Malinow

Malinow

Molecular Methods

Quantum Dots

Choquet

AMPAR

Category:Malinow

Diffusion and Cluster Model of LTP

Diffusion and Cluster Model of LTP

---

Model Space

The model is simulated in a 3D space with the following parameters

• There is a 3D XYZ coordinate grid
• The X-Y plane has 60x60 area
• The X-Y plane consists of real numbers: -30 to +30
• The Z axis is only 2 levels: 0 and -1
  • 0 represents the membrane surface
  • -1 represents intracellular space

Particle Types

There are 2 types of particles in the simulation

• 'Red' particle dots represent AMPA receptors
  • Red dots can randomly diffuse anywhere on the X-Y plane
  • Red dots only diffuse on the surface Z = 0
• 'Blue' particle dots represent PSD-95 molecules
  • Blue dots are contained in predefined PSD areas and cannot leave
  • Blue dots can exist at the surface Z = 0 or intracellularly Z = -1

Error creating thumbnail: File missing

two independent processes

In this model, there are two independently occurring processes.

• 1. Blue dots can be expressed at the surface or internalized within their PSD area
  • The Blue dot internalization/externalization rate properties are set by the Shouval cluster model equations.
• 2. Red dots diffuse on the X-Y plane with brownian motion
  • Each Red dot has an initial step size randomly drawn from a normal distribution with a mean = 1 and sd = .2

The step size for Red dots is dynamically altered when it's located in a PSD area

• In a PSD, the step size is reduced by a by some factor based on the number of Blue dots currently expressed at the surface of that PSD
• The more Blue dots at the surface, the more the step size is reduced
• The current step size function is:
  • f(R_step) = R * (10*(1 ⁄ B_n))
    • where R_step is the baseline Red dot step size
    • where B_n is number of Blue dots currently expressed at the PSD surface

Error creating thumbnail: File missing

Error creating thumbnail: File missing

Error creating thumbnail: File missing

Error creating thumbnail: File missing

• Dendrite: 1–10 spines per μm
• Spines: 0.5–2 μm in length
• PSD: 10,000 proteins (or 100 copies of 100 proteins)
• CaMKIIα: 7.4%
• CaMKIIβ: 1.3%
• SynGap: 2.1 pmol/20 μg
• NMDAR: 20 proteins
• AMPAR: 60 subunits (15 tetramers, 80% or 12 GluR1/GluR2 heteromers)
• PSD95: within 12 nm of surface

• adapted from: Sheng and Hoogenraad (2007) The postsynaptic architecture of excitatory synapses: a more quantitative view. Annu Rev Biochem, 76:823-47.

Experiment Notes

experimental notes and highlighted findings

---

Experiment Idea Notes

Project idea
GluR1 is dependent on NMDAR activiation to be driven into synapses, whereas GluR2 will readily enter synapses without NMDAR activation {{#info:For an interesting take on this see: Lu, Gray, Granger, During, Nicoll (2011) Potentiation of Synaptic AMPA Receptors Induced by the Deletion of NMDA Receptors Requires the GluA2 Subunit. J Neurophysiol 105: 923–928, 2011}}. Nicoll (2011) showed that in NMDAR knock-outs, GluR2 can rescue potentiation but not GluR1. But how long-lived is this scenario in WT neurons? Glutamate is known to cause bouts of AMPAR scatter/internalization (particularly GluR2-containing AMPARs {{#info:Tardin, Cognet, Bats, Lounis, Choquet (2003) Direct imaging of lateral movements of AMPA (GluR2) receptors inside synapses. EMBO}}), and in a model where synapses compete for AMPAR expression, the absence of NMDARs could result in rapid shifts synaptic AMPAR expression. But that would not be a good way to maintain potentiation at important synapses. The synapses become important because they are activated coincidentally input elsewhere on the dendrite. This is how neural networks form associations. NMDARs detect this coincidental synaptic activity and 'tag' the synapses involved. This 'tag' allows these synapses to set up a molecular environment that allows them to not only increase potentiation, but to sustain their potentiated state. This synapse is now faced with the challenge of retaining potentiation, even when input doesn't activate the local NMDARs. Again, glutamate alone is enough to depolarize the neuron, but it will also cause AMPAR scatter/internalization; however, synapses that express GluR1 homomerics will have a moderate calcium influx, even when NMDARs are not activated. So while this moderate calcium influx may not be enough to drive large potentiation cascades like NMDARs, it could be enough to sustain the potentiated state of the synapse when the neuron is bombarded with non-NMDAR-activating signals.

Background Narrative

Overall, we should focus on the key differences in GluR1 vs GluR2 expression in terms of how they are trafficked and their unique roles in potentiation. We know that GluR1 is Ca²⁺ permeable and GluR2 isn't, likewise GluR1 is inward rectifying, GluR2 is not. It should be safe to assume these differences are very meaningful in LTP. Aside from those ion channel differences, GluR1 has a longer C-tail producing differences in trafficking, mobility, and intracellular protein association. Other key LTP-related pathways and mechanisms include: NMDAR-associated calcium influx activates CaMKII, which is rapidly translocated to the active synaptic terminal {{#info: Choquet 2010: NMDAR activation promoted the rapid translocation of aCaMKII::GFP to synaptic sites (marked by Homer1C::DsRed), after which AMPARs were completely immobilized during the 1 min posttranslocation recording period}}. Once situated, CaMKII can increase AMPAR activity at synapses {{#info: Choquet 2010: tCaMKII (constitutively active) promotes immobilization of endogenous GluR1 (containing) AMPARs (both synaptic and extrasynaptic), and to a much lesser extent GluA2 (containing) AMPARs, but CaMKII direct phosphorylation of AMPARs unnecessary for synaptic trapping.}}, but direct phosphorylation is not required for AMPAR expression at synapses {{#info: Malinow (2000): Although such phosphorylaton may enhance the function of synaptic receptors, this phosphorylation does not seem to be required for receptor delivery. tCaMKII can deliver mutated GluR1 (S831A - mutated CaMKII p-site) to the synapse, indicating that some protein(s) other than GluR1 must be substrate(s) of CaMKII (we know Stargazin is one of them)}}. CaMKII perhaps influences AMPAR expression via its PDZ binding domain {{#info: Malinow (2000) found that co(over)expression of tCaMKII and mutate GluR1 (GluR1::T887A::GFP) at its PDZ binding domain completely blocked synaptic response amplitude and rectification, and in fact depressed transmission in hippocampal slice neurons}} {{#info:Choquet 2010 found that the GluA1 - SAP97 interaction unnecessary for CaMKII-dependent synaptic trapping}} {{#info:Choquet 2007 found that GluR2 surface expression was reduced by half when its PDZ binding site was deleted, but lateral diffusion of GluR2 was not changed by this PDZ mutation. Suggesting the PDZ-Binding site of GluR2 controls surface expression, not lateral mobility}} resulting surface expression at synaptic slots; at active synapses CaMKII can also complex with NMDARs, resulting in a sustained increase in activity of both proteins. There strong evidence to suggest that CaMKII works through Stargazin {{#info:Choquet 2007 found that tCaMKII caused a robust immobilization of Stargazin, but not a mutated version of Stargazin (S9A) lacking the CaMKII phosphorylation site, and went on to show the critical importance of Stargazin binding to AMPARs in synaptic trapping}} to increase synaptic trapping of AMPARs {{#info:Choquet 2007 used Stargazin and PSD-95 compensetory mutants to show that the synaptic targeting of Stargazin is dependent on the presence of synaptic PSD-95, and this interaction helped immobilize both GluR1 and GluR2 receptors at synapses}}.

---

To follow up on

• Evidence shows the PDZ site is important for surface expression, not trapping at synapses.
  • suggests PDZ is not Stargazin interaction site
  • None of the AMPAR subunits bind directly PSD-95 - what other TARP proteins are important for SAP interaction
• the interaction between TARP proteins and AMPARs can be disrupted by glutamate
• Other MAGUKs interact with Stargazin such as SAP-102 and PSD-93
  • the interaction of Stargazin with SAP102 and then with increasing level of PSD-95/93 is involved in the higher trapping efficiency of AMPAR at mature synapses
• The PKA phosphorylation of Stargazin C terminus prevents Stargazin binding to PSD-95
• there are apparent GluA1 subunit-specific effect of CaMKII. Although CaMKII triggers the immobilization of both GluA1 and GluA2 containing endogenous AMPARs, it immobilizes homomeric GluA1 but not GluA2 homomeric AMPARs.
• the neuronal pentraxin NARP and NP1 are enriched at excitatory synapses and interact directly with all of the four AMPAR subunits inducing AMPARs surface clustering. NARP and NP1 could thus act as AMPARs stabilizing extracellular factors.
• LTP at the dentate gyrus is independent of CaMKII activity. Also, CaMKII activity is not necessary for LTP early in development at the CA1 region. Further studies will be necessary to determine whether other kinases known to be important for LTP induction, such as PKA, PI3-K, PKC, and MAPK, also trigger AMPAR immobilization.
• Our findings thus raise the possibility that during LTP, CaMKII activation triggers both classical LTP and PPD. It is interesting to note that LTP is frequently accompanied by a decrease in paired-pulse facilitation (PPF)

---

Zac Email
Here are the different labeling techniques that might be applicable with recombinant expression of AMPARs. Roughly ranked from most to least likely to succeed, separated by large vs small AMPAR N-terminal additions. The references in parentheses are for background on the technique.

Large AMPAR N-terminal addition

• Halotag (Promega): AMPAR-enzyme, QD-Halotag substrate
• AMPAR-streptavidin, QD-biotin

Small AMPAR N-terminal addition

• AMPAR-FLAG, QD-anti-FLAG
• AMPAR-biotin ligase recognition peptide, QD-biotin + biotin ligase (Lu Ting 2013 PLOSONE)
• AMPAR-peptide A, QD-peptide B, which binds peptide A (Zhang Kodadek 2000 NatBiotech)
• AMPAR-unnatural amino acid azide, QD-propargyl (Chaterjee Schultz 2013 PNAS)

---

Choquet 2010 CaMKII triggers the diffusional trapping of surface AMPARs through phosphorylation of stargazin

• NMDAR activation promotes rapid translocation of aCaMKII::GFP to synapses, causing AMPAR trapping at 1 min (only synapses with CaMKII translocation)
• tCaMKII (active prion) promotes immobilization of endogenous GluR1 (containing) AMPARs (both synaptic and extrasynaptic), and to a much lesser extent GluA2 (containing) AMPARs.
• CaMKII direct phosphorylation of AMPARs unnecessary for synaptic trapping
• GluA1 - SAP97 interaction unnecessary for CaMKII-dependent synaptic trapping
• Stargazin increased tCaMKII-mediated trapping of recombinant GluA1 (homomeric), but tCaMKII had no effect on mobility of recombinant GluA2 (homomeric)
• Stargazin phosphorylation (by tCaMKII) is necessary for GluA1 trapping; blocking phosphorylation caused AMPAR mobility to significantly increase.
• intriguing finding: GluA1 subunit-specific effect of CaMKII, where it immobilizes recombinant GluA1 but not GluA2 homomeric AMPARs.
• findings consistent with specific role of GluA1 in activity-dependent trafficking - but Stargazin can bind all subunits??
• findings raise possibility that during LTP, CaMKII activation triggers both classical LTP and PPD. Interesting that LTP is frequently accompanied by PPD (opposite of PPF: paired-pulse facilitation)

---

Experiments

2000

Hayashi, Shi, Esteban, Piccini, Poncer, Malinow • 2000 • Science - PDF

Driving AMPA Receptors into Synapses by LTP and CaMKII: Requirement for GluR1 and PDZ Domain Interaction

Expand to view experiment summary

---

ABSTRACT

• AMPARs with an electrophysiological tag were expressed in rat hippocampal neurons.
• Long-term potentiation (LTP) or increased activity of CaMKII induced delivery of tagged AMPARs into synapses.
• mutating GluR1's CaMKII p-site had no effects on synaptic delivery (Choquet 2010 confirms this)
• mutating GluR1's PDZ domain blocked delivery (NB: Choquet 2010 found opposite ^NOTE: {{#info:In their 2010 study Choquet coexpressed tCaMKII with HA:GluA1 lacking the PDZ-binding domain (HA-GluA1D7) and found that HA-GluA1D7 mobility at synapses was strongly reduced by tCaMKII - their measure was diffusion rate and synaptic trapping, which to me is a weaker measure than Malinow's methods which directly test the functional electrophysiological properties conferred to the synapse by GluR1}})
• results show LTP and CaMKII activity drive GluR1 to synapses by mechanism requiring GluR1 and PDZ proteins

FINDINGS

Does tCaMKII-GFP enhanced transmission?

• To examine effect of elevated CaMKII activity, we used tCaMKII-GFP.
  • Genes delivered to neurons in organotypically cultured hippocampal slices, using Sindbis virus
  • * Neurons expressing trans-genes identified by GFP and whole-cell recordings.
• expression of construct increased tCaMKII activity in BHK cells.
• In neurons expressing construct GFP was detected in dendritic arbors and spines.
• we measured synaptic responses in two nearby neurons, one with tCaMKII-GFP one WT.
• tCaMKII-GFP enhanced synaptic transmission but with no effect on rectification

Does tCaMKII enhance transmission via GluR1?

• We used ephys assay to examine if increase in AMPAR-mediated transmission was due to delivery of receptors to synapses.
  • The current-voltage (I-V) relationship of AMPARs is determined by GluR2 subunit (GluR2 has linear I-V relations; GluR1 homomerics are rectified at 140 mV). Most AMPARs in hippocampal pyramidal cells contain the GluR2 subunit.
• We overexpressed GluR1::GFP subunit in hippocampal slice neurons. Most resulting recombinant AMPARs lacked GluR2. Recombinants were functional and showed complete inward rectification in HEK293 cells. Thus, incorporation of these recombinant receptors into synapses would be expected to increase rectification of synaptic responses.

Can CaMKII activity drive recombinant GluR1-GFP into synapses?

• GluR1-GFP is widely distributed throughout dendritic arbors, but little is incorporated into synapses in the absence of activity.
  • In agreement with this, expression of GluR1-GFP had no effect on amplitude or rectification.
• To determine if CaMKII activity could drive recombinant GluR1-GFP into synapses,
  • we coexpressed GluR1-GFP and tCaMKII using an internal ribosomal entry site (IRES) construct.
  • BHK cells expressing this construct showed increased constitutive tCaMKII activity, and slices expressing this construct showed GluR1-GFP expression.
• Pairwise recordings comparing infected and noninfected cells showed infected cells had enhanced transmission, due to increase of tCaMKII activity.
• Notably, infected cell transmission showed increased rectification, indicating a contribution of the homomeric GluR1-GFP to transmission.
• This effect on rectification was due to coexpression of the two proteins, because transmission onto cells expressing either tCaMKII or GluR1-GFP alone had rectification comparable to that in uninfected cells.
• These results show that tCaMKII activity induces the insertion of homomeric GluR1-GFP into the synapse.

Is GluR1 phosphorylation by CaMKII sufficient to drive LTP?

• GluR1 is phosphorylated by CaMKII at Ser831 during LTP.
• To examine if direct phosphorylation of the receptor at this site is required for delivery, we substituted Ser831 with Ala, thus creating GluR1(S831A)-GFP.
• This mutation did not block delivery.
• Expression of this construct alone changed neither amplitude nor rectification, and coexpression with tCaMKII produced potentiated transmission that showed the same increase in rectification as that seen with GluR1-GFP-IREStCaMKII

Is the TGL (PDZ consensus) sequence of GluR1 important for its synaptic trafficking/rectification when tCaMKII is overexpressed

• The subcellular localization of many membrane proteins is controlled by associations with a class of proteins containing PDZ domains.
• In particular, the cytosolic C-terminus of such surface proteins has a PDZ sequence that when mutated prevents associations. GluR1 has this C-terminus TGL consensus sequence.
• We mutated the GluR1 sequence from TGL to AGL, creating GluR1(T887A)-GFP
• GluR1(T887A)-GFP expressed in HEK293 cells formed functional AMPARs with normal rectification.
• GluR1(T887A)-GFP expressed in hippocampal neurons was detected in dendrites, and showed no effect on transmission when expressed alone.
• Coexpression of GluR1(T887A)-GFP and tCaMKII in hippocampal slice neurons completely blocked synaptic response amplitude and rectification, and in fact depressed transmission

Does evoked LTP do the same thing as tCaMKII overexpression, in mediating GluR1 synaptic trafficking/rectification?

• To determine if LTP delivers AMPARs to synapses through a similar mechanism, we examined LTP in cells expressing GluR1-GFP.
• Whole-cell recordings were obtained from cells expressing or not expressing GluR1-GFP.
• LTP was induced with a pairing protocol.
• We added APV to the bath 30 min after potentiated transmission was measured, in order to isolate pure AMPAR–mediated responses.
• The holding membrane potential was then switched to measure rectification of the AMPAR–mediated responses.
• Similar to the effect of coexpressed CaMKII and GluR1-GFP, rectification was increased after LTP in cells expressing GluR1-GFP compared to cells not expressing GluR1-GFP.

Is the PDZ GluR1 binding domain important during LTP?

• We next examined the effects of GluR1(T887A) on LTP.
• As shown above, GluR1(T887A) has a mutated PDZ-interaction site that completely blocks the potentiation produced by tCaMKII.
• we recorded synaptic responses from cells expressing either GluR1-GFP or GluR1(T887A)-GFP.
• After LTP pairing, GluR1-GFP control cells displayed stable potentiation lasting at least 50 min.
• GluR1(T887A)-GFP cells displayed a very different response after LTP pairing: these cells had short-lasting potentiation that decayed over 20 min and after 45 min the responses were significantly depressed from baseline.
• In 4 of the 21 experiments with GluR1(T887A), a control (nonpaired) pathway was monitored, which did not show depression

SUMMARY

• Here, we generated electrophysiologically tagged receptors to monitor their synaptic delivery during LTP and increased tCaMKII. In the absence of plasticity-inducing stimuli, we saw no evidence for their contribution to transmission (consistent with previous results indicating that in the absence of evoked activity, GluR1 is retained within the dendrite).
• Upon coexpression with constitutively active tCaMKII or following LTP induction, we see that tagged receptors contribute to transmission, indicating their delivery to synapses.
• Previous studies indicate that LTP induction increases the CaMKII-dependent phosphorylation of GluR1 at Ser831. Although such phosphorylaton may enhance the function of synaptic receptors, this phosphorylation does not seem to be required for receptor delivery: tCaMKII can deliver GluR1(S831A)-GFP to the synapse. Our results indicate that some protein(s) other than GluR1 must be substrate(s) of CaMKII (Choquet: Stargazin) and participate in the regulated synaptic delivery of AMPARs.
• The most surprising of our results relate to the effects of GluR1(T887A). This protein forms functional receptors and has no detectable effects on basal synaptic transmission. However, this mutant receptor can block the effects of tCaMKII and LTP (24). This has several implications:

1. It reinforces the view that CaMKII and LTP act through similar mechanisms.
2. It indicates that both CaMKII-potentiation and LTP exert their effects through GluR1
3. It indicates that an interaction between GluR1 and a protein with a PDZ domain plays a key intermediate in these forms of plasticity
4. GluR1(T887A) depresses transmission, but only after increased CaMKII or LTP.

• This last finding suggests that activity enables the mutant protein to interrupt a constitutive delivery of endogenous AMPA-Rs (25).

“

These results demonstrate that incorporation of GluR1-containing AMPA-Rs into synapses is a major mechanism underlying the plasticity produced by activation of CaMKII and LTP. This process requires phosphorylation of protein(s) other than GluR1. Furthermore, this delivery requires interactions between the COOH-terminus of GluR1 and PDZ domain proteins.

”

2009

Kessels, Kopec, Klein, Malinow • 2009 • Nat Neurosci. - PDF

Roles of stargazin and phosphorylation in the control of AMPA receptor subcellular distribution

Expand to view experiment summary

---

Abstract

• Understanding how the subcellular fate of newly synthesized AMPA receptors (AMPARs) is controlled is important for elucidating the mechanisms of neuronal function. We examined the effect of increased synthesis of AMPAR subunits on their subcellular distribution in rat hippocampal neurons. Virally expressed AMPAR subunits (GluR1 or GluR2) accumulated in cell bodies and replaced endogenous dendritic AMPAR with little effect on total dendritic amounts and caused no change in synaptic transmission. Coexpressing stargazin (STG) or mimicking GluR1 phosphorylation enhanced dendritic GluR1 levels by protecting GluR1 from lysosomal degradation. However, STG interaction or GluR1 phosphorylation did not increase surface or synaptic GluR1 levels. Unlike GluR1, STG did not protect GluR2 from lysosomal degradation or increase dendritic GluR2 levels. In general, AMPAR surface levels, and not intracellular amounts, correlated strongly with synaptic levels. Our results suggest that AMPAR surface expression, but not its intracellular production or accumulation, is critical for regulating synaptic transmission.

• Coexpressing stargazin (STG) or mimicking GluR1 phosphorylation enhanced dendritic GluR1 levels by protecting GluR1 from lysosomal degradation. However, STG interaction or GluR1 phosphorylation did not increase surface or synaptic GluR1 levels. Unlike GluR1, STG did not protect GluR2 from lysosomal degradation or increase dendritic GluR2 levels

Overexpressed GluR1 accumulates in cell bodies, not in dendrites

• Our data indicate that the subcellular distribution is differently controlled for GluR1- and GluR2-containing AMPARs and depends on their phosphorylation status and interaction with STG. Notably, we did not find any correlation between the dendritic accumulation of AMPAR subunits and synaptic strength, indicating that merely increasing dendritic levels of AMPARs is not sufficient to generate synaptic plasticity.
• After 2.5 d of expression, the intensity of GluR1 staining in infected cell bodies was 8-fold greater than that of nearby uninfected cell bodies. In contrast, dendritic levels of GluR1 in infected cells were only moderately (1.6x) higher than neighboring uninfected neurons.
• This was not because recombinant GluR1 couldn't make it to the dendrite, when only examining recombinant GluR1, there wasn't a large difference between body and tail, suggesting that dendrites actively control total AMPAR expression.
• Overexpression of GluR1 was not accompanied by an increase of GluR2 in dendrites, consistent with the finding that (over-expressing) dendritic GluR1 subunits predominantly represents a pool of homomeric receptors.
• total dendritic levels of GluR1 were on average increased by 64% by GluR1 infection
• the ratio of recombinant (homomeric) to endogenous GluR1 in dendrites was ~2:1 (1.8 to 1 after 2–3 d of infection).

Phosphorylation drives GluR1 accumulation in dendrites

• Phosphorylation events on the C tail of GluR1 have been shown to influence their synaptic trafficking; here, we examined whether they also affect the total dendritic levels of GluR1.
• GluR1 can be phosphorylated on serine 818 (S818) by PKC, on S831 by CaMKII or PKC, and on S845 by PKA {{#info:The PKA phosphorylation of Stargazin C terminus prevents Stargazin binding to PSD-95 (Chetkovich et al., 2002)}}. All of these activity-dependent phosphorylation events are most strongly mimicked by substituting these residues with aspartates (GluR14D).
• Viral expression of GluR14D resulted in a significant increase in dendritic GluR1 levels compared with wild-type GluR1

GluR1 can reach dendrites, but are targeted to lysosomes unless phosphorylated at C-tail

• We examined whether the mechanisms controlling dendritic GluR1 levels could involve regulated targeting to lysosomes, which has been shown to affect AMPAR levels
• When we blocked lysosomal degradation in GFP-GluR1–infected hippocampal slices (chloroquin and leupeptin) for 4 h, the amount of GluR1 in the dendrites was increased (2.76x) and approached that of dendrites infected with GFP-GluR14D
• These data suggest that overexpressed GluR1-containing receptors can reach dendritic areas, but they are targeted to lysosomes unless they are phosphorylated at their C tail.

Stargazin interaction drives GluR1 accumulation in dendrites

• To test whether increased amounts of TARPs would enhance GluR1 levels in dendritic compartments, we generated Sindbis viruses that expressed Stargazin with either GFP or GFP-GluR1.
• Cells overexpressing both GFP-GluR1 and Stargazin had a significant decrease in somatic levels of GluR1 (4x with Stargazin 8x without)
• On the other hand, dendritic GluR1 levels were increased compared with cells overexpressing GluR1 alone (2.7x with Stargazin vs 1.6x without)
• Overexpression of Stargazin with GFP did not alter the amounts of endogenous GluR1 present in dendrites suggesting that the expression levels of endogenous GluR1 subunits and TARPs are balanced under physiological conditions.
• STG can be phosphorylated at nine different serine residues in its cytoplasmic tail by PKC and/or CaMKII, and the majority of endogenous TARPs are phosphorylated under basal conditions. Because overexpressed recombinant STG remains mainly nonphosphorylated, we evaluated whether STG phosphorylation affects dendritic GluR1 levels by expressing a phospho-mimetic STG (STG9D).
• Coexpression of GluR14D and STG9D did not give an additive effect to the total dendritic GluR1 levels compared with expression of GluR14D or coexpression of GluR1 and STG9D
• We reasoned that both the GluR1-STG interaction and GluR1 phosphorylation use overlapping mechanisms to increase dendritic GluR1 levels. Indeed, binding STG also protected GluR1 from lysosomal targeting.

STG interaction does not drive GluR1 surface expression

• To examine whether interaction with STG stabilized dendritic GluR1 by translocating AMPARs to the cell surface, we measured the amount of surface GluR1 in STG-infected CA1 neurons relative to uninfected neighbors.
• We were surprised to find that expression of STG did not elevate endogenous GluR1 levels on the surface of dendrites or cell bodies as antibody staining and biotinylation assays have previously shown STG to increase recombinant AMPAR levels on the surface of heterologous cells.
• Furthermore, overexpression of both STG and GFP-GluR1 led to a modest increase in GluR1 subunits on the cell surface, which was indistinguishable to expression of GFP-GluR1 alone
• Additional phosphorylation events (that is, coexpression of GluR1-4D and STG-9D) also had no effect on GluR1 surface expression
• To further examine surface AMPAR levels, we obtained paired whole-cell recordings from neighboring infected and uninfected neurons and measured the currents induced by bath application of AMPA.
• As has been previously shown, AMPA-induced currents were substantially increased in neurons overexpressing STG34.
• To determine the proportion of these increased currents that results from preventing AMPAR channel desensitization by STG we repeated these experiments in the presence of drugs that block desensitization for both the flip (cyclothiazide) and flop (PEPA) AMPAR isoforms (the flop isoform is dominant in CA1 neurons).
• In the presence of these drugs, AMPA-evoked currents in infected cells were very similar to those in neighboring uninfected cells, confirming that STG primarily enhances bath-applied AMPA responses in hippocampal neurons by reducing AMPAR desensitization ^FIG: {{#info: Simultaneous whole-cell recordings of infected (black) and neighboring uninfected (gray) cells showed that overexpression of STG or GFP-GluR1 and STG markedly increased responses to bath-applied AMPA. (f) Neurons expressing STG or GFP-GluR1 and STG did not show changes in their responses to bath-applied AMPA in the presence of the desensitization blockers cyclothiazide and PEPA CLICK AWAY FROM IMAGE TO CLOSE }}
• Furthermore, AMPA-evoked responses were also left unchanged when we overexpressed both GFP-GluR1 and STG, suggesting that even with an excess of GluR1, the STG interaction is not sufficient to drive GluR1 to the surface.
• These data indicate that enlarging the dendritic intracellular GluR1 pool does not lead to an enhancement of surface GluR1.

Simultaneous whole-cell recordings of infected (black) and neighboring uninfected (gray) cells showed that overexpression of STG or GFP-GluR1 and STG markedly increased responses to bath-applied AMPA. (f) Neurons expressing STG or GFP-GluR1 and STG did not show changes in their responses to bath-applied AMPA in the presence of the desensitization blockers cyclothiazide and PEPA

2007

Kopec, Real, Kessels, Malinow • 2007 • J Neuro - PDF

GluR1 links structural and functional plasticity at excitatory synapses

Expand to view experiment summary

---

Abstract

• Long-term potentiation (LTP), a cellular model of learning and memory, produces both an enhancement of synaptic function and an increase in the size of the associated dendritic spine. Synaptic insertion of AMPA receptors is known to play an important role in mediating the increase in synaptic strength during LTP, whereas the role of AMPA receptor trafficking in structural changes remains unexplored. Here, we examine how the cell maintains the correlation between spine size and synapse strength during LTP. We found that cells exploit an elegant solution by linking both processes to a single molecule: the AMPA-type glutamate receptor subunit 1 (GluR1). Synaptic insertion of GluR1 is required to permit a stable increase in spine size, both in hippocampal slice cultures and in vivo. Synaptic insertion of GluR1 is not sufficient to drive structural plasticity. Although crucial to the expression of LTP, the ion channel function of GluR1 is not required for the LTP-driven spine size enhancement. Remarkably, a recombinant cytosolic C-terminal fragment (C-tail) of GluR1 is driven to the postsynaptic density after an LTP stimulus, and the synaptic incorporation of this isolated GluR1 C-tail is sufficient to permit spine enlargement even when postsynaptic exocytosis of endogenous GluR1 is blocked. We conclude that during plasticity, synaptic insertion of GluR1 has two functions: the established role of increasing synaptic strength via its ligand-gated ion channel, and a novel role through the structurally stabilizing effect of its C terminus that permits an increase in spine size.

Other Studies

Shouval HZ • 2005 • PNAS - PDF

Clusters of interacting receptors can stabilize synaptic efficacies

Expand to view experiment summary

---

Other papers by Shouval

• A network of spiking neurons that can represent interval timing
• Translational switch for long-term maintenance of synaptic plasticity

Abstract

“ Synaptic weights store memories that can last a lifetime. Yet, memory depends on synaptic protein receptors that are recycled in and out of the membrane at a fast rate, possibly several times an hour. Several proposals to bridge this vast gap in time scales between memory and its molecular substrate have relied on bistable molecular switches. Here, we propose an alternative to this approach based on clusters of interacting receptors in the synaptic membrane. We show that such clusters can be metastable and that the lifetime of such clusters can be many orders of magnitude larger than the lifetime of the receptors of which they are composed. We also demonstrate how bidirectional synaptic plasticity can be implemented in this framework. ”

Introduction

• In this paper, I propose a theory in which the stability of synaptic efficacies is based on local interactions between receptors within a single synapse. Specifically, I propose that interactions between receptors within a cluster can alter the trafficking of receptors in and out of the synaptic membrane, thereby creating a metastable synaptic state that significantly increases the stability of synaptic efficacy without changing the mean dwell time of receptors in the synaptic membrane.
• Statistical fluctuations in the number of receptors are a signature of this model that might be used to distinguish it from other synaptic models.

Mathematical Methods

The variable S_ij is an occupation variable of the lattice site denoted by indices i and j. If the site is occupied, S_ij = 1; otherwise, S_ij = 0. Insertion of a new receptor into the membrane can occur at any unoccupied site in the lattice, and internalization of a receptor can occur only at occupied sites. In this formulation, internalization occurs at a fixed rate, independent of interaction with other receptors. I used a fixed internalization rate μ = 1 ⁄ τ_in in per unit time, which implies that the probability of internalizing a receptor at site S_{i, j} in a small time step Δt is:

Internalization Probability

Pⁱⁿ( i,j, t:t + Δ_t ) = S_ijμΔ_t

• This equation is simply saying that the probability Pⁱⁿ of a receptor internalizing from site S with coordinates i,j at time-point t:t plus the elapsed time Δ_t is equal to the occupation state of that lattice site S_ij (1 or 0) × an internalization rate constant μ = 1⁄τ_in × a small time step Δ_t
• In a nutshell the probability of a receptor internalizing is equal to the internalization rate constant per unit time. Since S and μ = 1 and the step size for Δ_time = .01 then a receptor internalizes once every 100 steps

Parameter        Typical Values        Description

S_i,j              1,0                    Lattice State

μ              1              μ = 1 ⁄ τ_in        Internalization Constant

τ_in              1              τin = 1 ⁄ μ        Dwell Time

Δt              .01                          Time Step

h_k              0,1,2,3,4                    Field

L₁              1.5                    Lattice Repulsion

β              50                    Slope Constant

ρ              0.95                    Position Probability

r              10                     Transition Rate

• Throughout this paper, we use μ = 1, which implies that the mean dwell time of a receptor in the membrane is 1 unit of time. Typically, we use a time step δt less than 0.01, which is significantly smaller than the other time constants in this system.
• Inserting a new receptor into an unoccupied site depends on the occupation in the vicinity of the unoccupied site. I calculate a "field" h_k(i, j) at each unoccupied site i,j that measures the number of membrane-embedded receptors in the local neighborhood.
• The field h_k will determine the conditional probability of inserting a new receptor into an unoccupied site. A typical parameter used in our simulations is L1 equals 1.5; however, similar stability is obtained for the range of L1 equals 1.2 to 2.0. The lattice repulsion constant used for the second population is L2 equals 0.9

• L = lattice repulsion constant

• To determine insertion probability we use

Insertion Probability

P_k(i,j) = 1 / ( 1 + exp( -βh_k(i,j) ) )

• This equation is saying the probability of insertion varies smoothly from 0 to 1 as a function of the field h
• (1 / (1 - e(-50×1.5))) × 100_steps = .5
• (1 / (1 - e(-50×2.5))) × 100_steps = .3
• ^FIG: {{#info: (A) Receptors in the synapse are internalized stochastically at a constant rate, and their probability of staying in the synapse decays exponentially with a time constant of 1. CLICK AWAY FROM IMAGE TO CLOSE }} ^FIG: {{#info: (B) The rate of insertion depends of the number of nearest neighbors. Given the occupation state (Left), a field is calculated (Right). The probability of inserting a new receptor is proportional to this field. The field can be computed from convolving the nearest-neighbor function (Center) with the state. The field is higher within the cluster and close to its boundaries than outside the cluster or near the isolated receptor. CLICK AWAY FROM IMAGE TO CLOSE }}

• which varies smoothly from 0 to 1 as a function of hk(i, j). The constant β is the slope of this function. Stability increases for larger β. I typically use β equals 50. However, values of β greater than 25 are sufficient for stability of up to about 1,000 time steps, with 49 receptors in the initial state. This stability depends on other parameters, such as L1. The probability of inserting a receptor in an unoccupied site in a very small time step Δt is then

P_k^ex(i,j) = (1 - S_ij)(ρ_k r Δt P_k(i,j))

• .95 * 10 * .01 * P(1) ≈ .1

• where ρ_k is the probability that a receptor of type k is present in a position near the empty site, and r is the rate of transition into the empty site. Typically, we use ρ₁ equals 0.95 and r equals 10, which implies that for P_k about 1, the average time for inserting a receptor into a vacant site with a high h_k is about 0.1 units of time, significantly faster than the internalization rate and slower than the typical time step used.
• The key to stability is not the identity of specific parameters, such as Pk and r, but their consequence that the characteristic time for insertion into an empty site in a cluster is much shorter than the characteristic time of removing a receptor from a cluster. To reduce run time, we use parallel dynamics. The use of parallel dynamics is not a problem because we use small time steps in which a very small number of events occur across the whole lattice. I ran a few random sequential simulations and obtained indistinguishable results.

Results

• The cluster theory of synaptic stability is based on several assumptions:

• (i) Synaptic efficacy is proportional to the number of postsynaptic AMPA receptors.
• (ii) Receptors in the postsynaptic density are clustered.
• (iii) The insertion rate of a receptor in the vicinity of other receptors in the cluster is much higher than for an isolated receptor.
• (iv) The rate of receptor removal from the cluster is independent of interactions with other receptors in the cluster.

• Assumptions i–iii are essential assumptions of this model, whereas assumption iv could be altered while preserving the main features of the model. It is important to note that the insertion rate (on rate) and removal rate (off rate) are controlled independently and not governed by a single parameter, which is important for the robust functioning of the model and makes it formally distinct from an Ising spin model of statistical physics

USEFUL PROBABILITY EQUATIONS

---

• ----------------------------------------*
• PROBABILITY OF EXACT SEQUENCE (e.g. HHHHTT)
• ----------------------------------------*
• P(x) = (p^k) * ((1-p)^(n-k))
• ----------------------------------------*

• ----------------------------------------*
• BINOMIAL RANDOM VARIABLE (BERNULLI)
• ANY SEQUENCE (e.g. 'K' HEADS IN 'N' FLIPS)
• ----------------------------------------*
• P(x) = choose(n,k) * (p^k) * ((1-p)^(n-k))
• ----------------------------------------*

• ----------------------------------------*
• BAYES
• ----------------------------------------*
• P(A¡B) = P(B¡A)*P(A) / [P(B¡A)*P(A) + P(B¡~A)*P(~A)]
• ----------------------------------------*
• Bayes theorem is used for testing conditional probabilities when we know the
• probability of the occurence of event A, and the probability of the occurence
• of event B given that event A has already occurred.
• ----------------------------------------*

• ----------------------------------------*
• PMF Probability Mass Function
• ----------------------------------------*
• PMF is for descrete non-continuous variables
• PMF is a general case for Bernoulli, and can be used for Bernoulli
• The PMF for the variable X is denoted px
• If x is any possible value of X, px(x) = P({X = x})
• ----------------------------------------*
• PMF = [(factorial(n)) / ( (factorial(*a) * factorial(*b) * factorial(*c) )] *
• [P(A^*a) * P(B^*b) * P(C^*c)]
• given that x is a single observation from set X
• where n is total number of x sampled from set X
• where *a is x observations from group A of set X
• where *b is x observations from group B of set X
• where *c is x observations from group C of set X
• where P(A^*a) is the probability of group A to the *a
• where P(B^*b) is the probability of group B to the *b
• where P(C^*c) is the probability of group C to the *c
• and so forth for {A,B,C,...}
• ----------------------------------------*

• ----------------------------------------*
• Geometric Random Variable (GRV)
• ----------------------------------------*
• The GRV is the number of X coin tosses needed for a head to come up for the first time
• defined as px(k) = the probability of x for the k-ith toss
• ----------------------------------------*
• px(k) = ((1-p)^(k-1)) * p
• where p is the probability of flipping Heads on a coin
• where x is the event of getting a Heads
• where k is the number of flips
• where 1-p is the probability of Tails
• where k is the number of flips up to, and including, the first success
• ----------------------------------------*

• ----------------------------------------*
• Poisson Random Variable (PRV)
• ----------------------------------------*
• Use Poisson to calculate PMF when P is really small and N is really big
• ----------------------------------------*
• (exp(-(n*p))) * (((n*p)^k) / factorial(k))
• where n = the number of trials
• where p = probability of H
• where k = the number of successful hits of H
• ----------------------------------------*

• S_ij
• S_ij = x²
• √1 − e²
• −
• ±
• ×
• ÷
• ⁄

}}

Scholarpedia Entry

---

• Link to Article

One possible mechanism that can stabilize synaptic efficacies is a bi-stable molecular switch. Suppose we have a kinase that is active when phosphorylated, and can auto-phosphorylate itself ( <figref>AutoPhospho.jpg</figref>a). If these kinases interact with neighboring kinase (cooperativity), the resultant protein complex can exhibit an interesting property.This kinase complex, when brought into a fully activated (phosphorylated) state by a strong synaptic stimulus, can sustain its active state via cooperative interaction. If one of the kinase subunit is dephosphorylated by phosphatase or is replaced by an unphosphorylated kinase, the neighboring active kinase in the complex will quickly turn this subunit into an active state. Thus, the protein complex can sustain its active state (memory). If, on the other hand, the incoming signal is weak, phosphatase quickly brings the protein complex back to the basal state. Thus, this protein complex has two states (“bi-stable”).

a. Schematic diagram of an autophosphorylation loop. Here it is assumed that phosphorylation activates the kinase, this active kinase in turn phosphorylates more of the yet unphosphorylated kinase. This kinase can be dephosphorylated by a phosphatase. b. One candidate molecule for an autophosphorylation loop is CaMKII. Twelve CaMKII molecules assemble into a holoenzyme composed of two hexameric rings of CaMKII subunits. Autophosphorylation of CaMKII takes place in a cooperative manner when two Ca2+-CaM molecules are bound to two neighboring subunits.

One candidate of such a postsynaptic kinase complex is CaMKII (Ca²⁺/CaM dependent protein kinase II). In fact, CaMKII is the key regulator of LTP induction (E-LTP) and appears to have a capacity to fulfill the requirement for bi-stable memory molecule (Lisman and Goldring, 1988). Twelve CaMKII subunits assemble into a holoenzyme structure composed of two hexameric rings of subunits (Lisman et al., 2002, Bradshaw et al. 2003). CaMKII molecules are activated by binding Ca²⁺-CaM. Autophosphorylation of CaMKII takes place in a cooperative manner when two Ca²⁺-CaM molecules are bound to two neighboring subunits in the CaMKII holoenzyme. For this reason, CaMKII requires a non-zero level of signal (Ca²⁺-CaM) to maintain its activity.

Several modeling and experimental studies were conducted to test if CaMKII can serve in fact as a bi-stable switch. Some mathematical models exhibit bi-stability (Lisman and Zhabotinsky, 2001; Miller et al., 2005), while other modeling study shows no bi-stability (Kubota and Bower, 2003). Some of the kinetic parameters of the CaMKII system are still unknown and the topology of kinetic pathways in these two modeling studies differs significantly. Further computational and theoretical works are required to determine if CaMKII autophosphorylation in fact allows bi-stability or it is robustly monostable (regardless of parameter values). On the experimental side, a systematic study was conducted by Bradshaw and co-workers to test the bistability of CaMKII-protein phosphatase system. This in vitro study, however, shows no sign of bi-stability but the CaMKII-protein phosphatase system responds sharply to Ca²⁺ signals (ultra-sensitivity) (Bradshaw et al. 2003). It is extremely important to note that switching on (phosphorylation) and off (dephosphorylation) of CaMKII must NEVER be confused with bi-stability. Although an in vitro assay differs significantly from the intra-cellular environment, the bi-stability of CaMKII in vivo has not yet been demonstrated either.

Another potential problem of CaMKII hypothesis is, while the importance of CaMKII for the induction of LTP is well-established (Lisman et al., 2002), its role in the maintenance of L-LTP or long-term memory is still unclear. Several experimental studies (Malinow et al., 1989, Otmakhov et al., 1997) show that blocking CaMKII activity during the maintenance phase did not influence L-LTP. In addition, the putative target of CaMKII, AMPA receptor, seems only transiently phosphorylated after protocols that induce long-term memory (Whitlock et al., 2006).

Protein synthesis and bi-stability of a translational switch

Error creating thumbnail: File missing

A bi-stable translational switch. A schematic diagram of a positive feedback loop between translation and a translation factor X. Such positive feedback might result in bi-stability.

A detailed model for the protein synthesis dependent induction of L-LTP has been proposed by Smolen et al. (2006). However, this model does not propose a mechanism as to how L-LTP can persist beyond the characteristic time scales of the different molecules within the network.

An intriguing possibility is that a biochemical network involving protein synthesis may serve as a bi-stable switch and contribute to synaptic stability (Blitzer et al., 2005). In fact, the molecular machinery needed for protein translation resides in dendrites (even in spines) and an increasing number of experimental studies support the notion that dendritic protein synthesis is required for many forms of long-term plasticity (Sutton and Schuman, 2006). This localized protein translation could then account for the synapse specificity of plasticity. <figref>TranslationLoop_simp.jpg</figref> shows a (hypothetical) positive feedback loop involving translation factor(s) and a kinase protein X. In this diagram, the translation factor(s) regulates the translation of a specific kinase X, which in turn up-regulates the activity of the same translation factor(s). A biochemical network with such a positive feedback loop could exhibit bi-stability.

A potential problem for this hypothesis is that inhibitors of translation inhibit L-LTP (and L-LTD) only when applied before or shortly after stimulation. A widely accepted interpretation of this finding is that protein synthesis is important only during the induction (or consolidation) phase of plasticity. However, a bi-stable feedback loop of a translation factor and a protein kinase (if it exists) may respond differentially to the inhibitors during induction phase and during maintenance phase.

The cluster model

The cluster theory of synaptic stability. a. Receptors in the PSD tend to cluster. When a receptor is removed due to turn over or trafficking, it is rapidly replaced by another receptor. b. A simulation of a 7x7 cluster of receptors shown at times t=0, 50 and 100 (one time unit corresponds to the average dwell-time of a receptor). While receptors are rapidly removed and replaced, the cluster remains stable. c. The number of receptors as a function of time in two independent simulations.

The cluster theory of synaptic stability (Shouval, 2005) offers an alternative account for the synaptic stability. This theory is based on several assumptions: (1) Synaptic efficacy is proportional to the number of postsynaptic receptors, for example AMPA receptors. (2) Receptors in the postsynaptic density are clustered. (3) The insertion rate of a receptor in the vicinity of other receptors in the cluster is much higher than for an isolated receptor. (4) The rate of receptor removal from the cluster is independent of interactions with other receptors in the cluster. Assumptions 1-3 are essential assumptions of this model, while assumption 4 could be altered while preserving the main features of the model.

Simulations of such networks ( <figref>Cluster_Fig_tiff.jpg</figref>b, c) show that although individual receptors within a cluster are rapidly removed or replaced, the cluster itself remains stable for an extended period of time. The exact instantaneous number of receptors in a cluster fluctuates; however, the mean number of receptors remains stable for a time period much longer than the dwell-time of a single receptor. These receptor clusters are not in a stable steady state; instead they are in a metastable state and will eventually decay.

In fact, the life-time of clusters depends linearly on the dwell-time of receptors and increases steeply with the initial size of the cluster. For example, if we assume a receptor dwell-time of 20 minutes then the median life-time for a 9x9 cluster of receptors can be more than one year. As in other theoretical models, the experimental test of this hypothesis is necessary.

Other Notes

Lu W, Gray JA, Granger AJ, During MJ, Nicoll RA. • 2011 • J Neurophysiol - PDF

Potentiation of synaptic AMPA receptors induced by the deletion of NMDA receptors requires the GluA2 subunit

expand to view study notes

---

We thus tested whether GluA2 is required for the potentiation of AMPAR EPSCs following the loss of NMDARs. In the GRIA2fl/fl mice, deleting GluA2 caused about a 50% reduction in the AMPAR EPSC (Fig. 3A ) with no change in the glutamate-evoked AMPAR-mediated outside-out patch currents (B ). Interestingly, in GRIA2fl/flGRIN1fl/fl mice the loss of NMDARs failed to enhance AMPAR EPSCs. In fact, there was a significant further decrease (Fig. 3A ). Neither genetic manipulation changes the PPR. In addition, as expected, deletion of GluA2 caused strong inward rectification. Finally, no difference was found in glutamate evoked AMPAR-mediated currents in outside-out patches (Fig. 3B ). Therefore in contrast to GluA1, the GluA2 subunit is critical for synaptic potentiation of AMPARs following NMDAR deletion.

RANDOM NOTES

---

Proteins that interact with AMPARs

Error creating thumbnail: File missing

Song and Huganir 2002. Use of yeast two-hybrid method to identify AMPA-receptor-interacting proteins was crucial for the rapid progress in this field and has helped to identify a large complex of such proteins. AMPA-receptor-associated protein complex. AMPA receptors are associated with a large protein network. The GluR2 subunit specifically binds to several proteins, including glutamatereceptor- interacting protein (GRIP) 1, GRIP2, protein interacting with C kinase (PICK1) and N-ethylmaleimide sensitive factor (NSF). GRIP1, GRIP2 and PICK1 in turn bind to other proteins, including GRIP-associated proteins (GRASPs), EphB receptor tyrosine kinases, ephrins, kinesin 5 (KIF5) and protein kinase C-alpha (PKCa). The GluR1 subunit binds to synapse-associated protein 97 (SAP97, also known as hDLG) and protein 4.1 (4.1 N). All four AMPA-receptor subunits bind to neuronal activity regulated pentraxin (NARP) and stargazin. Stargazin binds, in turn, to the synaptic scaffolding protein postsynaptic density 95 (PSD95). These associated proteins appear to play an important role in the membrane trafficking of the receptors by escorting the receptor from the cell body to the synapse. In addition, this large complex might regulate novel downstream signal transduction pathways that emanate from the AMPA receptor. Abbreviations: CC, coiled-coil domain; GK, guanylate kinase domain; PDZ, PSD95/Dlg/ZO1 domain; SH3, SRC homology 3 domain.

Qdots

• Quantum Dots

• Qdot 655 Goat F(ab')2 Anti-Rabbit IgG Conjugate
• Qdot Methods Handbook
• Qdot Color Visualizer

Getting a Qdot into the cell

1. Conjugate Qdot with secondary antibody fab
2. Incubate tissue with primary antibodies for AMPAR and PSD95
3. Puff Qdots onto cell body, these will bind the primary at AMPAR N-terminus
4. When AMPARs internalize the Qdot will be dragged into cell
5. Cleave N-terminus of AMPAR to liberate Qdot
6. Qdot can then bind the primary ligated to PSD95

Notes

• Molecular Methods
• FLASH technology
• Bredt
• minisog - gfp
• Acidic basic polypeptide recognition sequences
• Talk with nanotech group about various ways to conj. Qdots
• Nichol and England - couple Qdot to AMPAR agonist
• Have simulation be a competitive model where AMPARs are competing during LTP
• Quantitative review on synaptic numbers (Sheng)

PALM STORM There are two major groups of methods for functional super-resolution microscopy:

1. Deterministic super-resolution: The most commonly used emitters in biological microscopy, fluorophores, show a nonlinear response to excitation, and this nonlinear response can be exploited to enhance resolution. These methods include STED, GSD, RESOLFT and SSIM.

2. Stochastical super-resolution PALM STORM: The chemical complexity of many molecular light sources gives them a complex temporal behaviour, which can be used to make several close-by fluorophores emit light at separate times and thereby become resolvable in time. These methods include SOFI and all single-molecule localization methods (SMLM) such as SPDM, SPDMphymod, PALM, FPALM, STORM and dSTORM.

NRSA

• Dominant negative PSD95

{{Article|AUTHORS|YEAR|JOURNAL - [http://domain.com/linktofile.pdf PDF]|PMID|TITLE}}
{{ExpandBox|Expand to view experiment summary|
}}<!-- END ARTICLE -->