1. Neuroscience
Download icon

Release probability increases towards distal dendrites boosting high-frequency signal transfer in the rodent hippocampus

  1. Thomas P Jensen  Is a corresponding author
  2. Olga Kopach
  3. James P Reynolds
  4. Leonid P Savtchenko
  5. Dmitri A Rusakov  Is a corresponding author
  1. Queen Square UCL Institute of Neurology, University College London, United Kingdom
Research Article
  • Cited 0
  • Views 771
  • Annotations
Cite this article as: eLife 2021;10:e62588 doi: 10.7554/eLife.62588

Abstract

Dendritic integration of synaptic inputs involves their increased electrotonic attenuation at distal dendrites, which can be counterbalanced by the increased synaptic receptor density. However, during network activity, the influence of individual synapses depends on their release fidelity, the dendritic distribution of which remains poorly understood. Here, we employed classical optical quantal analyses and a genetically encoded optical glutamate sensor in acute hippocampal slices of rats and mice to monitor glutamate release at CA3-CA1 synapses. We find that their release probability increases with greater distances from the soma. Similar-fidelity synapses tend to group together, whereas release probability shows no trends regarding the branch ends. Simulations with a realistic CA1 pyramidal cell hosting stochastic synapses suggest that the observed trends boost signal transfer fidelity, particularly at higher input frequencies. Because high-frequency bursting has been associated with learning, the release probability pattern we have found may play a key role in memory trace formation.

Introduction

Information processing in brain circuits relies on dendritic integration of excitatory inputs that occur at varied distances from the soma. Whilst the cable impedance of dendrites attenuates electric signals arriving from distal synapses, some nerve cells appear well equipped to counter this trend. In hippocampal CA1 pyramidal neurons, synaptic receptor numbers increase monotonically with the synapse-soma distance (Andrasfalvy and Magee, 2001; Nicholson et al., 2006). This, combined with the regenerative properties of local ion channels (Cash and Yuste, 1999; Magee, 1999), can provide efficient, relatively homogeneous summation of synaptic inputs across the dendritic tree of these cells (Cash and Yuste, 1999; Magee, 2000; Magee and Cook, 2000). In contrast, distal synapses on cortical L5 pyramidal neurons appear relatively ineffective in somatic excitation (Williams and Stuart, 2002). A combination of 3D electron microscopy with biophysical modelling of CA1 pyramidal cells has also argued that the greater occurrence of excitatory synapses towards the dendritic branch origin helps to normalise input efficacy along the dendrites (Katz et al., 2009). However, it remains largely unknown whether the other fundamental synaptic property, neurotransmitter release probability (Pr) remains constant along the dendrites. Because Pr defines how much information synaptic connections transmit over time (Zador, 1998), its distribution pattern is critical to the rules of dendritic input integration.

A recent elegant study has combined electron microscopy with extracellular (paired-pulse) afferent activation of multiple synapses in basal dendrites of CA1 pyramidal cells, to find the average Pr values decreasing towards the cell periphery (Grillo et al., 2018). Thus, a few proximal connections appear to have a greater impact on the cell output than more numerous synapses occurring distally. This observation raised the question of the synaptic connectome efficiency, suggesting that, during continued afferent spiking, distal inputs are weakened. Although highly supra-linear local events, such as dendritic spikes could, in principle, compensate for this trend (Larkum and Nevian, 2008; Branco and Häusser, 2009), how prevalent such events are remains debatable. Furthermore, if Pr tends to decrease towards more distal apical dendrites in CA1 pyramidal cells, the suggested input summation rules in these cells (Magee, 2000; Magee and Cook, 2000; Branco and Häusser, 2011), and at least in some other principal neurons (Araya et al., 2006), would be broken during sustained network activity.

Methodologically, paired-pulse recordings of multi-synaptic responses (Grillo et al., 2018), while revealing the trend, do not provide direct readout of either Pr, paired-pulse ratios (PPRs), or the Pr distribution among individual connections. This makes it difficult to evaluate how these properties of Pr affect dendritic integration. To address these challenges, here we monitored Pr in individual CA3-CA1 synapses, by systematically employing single-synapse optical quantal analyses (OQA) (Oertner et al., 2002; Emptage et al., 2003), based on Ca2+ imaging in individual dendritic spines, as established previously (Sylantyev et al., 2013; Boddum et al., 2016). In a complementary approach, we employed the genetically encoded optical glutamate sensor iGluSnFR, as established earlier (Jensen et al., 2017; Jensen et al., 2019; Henneberger et al., 2020; Kopach et al., 2020), to document a spatial trend of Pr values across the s. radiatrum. Exploring the results with a realistic biophysical model of the CA1 pyramidal cell (Migliore et al., 1999) equipped with multiple stochastic synaptic inputs, revealed how the observed trends in the Pr pattern along dendrites could affect synaptic input integration in CA1 pyramidal cells.

Results

Monitoring release probability with optical quantal analysis

We used 300–350 µm transverse hippocampal slices of 3- to 4-week-old rats (Materials and methods). First, we held CA1 pyramidal cells in whole-cell (Vm = −65 mV) and dialysed them with the red morphological tracer Alexa Fluor 594 (50 µM) and Ca2+-sensitive indicators as detailed in Materials and methods and described previously (Sylantyev et al., 2013; Zheng et al., 2015). We visualised cell morphology with two-photon excitation (λx2p = 800 nm), and placed the extracellular stimulating pipette in the 10–20 µm proximity of apical dendrites (Figure 1A, Figure 1—figure supplement 1A). The system was focused on individual dendritic spines that responded to paired-pulse stimuli (50 ms apart) with localised Ca2+ transients in a stochastic manner (Figure 1B, Figure 1—figure supplement 1B). Because individual dendritic spines of CA1 pyramidal cells host almost exclusively only one CA3-CA1 synapse (Harris and Stevens, 1989; Bloss et al., 2018), the first-pulse Ca2+ signal success counts over 14–30 trials provided direct readout of Pr at individual synapses (P1 in Figure 1B, Figure 1—figure supplement 1C), as we showed earlier (Sylantyev et al., 2013; Boddum et al., 2016). In these tests, the amplitudes of successful first-pulse release signals were consistently above two standard deviations of the average signal during release failures (Figure 1—figure supplement 1D), enabling us to readily calculate Pr = P1. The second-pulse release probability was estimated using two complementary methods (Figure 2B): one in which all trials were assessed for 1st and 2nd release successes and failures (P2), and one in which trials with the first successful response were ignored (P2*). The latter method eliminates false-positive detection of the second-pulse success, at the expense of reduced trial numbers. The curvilinear distance from the spine of interest to the cell soma was subsequently recorded (Materials and methods).

Figure 1 with 1 supplement see all
Optical quantal analysis at individual CA3-CA1 synapses reports higher release probability towards distant dendrites.

(A) CA1 pyramidal cell held in whole-cell mode (acute hippocampal slice), dialysed with 50 µM AF 594 and 300 µM Fluo-8 (75 µm z-stack average, λx2p = 800 nm; AF 594 channel). Dotted rectangles (S1 and S2), two ROIs to record from two dendritic spines; stimulating electrode positions are illustrated by dotted cones. (B) Image panels, ROIs as in (A), shown at higher magnification; arrows, linescan positioning at the dendritic spines of interest. Traces, Ca2+ signal (ΔG/R, Fluo-8 green-channel signal ΔG related to red-channel AF 594 signal R) recorded as the width-integrated linescan intensity, at two spines as indicated, in response to paired-pulse afferent stimuli (arrows). Release failures and responses to the first pulse can be clearly separated (Figure 1—figure supplement 1D). P1 and P2 show average release probability in response to the 1st and 2nd stimulus, respectively, calculated as P1 = {(1,0) + (1,1)} / N and P2 = {(0,1) + (1,1)} / N, and P2* is the adjusted 2nd-stimulus release probability calculated as P2* = (0,1) / {(0,0) + (0,1)} where brackets indicate the counts of paired-pulse successes (1) or failures (0), and N is the number of trials. (C) Average release probability (Pr; shown as P1 in B) at individual synapses plotted against distance to the soma. Solid line, linear regression (p value to reject H0 = zero slope and Pearson's r shown; n = 67). (D) Paired-pulse ratio P2/P1 plotted against distance to the soma. Other notations as in (C) (n = 41; average PPR mean ± SEM: 2.14 ± 0.17; spines with no reliable detection of 2nd responses, and no detectable (0,1) responses, were excluded). (E) Adjusted paired-pulse ratio PPR*=P2*/P1 plotted against distance to the soma. Other notations as in (C) (n = 41; average PPR*=2.05 ± 0.18).

Figure 2 with 1 supplement see all
Selected features of excitatory synapses with respect to their dendritic location.

(A) Average release probability (Pr) at individual synapses plotted against distance to the first dendrite branching point. Solid line, linear regression (p value to reject H0 = zero slope and Pearson's r shown; n = 67). (B) Percentage difference in Pr between two synapses on one dendritic branch (as in Figure 1A, Figure 1—figure supplement 1A), plotted against the distance between them along the branch. Other notations as in (A) (n = 15). (C) Average release probability (Pr) plotted against relative synapse position at the dendritic branch: synapse co-ordinate was scaled to the 0–1 range representing the branch origin and the end, as indicated (n = 63; several spines with unidentifiable distal branch ends were excluded). (D) Probability of release success upon both afferent stimuli, plotted against distance from the soma. Other notations as in (A) (n = 40). (E) Apparent spine density along the dendrite (smallest/thinnest spines could be undetectable, see Discussion), plotted against distance from the soma. Other notations as in (A) (n = 65; several spines with unidentifiable local spine density were excluded). (F) Release probability (Pr), plotted against distance from the soma. Other notations as in (A) (n = 67).

A systematic application of this protocol at n = 67 individual synapses (53 cells) has revealed a highly significant trend towards higher Pr at more distal synapses (Figure 1C), with average Pr = 0.36 ± 0.02 (mean ± SEM). The linear regression for the data suggested a rise in average Pr from ~0.2 at 50 µm to ~0.5 at 300 µm (range 0.05–0.7; Figure 1C). Over this distance range, the PPR of release probabilities decreased, on average, from ~3 to ~1.1, for either P2 or P2* counts (Figure 1D–E; experiments with no reliable 2nd response detection and no 'failure-success' responses were not included). While these relationships show relatively low values of correlation coefficient, the interpretation of Pearson's r that is commonly adopted for a specimen-centred design is irrelevant in the present context (see Discussion).

There observations appear at variance with an elegant earlier report which detected no distance-dependent Pr trends along apical dendrites of CA1 pyramidal cells, based on the use-dependent blockade of NMDARs by MK801 (Smith et al., 2003). However, the MK801-based approach is sensitive to local glutamate spillover (Scimemi et al., 2004) and, in that case, involved a Mg2+-free solution, both of which could mask the Pr trend. Intriguingly, that study documented 15–50% lower average PPR values in distal compared to proximal synapses, consistently over 25–200 ms paired-pulse intervals, albeit without reaching statistical significance in a six-cell sample (Smith et al., 2003).

The centrifugal trend in Pr values remained significant when the distance was measured from the primary apical dendrites (Figure 2A), suggesting that this trend applies to oblique branches rather than simply reflecting a position along the main dendrite. Interestingly, the analysis of Pr values recorded in pairs of synapses located on the same dendrite has revealed that the connections with similar Pr tend to occur close to one another (Figure 2B). Thus, 'homogenisation' of Pr among local synapses, which was detected previously in cultured neurons (Branco et al., 2008), is likely to occur in ex vivo brain tissue. At the same time, we found no trends in Pr values with respect to the synapse position relative to the local dendrite branching point (Figure 2C; the centrifugal Pr trend in these data does not reach statistical significance because branch length is much smaller than the overall dendritic length, thus providing much reduced statistical power). The prevalence of larger, more densely packed spines near the dendritic branch origins was earlier reported to reflect normalisation of synaptic signals integrated at the soma (Katz et al., 2009). In this context, no gradient in Pr values within a branch (Figure 2C) suggests that the above normalisation should still be valid during sustained afferent activity.

Because higher Pr are normally associated with lower PPRs (as in Figure 1D), the probability of both stimuli initiating successful release showed only a barely detectable increase with the spine-soma distance (Figure 2D), whereas the second-release Pr values were evenly spread along the dendrites (Figure 2—figure supplement 1A). However, the longitudinal density of optically identifiable dendritic spines increased towards distal dendrites (Figure 2E), also showing positive correlation with the Pr (Figure 2F), but no correlation with the PPR values (Figure 2—figure supplement 1B).

Monitoring glutamate release at CA3-CA1 synapses with an optical sensor

We have previously shown (Jensen et al., 2017; Jensen et al., 2019) that the patterns of release probability at CA3-CA1 synapses can be successfully gauged by monitoring presynaptic glutamate release with the genetically encoded optical sensor iGluSnFR (Marvin et al., 2018). While the earlier work focused on individual axonal boutons tracked from one presynaptic cell, here we attempted to employ a similar method using local extracellular stimulation. Once iGluSnFR (AAV9.hSynap.iGluSnFR. WPRE.SV40) was expressed in the area CA1 s.radiatum, we therefore focused on individual presynaptic boutons that showed a fluorescence response to paired-pulse stimuli applied locally to Schaffer collaterals; the distance between the bouton and the s. pyramidale border was documented (Figure 3A). We thus recorded fluorescence responses to paired-pulse stimuli (50 ms apart) using a high-resolution spiral linescan ('Tornado') mode, as described earlier (Jensen et al., 2017; Jensen et al., 2019; Figure 3B, image panels).

Figure 3 with 1 supplement see all
Evoked glutamate release from Schaffer collaterals shows lower paired-pulse ratios at greater distances from pyramidal cell bodies.

(A) Experimental design: area of the hippocampal slice with iGluSnFR expressed in neuronal membranes (green channel); arrow, measured distance d between CA1 pyramidal cell body layer and the axonal bouton of interest; stim, stimulating electrode. (B) Examples of recorded axonal boutons (image panels, dotted circles; position of spiral 'Tornado' linescans is illustrated) showing characteristic glutamate signals in response to two afferent stimuli 50 ms apart (green traces, individual trials; black, average), at two distances from the s. pyramidale, as indicated. The lack of release failures reflects detection of glutamate escaping from multiple neighbouring synapses. (C) Paired-pulse ratio for optical glutamate signals: ΔF/F0(1) / ΔF/F0(2) averaged over 18–36 trials at individual boutons, plotted against distance to the soma. Other notations as in Figure 1C (n = 33). (D) Amplitude of the first glutamate response, ΔF/F0(1) averaged over 18–36 trials at individual boutons, plotted against distance to the soma. The amplitude values reflect the average amount of glutamate released from the bouton of interest, and glutamate escaping from its neighbours; other notations as in (C) (n = 33).

It turned out that in such recordings we could not reliably detect failures, throughout the trials (examples in Figure 3B). This has a simple explanation. In the hippocampal neuropil, released glutamate molecules can travel for up to ~2 μm from the synapse (Armbruster et al., 2020), not necessarily entering other synaptic clefts (see Discussion). Given the density of glutamatergic connections in area CA1 of ~2.0 μm−2 (Rusakov and Kullmann, 1998), individual synapses have >60 neighbours within the 2 μm proximity (i.e. within the spherical volume of ~33 μm3). Furthermore, two-photon excitation imaging collects emission signal integrated over a ~ 1-μm-thick focal plane. Thus, extracellular stimulation is likely to generate an iGluSnFR fluorescence transient due to glutamate spillover even if only a small fraction of local axons gets excited. This implies that the iGluSnFR signals reported here reflect release activity of several local synapses, which makes this approach suitable for PPR measurements rather than for direct Pr estimates.

Measuring the amplitudes of paired-pulse iGluSnFR signal transients thus revealed a clear trend towards lower PPR, hence higher Pr values, with greater distances from the s. pyramidale (PPR mean ± SEM: 1.49 ± 0.01, n = 33; regression at p<0.001; Figure 3C). This observation was qualitatively consistent with the PPR data obtained with the OQA of Ca2+ signals (Figure 1D-E) . In contrast, the first iGluSnFR response amplitude on its own showed no distance dependence (ΔF/F0, mean ± SEM: 37 ± 0.5%; range 0.18–0.81; Figure 3D). The latter was expected because, in addition to Pr per se, the iGluSnFR signal amplitude depends on several poorly controlled local concomitants, such as the degree of glutamate spillover (which in turn depends on the local expression of iGluSnFR and glutamate transporters) and/or density of activated axons.

In a complementary approach, we recorded iGluSnFR responses to a short burst of five afferent stimuli (at 20 Hz), in an attempt to relate their use-dependent release properties to their location with respect to the s. pyramidale. To optimise ROI selection under burst stimulation, these recordings were carried out in a frame (time-lapse) mode, as detailed previously (Henneberger et al., 2020; Kopach et al., 2020; Figure 3—figure supplement 1A). While such recordings have relatively low temporal resolution, the underlying (fast) fluorescence kinetics could be reconstructed using a straightforward fitting procedure (Materials and methods; Figure 3—figure supplement 1B). We used the slope of linear regression among the five ΔF/F0 peak points that correspond to the five stimuli onsets (Figure 3—figure supplement 1B) as a crude indicator of short-term release plasticity during the burst. Intriguingly, the slope values increased significantly with greater distances to the s. pyramidale (Figure 3—figure supplement 1C), suggesting greater fidelity in signal transfer by spike bursts towards more distal dendrites, as further explored below.

A realistic biophysical model explains the role of the release probability trend

To understand how the uneven distribution of Pr values affects signal handling by the postsynaptic cell, an earlier study used simulations with a sphere-and-cylinder cell model (Grillo et al., 2018). Here, we employed a realistic multi-compartmental model of a reconstructed CA1 pyramidal cell (Migliore et al., 1999), with 50 excitatory synapses distributed along apical dendrites, so that their positions, density, and Pr could be set as required (Figure 4A, Materials and methods). In the first test, we asked whether and how the documented centrifugal increase in Pr and synaptic density (termed 'Pr trend' and 'density trend', respectively) changes cell output under a synchronous discharge of Schaffer collaterals. A series of 100 paired-pulse stimulus test runs was simulated, each generating stochastic 'glutamate release' at the 50 synapses, either with the same average Pr (0.36) at uniformly scattered synapses, or with the Pr values distributed along the regression line (Figure 1C), or, additionally, with the synaptic density trend as found (Figure 2E), with the average Pr = 0.36 kept unchanged throughout, to ensure the unchanged efficacy of the overall synaptic input.

Figure 4 with 1 supplement see all
Computer simulations of CA1 pyramidal cell with stochastic excitatory synapses.

(A) Diagram, NEURON model of a reconstructed CA1 pyramidal cell (Migliore et al., 1999) (ModelDB 2796; variable time step dt, t = 34°C). 50 excitatory inputs (blue dots) generate bi-exponential conductance change (rise and decay time, 1 ms and 20 ms, respectively) stochastically, in accord with Pr. Traces, simulated somatic response to paired-pulse stimuli (50 ms apart), with Pr distributed either uniformly randomly (black), or in accord with the distance-dependent trend (as in Figure 1C; red), and both Pr and synaptic density trends (as in Figure 2E, blue; same average Pr = 0.36 ). (B) Summary of simulation tests in (A); dots, individual runs (n = 100); bars, mean EPSP amplitude (left, mean ± SEM: 2.90 ± 0.060, 3.27 ± 0.061, 3.20 ± 0.068, for the three conditions, respectively, as indicated) and paired-pulse ratios (right, mean ± SEM: 1.96 ± 0.047, 1.57 ± 0.036, 1.52 ± 0.0437, notation as above) are shown; ***p<0.005. (C) Input-output spiking rate relationship over the physiological range of input firing frequencies (per axon, bottom axis; total, top axis); hollow circles, uniform distribution of Pr; solid symbols, Pr follows the distance-dependent trend (as in Figure 1C); mean ± SEM are shown (n = 100 simulation runs). (D) Trace: A characteristic cell spiking burst (model as in A) in response to a Poisson-process afferent spiking input (~50 Hz per synapse) incorporating the experimental kinetics of short-term plasticity (STP) at CA3-CA1 synapses (Mukunda and Narayanan, 2017) (see Figure 4—figure supplement 1C–F for detail). Graph: Input-output spiking rate relationship across the physiological range of average input firing frequencies, with experimental STP incorporated; other notations as in (C); the top abscissa scale is nonlinear because STP affects average Pr in a biphasic, non-monotonous manner (see Figure 4—figure supplement 1C).

The results show that the Pr trend, on average, adds ~13% to the single-pulse EPSP amplitude while decreasing PPR by ~20%, compared to the evenly distributed Pr, whereas adding the density trend produces little further change (Figure 4A–B). Qualitatively similar results were obtained for the five-pulse burst tests based on the iGluSnFR data, suggesting that the Pr trend, when combined with the five-pulse slope trend (Figure 3—figure supplement 1C), boosts the voltage transfer value during the burst activity by ~15% (Figure 4—figure supplement 1A–B).

However, the main role of Pr in synaptic signal integration and transfer is played out when a time series of afferent spikes, rather than one synchronous discharge, generate a postsynaptic spiking response (Thomson, 2000; Williams and Stuart, 2002; Williams and Atkinson, 2007; Grillo et al., 2018). Therefore, in the second test, we simulated a Poisson process of stochastic synaptic discharges, across the physiological range of frequencies, and monitored the postsynaptic cell output. Experimental attempts to explore stochastic input to principal neurons have often employed synchronous (extracellular) activation of multiple afferent fibres, which is unlikely to happen in an intact brain where individual inputs can generate independent spike series. Thus, relatively intense synaptic input activity seen in pyramidal cells in vivo could correspond to a relatively low spiking frequency at the individual multiple afferents converging onto these cells (Bähner et al., 2011; Kowalski et al., 2016). These considerations suggest that short-term plasticity, if any, of intense synaptic input is actually driven mainly by the evolution of Pr values over low or moderate spiking frequencies at individual axons.

To understand whether and how the distance dependence of Pr influences spiking output of the postsynaptic pyramidal cell, we first compared the case of uniform Pr values (Pr = 0.36), with that of the Pr trend (the spine density trend was ignored as it had a negligible effect on EPSCs; Figure 4B). The outcome shows that having the Pr trend provides a clear advantage for signal transfer fidelity, which is particularly prominent at higher input frequencies (Figure 4C). However, in these simulations we assumed no use-dependent changes in the transmission efficacy as set by Pr. In fact, a recent study of the CA3-CA1 circuitry has established the time-dependent degree of (presynaptic) short-term plasticity (STP) during afferent bursts, over a wide range of firing frequencies (Mukunda and Narayanan, 2017). In brief, during repetitive presynaptic activity, Pr undergoes prominent facilitation and depression within the first 2–4 discharges, followed by a near-constant Pr value that depends on the average input frequency (Mukunda and Narayanan, 2017). We have incorporated this STP algorithm (Figure 4—figure supplement 1C) and successfully validated it in our model by comparing simulated and recorded outcomes of the fixed-frequency afferent bursts (Figure 4—figure supplement 1D–F). Equipped with this STP mechanism, we generated Poisson-process afferent input in our model and found, once again, that the Pr trend improves signal transfer, with the effect monotonically increasing with higher frequencies of afferent firing (Figure 4D).

Discussion

The way information is handled and stored by brain circuits depends largely on the rules of synaptic signal integration by principal neurons. Passive electric properties of neuronal dendrites impose distance-dependent attenuation of synaptic receptor current. Thus, distal synapses should have a progressively weaker influence on somatic spike generation. If true, this would mean that neurons with large dendritic trees possess a highly inefficient synaptic connectome. However, studies in hippocampal CA1 pyramidal cells have found that synaptic receptor numbers increase with the synapse-soma distance (Andrasfalvy and Magee, 2001; Nicholson et al., 2006) and that ion channel properties at distal dendrites enable local regenerative events (Cash and Yuste, 1999; Magee, 1999). These features seem to underpin near-linear summation of synaptic inputs across the entire tree (Cash and Yuste, 1999; Magee, 2000; Magee and Cook, 2000), thus rescuing the efficiency of dendritic signal integration. However, equal contribution of individual axo-dendritic synapses to somatic signal assumes that their average discharge rates are similar. This in turn directly depends on Pr and its use-dependent plasticity at individual synapses (Williams and Stuart, 2002; Williams and Atkinson, 2007). It was therefore a surprising discovery that the Pr values at the basal dendrites of CA1 pyramidal cells showed a centrifugal decrease, at least in conditions of synchronous, multi-synaptic paired-pulse stimuli (Grillo et al., 2018). If common, this trend, again, would question the efficiency of synaptic connectome: it seems wasteful to make so many distal synaptic connections that have so little impact.

Assessing Pr using optical quantal analysis and paired-pulse ratios

Here, we used OQAs to measure Pr at individual synapses in apical dendrites of CA1 pyramidal cells and found the opposite: Pr values were increasing with the synapse-soma distance along the dendrites. There are certain advantages of the OQA over electrophysiological PPR measurements. Firstly, OQA requires only single-pulse stimulation and establishes absolute, rather than relative, Pr values and their distribution pattern along the dendrites. Secondly, OQA deals with confirmed afferent activation of individual synapses and thus avoids the potential dependence between excitability of an axon and Pr value at its synapses, which would be masked in multiple-fibre stimulation. Thirdly, intrinsic differences in the short-term plasticity kinetics among different synaptic populations may contribute to the variation in PPRs. As for the Pr assessment based on the use-dependent NMDA receptor blocker MK-801, the progressive blockade of the NMDAR current may involve a degree of local inter-synaptic NMDAR activation via glutamate spillover (Scimemi et al., 2004). The latter should have a much smaller effect on the postsynaptic optical Ca2+ signal which, classically, displays S-shape sensitivity to the underlying NMDAR current, so, it is virtually insensitive to small current increments near the bottom (release failure) and near the top (release success) of the scale. Indeed, multiple studies involving classical OQA or single-synapse LTP induction have indicated that, under suitable protocols, postsynaptic Ca2+ signals elicited either by local afferent stimuli or by glutamate uncaging, do not spread to neighbouring spines (Oertner et al., 2002; Emptage et al., 2003; Boddum et al., 2016; Henneberger et al., 2020).

The technically strenuous OQA has its own limitations, of which the main one is the low signal-to-noise ratio, at least at some connections. In addition, the high-affinity (slowly unbinding) Ca2+ indicators required to detect dendritic spine signals tend to saturate rapidly, thus preventing reliable Pr readout for more than ~2 successive stimuli. We therefore, firstly, focused on synapses that showed a clear distinction between failures and successes (Figure 1B, Figure 1—figure supplement 1C,D), noting that this sampling still safely included the Pr range from 0.1 to 0.9 (Figure 1D). Secondly, we used two PPR readouts as a gauge for Pr variations, with the adjusted PPR measure avoiding any uncertainty about second-pulse detection of successes or failures (Figure 1E). Both PPR measures pointed to a clear centrifugal trend for Pr values (Figure 1D–E).

To further address a potential bias in selecting dendritic spines, we employed an alternative experimental design, in which Pr trends were assessed in the bulk of synapses in area CA1 by imaging evoked glutamate release with the genetically encoded optical sensor iGluSnFR. Similar to the earlier method (Grillo et al., 2018), we used PPR values to assess position-dependent variations in Pr. While the earlier method referred to the position of the stimulation electrode with respect to the patched postsynaptic cell morphology, the present approach provided an exact spatial reference to the site of glutamate release in the s. radiatum. As explained in the Results, iGluSnFR signals registered here under extracellular stimuli are most likely to reflect release of glutamate from several local synapses. This makes such an approach suitable for PPR measurements rather than for direct Pr estimates. The outcome of these experiments was consistent with the OQA data.

In the Pr-related data scatters presented here, Pearson's correlation r was relatively low (Figures 12). Indeed, the unexplained variance in the scatters, which is (1−r2), reflects large biological variability of Pr among synapses, as expected. Classically, low Pearson's r between variables x and y is interpreted as 'weak' correlation, because in such cases sampling a certain x value provides poor prediction for the corresponding y value, and vice versa. This prediction power could be critical, for instance, in a clinical setting where measurement x from an individual is used to gauge where this individual is on the y scale. However, in establishing the significance of a population-average trend such prediction power is often irrelevant because the uncertainty about the trend will diminish with sampling (which would effectively cancel out the symmetrical noise around the trend). For instance, pooling and averaging Pr values over 25 μm distance bins in Figure 1C data will increase Pearson's r to 0.80 although this would represent the same Pr dataset. In the pooling exercise, we could sample 'groups of synapses' rather than individual synapses because multi-synapse recording is allowed by the context, as in Figure 3 data or in earlier studies (Grillo et al., 2018). Thus, we must consider the statistical significance of the trend (p value for the non-zero slope) as a factor of prevailing importance, in the present context.

Possible implications for dendritic signal integration

We have thus found that both classical OQA and the iGluSnFR approach point to the centrifugal increase of Pr in apical dendrites of CA1 pyramidal cells. Intriguingly, an earlier study has found that at CA3-CA1 synapses release probability detected with OQA varies with the size of synaptic contacts (Holderith et al., 2012). This finding, combined with the evidence for greater AMPAR receptor numbers at more distal synapses (Andrasfalvy and Magee, 2001; Nicholson et al., 2006), is fully consistent with the notion of Pr increasing with the distance to the soma in these cells. Such data lend support to the idea that synaptic organisation can powerfully compensate for the 'electrotonic weakness' of synapses in distal dendrites. The effects found here are similar to the location-dependent features of synaptic inputs revealed with paired recordings between L2/3 and L5 pyramidal neurons in the neocortex (Williams and Stuart, 2002; Williams and Atkinson, 2007). Clearly, the occurrence of the higher-efficacy synapses at distal dendrites must boost temporal summation of local CA3-CA1 inputs (Stuart and Spruston, 2015). This is consistent with the observation that LTP induction there involves local sodium spikes (Kim et al., 2015). Furthermore, it has recently been found that individual CA3-CA1 axons make multiple synapses in distal (rather than proximal) dendrites of CA1 pyramidal cells (Bloss et al., 2018). This further argues for added efficiency of distal synaptic connections, and is in line with the relatively high dendritic spine density found here. Because the spine density estimate could be affected by limited resolution of two-photon excitation imaging (which may not necessarily detect smallest or thinnest spines), it would be prudent to assume that here we refer mainly to larger, mushroom-type dendritic spines.

Notwithstanding methodological differences, comparing the present results with the earlier findings (Grillo et al., 2018) suggests that the dendritic integration traits at basal and apical inputs in CA1 pyramidal cells are starkly different. Excitatory inputs to basal dendrites in the s. oriens include axons of other pyramidal cells, septal fibre, and commissural axons from the contralateral hippocampus. Interestingly, the spine density here increases almost twofold away from the soma (Bannister and Larkman, 1995; Ballesteros-Yáñez et al., 2006). Whether and how these different inputs cluster within different parts of the basal dendrites, and how their spiking activities differ, might shed light on the adaptive role of the apparently 'inefficient' Pr pattern there. For instance, biophysical modelling of a simplified neuron suggested that the Pr and PPR trends in basal dendrites help to facilitate supra-linear summation of local generative events such as dendritic spikes (Grillo et al., 2018). Indeed, such events have long been considered as a key mechanism of distal dendritic signalling (Larkum and Nevian, 2008; Branco and Häusser, 2009).

In the present study, we explored a realistic model of a CA1 pyramidal cell (Migliore et al., 1999) hosting excitatory synapses that incorporated varied Pr values, and were scattered across the apical dendrites, in accord with our experimentally observed trends. Simulations suggested that having the centrifugal Pr trend boosts synaptic signal transfer across the physiological frequency range, with the effect monotonically increasing with higher firing frequencies. Importantly, in an intact rat brain, CA1 pyramidal cells tend to fire in short, high-frequency bursts (Bähner et al., 2011; Kowalski et al., 2016) associated with sharp-wave ripple complexes (Bähner et al., 2011). The latter type of synchronised neuronal activity is thought to represent a powerful mechanism of memory consolidation in the hippocampus (Gruart et al., 2006; Buzsáki, 2015). In fact, short bursts of activity have long been considered as a key unit of information transfer by stochastic synapses (Lisman, 1997). Thus, the Pr trend found here in apical dendrites must play an essential role in supporting and enhancing the cellular underpinning of learning and memory in the brain.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Chemical compound, drugPicrotoxinTocrisCat. # 1128
Chemical compound, drugD-SerineSigma AldrichCat. # S4250
Chemical compound, drugOregon Green 488 BAPTA-1, Hexapotassium SaltThermoFisherCat. #O6806
Chemical compound, drugAlexa Fluor-594 HydrazideThermoFisher ScientificCat.# A10438
Chemical compound, drugFluo-8, potassium saltStratech ScientificCat.# 21087-AATOriginal Source: (Teflabs)
No longer exists
Chemical compound, drugFluo-4, Pentapotassium SaltThermoFisher ScientificCat.# F14200
Chemical compound, drugAgaroseSigma-AldrichCat. #9539; CAS: 9012-36-6Freshly prepared (4%)
Chemical compound, drugD-glucoseSigma-AldrichCat. #G8270; CAS: 50-99-7Freshly prepared (10 mM)
Chemical compound, drugKClSigma-AldrichCat. #P9333; CAS: 7447-40-7
Recombinant DNA reagentAAV9.hSynap.iGluSnFr.WPRE.SV40Penn Vector CoreAddgene
Cat. # 98929-AAV9
Strain, strain background
(Sprague-Dawley rat)
Sprague-DawleyCharles River UKCrl: CD (SD)
Strain: 0204
Strain, strain background
(C57BL/6J mouse)
C57BL/6JCharles River UKC57BL/6NCrl
Strain: 0159
Software, algorithmImageJNIHRRID:SCR_003070
https://imagej.nih.gov/ij/
Software, algorithmpClamp10Molecular DevicesRRID:SCR_011323
https://www.moleculardevices.com/products/axon-patch-clamp-system/acquisition-and-analysis-software/pclamp-software-suite
Software, algorithmOriginProOriginLab IncRRID:SCR_014212
https://www.originlab.com/origin
Software, algorithmMES 4.x-5.xFemtonics Ltd.RRID:SCR_018309
https://uk.mathworks.com/products/connections/product_detail/femtonics-mes.html
Software, algorithmWinWCP Versions 4.x-5.xStrathclyde Electrophysiology SoftwareRRID:SCR_014270 http://spider.science.strath.ac.uk/sipbs/software_ses.htm
Software, algorithmMatlabMathworksRRID:SCR_001622 https://uk.mathworks.com/products/matlab.html
Software, algorithmNEURON 7.6 × 64https://www.neuron.yale.edu/neuron/RRID:SCR_005393
Software, algorithmKinetics of CA1 pyramidal neuronhttps://senselab.med.yale.edu/ModelDB/ShowModel?model=2796#tabs-2RRID:SCR_005393
model = 2796#tabs-2
Software, algorithmreconstructed CA1 pyramidal neuronshttps://senselab.med.yale.edu/ModelDB/ShowModel?model=7509#tabs-1RRID:SCR_005393
model = 7509#tabs-1
OtherMulticlamp 700BMolecular DevicesRRID:SCR_018455
OtherOlympus FluoView1000OlympusRRID:SCR_014215
OtherFemto3D RCFemtonicsFemto3D RC
OtherBioRad Radiance 2100BioRadRadiance 2100
OtherLeica VT1200S vibratomeLeica BiosystemsRRID:SCR_018453

Animals and hippocampal slice preparation

Request a detailed protocol

Animal procedures were subject to local ethical approval and adhered to the European Commission Directive (86/609/ EEC) and the United Kingdom Home Office (Scientific Procedures) Act of 1986. Male Sprague Dawley rats (P20-P28) were sacrificed using an overdose of isoflurane. Animals were kept in groups (5–8 per cage) under standard housing conditions with 12 hr light–dark cycle and free access to food pellets and drinking water. Following decapitation brains were rapidly removed, hippocampi dissected out and transverse slices (350 µm thick) were prepared on a Leica VT1200S vibratome. The slicing was performed in ice-cold slicing solution that contained (in mM): 60 NaCl, 105 sucrose, 26 NaHCO3, 2.5 KCl, 1.25 NaH2PO4, 7 MgCl2, 0.5 CaCl2, 11 glucose, 1.3 ascorbic acid, and three sodium pyruvate. Alternatively, slices were cut in an ice-cold slicing solution that contained (in mM): 64 NaCl, 2.5 KCl, 1.25 NaH2PO4, 0.5 CaCl2, 7 MgCl2, 25 NaHCO3, 10 D-glucose, and 120 sucrose. All solutions were bubbled with 95% O2 plus 5% CO2, pH adjusted to 7.4. Once cut, slices were incubated in the oxygenated slicing solution at 34°C for 15 min and then allowed to equilibrate to room temperature for 15 min following which they were transferred to either a continuously oxygenated humid interface chamber, or a submersion chamber. Both chamber types contained an oxygenated artificial cerebrospinal fluid (aCSF) solution containing (in mM): 120 NaCl, 10 glucose, 2.5 KCl, 1.3 MgSO4, 1 NaH2PO4, 25 NaHCO3, 1.3 MgCl2, 2 CaCl2, with an osmolality of ~300 mOsm. Slices were then allowed to recover at room temperature for 1–5 hr prior to being transferred to the recording chamber of the microscope constantly perfused with 32–34°C.

Electrophysiology ex vivo

Request a detailed protocol

Whole-cell patch-clamp recordings were performed from CA1 pyramidal cells visualised using infrared differential contrast imaging. Thin-walled borosilicate glass capillaries were used to fabricate recording electrodes with a resistance of 2.5–3.5 MΩ. Intracellular pipette solution contained (in mM) KCH3O3S 135, HEPES 10, Na2-Phosphocreatine or di-Tris-Phosphocreatine 10, MgCl2 4, Na2-ATP 4, Na-GTP 0.4, 5 QX-314-Bromide (pH adjusted to 7.2 using KOH, osmolarity 290–295). Cell-impermeable Ca2+ dyes detailed below and the Ca2+ insensitive morphological tracer Alexa Fluor 594 hydrazide (50 μM) were routinely added to the intracellular solution. Throughout recordings cells were held in voltage clamp at −65 mV, and the aCSF routinely supplemented with 100 µM Picrotoxin (Tocris Bioscience) and 30 µM D-Serine. Electrophysiological recordings were carried out by using a set of remotely controlled micromanipulators and XY-translation stage (Luigs and Neumann) with a Multiclamp 700B amplifier (Molecular Devices). Signals were digitised at 10 kHz and stored for off-line analysis using WinWCP V4.1.5–4.7 (John Dempster, University of Strathcyde) or PClamp 10.4–10.5 (Molecular Devices).

Two-photon excitation imaging: optical quantal analysis

Request a detailed protocol

We used a Radiance 2100 imaging system (Zeiss–Bio-Rad; 60x Olympus objective, NA0.9) or a Femtonics Femto3D-RC imaging system (25x Olympus objective, NA1.05) both optically linked to two femtosecond pulse lasers MaiTai SpectraPhysics-Newport and integrated with patch-clamp electrophysiology.

Intracellular Ca2+ responses were routinely imaged in the Ca2+-sensitive (green) emission channel filtered to the bandwidth of 500–550 nm, with either of the Ca2+ sensitive dyes Oregon Green BAPTA-1 (250 μM, Invitrogen), Fluo-4 (400 μM, Invitrogen) or Fluo-8 (300 µM, Stratech Scientific). For optical quantal analysis, synaptically evoked responses in dendritic spines were triggered by minimal electrical stimulation using a monopolar glass stimulating electrode filled with aCSF and placed 10–20 µm from the dendritic branch targeted. To identify active synapses relatively fast (10 Hz) frame scans of the local dendrites were viewed whilst three 100 μs square pulses of 2–10 V were delivered with a 25 ms inter-stimulus interval using a constant voltage isolated stimulator (model DS2A-mkII, Digitimer Ltd, Welwyn Garden City, UK). This protocol was repeated until a Ca2+ response confined to a spine head was observed, then 400–700 ms line scans of the active spine were then recorded at a line scan rate of 500 Hz. In order to reduce the inherent bias toward high release probability synapses, a dual stimulus protocol (50 ms inter-stimulus interval) was employed to ensure response stability at synapses with low initial release probability. Line scans were repeated once every 30 s with 14-26 trials to assess release probability while correcting manually for focus fluctuations in the Alexa (red) channel. Line scan profiles were extracted using ImageJ 1.x (Schneider et al., 2012) or Femtonics MES six and routinely documented as ΔG/R where ΔG = G - G0 stands for the fluorescence signal in the green channel G with the baseline fluorescence G0 (averaged over the baseline time window) subtracted, and R stands for the Alexa fluorescence in the red channel (corrected for photobleaching, if any). Where recordings of synaptic responses were made at two sites along a single dendritic branch (distal and proximal to the branch point), equal numbers of recordings, first proximal–distal and distal–proximal, were obtained to remove bias in the temporal order by which the recordings were carried out. Experiments were excluded if the synapse became non-responsive, if spine morphology visibly changed during recording or if significant drift of the stimulation electrode occurred. Release probability (Pr) was defined as the probability of success on the first stimulus, as shown as P1 in figures: sum of successful trials / total number of trials, or {(1,0) + (1,1)} / (all); here brackets indicate counts of successes (1), failures (0), and all paired-stimulus trials (all). Where a second response was clearly visible over the first, P2 was also measured and defined as the sum of responses to the second stimulus + responses to both stimuli / total number of trials, or {(0,1) + (1,1)} / (all). PPR was defined as P2/P1 = P2/Pr. To account for some uncertainty over the identification of (1,1) trials (because of Ca2+ signal saturation upon the second stimulus), we also calculated adjusted PPR* = P2*/P1 where P2* = (0,1) / {(0,0) + (0,1)}, so that (1,1) counts were ignored.

Two-photon excitation imaging: morphological measurements

Request a detailed protocol

For morphological tracing purposes, wide-field high-resolution images of the apical dendritic tree (>75 µm2) were acquired at the maximal optical resolution as a series of individual 3-D stacks (50–100 μm deep each) in the Alexa emission channel (550–650 bandwidth; λx2p = 800 nm), collected in image frame mode (Biorad: 512 × 512 pixels, 8-bit; Femto3D > 512×512 pixels, 16-bit) at 1.5–2.5 µm steps. These images were used to determine the location of the individual synapses in relation to the CA1 pyramidal cell soma, first the averaged Alexa 594 image stack was used to form a template defining the two-dimensional x-y path (along the dendrite) between soma and spine of interest. The start point of the distance measurement was defined as the centre of an oval shaped to fit the edges of the imaged soma, from this point the segmented line function in ImageJ was used to trace the path along the dendritic tree between the soma and the spine of interest ending at the base of the spines shaft. This line was then saved as a region of interest and then overlaid onto the original 3D image stack. The 'reslice' function in ImageJ was then used to form a two-dimensional y-z image in which the y-axis represents the length of the template line and the z-axis representing the deviation in the depth of the slice thus enabling simple measurement of distance from soma to synapse also accounting for z-plane deviation. With this method, the distances from the origin of and to the end of the parent dendrite were also obtained. Higher magnification images of the same resolution were also acquired and the density of spines local to the synapse estimated by counting the total number of visible protrusions along a 10 µm portion of dendrite centred on the synapse of interest.

Viral transduction for labelling Schaffer collateral axonal boutons

Request a detailed protocol

For ex vivo imaging of axonal boutons, viral transduction in vivo via neonatal intracerebroventricular (ICV) injections in both male and female C57BL/6J mice (Charles River Laboratories) were used, as we detailed earlier (Henneberger et al., 2020; Kopach et al., 2020). Briefly, an AAV virus expressing the neuronal optical glutamate sensor, AAV9.hSynap.iGluSnFR.WPRE.SV40 (supplied by Penn Vector Core, PA) was injected into the cerebral ventricles of neonates (P0-P1) during aseptic surgery. The viral particles were injected at 2 µl per hemisphere (2.5–5 × 109 genomic copies in total), at a rate not exceeding of 0.2 µl/s, 2 mm deep, guided to a location approximately 250 µm lateral to the sagittal suture and 500–750 µm rostral to the neonatal coronary suture. After animals received AAV injections, they were returned to the mother in their home cage; they were systematically kept as a group of litters and monitored for days thereafter, to ensure that no detrimental side effects appear. Satisfactory transduction of iGluSnFR in vivo occurred within 3–4 weeks.

Two-photon excitation (2PE) imaging of glutamate release with iGluSnFR iGluSnFR fluorescence was recorded in the green emission channel under 2PE at λx2P=910 nm, using a Femtonics Femto3D-RC imaging system or an Olympus FV10MP imaging system, both optically linked to a Ti:Sapphire MaiTai femtosecond-pulse laser (SpectraPhysics-Newport), equipped with galvo scanners, and integrated with patch-clamp electrophysiology. In the s.radiatum, we focused on individual axonal boutons that could be visualised by iGluSnFR expression and showed a consistent optical response to electric stimuli applied to Schaffer collaterals. To minimise photodamage, only a single focal section through the region of interest (ROI) containing selected axonal fragments was imaged, at laser power not exceeding 3–6 mW under the objective. The focal plane was regularly adjusted, to account for specimen drift. To record optical signals with high spatiotemporal resolution while minimising photodamage, we employed the scanning mode of spiral ('tornado') linescans centred at the bouton of interest, as detailed previously (Jensen et al., 2017; Jensen et al., 2019). In these experiments, we recorded responses to paired-pulse stimuli at 20 Hz, normally collecting 20–30 trials ~ 30 s apart, and documented the shortest distance between the recorded bouton and the s. pyramidale border. The iGluSnFR fluorescence response to afferent stimulation was expressed as (F(t)- F0) / F= ΔF / F0, where F(t) stands for fluorescence intensity over time, and F0 is the baseline intensity averaged over ~150 ms prior to the first stimulus.

In a complementing experiment, we applied five-pulse stimuli (at 20 Hz) to Schaffer collaterals and used time-lapse imaging in frame-scan mode, as detailed previously (Kopach et al., 2020), thus providing short pixel dwell time (~0.5 µs) to enable signal registration over larger imaged regions, albeit at the expense of temporal resolution. This approach was chosen because regions of interest had to be selected off-line post hoc, based on their overall morphological stability: burst stimulation could lead to microscopic focal drifts or tissue changes in some areas, which had to be avoided. Therefore, the experimental time course of iGluSnFR featuring a limited number of time points was, in some cases, fitted with the underlying theoretical kinetics of five overlapping ΔF / F0 signals so that: ΔF/F0=Aiexp((tΔt(i1))τ1) (i = 1,..., 5) where Ai is the ith signal amplitude (fitted directly to the recoded amplitude), Δt = 50 ms (inter-spike interval), and the decay constant τ obtained from fitting the signal decay (tail) after the fifth pulse.

NEURON modelling

Request a detailed protocol

We employed a multi-compartmental NEURON (Hines and Carnevale, 2001) model of a reconstructed CA1 pyramidal neuron, with a full set of experiment-adjusted membrane currents, uploaded from the NEURON 7.6 × 64 database (https://senselab.med.yale.edu/modeldb; models 2796 and 7509). Simulations were performed using a variable time step dt; the cell axial-specific resistance Ra and capacitance Cm were set at Ra = 90 Om·cm, and Cm = 1 mF/cm2, throughout simulations. Fifty synapses were distributed over the dendritic apical tree, between 40 and 350 µm from the soma, either uniformly randomly or scattered randomly in accord with the experimental statistical trend, as specified. Computation tests for any set of parameters routinely consisted of 100 runs (trials). To avoid any bias arising from a particular (fixed) distribution of synapses along the dendrites, this distribution was generated anew at each trial, albeit with the same probability density function. However, in the tests where non-uniform and uniform distributions of Pr were compared, the random generator seed was kept unchanged.

At individual synapses, excitatory synaptic conductance gs(t) was modelled using the dual-exponential formalism realised in the NEURON function Exp2Syn. The dynamics models synaptic activation as a change in synaptic conductance with a time course given by: gs=Gm(exp(t/τ1)exp(t/τ2)) where τ1 and τ2 are the rise and decay time constants, respectively, and Gm is the factor defined by the peak synaptic conductance. The reverse potential of excitatory synapses was zero, in line with that for AMPA and NMDA receptor types. In line with the data reporting that synaptic strength increases centrifugally in apical dendrites of CA1 pyramidal cells (Andrasfalvy and Magee, 2001; Nicholson et al., 2006), Gm values were set proportional to the function f(x) = (0.51 + 0.002x) where x is the synapse-soma distance, and A is scaling factor (NEURON NetCon object), which in our case sets single-synapse conductance Gm (in µS). In simulations exploring subthreshold postsynaptic excitation, A was set at 0.002 µS, and in tests exploring the spiking input - output relationship A was set at 0.06 µS.

To replicate our experimental observations of release probability, the average Pr (P1) was set at 0.36, and P2 at 0.77. These values remained constant throughout the synaptic population in the case of the uniform Pr distribution. In the case of the experimental (centrifugal) Pr trend, the Pr (P1) and P2 values were set as a function of the synapse-soma distance x: P1 = (0.18 + 0.0012x), and P2 = (0.18 + 0.0012x) ∙ (3.31–0.0074x), reflecting experimental regressions, with the average Pr value kept at 0.36 throughout, to ensure the unchanged overall efficacy of integrated synaptic input. Calculations started 300 ms following full stabilisation of the NEURON model.

Sampling and statistics

Request a detailed protocol

The present study contained no longitudinal experiments or biological hypotheses based on multiple comparisons or experimental samples involving bootstrapping. Dendritic spines or axonal boutons were sampled in an arbitrary manner, as they appeared in the focal plane showing distinct morphology and clear (above two standard deviations of the no-response noise) stimulus-induced fluorescence responses. No exclusion criteria were applied to animals or slices, experiment sessions were discarded if the dendritic spine or axonal bouton became non-responsive, or if a significant drift of the stimulation electrode or preparation occurred. Blinding was not applicable to experimental manipulations during live recording sessions.

In real-time experiments, the statistical units were either individual dendritic spines, individual axonal boutons, or groups of axonal boutons where applicable, with the effect of experimental manipulation being the only factor of interest. In OQA experiments, 1–2 dendritic spines were successfully recorded from individual CA1 pyramidal cells, 1–2 cells were recorded per slice / animal. In iGluSnFR experiments, axonal boutons or group of boutons were sampled as explained in the text. The paired 'baseline-effect' comparison was therefore employed in all such experiments, in accord with the convention of electrophysiological experiments. The sample size was not predetermined because the variability of measured parameters - be it the variance for mean estimates or the unexplained Pearson's variance for regression estimates - was not known a priori, and there was no known restriction or bias in the cohort sampling. No repeated measures were included in testing biological hypotheses. Experimental readout involved live recording sessions carried out once per statistical unit.

Statistical data were routinely presented as mean ± SEM and sample size. Testing statistical significance of correlation / linear regression, involved a standard t-test for the slope parameter (equivalent to the correlation test), and included Pearson's r. Shapiro-Wilks tests for normality produced varied results across raw data samples. Accordingly, we used either the paired-sample t-test, or the paired-sample non-parametric Wilcoxon Signed Ranks test, where indicated. The statistical software in use was Origin 2019 (Origin Lab; RRID: SCR_014212).

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1–4.

References

Decision letter

  1. Sacha B Nelson
    Reviewing Editor; Brandeis University, United States
  2. John R Huguenard
    Senior Editor; Stanford University School of Medicine, United States
  3. Sacha B Nelson
    Reviewer; Brandeis University, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Thank you for submitting your article "Release probability increases towards distal dendrites boosting high-frequency signal transfer" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Sacha B Nelson as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by John Huguenard as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional analyses are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

Prior work has established that neuronal sensitivity to released transmitter scales with distance from the axon so as to mitigate limitations of integrating more distal synapses. Here, the authors show for the first time that presynaptic release of glutamate is also "scaled" with distance. This is an important finding that potentially changes how we think about synaptic integration in these well studied hippocampal neurons, but reviewers raise concerns over aspects of the analyses that need to be addressed. Acceptability of the manuscript will be assessed after it is clearer whether or not the reanalysis supports the central claims of the paper.

Title: It is eLife policy that the title should make clear the biological system under investigation. This could be accomplished by including reference to "…the mammalian hippocampus" or at least "hippocampus" or "hippocampal neurons."

Essential revisions:

The full reviews are included below to help clarify the concerns. Each of the concerns raised should be addressed, however many of these require simply textual clarifications. Key additional analyses required are:

1) The reanalysis suggested by reviewer #3.

2) An analysis of baseline noise distributions and of how apparent Ca+ and iGluSnFR "failure" signals vary with the site and intensity of stimulation, coupled to a more detailed quantitative argument as to why spillover affects the latter, but not the former measurements.

Reviewer #1:

The authors use optical quantal analysis and iGluSnFR measurements of glutamate release at CA3-CA1 synapses to convincingly demonstrate a gradient in release probability that favors distal synapses, matching in many respects, the distance-dependent scaling demonstrated postsynaptically by Magee and others. This is an elegant study that reveals a new feature of hippocampal synaptic physiology. Although the net effect is modest (~15%) the authors also use a realistic biophysical model to demonstrate that the observed gradient improves information transfer to CA1 neurons.

Figure 1: why are 1/3 of the synapses in C. missing in D. Were these synapses for which a second response was not clearly visible? Is this presumed due to saturation? Some brief description of the reason for exclusion should be provided.

Reviewer #2:

In this study, Jensen and colleagues use optical imaging of synaptic calcium signals and glutamate release and report that presynaptic release probability increases in CA1 pyramidal cell dendrites where more distal synapses have a higher release probability. These results complement earlier observations on the increase in postsynaptic efficacy in distal dendrites. While the findings presented in this manuscript address a fundamental question in synapse biology and are potentially important, further documentation is needed to ensure validity of these results and realize their full impact.

1) The findings presented here contradict an earlier thorough study by Magee and colleagues that addressed the same question and reached opposite conclusions. I think the authors should discuss this work (Smith et al., 2003) and address the potential discrepancy.

2) It is important to document baseline noise distributions for calcium indicator as well as glutamate probe measurements. It is critical to demonstrate the reliability of success/failure classification in individual recordings. What is the variation in response amplitudes? How do they compare to baseline fluorescence fluctuations in the same trace?

3) As the senior author and colleagues proposed 14 years ago PPR can be release-independent (Volynski et al., 2006) and therefore makes a poor predictor of release probability. Given the authors have near single synapse resolution, it is surprising that they heavily rely on PPR measure for their arguments. While paired pulse stimulation is a good tool to reduce the bias towards potential high Pr synapses, failure analysis (first response success probability) should be more emphasized over paired pulse ratios. It is worrisome that iGluSnFR experiments solely rely on PPR measure. The authors indicate that they cannot detect failures due to spill over contribution to their measurements. Wouldn't spill-over be more of a concern for calcium signals as they rely on NMDA receptors? Given this group's track record in the field, I would have expected more insight into this issue. I think the authors should at least make sure that this is not a simple technical issue (e.g. location and intensity of stimulation etc.).

Reviewer #3:

I am not able to assess the manuscript in its current form, as I have doubts about the extraction of quantal parameters from the fluorescence traces. The description in the methods does not seem to match the examples provided. I suggest re-analysis of the data (not just of the example traces!).

In the Materials and methods, P2 is defined as "the sum of responses to the second stimulus + responses to both stimuli/total number of trials". There are 4 possible outcomes of paired pulse stimulation: double failure (0,0), single responses (1,0 and 0,1) and double responses (1,1). The author's definition sounds like P2 = [(0,1) + (1,1)] / [(0,0) + (1,0) + (0,1) + (1,1)], which makes sense.

Looking at the example traces shown in Figure 1B and Figure 1—figure supplement 1C, however, the given values of P2 correspond to [(1,0) + (0,1) + (1,1)] / [(0,0) + (1,0) + (0,1) + (1,1)], which is actually a different quantity, (P1+P2). The resulting calculated PPRs are thus not correct.

Given the SNR of the (best) examples, it seems very risky to try to distinguish between (1,0) and (1,1). My recommendation would be to define P2 = (0,1) / [(0,0) + (0,1)], which can be reliably extracted. Unfortunately, this requires complete re-analysis of all raw data and may change results and conclusions.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Release probability increases towards distal dendrites boosting high-frequency signal transfer in the rodent hippocampus" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Sacha Nelson as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by John Huguenard as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

The authors perform sophisticated optical measurements of spine calcium and extracellular glutamate in hippocampal tissue to investigate synaptic properties on the long apical dendrite of CA pyramidal cells. They report that synapses close to the soma displayed pronounced paired-pulse facilitation and lower release probabilities compared to distal synapses. The correlations of these properties with synaptic location are statistically significant, but very weak, indicating that the major causes of variability are still not understood. Interesting for researchers in the field, exactly the opposite gradient of synaptic properties was recently reported for the basal dendrites of the same cell type.

Summary:

After receiving clarifications of the analysis methods in the initial round of revision, reviewer 3 has raised some additional concerns. Other points raised by reviewers 1 and 2 have been adequately addressed. Specific revisions are suggested below. The full text of review #3 is also included for clarity.

Essential revisions:

1) Reviewer 3 correctly points out that the effect size for the central observation is small. This does not necessarily preclude publication, but this point should be made clearer to the reader both by including the correlation coefficients in the figures and adding consideration of this issue to the Discussion.

2) The analysis of the burst experiments should be revised along the lines suggested below, or these experiments should be removed or the concern raised adequately rebutted.

3) Whether or not the average Pr and facilitation were kept constant for the different modeling scenarios should be clarified. If this is not the case, it will be necessary to revise the modeling portion of the study, or to make a convincing argument as to why this is not required in order to demonstrate the functional significance of the observed trend.

Reviewer #3:

Jensen et al., use calcium- and glutamate-imaging to assess the properties of presynaptic terminals impinging on the dendrites of CA1 pyramidal cells at different distances from the soma. They find a trend towards higher release probabilities and less facilitation at distal synapses. Synapses that are close together (< 40 um) had similar release probabilities. It is reassuring to see a similar distance-dependent decrease of paired-pulse ratios using two very different parameters, single spine calcium and local glutamate (pooled from several synapses). Opposite gradients have been described for synapses on the proximal dendrites of CA1 neurons (Grillo, 2018), which makes this study interesting and controversial. The authors then use a Neuron model to compare synaptic inputs with uniform properties to inputs that reflect the detected trend to higher Pr at distal inputs. Higher frequencies are indeed better transmitted in the model with a gradient in Pr. This seems to be a good way to test the intuition about the impact of the scaling effect, but I have some questions about the simulated scenarios (below).

While the spine calcium imaging experiments are performed with a local stimulation electrode positioned close to each responding spine, the glutamate experiments use a single stimulation electrode position in stratum radiatum, but read-out in different layers. Although GluSnFR expression is punctate, the optical measurements represent bulk extracellular glutamate and not individual synapses. Therefore, the distance from the simulation electrode is likely the key parameter determining max df/f at different positions. The paired pulse ratio is the only parameter that can be extracted from the glu imaging experiments.

1) The discussion of correlations is focused on their p-values (which indicate that the slope of a linear regression is not zero). Biologically more interesting are the correlation coefficients, which indicate (after squaring) that only a small part of the total variability in Pr is determined by synapse position. Correlations with R squared = 0.18 (Figure 1C) or 0.14 (Figure 2A) are considered "very weak", which is not necessarily what you expect given the title of the manuscript. As all data points are shown, readers will form their own opinion and realize that synapse position has only a weak impact on presynaptic properties. Still, I recommend showing the numerical value of r (or r squared) on all panels with linear fits (and relegate "n" to the figure legends).

2) Figure 3C shows strong facilitation at proximal synapses, linear behavior at distal synapses, which the authors take as supporting evidence that Pr is higher at distal synapses. However, the first pulse amplitude is similar (Figure 3D, averaging failures and successes), which the authors explain away by saying these measurements average glutamate from at least 60 synapses, some of which might not be stimulated. In other words, given the position of the stimulation electrode distal from the cell body layer, the fraction of activated axons is likely higher at distal ROIs, compensating for their lower Pr. This argument (from the authors!) renders the burst experiments moot (Figure 3—figure supplement 1), as the read-out is df/f (measured in large ROIs), which is mainly a function of the density of active axons. Thus, the burst response slope is expected to be steepest close to the tip of the stimulation electrode. Indeed, ROIs close to the cell body layer (80-100 μm from soma layer) showed no response at all in these experiments (Figure 3—figure supplement 1C)! The burst experiments should therefore be removed unless there is a possibility to normalize the data to the 1st pulse responses (as in PPR analysis).

3) Modeling (subsection “A realistic biophysical model explains the role of the release probability trend”), "with the Pr values distributed in accord with our data (Figure 1C)". According to the Materials and methods, you used the slope of the linear fit, which is not the same as the distribution of the measured Pr values (which are much more variable). This has to be clear in the text. In Figure 1C, some synapses have distances > 300 um, but the most distal synapses in the model seem to be just 150 μm from the soma. Is there a reason why you did not place synapses on the distal apical dendrite of the model? Given you put only proximal synapses in your model, was the average release probability and the PPR in the "Pr trend" simulation identical to the average from the measurements? The comparison of the different model scenarios only makes sense if the average Pr and facilitation was identical in all scenarios. Please show the distribution of synaptic properties for all scenarios. If the gradients really matter, I would also expect a comparison of the frequency response to proximal vs distal inputs (as in Grillo, 2018).

https://doi.org/10.7554/eLife.62588.sa1

Author response

Essential revisions:

The full reviews are included below to help clarify the concerns. Each of the concerns raised should be addressed, however many of these require simply textual clarifications. Key additional analyses required are:

1) The reanalysis suggested by reviewer #3.

2) An analysis of baseline noise distributions and of how apparent Ca+ and iGluSnFR "failure" signals vary with the site and intensity of stimulation, coupled to a more detailed quantitative argument as to why spillover affects the latter, but not the former measurements.

In brief, we have (a) added the PPR analysis suggested by reviewer #3 and corrected the related presentation errors (original analyses did use correct PPR formulae throughout); (b) added the signal-to-noise ratio data for all dendritic spines, (c) dealt systematically with all other reviewers' comments by revising and appending the text and illustrations, as further detailed below.

Reviewer #1:

The authors use optical quantal analysis and iGluSnFR measurements of glutamate release at CA3-CA1 synapses to convincingly demonstrate a gradient in release probability that favors distal synapses, matching in many respects, the distance-dependent scaling demonstrated postsynaptically by Magee and others. This is an elegant study that reveals a new feature of hippocampal synaptic physiology. Although the net effect is modest (~15%) the authors also use a realistic biophysical model to demonstrate that the observed gradient improves information transfer to CA1 neurons.

Figure 1: why are 1/3 of the synapses in C. missing in D. Were these synapses for which a second response was not clearly visible? Is this presumed due to saturation? Some brief description of the reason for exclusion should be provided.

Indeed, a proportion of synapses showed no detectable 0-1 responses whereas 1-1 responses could not be reliably resolved, either, from the decay of the first response, due to dye saturation. Whilst these data were excluded from the PPR analyses, we note that direct Pr measurements required the first-pulse scores only. The explanation has been added (subsection “Monitoring release probability with optical quantal analysis”, Figure 1D legend).

Reviewer #2:

In this study, Jensen and colleagues use optical imaging of synaptic calcium signals and glutamate release and report that presynaptic release probability increases in CA1 pyramidal cell dendrites where more distal synapses have a higher release probability. These results complement earlier observations on the increase in postsynaptic efficacy in distal dendrites. While the findings presented in this manuscript address a fundamental question in synapse biology and are potentially important, further documentation is needed to ensure validity of these results and realize their full impact.

1) The findings presented here contradict an earlier thorough study by Magee and colleagues that addressed the same question and reached opposite conclusions. I think the authors should discuss this work (Smith et al., 2003) and address the potential discrepancy.

This is a legitimate request. To gauge release probability along the dendritic tree, Smith et al., used an MK801-based approach (their Figure 8A) while recording NMDAR responses in a Mg-free solution and 2.5 mM Ca2+. While they detected no significant difference between proximal and distal synapses, the removal of Mg2+ might have had a significant, possibly heterogeneous, effect on Pr across synaptic populations. In addition, the MK801-based approach is sensitive to the extent of local extrasynaptic glutamate escape (spillover) (Scimemi et al., 2004), which could bias comparative analyses of Pr.

Intriguingly, Smith et al' s own data show 15-50% lower average PPR values (consistently over 25-200 ms paired-pulse intervals) in distal compared to proximal synapses (their Figure 8Biii). Although their dataset did not reach statistical significance at n = 6, it clearly pointed to the trend reported by us. We have added the corresponding discussion (subsection “Monitoring release probability with optical quantal analysis”).

2) It is important to document baseline noise distributions for calcium indicator as well as glutamate probe measurements. It is critical to demonstrate the reliability of success/failure classification in individual recordings. What is the variation in response amplitudes? How do they compare to baseline fluorescence fluctuations in the same trace?

In our hands, the distinction between Ca2+ signal failures and successes, at least in response to the first stimulus, was unambiguous, as shown by some detailed analysis in our previous work (Sylantiev et al., 2013; Boddum et al., 2016). To follow the Reviewer's request, here we have measured the first-response amplitudes of successes and failures (over the same sampling interval post-pulse) in all trials throughout; the plot of average values (per spine, mean ± SD) has been added to Figure 1—figure supplement 1D. These data show that the success amplitude was consistently above 2SD of the failure signal fluctuations, and that there were no distance-dependent trends. The text has been expanded accordingly (subsection “Monitoring release probability with optical quantal analysis”).

iGluSnFR and OQA: Our previous work detailed a robust OQA approach for the iGluSnFR signals recorded in a single axon in response to action potentials elicited in one presynaptic cell (Jensen et al., 2017; 2019). However, attempts to employ a similar method under local extracellular stimulation turned out to be much less straightforward because in most cases it was not possible to separate out a glutamate spillover signal originating from neighbouring synapses. There are good reasons for that. In the hippocampal neuropil, released glutamate molecules can travel for up to ~2 μm (e.g., Armbuster et al., 2020) whereas individual synapses are likely to have >60 neighbours within the 2 μm proximity radius (sphere volume of ~33 μm3 at synaptic density of ~2 μm-2). Furthermore, 2PE imaging integrates fluorescent signal collected over an ~1 μm thick focal plane. Thus, local extracellular stimulation is likely to generate a glutamate spillover signal even if it excites only a small fraction of local axons. The iGluSnFR signals induced by local extracellular stimuli tend to report, therefore, release activity of a small group of local synapses, which makes this approach usable for PPR measurements rather than for direct Pr estimates. We have added the corresponding discussion (subsection “Monitoring glutamate release at CA3-CA1 synapses with an optical sensor”).

3) As the senior author and colleagues proposed 14 years ago PPR can be release-independent (Volynski et al., 2006) and therefore makes a poor predictor of release probability. Given the authors have near single synapse resolution, it is surprising that they heavily rely on PPR measure for their arguments. While paired pulse stimulation is a good tool to reduce the bias towards potential high Pr synapses, failure analysis (first response success probability) should be more emphasized over paired pulse ratios. It is worrisome that iGluSnFR experiments solely rely on PPR measure. The authors indicate that they cannot detect failures due to spill over contribution to their measurements. Wouldn't spill-over be more of a concern for calcium signals as they rely on NMDA receptors? Given this group's track record in the field, I would have expected more insight into this issue. I think the authors should at least make sure that this is not a simple technical issue (e.g. location and intensity of stimulation etc.).

Indeed, in the past we were able to detect a release-independent component of PPR at paired-pulse intervals of 5 ms (Volynski et al., 2006), but it has long been argued that such phenomena fade away at 50 ms intervals. Nonetheless, we have added an alternative PPR measure, which was suggested by reviewer #3 and which deals precisely with the release-independent probability of the second release (Figure 1E). This addition had no effect on our conclusions.

As explained above, 2PE iGluSnFR imaging under extracellular stimulation is likely to pick up glutamate spillover signals originating at numerous neighbouring synapses. However, the overwhelming numbers of high-affinity glutamate transporters (with or without iGluSnFR present) normally ensure that such spillover can produce only a tiny NMDAR current at neighbouring synapses (e.g., Armbuster et al., 2020). Given the classical non-linear and saturating (S-shaped) sensitivity of postsynaptic Ca2+ fluorescence to NMDAR current, spillover should have little effect on OQA readout near the bottom (release failure) and near the top (release success) of the scale. Indeed, multiple studies involving classical OQA or single-synapse LTP induction have indicated that, under suitable protocols, postsynaptic Ca2+ signals elicited either by local afferent stimuli or by glutamate uncaging, do not spread to neighbouring spines. We have added the corresponding explanations (subsection “Assessing Pr using optical quantal analysis and paired-pulse ratios”).

Reviewer #3:

I am not able to assess the manuscript in its current form, as I have doubts about the extraction of quantal parameters from the fluorescence traces. The description in the Materials and methods does not seem to match the examples provided. I suggest re-analysis of the data (not just of the example traces!).

In the Materials and methods, P2 is defined as "the sum of responses to the second stimulus + responses to both stimuli/total number of trials". There are 4 possible outcomes of paired pulse stimulation: double failure (0,0), single responses (1,0 and 0,1) and double responses (1,1). The author's definition sounds like P2 = [(0,1) + (1,1)] / [(0,0) + (1,0) + (0,1) + (1,1)], which makes sense.

Looking at the example traces shown in Figure 1B and Figure 1—figure supplement 1C, however, the given values of P2 correspond to [(1,0) + (0,1) + (1,1)] / [(0,0) + (1,0) + (0,1) + (1,1)], which is actually a different quantity, (P1+P2). The resulting calculated PPRs are thus not correct.

Given the SNR of the (best) examples, it seems very risky to try to distinguish between (1,0) and (1,1). My recommendation would be to define P2 = (0,1) / [(0,0) + (0,1)], which can be reliably extracted. Unfortunately, this requires complete re-analysis of all raw data and may change results and conclusions.

We fully appreciate this comment and apologise for the technical glitch: the Pr values in question were left in the figures from the previous (discarded) example traces – the replacement of labelling was overlooked, which has now been corrected. However, all the original PPR analyses were carried out in accord with the correct formula where P2 = {(0,1) + (1,1)} / (number of trials). In fact, all the success-failure scores and all related data, for all spines, could be found in the Source data file supplied with the manuscript (Figure 1—source data 1). We do appreciate the suggestion to use an alternative / adjusted P2 definition and have added these data to Figure 1E and illustrations. As expected, both the original PPR and adjusted PPR values provided qualitatively identical conclusions.

We note that the distinction between Ca2+ signal failures and successes, at least in response to the first stimulus, was unambiguous, as shown by some detailed analysis in our previous work (Sylantiev et al., 2013; Boddum et al., 2016). To provide a quantitative reference, we have measured the first-response amplitudes of successes and failures (over the same sampling interval post-pulse) in all trials throughout. The plot of average values (per spine, mean ± SD) has been added to Figure 1—figure supplement 1D. These data show that the success amplitude was consistently above 2SD of the failure-signal fluctuations, and that there were no distance-dependent trends. The text has been expanded accordingly (subsection “Monitoring release probability with optical quantal analysis”).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Essential revisions:

1) Reviewer 3 correctly points out that the effect size for the central observation is small. This does not necessarily preclude publication, but this point should be made clearer to the reader both by including the correlation coefficients in the figures and adding consideration of this issue to the Discussion.

While we appreciate this comment, the interpretation of Pearson's r depends on the context. In our case, the unexplained variance (1-r2) reflects inherent biological variability of Pr among individual synapses, which is a well acknowledged phenomenon. The central effect we see is not small, it is rather large (Pr changes from ~0.2 at proximal to ~0.5 at distal synapses), but it is the large Pr variability that leads to a relatively low r. The reviewer correctly points out that low r values imply poor predictability of the regression trend based on an individual specimen: thus, high Pearson's r could be critical in clinical studies that seek to classify an individual's condition. But this is irrelevant to our quest because we are not interested in the action of individual synapses. As nerve cells are incessantly bombarded by synaptic discharges, our focus is on the overall trend (that transpires over virtually unlimited sampling), rather than on the symmetric variability / noise around this trend. In this context, high Pr variability is effectively cancelled out as we observe the average trend: the latter, in turn, has a significant effect on signal integration, as shown by our simulations.

To illustrate our point further, here we pooled and averaged all individual Pr values over the respective 25 μm distance bins. This narrowed down the Pr scatter along the regression line and thus increased Pearson's r to a 'strong' ~0.80. This synapsepooling exercise is somewhat representative of the case of multi-synaptic responses, as our iGluSnFR recordings or in Grillo et al., 2018. Judging by Pearson's r, the effect here appears much 'stronger' than we had with individual Pr values (r ~0.41), but in reality, it represents exactly the same Pr data set. We would therefore argue against overinterpreting Pearson's r as an indicator of the effect 'strength' of ‘weakness', in the present context.

We have expanded our explanations as a discussion point (subsection “Monitoring release probability with optical quantal analysis”, subsection “Possible implications for dendritic signal integration”) and revised figure labelling as suggested.

2) The analysis of the burst experiments should be revised along the lines suggested below, or these experiments should be removed or the concern raised adequately rebutted.

The corresponding reviewer's critique reads "Thus, the burst response slope is expected to be steepest close to the tip of the stimulation electrode. Indeed, ROIs close to the cell body layer (80-100 μm from soma layer) showed no response at all in these experiments (Figure 3—figure supplement 1C)!…".

There must have been some confusion here. The data in question (Figure 3—figure supplement 1) deal with the regression slope between first - fifth ΔF/F response peaks, which we employed as a crude short-term plasticity indicator during the burst. This slope indicator (in %ΔF/F s-1 units) has nothing to do with the absolute ΔF/F amplitudes, hence little to do with the synaptic density or electrode position. It is this regression slope (between first - fifth response peaks) that was close to zero in Figure 3—figure supplement 1C whereas the ΔF/F amplitudes were equally large at proximal and distal sites, as shown in Figure 3D. We have clarified this further (subsection “Monitoring glutamate release at CA3-CA1 synapses with an optical sensor”; Figure 3—figure supplement 1C legend).

3) Whether or not the average Pr and facilitation were kept constant for the different modeling scenarios should be clarified. If this is not the case, it will be necessary to revise the modeling portion of the study, or to make a convincing argument as to why this is not required in order to demonstrate the functional significance of the observed trend.

We can only apologise for not making it abundantly clear that the average Pr = 0.36 has been kept constant throughout simulations: comparing cases with different overall synaptic inputs would indeed be a pointless exercise. The clarification has been added (subsection “A realistic biophysical model explains the role of the release probability trend”, subsection “NEURON modelling”).

Reviewer #3:

1) The discussion of correlations is focused on their p-values (which indicate that the slope of a linear regression is not zero). Biologically more interesting are the correlation coefficients, which indicate (after squaring) that only a small part of the total variability in Pr is determined by synapse position. Correlations with R squared = 0.18 (Figure 1C) or 0.14 (Figure 2A) are considered "very weak", which is not necessarily what you expect given the title of the manuscript. As all data points are shown, readers will form their own opinion and realize that synapse position has only a weak impact on presynaptic properties. Still, I recommend showing the numerical value of r (or r squared) on all panels with linear fits (and relegate "n" to the figure legends).

While we appreciate this comment, the interpretation of Pearson's r depends on the context. In our case, the unexplained variance (1-r2) reflects inherent biological variability of Pr among individual synapses, which is a well acknowledged phenomenon. The central effect we see is not small, it is rather large (Pr changes from ~0.2 at proximal to ~0.5 at distal synapses), but it is the large Pr variability that leads to a relatively low r. The reviewer correctly points out that low r values imply poor predictability of the regression trend based on an individual specimen: thus, high Pearson's r could be critical in clinical studies that seek to classify an individual's condition. But this is irrelevant to our quest because we are not interested in the action of individual synapses. As nerve cells are incessantly bombarded by synaptic discharges, our focus is on the overall trend (that transpires over virtually unlimited sampling), rather than on the symmetric variability / noise around this trend. In this context, high Pr variability is effectively cancelled out as we observe the average trend: the latter, in turn, has a significant effect on signal integration, as shown by our simulations.

To illustrate our point further, here we pooled and averaged all individual Pr values over the respective 25 μm distance bins. This narrowed down the Pr scatter along the regression line and thus increased Pearson's r to a 'strong' ~0.80. This synapsepooling exercise is somewhat representative of the case of multi-synaptic responses, as our iGluSnFR recordings or in Grillo et al., 2018. Judging by Pearson's r, the effect here appears much 'stronger' than we had with individual Pr values (r ~0.41), but in reality, it represents exactly the same Pr data set. See Author response image 1.

Author response image 1

We would therefore argue against overinterpreting Pearson's r as an indicator of the effect 'strength' of ‘weakness', in the present context.

We have expanded our explanations as a discussion point (subsection “Monitoring release probability with optical quantal analysis”, subsection “Possible implications for dendritic signal integration”) and revised figure labelling as suggested.

2) Figure 3C shows strong facilitation at proximal synapses, linear behavior at distal synapses, which the authors take as supporting evidence that Pr is higher at distal synapses. However, the first pulse amplitude is similar (Figure 3D, averaging failures and successes), which the authors explain away by saying these measurements average glutamate from at least 60 synapses, some of which might not be stimulated. In other words, given the position of the stimulation electrode distal from the cell body layer, the fraction of activated axons is likely higher at distal ROIs, compensating for their lower Pr. This argument (from the authors!) renders the burst experiments moot (Figure 3 supplement 1), as the read-out is df/f (measured in large ROIs), which is mainly a function of the density of active axons.

We note here that the ΔF/F amplitude generated under extracellular stimulation is a poor predictor of Pr because of multiple, poorly-controlled factors affecting the optical glutamate spillover signal, such as: varied local synaptic density or inter-synaptic distance (e.g., a sharp increase in the occurrence of multi-synapse axonal boutons along the s. radiatum – s. lacunosum moleculare direction has been reported, Bloss et al., 2018), varied axonal density or axonal excitability under the stimulating electrode, varied expression ratio for iGluSnFR vs GLT-1 transporters that would have a complex effect on glutamate escape per se as well as on the detected optical signal, etc. These issues have been discussed in the text.

Thus, the burst response slope is expected to be steepest close to the tip of the stimulation electrode. Indeed, ROIs close to the cell body layer (80-100 μm from soma layer) showed no response at all in these experiments (Figure 3—figure supplement 1C)! The burst experiments should therefore be removed unless there is a possibility to normalize the data to the 1st pulse responses (as in PPR analysis).

There must have been some confusion here. The data in question (Figure 3—figure supplement 1) deal with the regression slope between first - fifth ΔF/F response peaks, which we employed as a crude short-term plasticity indicator during the burst. This slope indicator (in %ΔF/F s-1 units) has nothing to do with the absolute ΔF/F amplitudes, hence little to do with the synaptic density or electrode position. It is this regression slope (between first - fifth response peaks) that was close to zero in Figure 3—figure supplement 1C whereas the ΔF/F amplitudes were equally large at proximal and distal sites, as shown in Figure 3D. We have clarified this further (subsection “Monitoring glutamate release at CA3-CA1 synapses with an optical sensor”; Figure 3—figure supplement 1C legend).

3) Modeling (subsection “A realistic biophysical model explains the role of the release probability trend”), "with the Pr values distributed in accord with our data (Figure 1C)". According to the methods, you used the slope of the linear fit, which is not the same as the distribution of the measured Pr values (which are much more variable). This has to be clear in the text.

The reviewer is correct: we should have said 'regression trend' or the like. Clarified (subsection “A realistic biophysical model explains the role of the release probability trend”, subsection “NEURON modelling”).

In Figure 1C, some synapses have distances > 300 um, but the most distal synapses in the model seem to be just 150 μm from the soma. Is there a reason why you did not place synapses on the distal apical dendrite of the model? Given you put only proximal synapses in your model, was the average release probability and the PPR in the "Pr trend" simulation identical to the average from the measurements?

Figure 4A: We must admit that placing labelling or scale bars separately from the embedded images leads to trivial unexpected glitches during figure panel arrangements. Author response image 2 is the original NEURON rendering, with the original μm scale.

Author response image 2

The synapses in the model were scattered as originally described. The scale bar in Figure 4A has therefore been corrected.

The comparison of the different model scenarios only makes sense if the average Pr and facilitation was identical in all scenarios.

We can only apologise for not making it abundantly clear that the average Pr = 0.36 has been kept constant throughout simulations: comparing cases with different overall synaptic inputs would indeed be a pointless exercise. The clarification has been added (subsection “A realistic biophysical model explains the role of the release probability trend”, subsection “NEURON modelling”).

Please show the distribution of synaptic properties for all scenarios. If the gradients really matter, I would also expect a comparison of the frequency response to proximal vs distal inputs (as in Grillo, 2018).

We have clarified further the setting of Pr in the Materials and methods. As for comparing the effects of proximal versus distal synapses with the Pr trend, this would mean that we simply compare synaptic groups with a stronger or weaker overall release efficacy. This reviewer has argued above that such comparisons are not very informative.

https://doi.org/10.7554/eLife.62588.sa2

Article and author information

Author details

  1. Thomas P Jensen

    Queen Square UCL Institute of Neurology, University College London, London, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing
    For correspondence
    t.jensen@ucl.ac.uk
    Competing interests
    No competing interests declared
  2. Olga Kopach

    Queen Square UCL Institute of Neurology, University College London, London, United Kingdom
    Contribution
    Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3921-3674
  3. James P Reynolds

    Queen Square UCL Institute of Neurology, University College London, London, United Kingdom
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  4. Leonid P Savtchenko

    Queen Square UCL Institute of Neurology, University College London, London, United Kingdom
    Contribution
    Software, Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Dmitri A Rusakov

    Queen Square UCL Institute of Neurology, University College London, London, United Kingdom
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    d.rusakov@ucl.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9539-9947

Funding

Wellcome Trust (212251_Z_18_Z)

  • Dmitri A Rusakov

European Research Council (323113)

  • Dmitri A Rusakov

European Commission (857562)

  • Dmitri A Rusakov

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was supported by the Wellcome Trust Principal Fellowship (212251_Z_18_Z), ERC Advanced Grant (323113) and European Commission NEUROTWIN grant (857562).

Ethics

Animal experimentation: Animal procedures were subject to local ethical approval and adhered to the European Commission Directive (86/609/ EEC) and the United Kingdom Home Office (Scientific Procedures) Act of 1986. Experiments were carried out under the UK HO Project licence PPL707524.

Senior Editor

  1. John R Huguenard, Stanford University School of Medicine, United States

Reviewing Editor

  1. Sacha B Nelson, Brandeis University, United States

Reviewer

  1. Sacha B Nelson, Brandeis University, United States

Publication history

  1. Received: August 29, 2020
  2. Accepted: January 12, 2021
  3. Accepted Manuscript published: January 13, 2021 (version 1)
  4. Version of Record published: January 25, 2021 (version 2)
  5. Version of Record updated: January 27, 2021 (version 3)

Copyright

© 2021, Jensen et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 771
    Page views
  • 155
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

  1. Further reading

Further reading

    1. Neuroscience
    Diptendu Mukherjee et al.
    Research Article Updated

    Drug addiction develops due to brain-wide plasticity within neuronal ensembles, mediated by dynamic gene expression. Though the most common approach to identify such ensembles relies on immediate early gene expression, little is known of how the activity of these genes is linked to modified behavior observed following repeated drug exposure. To address this gap, we present a broad-to-specific approach, beginning with a comprehensive investigation of brain-wide cocaine-driven gene expression, through the description of dynamic spatial patterns of gene induction in subregions of the striatum, and finally address functionality of region-specific gene induction in the development of cocaine preference. Our findings reveal differential cell-type specific dynamic transcriptional recruitment patterns within two subdomains of the dorsal striatum following repeated cocaine exposure. Furthermore, we demonstrate that induction of the IEG Egr2 in the ventrolateral striatum, as well as the cells within which it is expressed, are required for the development of cocaine seeking.

    1. Neuroscience
    Antonio HO Fonseca et al.
    Research Article Updated

    Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameters. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a data set of >4000 USVs emitted by mice, VocalMat detected over 98% of manually labeled USVs and accurately classified ≈86% of the USVs out of 11 USV categories. We then used dimensionality reduction tools to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat makes it possible to perform automated, accurate, and quantitative analysis of USVs without the need for user inputs, opening the opportunity for detailed and high-throughput analysis of this behavior.