Synapses pass information from one neuron to another, but their performance is idiosyncratic and unreliable. It was long recognised from electrophysiological work that isolating single synapses reveals variability that is otherwise lost when population responses are measured. In electrophysiology, separating out individual synaptic responses can be achieved either by reducing release probability (del Castillo and Katz, 1954), by blocking all other responses (McAllister and Stevens, 2000), using minimal stimulation (Isaac et al., 1996), or by examining connected pairs of neurons (Vyleta and Jonas, 2014) with time resolution in the ms range. In contrast, optical report of neurotransmission via membrane dyes (Griesinger et al., 2005), calcium dyes (Oertner et al., 2002; Enoki et al., 2009), voltage-sensing dyes (Popovic et al., 2015), or neurotransmitter-binding reporters offers direct spatial resolution of synaptic events, usually with a time resolution about two orders of magnitude slower. Quantitation and analysis of the relevant optical signals remain challenging (Helassa et al., 2018; James et al., 2019; Soares et al., 2019; Tagliatti et al., 2020).

Synapses are diverse in their composition and size (Jontes and Smith, 2000), and it is assumed that these architectural features correspond to functional differences (Cizeron et al., 2020). In principle, accessing individual synaptic inputs to a neuron in space and time should allow correlation of molecular architecture with functional synapse properties and thus reveal synaptic diversity, but functional properties might be less diverse than synapse composition itself (Farsi et al., 2021). Plastic changes in synaptic responses, generated by sensory input (Gambino et al., 2014), are widely taken to represent learning and memory mechanisms, and might in turn increase diversity. However, synaptic contributions to sensory selectivity seem to depend most on the number of synapses that are activated (Scholl et al., 2020) suggesting that synaptic diversity may not serve immediate functional purposes. Instead, because synapses use small numbers of molecules, and tend to operate randomly (Ribrault et al., 2011), diversity may be needed to provide robustness through redundancy. The lack of reliability of certain synapses may be key to their computational power and role in decision making (Evans et al., 2018). These observations strongly motivate us to develop and test optical tools that can report activity with individual synapse resolution, and with a dynamic range sufficient to discern different modes of release, and failures to release.

Early generations of genetically encoded reporters of glutamate release relied on FRET (Okumoto et al., 2005; Namiki et al., 2007). The advent of bright, single-wavelength genetically encoded reporters that directly bind neurotransmitters, such as iGluSnFR, have expanded the scope of optical report of synaptic transmission by speeding up report to the limit of optical microscopy (Marvin et al., 2013). This class of sensor has also been adapted to fold more quickly (using superfolder Green Fluorescent Protein (GFP)), and offer affinity and colour variants (Marvin et al., 2018; Helassa et al., 2018). For the purpose of subcellular targeting, the compact sensor architecture of iGluSnFR is easy to transplant into other host proteins.

Fusion of fluorescent probes to proteins with selective, organellar targeting allows their subcellular expression. For example, targeting of probes to synaptic vesicles (such as Synaptophluorin; Miesenböck et al., 1998) has enabled quantitation of vesicle fusion and recovery with high specificity (Balaji and Ryan, 2007; Chanaday and Kavalali, 2018) albeit with slow off-kinetics (Sankaranarayanan et al., 2000) compared to vesicle fusion. On the other hand, most genetically-encoded sensors of neurotransmitters themselves lack targeting to synaptic sites. Even when signals from the latter are localised to putative dendritic spines, the unknown proximity of the sensor to the release site introduces unwanted variability (Soares et al., 2019), because the glutamate signal diverges rapidly in space and time due to diffusion (Raghavachari and Lisman, 2004). Where spatial confinement of the reporter was achieved by exploiting targeting (Kim et al., 2020) or anatomy (Helassa et al., 2018; James et al., 2019; Duerst et al., 2020; Jensen et al., 2019; Mendonça et al., 2022), the quantitative improvement of the fluorescent report is striking.

Here, we report that fusion of iGluSnFR to the glutamate receptor auxiliary proteins γ-2 or γ-8 produces postsynaptic-targeted sensors with improved spatial resolution. The SnFR-γ2 reporter gives a high-contrast signal through enrichment at glutamatergic synapses, and surprisingly also shows more stable activity. Spontaneous and evoked release can be resolved at up to 5 Hz, allowing dissection of the presynaptic contribution to short-term plasticity and release parameters in cultured neurons at the single synapse level.

Several groups have pursued mutagenesis of the Glt1 glutamate-binding domain to reduce the affinity of iGluSnFR for glutamate (Helassa et al., 2018; Marvin et al., 2018), with aim of improving its spatial and temporal resolution. We examined several mutants using patch-clamp fluorometry (Figure 1—figure supplement 1). A particularly interesting mutant that has not been previously reported is the Y230F substitution that removes a hydroxyl group that coordinates glutamate in the Glt1 domain. This mutation increased ∆F/F, the fractional fluorescence change upon glutamate binding (possibly by reducing fluorescence in the absence of glutamate), and sped the kinetics of the off-response (Figure 1—figure supplement 1). As expected, the Y230F mutation also reduced the apparent affinity for glutamate. To assess the kinetics of this mutant in synapses, we turned to autaptic microisland neuronal cultures. In this preparation, a single patch-clamp electrode can produce escaping action potentials that trigger glutamate release from synaptic terminals and record the resulting postsynaptic currents. However, we were surprised to discover that the Y230F mutant gave almost no fluorescent response in autaptic hippocampal neurons following stimulation (Figure 1—figure supplement 2), whereas in the same cultures, the regular iGluSnFR gave robust fluorescent responses, phase-locked to the postsynaptic currents, as did Synaptophluorin (with about 100× slower off kinetics). Paradoxically, the Y230F was expressed on the neuronal surface and responded robustly to 10 mM glutamate perfusion over the autaptic neuron (Figure 1—figure supplement 2). Combined with the observation that the fluorescence from unmodified iGluSnFR on average occurs over a spatial extent much larger than an individual synapse (~10 µm², see below) this observation led us to hypothesise that the low-affinity sensor was on average not sufficiently close to the synaptic glutamate signal to properly report it. Other low-affinity sensors have been reported (Helassa et al., 2018; Marvin et al., 2018) but as outlined above, their localisation in relation to postsynaptic densities (and therefore the signal they report) is uncertain.

iGluSnFR is membrane targeted through a single pass pDisplay peptide (Marvin et al., 2013), which includes no localisation signal to concentrate sensors at or near synapses, even if its responses themselves provide a spatial readout of glutamate release. To address this aspect, we fused the iGluSnFR extracellular domain to AMPA-type glutamate receptor in order to target it to excitatory synapses. Inline fusion at a permissive site in AMPA receptor subunits GluA1 and GluA2 extracellular region produced a sensor with a very small ∆F/F (Figure 1—figure supplement 3). Instead, we turned to the auxiliary proteins (TARPs) that decorate the periphery of AMPA receptors. To place the iGluSnFR sensor domain on the extracellular side of the membrane we fused a single pass transmembrane helix (from the NETO2 kainate receptor auxiliary protein; Zhang et al., 2009 to avoid any adventitious competition against the AMPA receptor–TARP interaction) to the N-terminus of Stargazin (γ-2) or γ-8 (Figure 1A). These chimeric sensors had similar performance to the original iGluSnFR and associated normally with AMPA receptor complexes in HEK cells (Figure 1), also acting to modulate AMPA receptor gating. The two reporters had similar apparent affinity for glutamate (within a factor of 2) to iGluSnFR, quite distinct from published ‘low-affinity’ variants (Helassa et al., 2018). We named these reporters SnFR-γ2 and SnFR-γ8.

Figure 1 with 3 supplements see all

Download asset Open asset

Bulk rat hippocampal cultures infected with the AAV of iGluSnFR gave wide, diffuse and, in our hands, relatively infrequent fluorescent signals (see Figure 2 and Video 1). We made adeno-associated viruses (AAVs) of both SnFR-γ2 and SnFR-γ8. Hippocampal bulk cultures infected with these viruses revealed regular fluorescence spikes (typically about 1 Hz frequency, see Figure 2 and Video 2). At hand-picked regions of interest (ROIs), SnFR-γ2 and SnFR-γ8 showed a more localised signal (Figure 2).

Figure 2

Download asset Open asset

Video 1

Download asset

Video 2

Download asset

To address synaptic localisation quantitatively, we performed immunocytochemistry on bulk cultured hippocampal neurons (Figure 3). We live-labelled cells for GFP (corresponding to the cpGFP domain of iGluSnFR) to show surface localisation. Following fixing, we stained for PSD95 and MAP2 to mark postsynaptic densities and dendritic shafts, respectively. The signal for SnFR-γ2 and SnFR-γ8 was markedly more punctate than for iGluSnFR, as expected. We detected about 50% greater average intensity in PSD spots for SnFR-γ2 and SnFR-γ8 (p = 0.03 and 0.06, respectively), as compared to iGluSnFR, suggesting that SnFR-γ2 and SnFR-γ8 are weakly enriched at synapses and probably exchange with extrasynaptic sites. The PSD size was slightly reduced for SnFR-γ2 compared to iGluSnFR, but the number of PSD spots per neuron imaged was similar across all conditions.

Figure 3

Download asset Open asset

The demonstration that SnFR-γ2 and SnFR-γ8 are enriched at synapses suggested they might show reduced spatial spread of the fluorescent glutamate report in live cells. To investigate this, we co-infected the cultures with a lentivirus for Homer-tdTomato. The complexity of the SnFR-γ2 signal between intracellular (probably endoplasmic reticulum [ER]) membranes and synaptic sites forbade a simple measure of colocalisation between Homer and SnFR-γ2 in these experiments. Consistent with our results from immunocytochemistry (Figure 3), responsive SnFR-γ2 spots were enriched at Homer positive spots, presumably corresponding to synaptic connections (Figure 4A). Background-subtracted heat maps revealed a sharper signal for SnFR-γ2 compared to iGluSnFR (Figure 4B). Taking a line profile through the centre of the peak response showed that the apparent half-width of the responses during spontaneous neurotransmission was on average less than 500 nm (Figure 4D, E) or about three-fold narrower than for iGluSnFR in our cultures. When averaging across frames, the half-width at some sites was of the order of 300 nm, or in other words, probably limited by diffraction of the microscope (Figure 4D). For this spontaneous neurotransmission, mean amplitudes from both the peak and subsequent two frames from SnFR-γ2 were slightly increased compared to iGluSnFR (Figure 4F, G), possibly because SnFR-γ2 and SnFR-γ8 experienced a higher degree of saturation than their non-targeted version (see Discussion). The normalised peak fluorescence of the SnFR-γ2 response was also greater than that for iGluSnFR.

Figure 4

Download asset Open asset

Infection of autapses with SnFR-γ2 and SnFR-γ8 gave punctate GFP signal in the majority of cases, but for some neurons, the signal remained diffuse. Although not obviously related, we also regularly measured a near absence of evoked neurotransmission (Figure 5B) in about half the autaptic neurons following infection before DIV 3. Robust evoked glutamate release (as reported by fluorescence changes from the sensors) was present, and we failed to detect any change in VGLUT puncta size or number on MAP2-positive neurites in immunostaining experiments (Figure 5—figure supplement 1), when comparing to non-infected neurons or neurons infected with iGluSnFR. These observations suggested a profound postsynaptic deficit due to overexpression of SnFR-γ2 and SnFR-γ8. We reasoned that, given that cultured neurons are still developing in this time window, we could infect the neurons later (after 6 days in vitro, DIV) and recover the evoked currents. Indeed, a majority of autaptic neurons retained an appreciable evoked response following late infection (neurons giving excitatory postsynaptic current [EPSC] >100 pA: 7 of 31, DIV 3 infection and 24 of 45, DIV 6; Figure 5C). The fluorescent signal and presynaptic markers (Figure 5—figure supplement 2) were unaffected by the late infection. Given that infection with iGluSnFR does not affect evoked currents much (Figure 5C), we reasoned that auxiliary proteins in overexpression might be causing the postsynaptic deficit. Indeed, early infection (before DIV 3) of either of two Stg constructs tagged with mScarlet (with or without the fused N-terminal NETO transmembrane section) were highly deleterious to evoked currents in autapses (Figure 5F, G). Miniature currents were also strongly reduced in amplitude and frequency, to the point that we failed to detect minis in a large proportion of cells (Figure 5—figure supplement 2), whereas paired-pulse ratio (20-ms interval) was unaffected. However, a late infection (after DIV 6) had much less impact, suggesting a previously unheralded developmental pathology at the postsynapse from early Stargazin overexpression.

Figure 5 with 2 supplements see all

Download asset Open asset

In autaptic neurons infected with AAVs at DIV 6, the signal from iGluSnFR in response to evoked transmission at 5 Hz was again more diffuse than the response of SnFR-γ2 (Figure 6). We selected small ROIs and adjacent neighbour regions, again by hand. Whilst neighbour and principal fluorescence time series usually matched well for iGluSnFR (Figure 6C), for most ROIs in the SnFR-γ2 cells, the neighbour region was silent (Figure 6F). Correspondingly, the correlation coefficient between fluorescence time series for principal and neighbour ROIs was less for SnFR-γ2 (Figure 6).

Figure 6

Download asset Open asset

Having established these prerequisites, we returned to our original motivation for improving the spatial and temporal response of iGluSnFR, which was to evaluate synaptic transmission at individual connections. We recorded movies of autaptic neurons infected with iGluSnFR (Video 3), SnFR-γ2 (Video 4), or SnFR-γ8. At the same time, we subjected the neurons to trains of 10 stimuli at 5 Hz in 0.5, 2, and 4 mM calcium. Escaping action potentials were generated from short depolarising pulses (see Methods), and we recorded the evoked postsynaptic currents under voltage clamp. From these movies, we examined the simultaneous evoked fluorescent responses from individual ROIs.

Video 3

Download asset

Video 4

Download asset

Rather than choosing ROIs by eye, we employed a custom-written imageJ macro to systematically scan movies (25-Hz frame rate) for ROIs that show similar statistics to the expected responses. Responses at 2 mM calcium were most reliable across the different sensors and we used these to identify ROIs with >20 positive-going fluorescence transients per movie in which we made 50 stimulations. Composite ROIs were constructed from the contiguous regions identified. Strikingly, but perhaps unsurprisingly, the ROIs found in this way (Figure 7) were much smaller on average for SnFR-γ2 (mean areas: 2.5 vs. 9.6 μm²) than for iGluSnFR. On average, neurons infected with iGluSnFR gave about twice as many ROIs as either SnFR-γ2 or SnFR-γ8 (Figure 7C; 207 ROIs for iGluSnFR, 107 for SnFR-γ2, and 97 for SnFR-γ8, each over 6 neurons). For all but one neuron, the number of ROIs from SnFR-γ8 was too few to pursue a further analysis.

Figure 7 with 2 supplements see all

Download asset Open asset

Although iGluSnFR gave more ROIs, when we analysed the peak responses from each ROI, we saw that the responses of iGluSnFR in a subset of ROIs tended to run down (assessed as the magnitude of the last 10% of responses to the first 10%, Figure 7—figure supplement 1). This rundown represented as much as fourfold loss of the peak signal for some ROIs. Indeed at least half of automatically identified ROIs from the iGluSnFR movies showed rundown greater than 50% (Figure 7—figure supplement 1), whereas less than 7% of ROIs selected from SnFR-γ2 cells did. Classifying the ROIs in this way arguably lacks statistical rigour but indicates a large qualitative difference in the data obtained from the two sets of ROIs. There was no relation between the size of the compound ROI and the extent of rundown (Figure 7—figure supplement 1). Recordings were made on the same day with the same illumination conditions. Moreover, the average baseline fluorescence intensity (F₀) was stable during the recordings, and showed little change for either iGluSnFR or SnFR-γ2 in our recording conditions (Figure 7—figure supplement 2). This observation speaks against photobleaching as a source of the aberrant behaviour of iGluSnFR in some ROIs.

To understand if the rundown was a problem of the sensor, or perhaps simply an accurate report of the neurotransmission running down in these cells, we looked at how well the responses of the fluorescence reporters followed the summed postsynaptic electrical response to AP-induced release of glutamate, which we measured simultaneously. The EPSC peak amplitude typically reduced by about 40% over an entire run (see Figure 7—figure supplement 2), for both iGluSnFR and SnFR-γ2. Therefore, the iGluSnFR rundown exaggerated the current rundown and the SnFR-γ2 construct perhaps underestimated it, consistent with reporting different sources of glutamate (e.g., iGluSnFR preferentially reporting spillover). However, we note that changes in the peak response of only 30% are less easy to see in the noisy optical measurement (SNR ~3) than in the electrophysiology of evoked EPSCs (SNR ~1000).

Postsynaptic currents in iGluSnFR and SnFR-γ2 showed similar short-term plasticity (Figure 8A, B). With 2 mM calcium, the peak current amplitude exhibited mild depression during trains of five pulses. The initial amplitude was larger and showed a stronger reduction at 4 mM calcium, consistent with multivesicular release and vesicle depletion, whereas responses were small and did not change during the train for 0.5 mM calcium. We described the degree of depression with the ratio of amplitudes in response to the first and last stimulation in the group (Amplitude₅/Amplitude₁). For fluorescence responses, we obtained peak responses from ROI time series following automatic (but supervised) background subtraction and discarded ROIs with a low peak signal-to-noise ratio (>3, see Methods).

Figure 8

Download asset Open asset

In movies recorded from iGluSnFR expressing cells, there was no relation between the depression of the postsynaptic AMPA receptor response during each train and the average depression of the fluorescence signal over the ROIs (Figure 8C). In contrast, there was an excellent correlation between the SnFR-γ2 depression and the postsynaptic current depression at 2 and 4 mM calcium (Figure 8C) indicating that the synaptic release reported by SnFR-γ2 across tens of sites per neuron accurately represents the average synaptic response to glutamate release. At 0.5 mM calcium, one SnFR-γ2 cell gave an abnormal high potentiation (seven-fold). However, omitting this point did not reveal any correlation in the remaining data, so we kept this point in the dataset. The lack of correlation of signals from iGluSnFR is likely because of rundown and also possibly because indistinct localisation leads it to report stray glutamate as well as synaptic glutamate, particularly in conditions where repeated glutamate release occurs (5 Hz or above, see Discussion). The higher correlation in fluorescence between neighbouring sites that we observed for iGluSnFR (Figure 6) exemplifies this phenomenon.

Taking each train of five responses, we could plot the standard deviation of the five responses against the mean. In this way, we could estimate the coefficient of variation (CV) for a large number of synapses and responses (Figure 8D). Quantal analysis (del Castillo and Katz, 1954) suggests that the square of the CV is a simple function of the number of sites and the probability of release (see Methods). Even though we evoked EPSCs over presumably hundreds of connections, the selected ROIs allowed us to analyse the stoichiometry of release at putative single synapses optically. The number of ROIs is limited by the field of view and the focal depth of the objective lens. Taking the slopes from linear fits to paired SD/mean values, we performed a non-linear fit to the estimated squared CV values across the whole population of synaptic ROIs, allowing for a different release probability at each calcium condition, but common N (corresponding to the number of sites in an ROI). Most generally, N here relates to the number of release sites per ROI (independent of the number of synapses in the ROI), and therefore P_R is the neurotransmitter release probability at a single release site. Even though N is averaged over a large number of ROIs (synapses), we fixed N to be an integer to facilitate the search. For both conditions, N could not be uniquely determined (three data points but four unknowns) and the data were fit equally well with a range of N values. N and P_R are inversely correlated. However, for N < 3 the release probabilities were unfeasibly high (Figure 8E), so we took a conservative approach, selecting the lowest N value that gave P_R less than 50% for 0.5 mM Ca. Values of N above one could indicate either that each ROI contains multiple synapses, or, more likely given previous results on autapses, that the synapses show multivesicular release. As our results on individual ROIs below show, because practically no ROIs show N = 1 behaviour, the latter interpretation is also more likely here.

For CV analysis, independent of the choice of N value, the average release probability over all ROIs, estimated from SnFR-γ2, increased monotonically with Ca²⁺ concentration, as expected from decades of electrophysiological analysis (Figure 8G, 6/6 cells). By contrast, the release probability estimated from iGluSnFR responses only increased monotonically for two out of six cells (Figure 8G), suggesting it is a poor reporter of synaptic activity in this context. These results indicate a clear quantitative benefit of targeted SnFR-γ2 over the non-targeted iGluSnFR. Previous analysis on connected cultured hippocampal neurons using electrophysiology gave similar values (N = 5 and P_R = 0.4 from CV analysis Bekkers and Stevens, 1990) to the ones that we obtained from SnFR-γ2 at 2 mM Ca²⁺. Although the congruence between connections between single neurons and the per-ROI parameters we found here is encouraging, we note that the CV method applied in this way cannot resolve N and therefore only gives an approximate rank order of synaptic release probabilities across the different conditions. We would therefore not recommend to use it.

In order to provide a more detailed analysis of synaptic release parameters, we next asked if SnFR-γ2 could be used to measure the quantal properties of individual synapses, and if it offered any advantages over the non-targeted iGluSnFR. To do this, we performed automated baseline subtraction from each ROI and detected peak responses according to a manually determined pattern from the mean of all responses. For both iGluSnFR and SnFR-γ2, we observed fluorescence time series with clear calcium dependence and quantal behaviour (Figure 9—figure supplement 1). From 50 stimulations at each concentration of calcium (150 events in total), at about half of the ROIs from iGluSnFR we could build histograms and fit globally (see Figure 9—figure supplement 2 for workflow). For SnFR-γ2, 80% of the automatically-found ROIs were usable for quantal analysis, and these ROIs reported a range of release probabilities similar to those determined from the CV analysis (Figure 9). In the majority of ROIs, according to the Kolmogorov–Smirnov test for the goodness of fit, the binomial model gave a better fit than the Poisson model (Bhumbra and Beato, 2013; Malagon et al., 2016). Examples of imperfect fits are shown in Figure 9—figure supplement 3. For SnFR-γ2, the distributions of peak amplitudes gave clearer multiple peaks (Figure 9C, D) than iGluSnFR responses. However, these peaks were washed out above 3 or 4 components, even if up to 8–10 components were needed to cover the entire spread of amplitude data. The ranges of numbers of components and open probabilities determined from global fitting of individual synapses were slightly narrower for SnFR-γ2 than for iGluSnFR (Figure 9F, G).

Figure 9 with 3 supplements see all

Download asset Open asset

In summary, the results from the SnFR-γ2 sensor were more convincingly quantal. Yet global fitting allowed similar, if less consistent, quantal parameters to be determined from iGluSnFR responses. Identification of single quanta at 0.5 mM Ca²⁺ was challenging for both indicators, and this difficulty was more pronounced for SnFR-γ2, possibly because the smaller ROIs we obtained in this condition are inherently more noisy. This drawback meant that our quantal analysis (and particularly the estimation of the quantal size) relied on the equal spacing of the responses to multiple quanta at higher calcium concentrations. In turn, the best fits could only describe the histograms with limited accuracy (Figure 9C, D)—we expect that this could be improved by collecting more responses. The ROIs for iGluSnFR were on average much larger in size and number, and a larger fraction of them needed to be manually discarded because they showed strong rundown. A greater proportion of ROIs that were responsive for SnFR-γ2 could be described by quantal parameters, and very few were affected by rundown in the peak response that was seen in ~50% of the responsive ROIs in iGluSnFR-infected neurons.

The principal advantage of optical reporting of neurotransmission is direct access to individual synaptic responses. Taking optical responses from the field of view as group, SnFR-γ2 (but not iGluSnFR) accurately reported short-term depression, as measured simultaneously with electrophysiology, except in the noisy conditions of 0.5 mM calcium. On average, SnFR-γ2 also gave similar quantal parameters from global estimates of the CV to previous electrophysiological work (N = 5 and P_R = 0.4 from CV analysis on pairs of cultured rat neurons; Bekkers and Stevens, 1990). These observations suggest that SnFR-γ2 is a fair reporter of presynaptic release, and that postsynaptic short-term plasticity (e.g., from desensitisation) is limited enough to be neglected at the frequencies we used (up to 5 Hz) (Malagon et al., 2016). However, both iGluSnFR and SnFR-γ2 failed to report gross variation in synaptic release properties across individual cells. Are synapses then truly variable (Rosenmund et al., 1993)? The deviation of individual mean/SD values from responsive ROIs (stemming from distinct synapses, either neighbouring groups or individual sites) from the fitted line representing the global CV was limited, and the R² value was quite high for all conditions. However, the range of putative maximum release probabilities detected in individual cells was substantial (typically from 40% to 80%, at 4 mM calcium). One caveat is that our approach may be biased towards detecting synapses with higher release rates. Another important consideration is that the number of ROIs we typically observed (tens) is consistently fewer than expected from the number of synapses that would be expected to generate nA-scale evoked synaptic currents (hundreds). The reason for this disparity remains unclear. Correlation of the parameters of synaptic release determined from optical analysis to synaptic architectures, synapses altered by plasticity or experience and synapses whose properties are altered by pathological mutants, should yield information about cell-autonomous synaptic diversity (Matz et al., 2010).

Unlike at the neuromuscular junction (del Castillo and Katz, 1954), peaks in the histograms of evoked EPSC amplitudes are much less prominent at central synapses (Bekkers, 1994). Several mathematical treatments were proposed to deal with the smeared distribution that apparently comes from quantal variability at central synapses, including using Bayesian statistics and the gamma distribution (Bhumbra and Beato, 2013; Soares et al., 2019). Optical report of glutamate concentration signal eliminates postsynaptic receptors as a source of variability. Miniature currents are variable in amplitude, introducing unavoidable variance when measuring evoked synaptic currents or when indirectly reporting activity from NMDA receptor calcium flux (Oertner et al., 2002). Simulations suggest that most variability comes from the variation in glutamate released by each vesicle (Franks et al., 2003), but we saw little evidence for this. Perhaps SnFR-γ2 is saturated by even poorly loaded vesicles, but fitting distributions with release sites (N) up to 8 or more, corresponding to up to 8 quanta in high Ca²⁺, suggests this is not the case. AMPA-type glutamate receptors have a lower affinity for glutamate than SnFR-γ2, and so the invariant quantal size reported by SnFR-γ2 would in any case translate to less robust AMPA receptor activation. Synaptic AMPARs are probably often not saturated due to a steep dependence on geometric factors (Savtchenko and Rusakov, 2014), or because the number of AMPA receptors available is so variable. A similar “lack of dips” in histograms has been observed in distributions from iGluSnFR in postsynaptic sites (Soares et al., 2019; Heck et al., 2019), requiring extensive mathematical modelling to extract quantal parameters. In clear contrast, confining iGluSnFR to the presynaptic terminal (in the case that anatomy allows this) gives quantal amplitude distributions (Duerst et al., 2020; Dürst et al., 2019). In contrast with some previous work, for example using field stimulation (Heck et al., 2019), in our experiments in autapses, we stimulated glutamate release with a single action potential and obtained quantal histograms with separated peaks. In particular, we can confirm that changing the calcium concentration and examining the same terminal appears to be beneficial in determining quantal parameters (Duerst et al., 2020). On the other hand, in native tissue, the environment of synaptic terminals may reduce the ability of SnFR-γ2 to report quantal distributions, and future work should address this point.

Single AP stimulation enabled a clear view of the systematic fast loss of the iGluSnFR signal (Figure 8). Why does iGluSnFR run down and why does SnFR-γ2 instead have a stable response? We have no decisive mechanistic explanation. One possibility is that bleaching reduces ∆F/F because a large proportion of the background fluorescence signal comes from either intracellular or occluded sensor molecules. If bleaching (or another process that eliminates signal, like endocytosis) were more intense when glutamate is bound, and iGluSnFR were to in turn have more average occupancy by glutamate than the confined SnFR-γ2, this could specifically reduce ∆F. In such a scheme, SnFR-γ2 would be protected from glutamate exposure by seeing only fast glutamate transients that saturate the sensor and immediately disperse, as opposed to a larger iGluSnFR capacity that is saturated for longer periods by spillover (Figure 10). The diffusion within synapses of the same single TM protein with and without a PSD-binding ligand (from the Stg C-terminus) provides an excellent comparison for our purposes (Li and Blanpied, 2016). These studies show that even though the diffusion of any protein is impeded within the PSD, the membrane protein without any PSD ligand is not excluded. However, our results are consistent with the idea that the PDGFR motif of the original iGluSnFR is excluded from synapses, as corroborated by expansion microscopy (Aggarwal et al., 2022).

Figure 10

Download asset Open asset

Assuming equal expression, and the concentration of membrane proteins with a PDZ ligand inside PSDs (Li and Blanpied, 2016), the density of SnFR-γ2 must be less outside the synapse (also see Figure 6). On average, a greater capacity outside the synapse would mean that, even without being excluded from synapses, iGluSnFR is biased against measuring synaptic glutamate. The basal response of iGluSnFR might be greater in neurons (even though in HEK cells the response was similar) because it is always moving in relation to release sites, and can respond to distant glutamate better by covering greater areas (even within an ROI).

Neither iGluSnFR nor SnFR-γ2 can identify single release events in our conditions, but for different reasons. The iGluSnFR signal often runs down intensely, meaning that the quantal estimate ∆F changes during a recording and cannot be considered constant across different conditions. SnFR-γ2 cannot reliably report isolated single release events because the amplitude of the signal is too small. However, spontaneous neurotransmission in cultures (in the absence of TTX) could be readily resolved by SnFR-γ2, at spot-like ROIs. These optical responses may correspond to single or multiple vesicles released by action potentials. For short-term plasticity, whereby synapses are reactivated over 50-ms intervals or similar, the improved spatial profiles of SnFR-γ2 are expected to be beneficial (Helassa et al., 2018). In our experiments, we did not focus on ROIs with high-frame rate, but this is an obvious future application.

iGluSnFR has been used in a variety of organisms (James et al., 2019; Marvin et al., 2013; Borghuis et al., 2013), and here we expressed SnFR-γ2 only in cultured rodent neurons. Using mouse auxiliary proteins to anchor the glutamate reporter opposite to release sites may not be universal, and may influence synaptic transmission in other systems in undesirable ways. Stargazin overexpression reduced miniature current amplitude and frequency but did not affect paired-pulse response (Figure 5—figure supplement 2) and glutamate release was intact (from SnFR-γ2 signals). This strongly suggests a postsynaptic deficit in cultured hippocampal cells but in other neuronal types, effects might be different. When we made inadvertent recordings of GABAergic cells, which we subsequently discarded, we saw no response of the iGluSnFR sensors, but nor was there any obvious effect on evoked inhibitory current magnitudes. It is unclear how well this approach can translate across species, even though PSD binding should be conserved. Notably, sensor performance is always worse in the in vivo context, and our sensor, along with a similar recently reported approach (Aggarwal et al., 2022), is probably better suited to quantitative studies of synaptic transmission in vitro, rather than in vivo work, even though its performance should not be worse than the original iGluSnFR construct.

Why does overexpressing various forms of Stargazin in the first few days of culture create a specific postsynaptic deficit? Stargazin has a very long first intron (Letts et al., 1998). This long first intron of Stg is the target of three mutations (including the original stargazer mouse) which reduce or abolish γ-2 mRNA (Letts et al., 2003). Because long introns extend transcription times, they are thought to delay expression. The most curious aspect of our observations was that release was apparently normal, and presynaptic VGLUT1 markers were not affected. It is not routine to perform electrophysiological characterisation of synapses following molecular interventions before imaging, however, the synapses in neurons infected with Stargazin or chimeras on or before DIV 3 were non-functional, despite their otherwise normal appearance. It is likely that Stargazin is not the ideal host for the iGluSnFR sensor domain. Auxiliary proteins that are not complexed to AMPA receptors probably fill slots in the postsynaptic density, reducing postsynaptic currents. The performance of the SnFR-γ8 construct was similar to SnFR-γ2, but neuronal labelling was often less broad, giving fewer ROIs that responded. It is likely that tethered sensors preferentially select stronger synapses. The additional transmembrane helix from NETO2 appears to reduce expression (although this may indeed be desirable) and is quantitatively more damaging to synaptic transmission than Stargazin in a more native form (Figure 5).

Rather than obtaining an optimised sensor, we have instead demonstrated further proof of principle that postsynaptic tethering improves reporter characteristics (Soares et al., 2019), even if it reduces overall signal. Our targeted sensor did not have distinctly better fluorescence performance in terms of signal to noise compared to iGluSnFR. A recent report used the principle of tethering to localise the signal (Aggarwal et al., 2022). One disadvantage of targeting may be that non-synaptic signals of interest could be lost. More critically, the synaptic localisation does not guarantee to measure exclusively synaptic glutamate; waves of elevated glutamate from other sources including astroglia, substantial spillover or even changes in the optical properties of tissue could be mistaken for synaptic glutamate in this context, and this is a limitation of our approach. In organised brain tissue, two-photon excitation will not avoid this difficulty. Spectral variants with distinct (or no) targeting could be combined to get a fuller picture of both synaptic and spillover glutamate (see Hao and Plested, 2022 for discussion). Various approaches are known to augment the signal from iGluSnFR including using the superfolder variant of GFP (Marvin et al., 2018), mutagenesis (Figure 1—figure supplement 1), or a different promoter (we used human Synapsin). Indeed, the superfolder variant of iGluSnFR could be used very successfully for quantal analysis in cell culture, when analysis was confined to axonal projections (i.e., presynaptic sites, Mendonça et al., 2022). More work is needed to understand why targeting reduced the size of ROIs at postsynaptic sites in our hands, whereas well-confined signals are seen in axons. Finally, other tethers, either minimal ones (Li and Blanpied, 2016) or those based on other synaptic proteins, may also give better, more defined, or even orthogonal signals.

Custom software is available at https://github.com/agplested/SAFT (copy archived at swh:1:rev:c3f5615648978f9fdc82237c443ed29eadb2fc15).

1. 1. Hao Y
   2. Toulmé E
   3. König B
   4. Rosenmund C
   5. Plested A

   (2023) Zenodo

   Targeted sensors for glutamatergic neurotransmission.

   https://doi.org/10.5281/zenodo.7512561

1. Preprint

   1. Aggarwal A
   2. Liu R
   3. Chen Y
   4. Ralowicz AJ
   5. Bergerson SJ
   6. Tomaska F
   7. Hanson TL
   8. Hasseman JP
   9. Reep D
   10. Tsegaye G
   11. Yao P
   12. Ji X
   13. Kloos M
   14. Walpita D
   15. Patel R
   16. Mohr MA
   17. Tilberg PW
   18. Mohar B
   19. Looger LL
   20. Marvin JS
   21. Hoppa MB
   22. Konnerth A
   23. Kleinfeld D
   24. Schreiter ER
   25. Podgorski K
   26. The GENIE Project Team

   (2022) Glutamate Indicators with Improved Activation Kinetics and Localization for Imaging Synaptic Transmission

   bioRxiv.

   https://doi.org/10.1101/2022.02.13.480251

   • Google Scholar
2. 1. Balaji J
   2. Ryan TA

   (2007) Single-vesicle imaging reveals that synaptic vesicle exocytosis and endocytosis are coupled by a single stochastic mode

   PNAS 104:20576–20581.

   https://doi.org/10.1073/pnas.0707574105

   • PubMed
   • Google Scholar
3. 1. Bekkers JM
   2. Stevens CF

   (1990) Presynaptic mechanism for long-term potentiation in the hippocampus

   Nature 346:724–729.

   https://doi.org/10.1038/346724a0

   • PubMed
   • Google Scholar
4. 1. Bekkers JM

   (1994) Quantal analysis of synaptic transmission in the central nervous system

   Current Opinion in Neurobiology 4:360–365.

   https://doi.org/10.1016/0959-4388(94)90097-3

   • PubMed
   • Google Scholar
5. 1. Bhumbra GS
   2. Beato M

   (2013) Reliable evaluation of the quantal determinants of synaptic efficacy using Bayesian analysis

   Journal of Neurophysiology 109:603–620.

   https://doi.org/10.1152/jn.00528.2012

   • PubMed
   • Google Scholar
6. 1. Bindels DS
   2. Haarbosch L
   3. van Weeren L
   4. Postma M
   5. Wiese KE
   6. Mastop M
   7. Aumonier S
   8. Gotthard G
   9. Royant A
   10. Hink MA
   11. Gadella TJ

   (2017) MScarlet: a bright monomeric red fluorescent protein for cellular imaging

   Nature Methods 14:53–56.

   https://doi.org/10.1038/nmeth.4074

   • PubMed
   • Google Scholar
7. 1. Borghuis BG
   2. Marvin JS
   3. Looger LL
   4. Demb JB

   (2013) Two-Photon imaging of nonlinear glutamate release dynamics at bipolar cell synapses in the mouse retina

   The Journal of Neuroscience 33:10972–10985.

   https://doi.org/10.1523/JNEUROSCI.1241-13.2013

   • PubMed
   • Google Scholar
8. 1. Chanaday NL
   2. Kavalali ET

   (2018) Optical detection of three modes of endocytosis at hippocampal synapses

   eLife 7:e36097.

   https://doi.org/10.7554/eLife.36097

   • PubMed
   • Google Scholar
9. 1. Choi JH
   2. Yu NK
   3. Baek GC
   4. Bakes J
   5. Seo D
   6. Nam HJ
   7. Baek SH
   8. Lim CS
   9. Lee YS
   10. Kaang BK

   (2014) Optimization of AAV expression cassettes to improve packaging capacity and transgene expression in neurons

   Molecular Brain 7:17.

   https://doi.org/10.1186/1756-6606-7-17

   • PubMed
   • Google Scholar
10. 1. Cizeron M
    2. Qiu Z
    3. Koniaris B
    4. Gokhale R
    5. Komiyama NH
    6. Fransén E
    7. Grant SGN

    (2020) A brainwide atlas of synapses across the mouse life span

    Science 369:270–275.

    https://doi.org/10.1126/science.aba3163

    • PubMed
    • Google Scholar
11. 1. del Castillo J
    2. Katz B

    (1954) Quantal components of the end-plate potential

    The Journal of Physiology 124:560–573.

    https://doi.org/10.1113/jphysiol.1954.sp005129

    • PubMed
    • Google Scholar
12. Preprint

    1. Duerst C
    2. Wiegert JS
    3. Schulze C
    4. Helassa N
    5. Török K
    6. Oertner TG

    (2020) The Vesicular Release Probability Sets the Strength of Individual Schaffer Collateral Synapses

    bioRxiv.

    https://doi.org/10.1101/2020.08.02.232850

    • Google Scholar
13. 1. Dürst CD
    2. Wiegert JS
    3. Helassa N
    4. Kerruth S
    5. Coates C
    6. Schulze C
    7. Geeves MA
    8. Török K
    9. Oertner TG

    (2019) High-speed imaging of glutamate release with genetically encoded sensors

    Nature Protocols 14:1401–1424.

    https://doi.org/10.1038/s41596-019-0143-9

    • PubMed
    • Google Scholar
14. 1. Edelstein AD
    2. Tsuchida MA
    3. Amodaj N
    4. Pinkard H
    5. Vale RD
    6. Stuurman N

    (2014) Advanced methods of microscope control using μmanager software

    Journal of Biological Methods 1:e10.

    https://doi.org/10.14440/jbm.2014.36

    • PubMed
    • Google Scholar
15. Report

    1. Eilers PHC
    2. Boelens HFM

    (2005)

    Baseline correction with asymmetric least squares smoothing

    Leiden University Medical Centre.

    • Google Scholar
16. 1. Enoki R
    2. Hu YL
    3. Hamilton D
    4. Fine A

    (2009) Expression of long-term plasticity at individual synapses in hippocampus is graded, bidirectional, and mainly presynaptic: optical quantal analysis

    Neuron 62:242–253.

    https://doi.org/10.1016/j.neuron.2009.02.026

    • PubMed
    • Google Scholar
17. 1. Evans DA
    2. Stempel AV
    3. Vale R
    4. Ruehle S
    5. Lefler Y
    6. Branco T

    (2018) A synaptic threshold mechanism for computing escape decisions

    Nature 558:590–594.

    https://doi.org/10.1038/s41586-018-0244-6

    • PubMed
    • Google Scholar
18. 1. Faber DS
    2. Korn H

    (1991) Applicability of the coefficient of variation method for analyzing synaptic plasticity

    Biophysical Journal 60:1288–1294.

    https://doi.org/10.1016/S0006-3495(91)82162-2

    • PubMed
    • Google Scholar
19. 1. Farsi Z
    2. Walde M
    3. Klementowicz AE
    4. Paraskevopoulou F
    5. Woehler A

    (2021) Single synapse glutamate imaging reveals multiple levels of release mode regulation in mammalian synapses

    IScience 24:101909.

    https://doi.org/10.1016/j.isci.2020.101909

    • PubMed
    • Google Scholar
20. 1. Franks KM
    2. Stevens CF
    3. Sejnowski TJ

    (2003) Independent sources of quantal variability at single glutamatergic synapses

    The Journal of Neuroscience 23:3186–3195.

    https://doi.org/10.1523/JNEUROSCI.23-08-03186.2003

    • PubMed
    • Google Scholar
21. 1. Gambino F
    2. Pagès S
    3. Kehayas V
    4. Baptista D
    5. Tatti R
    6. Carleton A
    7. Holtmaat A

    (2014) Sensory-Evoked LTP driven by dendritic plateau potentials in vivo

    Nature 515:116–119.

    https://doi.org/10.1038/nature13664

    • PubMed
    • Google Scholar
22. 1. Granseth B
    2. Odermatt B
    3. Royle SJ
    4. Lagnado L

    (2006) Clathrin-Mediated endocytosis is the dominant mechanism of vesicle retrieval at hippocampal synapses

    Neuron 51:773–786.

    https://doi.org/10.1016/j.neuron.2006.08.029

    • PubMed
    • Google Scholar
23. 1. Griesinger CB
    2. Richards CD
    3. Ashmore JF

    (2005) Fast vesicle replenishment allows indefatigable signalling at the first auditory synapse

    Nature 435:212–215.

    https://doi.org/10.1038/nature03567

    • PubMed
    • Google Scholar
24. 1. Hao Y
    2. Plested AJR

    (2022) Seeing glutamate at central synapses

    Journal of Neuroscience Methods 375:109531.

    https://doi.org/10.1016/j.jneumeth.2022.109531

    • PubMed
    • Google Scholar
25. 1. Heck J
    2. Parutto P
    3. Ciuraszkiewicz A
    4. Bikbaev A
    5. Freund R
    6. Mitlöhner J
    7. Andres-Alonso M
    8. Fejtova A
    9. Holcman D
    10. Heine M

    (2019) Transient confinement of Cav2.1 Ca2+-channel splice variants shapes synaptic short-term plasticity

    Neuron 103:66–79.

    https://doi.org/10.1016/j.neuron.2019.04.030

    • PubMed
    • Google Scholar
26. 1. Helassa N
    2. Dürst CD
    3. Coates C
    4. Kerruth S
    5. Arif U
    6. Schulze C
    7. Wiegert JS
    8. Geeves M
    9. Oertner TG
    10. Török K

    (2018) Ultrafast glutamate sensors resolve high-frequency release at schaffer collateral synapses

    PNAS 115:5594–5599.

    https://doi.org/10.1073/pnas.1720648115

    • PubMed
    • Google Scholar
27. 1. Isaac JT
    2. Hjelmstad GO
    3. Nicoll RA
    4. Malenka RC

    (1996) Long-term potentiation at single fiber inputs to hippocampal CA1 pyramidal cells

    PNAS 93:8710–8715.

    https://doi.org/10.1073/pnas.93.16.8710

    • PubMed
    • Google Scholar
28. 1. James B
    2. Darnet L
    3. Moya-Díaz J
    4. Seibel SH
    5. Lagnado L

    (2019) An amplitude code transmits information at a visual synapse

    Nature Neuroscience 22:1140–1147.

    https://doi.org/10.1038/s41593-019-0403-6

    • PubMed
    • Google Scholar
29. 1. Jensen TP
    2. Zheng K
    3. Cole N
    4. Marvin JS
    5. Looger LL
    6. Rusakov DA

    (2019) Multiplex imaging relates quantal glutamate release to presynaptic ca2+ homeostasis at multiple synapses in situ

    Nature Communications 10:1414.

    https://doi.org/10.1038/s41467-019-09216-8

    • PubMed
    • Google Scholar
30. 1. Jontes JD
    2. Smith SJ

    (2000) Filopodia, spines, and the generation of synaptic diversity

    Neuron 27:11–14.

    https://doi.org/10.1016/s0896-6273(00)00003-9

    • PubMed
    • Google Scholar
31. 1. Kim Y
    2. Lee U
    3. Choi C
    4. Chang S

    (2020) Release mode dynamically regulates the RRP refilling mechanism at individual hippocampal synapses

    The Journal of Neuroscience 40:8426–8437.

    https://doi.org/10.1523/JNEUROSCI.3029-19.2020

    • PubMed
    • Google Scholar
32. 1. Letts VA
    2. Felix R
    3. Biddlecome GH
    4. Arikkath J
    5. Mahaffey CL
    6. Valenzuela A
    7. Bartlett FS
    8. Mori Y
    9. Campbell KP
    10. Frankel WN

    (1998) The mouse stargazer gene encodes a neuronal Ca2+-channel gamma subunit

    Nature Genetics 19:340–347.

    https://doi.org/10.1038/1228

    • PubMed
    • Google Scholar
33. 1. Letts VA
    2. Kang MG
    3. Mahaffey CL
    4. Beyer B
    5. Tenbrink H
    6. Campbell KP
    7. Frankel WN

    (2003) Phenotypic heterogeneity in the stargazin allelic series

    Mammalian Genome 14:506–513.

    https://doi.org/10.1007/s00335-003-2268-x

    • PubMed
    • Google Scholar
34. 1. Li TP
    2. Blanpied TA

    (2016) Control of transmembrane protein diffusion within the postsynaptic density assessed by simultaneous single-molecule tracking and localization microscopy

    Frontiers in Synaptic Neuroscience 8:19.

    https://doi.org/10.3389/fnsyn.2016.00019

    • PubMed
    • Google Scholar
35. 1. Lois C
    2. Hong EJ
    3. Pease S
    4. Brown EJ
    5. Baltimore D

    (2002) Germline transmission and tissue-specific expression of transgenes delivered by lentiviral vectors

    Science 295:868–872.

    https://doi.org/10.1126/science.1067081

    • PubMed
    • Google Scholar
36. 1. Malagon G
    2. Miki T
    3. Llano I
    4. Neher E
    5. Marty A

    (2016) Counting vesicular release events reveals binomial release statistics at single glutamatergic synapses

    The Journal of Neuroscience 36:4010–4025.

    https://doi.org/10.1523/JNEUROSCI.4352-15.2016

    • PubMed
    • Google Scholar
37. 1. Marvin JS
    2. Borghuis BG
    3. Tian L
    4. Cichon J
    5. Harnett MT
    6. Akerboom J
    7. Gordus A
    8. Renninger SL
    9. Chen TW
    10. Bargmann CI
    11. Orger MB
    12. Schreiter ER
    13. Demb JB
    14. Gan WB
    15. Hires SA
    16. Looger LL

    (2013) An optimized fluorescent probe for visualizing glutamate neurotransmission

    Nature Methods 10:162–170.

    https://doi.org/10.1038/nmeth.2333

    • PubMed
    • Google Scholar
38. 1. Marvin JS
    2. Scholl B
    3. Wilson DE
    4. Podgorski K
    5. Kazemipour A
    6. Müller JA
    7. Schoch S
    8. Quiroz FJU
    9. Rebola N
    10. Bao H
    11. Little JP
    12. Tkachuk AN
    13. Cai E
    14. Hantman AW
    15. Wang SS-H
    16. DePiero VJ
    17. Borghuis BG
    18. Chapman ER
    19. Dietrich D
    20. DiGregorio DA
    21. Fitzpatrick D
    22. Looger LL

    (2018) Stability, affinity, and chromatic variants of the glutamate sensor iglusnfr

    Nature Methods 15:936–939.

    https://doi.org/10.1038/s41592-018-0171-3

    • PubMed
    • Google Scholar
39. 1. Matz J
    2. Gilyan A
    3. Kolar A
    4. McCarvill T
    5. Krueger SR

    (2010) Rapid structural alterations of the active zone lead to sustained changes in neurotransmitter release

    PNAS 107:8836–8841.

    https://doi.org/10.1073/pnas.0906087107

    • PubMed
    • Google Scholar
40. 1. McAllister AK
    2. Stevens CF

    (2000) Nonsaturation of AMPA and NMDA receptors at hippocampal synapses

    PNAS 97:6173–6178.

    https://doi.org/10.1073/pnas.100126497

    • PubMed
    • Google Scholar
41. 1. Mendonça PRF
    2. Tagliatti E
    3. Langley H
    4. Kotzadimitriou D
    5. Zamora-Chimal CG
    6. Timofeeva Y
    7. Volynski KE

    (2022) Asynchronous glutamate release is enhanced in low release efficacy synapses and dispersed across the active zone

    Nature Communications 13:3497.

    https://doi.org/10.1038/s41467-022-31070-4

    • PubMed
    • Google Scholar
42. 1. Miesenböck G
    2. De Angelis DA
    3. Rothman JE

    (1998) Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins

    Nature 394:192–195.

    https://doi.org/10.1038/28190

    • PubMed
    • Google Scholar
43. 1. Namiki S
    2. Sakamoto H
    3. Iinuma S
    4. Iino M
    5. Hirose K

    (2007) Optical glutamate sensor for spatiotemporal analysis of synaptic transmission

    The European Journal of Neuroscience 25:2249–2259.

    https://doi.org/10.1111/j.1460-9568.2007.05511.x

    • PubMed
    • Google Scholar
44. 1. Oertner TG
    2. Sabatini BL
    3. Nimchinsky EA
    4. Svoboda K

    (2002) Facilitation at single synapses probed with optical quantal analysis

    Nature Neuroscience 5:657–664.

    https://doi.org/10.1038/nn867

    • PubMed
    • Google Scholar
45. 1. Okumoto S
    2. Looger LL
    3. Micheva KD
    4. Reimer RJ
    5. Smith SJ
    6. Frommer WB

    (2005) Detection of glutamate release from neurons by genetically encoded surface-displayed FRET nanosensors

    PNAS 102:8740–8745.

    https://doi.org/10.1073/pnas.0503274102

    • PubMed
    • Google Scholar
46. Software

    1. Plested A

    (2023) SAFT, version swh:1:rev:c3f5615648978f9fdc82237c443ed29eadb2fc15

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:ab8c9acfa92cfe80552c882c443cb16f70e2788e;origin=https://github.com/AGPlested/SAFT;visit=swh:1:snp:34fbc252c5eec382433674f5972bf22c4527e537;anchor=swh:1:rev:c3f5615648978f9fdc82237c443ed29eadb2fc15
47. 1. Popovic MA
    2. Carnevale N
    3. Rozsa B
    4. Zecevic D

    (2015) Electrical behaviour of dendritic spines as revealed by voltage imaging

    Nature Communications 6:8436.

    https://doi.org/10.1038/ncomms9436

    • PubMed
    • Google Scholar
48. 1. Raghavachari S
    2. Lisman JE

    (2004) Properties of quantal transmission at CA1 synapses

    Journal of Neurophysiology 92:2456–2467.

    https://doi.org/10.1152/jn.00258.2004

    • PubMed
    • Google Scholar
49. 1. Ribrault C
    2. Sekimoto K
    3. Triller A

    (2011) From the stochasticity of molecular processes to the variability of synaptic transmission

    Nature Reviews. Neuroscience 12:375–387.

    https://doi.org/10.1038/nrn3025

    • PubMed
    • Google Scholar
50. 1. Rosenmund C
    2. Clements JD
    3. Westbrook GL

    (1993) Nonuniform probability of glutamate release at a hippocampal synapse

    Science 262:754–757.

    https://doi.org/10.1126/science.7901909

    • PubMed
    • Google Scholar
51. 1. Rost BR
    2. Schneider F
    3. Grauel MK
    4. Wozny C
    5. Bentz C
    6. Blessing A
    7. Rosenmund T
    8. Jentsch TJ
    9. Schmitz D
    10. Hegemann P
    11. Rosenmund C

    (2015) Optogenetic acidification of synaptic vesicles and lysosomes

    Nature Neuroscience 18:1845–1852.

    https://doi.org/10.1038/nn.4161

    • PubMed
    • Google Scholar
52. 1. Sankaranarayanan S
    2. De Angelis D
    3. Rothman JE
    4. Ryan TA

    (2000) The use of phluorins for optical measurements of presynaptic activity

    Biophysical Journal 79:2199–2208.

    https://doi.org/10.1016/S0006-3495(00)76468-X

    • PubMed
    • Google Scholar
53. 1. Savtchenko LP
    2. Rusakov DA

    (2014) Moderate AMPA receptor clustering on the nanoscale can efficiently potentiate synaptic current

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130167.

    https://doi.org/10.1098/rstb.2013.0167

    • PubMed
    • Google Scholar
54. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez JY
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
55. 1. Scholl B
    2. Thomas CI
    3. Ryan MA
    4. Kamasawa N
    5. Fitzpatrick D

    (2020) Cortical response selectivity derives from strength in numbers of synapses

    Nature 590:111–114.

    https://doi.org/10.1038/s41586-020-03044-3

    • PubMed
    • Google Scholar
56. 1. Soares C
    2. Trotter D
    3. Longtin A
    4. Béïque JC
    5. Naud R

    (2019) Parsing out the variability of transmission at central synapses using optical quantal analysis

    Frontiers in Synaptic Neuroscience 11:22.

    https://doi.org/10.3389/fnsyn.2019.00022

    • PubMed
    • Google Scholar
57. 1. Tagliatti E
    2. Bello OD
    3. Mendonça PRF
    4. Kotzadimitriou D
    5. Nicholson E
    6. Coleman J
    7. Timofeeva Y
    8. Rothman JE
    9. Krishnakumar SS
    10. Volynski KE

    (2020) Synaptotagmin 1 oligomers clamp and regulate different modes of neurotransmitter release

    PNAS 117:3819–3827.

    https://doi.org/10.1073/pnas.1920403117

    • PubMed
    • Google Scholar
58. 1. Virtanen P
    2. Gommers R
    3. Oliphant TE
    4. Haberland M
    5. Reddy T
    6. Cournapeau D
    7. Burovski E
    8. Peterson P
    9. Weckesser W
    10. Bright J
    11. van der Walt SJ
    12. Brett M
    13. Wilson J
    14. Millman KJ
    15. Mayorov N
    16. Nelson ARJ
    17. Jones E
    18. Kern R
    19. Larson E
    20. Carey CJ
    21. Polat İ
    22. Feng Y
    23. Moore EW
    24. VanderPlas J
    25. Laxalde D
    26. Perktold J
    27. Cimrman R
    28. Henriksen I
    29. Quintero EA
    30. Harris CR
    31. Archibald AM
    32. Ribeiro AH
    33. Pedregosa F
    34. van Mulbregt P
    35. SciPy 1.0 Contributors

    (2020) Author correction: scipy 1.0: fundamental algorithms for scientific computing in python

    Nature Methods 17:261–272.

    https://doi.org/10.1038/s41592-020-0772-5

    • PubMed
    • Google Scholar
59. 1. Vyleta NP
    2. Jonas P

    (2014) Loose coupling between Ca2+ channels and release sensors at a plastic hippocampal synapse

    Science 343:665–670.

    https://doi.org/10.1126/science.1244811

    • PubMed
    • Google Scholar
60. 1. Zhang W
    2. St-Gelais F
    3. Grabner CP
    4. Trinidad JC
    5. Sumioka A
    6. Morimoto-Tomita M
    7. Kim KS
    8. Straub C
    9. Burlingame AL
    10. Howe JR
    11. Tomita S

    (2009) A transmembrane accessory subunit that modulates kainate-type glutamate receptors

    Neuron 61:385–396.

    https://doi.org/10.1016/j.neuron.2008.12.014

    • PubMed
    • Google Scholar

Author details

1. Yuchen Hao

   1. Institute of Biology, Cellular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
   2. Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin, Germany

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0042-6576

2. Estelle Toulmé

   Institute for Neurophysiology, Charité - Universitätsmedizin Berlin, Berlin, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8734-3484

3. Benjamin König

   1. Institute of Biology, Cellular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
   2. Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin, Germany

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

4. Christian Rosenmund

   1. Institute for Neurophysiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
   2. NeuroCure Cluster of Excellence, Berlin, Germany

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3905-2444

5. Andrew JR Plested

   1. Institute of Biology, Cellular Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
   2. Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin, Germany
   3. NeuroCure Cluster of Excellence, Berlin, Germany

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   andrew.plested@hu-berlin.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6062-0832

• 3,001

  views

• 405

  downloads

• 9

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.