (3) it tiles the retina with a uniform mosaic independent of the mosaic of parasol cells. Thus, the smooth cells send to the brain a coding of the visual input similar to that of the parasol cells, but each smooth cell reports upon a region of visual space about four times as big as that sampled by a parasol cell.

The smooth cells project to the lateral geniculate body, way station to the cortex. Why does the cortex need to view the same feature of the world through two Nintedanib in vitro different-sized apertures? Is there some other difference in the encoding transmitted by the smooth cell, something not revealed by testing with standard grating stimuli? And how do these separate representations

combine to create visual perception? Perhaps the nonstandard visual signals are somehow incorporated into the canonical pattern of visual cortical responses (Hubel and Wiesel, 1965). The alternative is that a fundamentally new concept of higher visual processing will be necessary. The broad view of the retina’s organization is now complete, but it remains studded with approximations— “around thirty” types of amacrine Lapatinib in vitro cell, “twelve to twenty” types of ganglion cell—and little has been said about synaptic connectivity. How do we get to the next level of precision? It is important here to recognize that the aim is a possibly utopian one: we seek an exact enumeration of the retina’s component cell types. This is different from the traditional view, which is that the brain is so hopelessly complex

(and plastic into the bargain) that the best hope is only a description of selected neural subcircuits, containing just a few types of neurons. Instead, the goal here is to be able to say: “These are the cells of the retina, and the list includes all the cell types that exist.” For rods, cones, horizontal, and bipolar cells, our present census is pretty STK38 definitive: we can identify the cell types and we can describe them quantitatively. But amacrine cells have been enumerated only in the rabbit retina, and retinal ganglion cells remain a struggle. All workers agree on their broad diversity, and different imaging methods repeatedly show the same cells; but a consensus on a classification of the ganglion cell types has not emerged. How do we get to a definitive description? In the past few years, strategies for introducing fluorescent labels into subgroups of retinal neurons have appeared (Feng et al., 2000; Huang et al., 2003; Huberman et al., 2009; Kim et al., 2008; Siegert et al., 2009; Yonehara et al., 2008, 2009). The importance of this advance is hard to overstate.

Brainstem tissues were maintained for 1 hr at RT in aCSF, bubbled with 95% O2 and 5% CO2, containing Rp-cGMPS (3 μM, dissolved in 0.1% DMSO), PTIO (100 μM, with 0.1% DMSO), or none of them (control, with 0.1% DMSO). After measuring wet weight of individual brainstem click here tissue samples, they were homogenized in 0.32 M sucrose, 4 mM HEPES-NaOH (pH 7.3 kept at <4°C), supplemented with EDTA-free protease inhibitor cocktail (Roche), phosphatase Inhibitor Cocktail 3 (Sigma), and kinase inhibitor sodium orthovanadate

(Sigma). For lipid extraction, an ice-cold methanol/chloroform (2/1) mixture was added to each pellet. Samples were centrifuged at 1,500 rpm for 5 min, and then supernatants were removed to extract neutral lipids. To extract acidic lipids, an ice-cold methanol/chloroform/HCl 12N (80/40/1) mixture was added to the remaining tissue pellets, vortexed thoroughly, find more and centrifuged at 1,500 rpm for 5 min. Supernatants were collected and a chloroform/0.1N-HCl (1/2) nonpolar/polar solvents mixture was added to create a phase split of liquid to separate lipids from remaining other constituents. The chloroform phase (lower phase) comprising lipids was collected and evaporated by a speed-vacuum centrifuge (Eppendorf Concentrator

5301). Dried lipids samples were then rapidly dissolved again in PBS containing the PIP2 sensor provided in the PIP2 Mass ELISA kit (K-4500, Echelon). PIP2 in each sample was then quantified following the manufacturer’s protocol. Luminometric analyses were performed by measuring the final signal absorbance at 450 nm using a microplate reader (Benchmark Plus 170-6930J1, Endonuclease Bio-Rad). PIP2 levels were normalized to the wet weight of brainstem tissue. Brainstem, heart, and liver tissues of P7 and P14 rats were homogenized in ice-cold buffer containing 50 mM Tris, 1 mM EDTA, 150 mM NaCl, 1% NP-40, and a protease inhibitor cocktail (Roche). Homogenates were then centrifuged at 12,000 rpm for 30 min. The protein concentrations of lysates were measured using Micro BCA Protein assay kit (Thermo Scientific) and equal amount of proteins were loaded onto a SDS polyacrylamide gel. After separation by

electrophoresis proteins were transferred to a PVDF membrane (Bio-Rad). Blotted membranes were blocked with 5% nonfat dry milk in Tris-buffer saline with 0.1% Tween-20. Proteins were detected with the specific primary rabbit antibodies anti-PGKI (Abcam) and anti-alpha-tubulin (Sigma) prior to incubation with horseradish peroxidase-conjugated anti-mouse or anti-rabbit secondary antibody (Millipore). Protein immunoreactivity was further detected using ECL plus western blotting detection reagents (Amersham). Quantitative analyses were performed with a cooled CCD camera coupled with AE- 6971 Light Capture instrument (ATTO) and analyzed with the supplied CS-Analyzer software (ATTO). Data were analyzed using IGOR Pro 4 (WaveMatrics), MS Excel 2003 (Microsoft), and Sigmaplot 11 (Synstat Software) softwares.

We then placed the animal into the MRI, acquiring functional volumes while alternating between microstimulation on and microstimulation off conditions every 24 s while the monkey fixated on a dot in the center of a gray screen. In both monkeys, microstimulation elicited strong activation throughout the OTS, as well as in an anatomically discontinuous region in the medial parahippocampal gyrus, which we term the medial place patch (MPP) for reasons discussed below. As with LPP, histological studies differ in their region labels for the area in which this activation resides, terming it TLO (Blatt and Rosene, 1998 and Blatt et al., 2003), TFO (Saleem et al., 2007), or VTF (Boussaoud et al., 1991). selleck products Additional

microstimulation-evoked activation was observed in extrastriate Romidepsin concentration visual areas V4V and putative DP and in the inferior branch of the posterior middle temporal sulcus (PMTS) (Figure 3). These areas are a subset

of the areas identified by tracing studies of the vicinity of LPP, which have shown reciprocal connectivity with medial parahippocampal areas, as well as extrastriate visual areas V3A, V3V, V4, FST, MST, LIP, and 7a; area TPO; retrosplenial cortex; and hippocampal subfield CA1 (Blatt and Rosene, 1998, Blatt et al., 2003 and Distler et al., 1993). Of the regions activated by microstimulation, we were particularly interested in the activation in the medial parahippocampal gyrus (MPP). Because this site is putatively located within parahippocampal cortex, it is well suited to carry scene information to the hippocampus, and, like LPP, it is potentially homologous to the human PPA. Furthermore, the region was also weakly activated by the place localizer in one hemisphere of M3, suggesting that it might respond to passive viewing of scenes (Figure S1C). We targeted this medial parahippocampal region as Carnitine dehydrogenase activated by microstimulation in monkey M1 (Figures S4A and S4B) and recorded a large proportion of scene-selective

single units (Figure 4A). Twenty-seven percent of visually responsive units (31/113) exhibited a scene selectivity index greater than one-third (median = 0.16; Figure 4B). While LPP and MPP exhibited similar latencies (LPP: 120 ± 42 ms; MPP: 123 ± 63 ms; p = 0.33, unequal variance t test), the duration of the neural response was nearly twice as long in LPP as compared to MPP (LPP: 155 ± 76 ms; MPP: 90 ± 70 ms; p < 10−14, unequal variance t test; Figure S4C). Additionally, none of 24 units recorded from grid holes between MPP and LPP were visually responsive, a significant difference from results in both regions (both p < 0.003, Fisher’s exact test; Figures S4D–S4G). These results indicate that MPP and LPP are distinct functional regions. To ensure that the scene selectivity observed in single units in LPP and MPP was not present throughout all ventral visual areas, we also recorded from 41 single units in a region 3 mm posterior to LPP (Figures 4 and S4H).

The larger, more obvious LFP, the positivity peaking at ∼30 ms, and the negativity peaking at ∼50 ms (P30/N50, Figures 1A–1C) appear to arise Compound Library purchase mainly from processes in the supragranular layers. The superficial P30 extends upward from a supragranular current source that we interpret as a “passive” CSD feature reflecting current return to the “active” current source, itself representing the initial activation

of supragranular pyramidal cells (by granule cell afferents from Layer 4). Passive current return happens because of the conservation of net electrical currents and electrical neutrality. N50 extends vertically from a superficial current sink (an asterisk in Figure 1C), whose physiological significance is less clear. As discussed below, we use the P30 to track LFP spread vertically. To get at lateral spread of LFPs, we focused analysis on the initial negativity associated with the frequency-selective

responses in Layer 4/lower Layer 3 (“1” and “2”, Figure 1); this negativity extends in a ventral direction from the current sinks in these locations, particularly the lower (Layer 4) one. Figure 2 shows Layer 4 MUA, CSD, and LFP responses to tones in two different A1 penetration sites. In each site, it is clear that the three signals were largest in response to same tone frequencies, and thus shared a common BF. However, while MUA and CSD responses to tones disappeared as the tone frequency moved away from the BF, the LFP response did not. Adriamycin order Tuning curves were derived by measuring mean Thymidine kinase response amplitudes over 10 ms periods, centered between 23 and 30 ms following the stimulus onset at a recording depth

within the Layer 4 (see Experimental Procedures). The mean amplitude of MUA, CSD, and LFP signals indicated change in the level of local neuronal firing, the magnitude of current sinks due to excitatory synaptic currents and the magnitude of LFP negativity caused by current sinks relative to the baseline levels, respectively. The period was chosen to be the time during both LFP and CSD signals were negatively deflected along with simultaneous increase in MUA. Figures 3A and 3B show the normalized tuning curves for LFP, CSD, and MUA signals in the two example cases shown in Figures 2A and 2B, respectively. The three types of tuning curve generally peak at the same tone frequencies. The same trend was observed across all recording sites (Figure 3C). BF estimates were not significantly different between the three signals (Friedman’s nonparametric repeated-measures ANOVA, χγ2 (2, n = 130) = 0.92, p = 0.2) (see Figure S1 available online). The tuning bandwidths of MUA, CSD, and LFP differed significantly from one another (Friedman’s nonparametric repeated-measures ANOVA, χγ2 (2, n = 130) = 85.2, p < 0.01), in an order of BWMUA < BWCSD < BWLFP (Tukey’s HSD test, all comparisons p < 0.05; Figure 3D).

The rate-limiting enzyme in polyamine biosynthesis is ornithine decarboxylase (ODC) (Pegg and McCann, 1988). Difluoromethylornithine (DFMO) is a suicide inhibitor of ODC. Administered to rats as a 2% solution in drinking water, DFMO lowers total polyamine levels significantly in all tissues examined (Danzin et al., 1979). Rats treated with DFMO for 2–21 days were used to determine the effects of reduced neuronal polyamine on CST. After 18 hr, 7 days, and 21 days of treatment, axonal MTs were labeled by injecting 35S-methionine into the vitreous of the eye and waiting 21 days for axonal transport to deliver labeled

tubulin to the optic nerve. Cold/Ca2+ fractionation of labeled optic nerve (Figure 1A) showed a significant decrease in CST after DFMO treatment (Figures

LY294002 manufacturer 1B and 1C). Fluorographs of S1, S2, and P2 fractions from control and DFMO-treated rats show a significant fraction of tubulin shifted from P2 to S1 fractions with DFMO treatment (Figure 1B). In control optic nerves, 52% of the total radiolabeled axonal tubulin was cold-insoluble tubulin, but in 7 day or 21 day DFMO-treated nerves, this fraction was <40% (Figure 1C; VX-809 manufacturer see also Figure S1 available online; Table S1) (p < 0.001), suggesting that polyamines are required for generation of cold-insoluble tubulin in axons. To determine whether a decrease in polyamines generally reduced cytoskeleton stability in axons or was specific for MTs, neurofilament (NFM) fractionation was analyzed in parallel. There was no change in NFM fractionation after DFMO treatment (Table S1). Next, we sought to determine whether polyamines were covalently added to tubulin in vivo and whether modified tubulin cofractionated with cold-insoluble tubulin. 14C-PUT axonal transport labeling experiments were until performed in rat optic nerves. Due

to high levels of endogenous polyamines and low specific activity of 14C-PUT, endogenous polyamine levels were lowered by 18 hr DFMO pretreatment. When axonal proteins were fractionated 21 days after 14C-PUT labeling, 70%–80% of the label was in P2 (not shown). Subsequent fractionation studies with higher-specific-activity 3H-PUT confirmed these results (Figure 2A). In optic nerves labeled by axonal transport of 3H-PUT, the only proteins with significant incorporation of labeled polyamines had the molecular weight (MW) of tubulin, although 35S-methionine labeled neurofilaments at the same time. Much lower levels of 3H-PUT were seen in S1, the cold-labile MT fraction. This suggested that polyamination of tubulin can occur before formation of stable MTs. Polyaminated tubulin may help nucleate and stimulate polymerization of tubulins and may also stabilize MTs after polymerization. 14C-PUT-labeled proteins in P2 were analyzed by gel filtration chromatography on a Toyopearl HW-55F (Supelco) column equilibrated in 6 M guanidine-HCl in MES to determine if 14C-PUT coeluted with tubulin.

Lentiviruses were harvested with the medium 46 hr after transfection, pelleted by centrifugation (49,000 × g for 90 min), resuspended in MEM, aliquoted, and snap-frozen in liquid N2. Only virus preparations with > 90% infection efficiency as assessed by EGFP

expression or puromycin resistance were used for experiments. For IDO inhibitor details of lentiviral constructs, see Supplemental Experimental Procedures. ESCs and iPSCs were treated with Accutase (Innovative Cell Technologies) and plated as dissociated cells in 24-well plates (H1: 1 × 104 cells/well; iPSCs: 1.5 × 104 cells/well) on day −2 (Figure 1B). Cells were plated on matrigel (BD Biosciences)-coated coverslips in mTeSR1 containing 2 μM thiazovivin (Bio Vision). On day −1, lentivirus prepared as described above (0.3 μl/well of 24-well plate) was added in fresh mTeSR1 medium containing polybrene (8 μg/μl, Sigma). On day 0, the culture medium was replaced with N2/DMEM/F12/NEAA (Invitrogen) containing

human BDNF (10 μg/l, PeproTech), human NT-3 (10 μg/l, PeproTech), and mouse laminin (0.2 mg/l, Invitrogen). Doxycycline (2 Selleckchem BLZ945 g/l, Clontech) was added on day 0 to induce TetO gene expression and retained in the medium until the end of the experiment. On day 1, a 24 hr puromycin selection (1 mg/l) period was started. On day 2, mouse glia cells were added in Neurobasal medium supplemented with B27/Glutamax (Invitrogen) containing BDNF and NT3; Ara-C (2 g/l, Sigma) was added to the

about medium to inhibit astrocyte proliferation. After day 2, 50% of the medium in each well was exchanged every 2 days. FBS (2.5%) was added to the culture medium on day 10 to support astrocyte viability, and iN cells were assayed on day 14 or 21 in most experiments. The efficiency of conversion of ESCs and iPSCs into iN cells was calculated by two approaches from counts of cell densities in four random fields on each coverslip (Figure 2D): (1) as the percentage of EGFP-positive lentivirally transduced cells that also express MAP2 or NeuN; (2) as the percentage of starting cells that become NeuN positive. Immunofluorescence experiments were performed essentially as described (Pang et al., 2011). Briefly, cultured iN cells were fixed in 4% paraformaldehyde in PBS for 20 min at room temperature, washed three times with PBS, and incubated in 0.2% Triton X-100 in PBS for 10 min at room temperature. Cells were blocked in PBS containing 10% goat serum for 1 hr at room temperature. Primary antibodies were applied overnight, cells were washed in PBS for three times and blocked with 10% goat serum for 15 min, secondary antibodies were applied for 1 hr. Transplanted iN cells in mouse striatum were analyzed by immunofluorescence staining after mice were transcardially perfused with saline followed by 4% paraformaldehyde.

Furthermore, it appears that frequency-specific patterns of interregional phase synchronization in large-scale networks can provide insight into how multiple contexts underlying a single episode can be recreated in the same network. Candidate coalitions of memory-related representations are also unveiled by methodologies tapping into longer temporal intervals. Methods for assessing functional connectivity in human fMRI data unveil sets of coactivations of regions subserving episodic recollection (e.g., Greenberg Palbociclib ic50 et al., 2005, Maguire et al., 2000 and Burianová et al., 2012). Within the animal domain, immediate early gene (IEG) mapping offers another opportunity

to examine the coactivation and possible coordination of neurons in multiple brain areas during memory retrieval—as reported by Wheeler et al. (2013) for context fear

conditioning. Whereas we used to think see more of plasticity-related gene activation as triggered solely by encoding and necessary for storage, research on reconsolidation (see “trace rebooting” above) has alerted us to the phenomena of gene activation during and after a retrieval session. While the timescale of IEG expression is at least three orders of magnitude slower than that studied in ECoG, obscuring whether gene activation is triggered by, required for, or is some epiphenomenon of memory retrieval, it nonetheless offers

an opportunity to examine the dynamics of trace activation across the brain. Wheeler et al. (2013) establish that the network interactions that are seen in IEG expression change as a representation consolidates over time. T.S. Eliot, whose insights into memory infiltrate our subtitles, saw that life had its retrospective, immediate, and prospective elements. The last of these applies even to memory itself, with a growing number of investigators considering planning from the perspective however of memory (Schacter and Addis, 2007 and Thom et al., 2013). The prospective aspect of memory research is also intriguing. Given our argument that contemporary conceptions of memory processing are diverting from our dual-trace and fixed storage heritage, we can usefully ask, “Where are we going”? Memory is traditionally measured in terms of the change in an individual’s behavior that results from their behavioral experience. This change reflects the encoding and retention over time of experience-dependent internal representations in the brain or of the capacity to reactivate or reconstruct such representations (Dudai, 2002). Representations, unless possibly of very elementary reflexes, are commonly postulated to be encoded in the spatiotemporal activity of neural circuits, ensembles, or Hebbian “cell assemblies” (Buzsáki, 2010).

The eCB system allows for multiple points of interaction with other signaling and neuromodulatory systems. selleck chemicals llc In addition to regulating release of classical

neurotransmitters like glutamate and GABA, CB1Rs can also control the release of several neuromodulators including serotonin, acetylcholine, dopamine, opioids, norepinephrine, and cholecystokinin (Alger, 2002; Kano et al., 2009; Schlicker and Kathmann, 2001). On the other hand, many of these neuromodulators actually couple to eCB synthesis by activating their respective Gq/11 protein-coupled receptors (for a comprehensive list, see Katona and Freund, 2012). Additionally, regulators of G protein signaling were recently shown to control Gq/11-coupled receptors and eCB mobilization (Lerner and Kreitzer, 2012), indicating how GPCRs themselves can fine-tune eCB release. Together, these studies not only support a general theme by which Gq/11-coupled GPCRs mobilize eCBs but demonstrate the existence of multiple routes for eliciting and regulating eCB release. On the other side of the synapse, functional interactions between CB1Rs and other receptors

have been identified. For example, at inhibitory terminals in the prefrontal cortex, D2-like receptors colocalize with CB1Rs where they appear to facilitate CB1R-mediated suppression of transmitter release (Chiu et al., 2010). This is probably due to a cooperative PD-0332991 manufacturer lowering of PKA activity, consistent with Phosphatidylinositol diacylglycerol-lyase similar observations

in the ventral tegmental area (Pan et al., 2008). In addition, work in visual cortical slices from young mice suggests that BDNF interferes with CB1R downstream signaling, thereby disrupting eCB-mediated suppression of neurotransmitter release (Huang et al., 2008). This might result from, at least in part, BDNF inhibiting CB1R function through a mechanism requiring cholesterol metabolism and altered membrane lipid raft function (De Chiara et al., 2010). At Schaffer collaterals, adenosine A1 receptors (A1Rs) colocalize with CB1Rs. Tonic activation of A1Rs can reduce the efficacy of CB1R-mediated inhibition of glutamate release (Hoffman et al., 2010). Also in the hippocampus, stimulating GluK1-containing kainate receptors at inhibitory terminals appears to actually facilitate CB1R signaling (Lourenço et al., 2010). The mechanism by which this occurs is not yet clear. Adding to the complexity of eCB signaling, evidence suggests that CB1Rs can associate with other GPCRs to form heteromeric complexes. Such interactions have been detected for CB1-D2, CB1-opioid, CB1-A2A, and CB1-orexin-1 receptor pairs (Hudson et al., 2010; Mackie, 2005; Pertwee et al., 2010). Strikingly, higher-order heteromeric complexes consisting of CB1, D2, and A2ARs have also been observed (Carriba et al., 2008).

Substituting 60 aa of the NLG1 stalk domain (aa 636–695) with the polylinker GAAAAA resulted in a mutant (NLG1-ΔSDfull) that is resistant to APMA (Figures 4A and 4B). Within this 60 residue stretch, deletion of aa 672–695 (NLG1-ΔSD3) and replacement selleck compound with the polylinker GAAAAA likewise abolished APMA-induced cleavage, whereas mutation of more membrane-distal sequences did not (aa 636–660, NLG1-ΔSD1; aa 654–677, NLG1-ΔSD2). Notably, we attempted to further resolve the precise cleavage site, but shorter deletions or single site mutants were all cleaved upon APMA treatment, potentially due to the presence of multiple MMP target sequences within this domain. Importantly, the ΔSD3 mutation

does not alter NLG1 localization, as GFP-NLG1-ΔSD3 exhibited a similar distribution pattern and synaptic enrichment as wild-type GFP-NLG1 when expressed in DIV21 hippocampal neurons (Figure S4A). Moreover, GFP-NLG1-ΔSD3 and GFP-NLG1 induced quantitatively similar spine formation when expressed in mouse cortical neurons in vivo from E15.5 to P17 and P18, indicating that GFP-NLG1-ΔSD3 retains the synaptogenic properties of wild-type NLG1 (Figures S4D and S4E). To address if NLG1-ΔSD3 is resistant to activity-dependent cleavage in neurons, we tested the effect of KCl depolarization in neurons expressing GFP-NLG1 and GFP-NLG1-ΔSD3. Following

2 hr of KCl incubation, synaptic GFP-NLG1 fluorescence decreased to 55.9% ± 5.4% of initial value, whereas GFP-NLG1-ΔSD3 exhibited no change upon KCl treatment (101.9% ± 6.9% of initial fluorescence Screening Library level; Figures S4A and S4B). To address if NLG1 cleavage occurs locally in response to increased synaptic activity, we released glutamate at single dendritic spines by two-photon laser-induced photolysis of (4-methoxy-7-nitroindolinyl)-glutamate (MNI-glutamate), while imaging dendrites Terminal deoxynucleotidyl transferase of neurons expressing GFP-NLG1-WT or GFP-NLG1-ΔSD3 (Figures 4C–4L). Analysis was performed in CA1 pyramidal neurons in organotypic hippocampal slices at a time corresponding to P14 with tdTomato (tdT) coexpression used as a cell fill. Stimulation near (∼1 μm) the distal

head of a dendritic spine (Spine 1) with 80 4-ms laser pulses at 2 Hz induced rapid loss of spine GFP-NLG1 within 1 min, whereas no change in fluorescence was detected in the neighboring dendritic shaft (ΔGFP/tdT: Spine 1: 0.61 ± 0.04, dendrite: 1.01 ± 0.02; Figures 4C, 4F, and 4J). We observed a smaller, partial loss of NLG1-GFP in neighboring spines (Spine 2, ΔGFP/tdT: 0.79 ± 0.15), possibly due to the diffusion of intracellular signals. Incubation with the MMP2/MMP9 inhibitor II (0.3 μM) or GM6001 (10 μM) abrogated glutamate-induced GFP-NLG1 loss (MMP2/MMP9i: ΔGFP/tdT: Spine 1, 0.89 ± 0.10; Spine 2, 0.97 ± 0.12; dendrite, 0.98 ± 0.11; Figures 4D, 4G, and 4K; GM6001: ΔGFP/tdT: Spine 1, 0.91 ± 0.04; dendrite, 0.96 ± 0.03; Figures S4F–S4H).