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Astrocyte GluN2C NMDA receptors control basal synaptic strengths of hippocampal CA1 pyramidal neurons in the stratum radiatum

  1. Peter H Chipman
  2. Chi Chung Alan Fung
  3. Alejandra Pazo Fernandez
  4. Abhilash Sawant
  5. Angelo Tedoldi
  6. Atsushi Kawai
  7. Sunita Ghimire Gautam
  8. Mizuki Kurosawa
  9. Manabu Abe
  10. Kenji Sakimura
  11. Tomoki Fukai
  12. Yukiko Goda  Is a corresponding author
  1. RIKEN Center for Brain Science, Japan
  2. Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology Graduate University, Japan
  3. Department of Animal Model Development, Brain Research Institute, Niigata University, Japan
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Cite this article as: eLife 2021;10:e70818 doi: 10.7554/eLife.70818

Abstract

Experience-dependent plasticity is a key feature of brain synapses for which neuronal N-Methyl-D-Aspartate receptors (NMDARs) play a major role, from developmental circuit refinement to learning and memory. Astrocytes also express NMDARs, although their exact function has remained controversial. Here, we identify in mouse hippocampus, a circuit function for GluN2C NMDAR, a subtype highly expressed in astrocytes, in layer-specific tuning of synaptic strengths in CA1 pyramidal neurons. Interfering with astrocyte NMDAR or GluN2C NMDAR activity reduces the range of presynaptic strength distribution specifically in the stratum radiatum inputs without an appreciable change in the mean presynaptic strength. Mathematical modeling shows that narrowing of the width of presynaptic release probability distribution compromises the expression of long-term synaptic plasticity. Our findings suggest a novel feedback signaling system that uses astrocyte GluN2C NMDARs to adjust basal synaptic weight distribution of Schaffer collateral inputs, which in turn impacts computations performed by the CA1 pyramidal neuron.

Editor's evaluation

This paper provides evidence that NMDA receptors containing the GluN2C subunit are expressed in hippocampal astrocytes and are involved in maintaining a wide distribution of presynaptic release probabilities at local excitatory synapses. Theoretical modelling suggests this wide distribution of synaptic efficacy is conducive to the expression of the synaptic plasticity. It will be interesting to see if future studies support a role for astrocytic NMDA receptors in memory storage.

https://doi.org/10.7554/eLife.70818.sa0

Introduction

Plasticity is a fundamental feature of neuronal connections in the brain, where experience-dependent changes in synaptic strengths over different time scales are crucial for a variety of processes ranging from neural circuit development, circuit computations to learning and memory (Feldman and Brecht, 2005; Bliss and Collingridge, 1993; Abbott and Regehr, 2004; Collingridge et al., 2010; Nicoll, 2017). Deciphering how neurons dynamically express different forms of synaptic plasticity while ensuring the optimal performance of the circuit remains a key challenge (Vitureira and Goda, 2013; Turrigiano, 2017; Nicoll, 2017; Brunel, 2016). In particular, the distribution of synaptic strengths is thought to reflect the capacity or the state of information storage of neural circuits (Barbour et al., 2007; Buzsáki and Mizuseki, 2014; Bromer et al., 2018). A better understanding of the cellular mechanisms that regulate synaptic strength distribution could therefore provide novel insights into the basis for the effective execution of circuit operations (Barbour et al., 2007).

The N-methyl-D-aspartate receptors (NMDARs) are a major ionotropic glutamate receptor type mediating excitatory synaptic transmission (Paoletti et al., 2013; Sanz-Clemente et al., 2013; Hansen et al., 2018). NMDARs are heteromeric assemblies consisting of four subunits, and seven NMDAR subunit genes have been identified that fall into three subfamilies: Grin1 encoding the obligatory GluN1 subunit, four Grin2 genes encoding GluN2A-D, and two Grin3 genes encoding GluN3A-B (Paoletti et al., 2013; Hansen et al., 2018). The differing subunit compositions confer NMDARs with distinct biophysical and pharmacological properties and contribute to their diverse biological activities. NMDARs are expressed throughout the central nervous system (CNS) and are crucial for normal brain function and plasticity. In particular, NMDARs that are typically composed of two GluN1 and two GluN2 subunits and are present postsynaptically, have been extensively studied for their role in memory mechanisms (Paoletti et al., 2013; Sanz-Clemente et al., 2013; Nicoll, 2017). NMDARs are also involved in pathological conditions such as epilepsy, ischemia, and traumatic brain injury (Hansen et al., 2017). In stark contrast to the multifaceted functions of neuronal NMDARs, very little is known of NMDAR functions in glial cells in the CNS. Amongst the better characterized glial NMDARs, NMDARs in oligodendrocytes play a role in axonal energy metabolism (Saab et al., 2016) and exacerbate pathological conditions (Káradóttir et al., 2005). In astrocytes, NMDARs have been long thought to be absent except following ischemia in vivo or under anoxia in vitro that promote their aberrant expression (Krebs et al., 2003; Gottlieb and Matute, 1997). Nevertheless, there are reports of GluN2C mRNA expression in astrocytes in the adult brain (Watanabe et al., 1993; Karavanova et al., 2007; Ravikrishnan et al., 2018), and several studies provide physiological evidence in support of astrocyte NMDAR expression under non-pathological conditions (Schipke et al., 2001; Serrano et al., 2008; Lalo et al., 2006; Letellier et al., 2016). The precise function for astrocyte NMDARs, however, has remained a matter of debate (e.g. Kirchhoff, 2017), and the role for astrocyte GluN2C has not yet been identified.

Astrocytes regulate a diverse set of essential brain activities, from neurovascular coupling, metabolic support, and ionic homeostasis (e.g. Attwell et al., 2010; Giaume et al., 2010; Simard and Nedergaard, 2004) to synaptic connectivity and function (e.g. Araque et al., 2014; Clarke and Barres, 2013). Recent studies have highlighted a role for astrocytes in the bidirectional control of basal synaptic transmission (Panatier et al., 2011; Perea and Araque, 2007; Di Castro et al., 2011; Martin-Fernandez et al., 2017; Schwarz et al., 2017), which likely involves signaling via their perisynaptic processes (Bindocci et al., 2017; Bazargani and Attwell, 2016). Moreover, mounting evidence suggests that astrocytes are not only capable of either simply potentiating (Panatier et al., 2011; Perea and Araque, 2007; Jourdain et al., 2007) or depressing synaptic transmission (Martín et al., 2015; Pascual et al., 2005), but a single astrocyte, which contacts tens of thousands of synapses, can concurrently mediate bi-directional synapse regulation at separate synaptic contact sites (Schwarz et al., 2017; Covelo and Araque, 2018). Astrocytes likely regulate neural circuit functions at multiple levels, as suggested by observations of both local and global astrocyte signaling that are triggered in a synapse, neuron, or circuit-specific manner (Martin-Fernandez et al., 2017; Martín et al., 2015; Chai et al., 2017; Deemyad et al., 2018; Dallérac et al., 2018; Santello et al., 2019). However, despite these advances, the basic mechanisms by which astrocytes detect and adjust synaptic transmission and the extent of their impact on local synaptic circuit activity are not fully understood.

Astrocytes express a plethora of neurotransmitter receptors and membrane transporters that are thought to modulate synaptic transmission (Araque et al., 2014; Bazargani and Attwell, 2016), including perisynaptic astrocyte glutamate transporters that influence the magnitude and kinetics of postsynaptic glutamate receptor activation and membrane depolarization (Pannasch et al., 2014; Murphy-Royal et al., 2015) and astrocyte metabotropic glutamate receptors (mGluRs) whose activation promote gliotransmitter release to provide feedback control of synaptic transmission (Panatier et al., 2011; Schwarz et al., 2017; Covelo and Araque, 2018; Araque et al., 2014; Bazargani and Attwell, 2016). Given the often shared expression of neurotransmitter receptors and transporters between astrocytes and neurons, an important challenge is to decipher the synaptic activity-dependent functions of astrocyte receptors and transporters independently of the functions of their neuronal counterparts. Notably, many of the prior studies have focused on pathological brain states (Krebs et al., 2003; Gottlieb and Matute, 1997) or used young brain tissue (e.g. Panatier et al., 2011) or culture preparations (e.g. Schwarz et al., 2017) in which the expression pattern of the astrocyte receptors and channels differ substantially from the adult brain in basal conditions (Sun et al., 2013; also see Boisvert et al., 2018). Consequently, the cellular basis and the network consequences of astrocyte-synapse interactions mediated by the astrocyte receptors and channels in the healthy adult brain remain to be clarified.

In a previous study, we identified a role for astrocyte signaling in regulating synaptic strength heterogeneity in hippocampal neurons, which involved astrocyte NMDARs (Letellier et al., 2016). Heterogeneity was assessed by comparing the presynaptic strengths of two independent inputs to pyramidal neurons, which was estimated by the paired-pulse ratio (PPR) of excitatory postsynaptic current (EPSC) amplitudes, a parameter inversely related to presynaptic release probability (Dobrunz and Stevens, 1997). Although basal PPRs were uncorrelated between the two inputs, surprisingly, inhibiting astrocyte NMDARs promoted the correlation of PPRs. This indicated that astrocyte NMDARs contributed to enhancing the differences in basal presynaptic strengths represented by the two different inputs (Letellier et al., 2016). Such pair-wise comparison of PPRs, however, is limited to providing an indirect measure of variability, and it remains unclear how astrocyte NMDARs control the overall shape of the PPR distribution. Furthermore, the crucial roles for neuronal NMDARs in regulating synaptic transmission and plasticity have confounded the interpretation of astrocyte NMDAR functions, which still remain enigmatic (Kirchhoff, 2017). Here, we further investigated astrocyte NMDAR-dependent regulation of synaptic strength in hippocampal CA1 pyramidal neurons in adult mice, focusing on the mode of regulation of presynaptic strength distribution, the NMDAR type responsible, and probing the potential layer specific regulation in area CA1. Our findings identify a role for astrocyte GluN2C NMDARs in maintaining broad presynaptic strength diversity specifically in the stratum radiatum (SR) by enhancing both strong and weak synapses. Mathematical modeling suggests that presynaptic strength diversity strongly influences the expression of long-term synaptic plasticity, which in turn, is important for network stability and learning and memory (Malenka and Bear, 2004; Collingridge et al., 2010; Royer and Paré, 2003; Zenke and Gerstner, 2017). Our findings suggest astrocyte GluN2C NMDARs as a key player in linking the regulation of synaptic strength distribution to the expression of synaptic plasticity that promotes optimal circuit performance.

Results

NMDAR-dependent regulation of PPR variability in hippocampal CA1 pyramidal neurons

In order to characterize the properties of astrocyte NMDAR-dependent modulation of synaptic strength, here we devised a simplified assay using a single stimulating electrode to monitor PPR variability of Schaffer collateral synapses. Hippocampal slices were prepared from adult mouse brain (P60-120), and EPSCs to a pairwise stimulation (50 ms inter-stimulus interval) of Schaffer collateral axons were recorded in CA1 pyramidal neurons (Figure 1A; Figure 1—figure supplement 1). MK801 (1 mM) was infused into the postsynaptic cell via the patch pipette, and after 10–15 min but prior to starting the experiment, Schaffer collaterals were stimulated at 0.1 Hz for at least 45 times to pre-block synaptic NMDARs. This resulted in over 92 % inhibition of synaptic NMDAR currents (Bender et al., 2006; Figure 1—figure supplement 1; see Materials and Methods). Upon confirming the block of postsynaptic NMDARs, the baseline EPSC recordings were taken; subsequently, NMDAR inhibitors were bath applied to test their effects on PPR variance. Under the block of postsynaptic NMDARs, any effects of NMDAR inhibitors, if observed, would be expected to reflect the influence of NMDARs present either in astrocytes or the presynaptic neurons. Notably, in mature brains which we used for our experiments, the contribution of presynaptic NMDARs in general had been suggested to be minimal (Shih et al., 2013). Upon bath applying MK801 (50 µM) or AP5 (50 µM), the population variance (δ2) of PPRs was reduced without a significant change in the mean (x)(Figure 1B–D; MK801 δ2 P = 0.016, x-P = 0.856; AP5, δ2 P = 0.047; x-P = .162). This overall decrease in PPR variance, when examined at the level of individual inputs, was associated with a change in PPR (ΔPPR) that was negatively correlated to the baseline PPR: PPR of some inputs increased while others decreased by the NMDAR antagonist application (Figure 1E–G). Furthermore, consistent with a lack of change in the mean PPR upon blocking NMDARs, a linear fit to the data intercepted the x-axis near the baseline mean PPR (x-axis intercept: MK801 = 1.97; AP5 = 1.95).

Figure 1 with 5 supplements see all
NMDARs contribute to synaptic strength diversity of Schaffer collateral synapses.

(A) Experimental scheme to assess the role of non-postsynaptic NMDARs. MK801 was perfused through the patch pipette (iMK801) to the CA1 pyramidal neuron for 10–15 min, and pairs of stimuli (Δt = 50 ms) were applied at least 45 times at 0.1 Hz to pre-block postsynaptic NMDARs prior to bath applying NMDAR antagonists (bottom). (B–D) Left: representative EPSC traces (average of 20 sweeps) to pairs of pulses (Δt = 50 ms) applied to Schaffer collateral axons before (baseline) and after wash-on of vehicle control (B), external MK801 (eMK801: 50 µM) (C), or AP5 (50 µM) (D). Right: histograms of the PPRs recorded during the baseline and vehicle or drug application periods were fit with single Gaussian curves. The p values for the comparison of the baseline to the experimental periods for the population mean (x-) and variance (δ2) were obtained by two-tailed paired sample t-test and one-tailed f-test for equal variances, respectively. (E–G) Scatterplots of the change in PPR vs. the initial PPR in vehicle control (E), MK801 (F), or AP5 (G) where ΔPPR = PPRexperimental-PPRbaseline; linear fits, Pearson’s correlation coefficients (r) and p values are as indicated. X intercept: MK801 = 1.97, AP5 = 1.95. (H–J) Plots of normalized EPSC amplitudes before and during the application of NMDAR antagonists (shaded area); n, number of inputs examined for each experiment. Baseline and experimental periods are indicated (black bars). Right: summary bar graph. * p < 0.05, Mann-Whitney U-test. Control = 30 inputs, 15 cells, 13 mice; MK801 = 28 inputs, 14 cells, 9 mice; AP5 = 24 inputs, 12 cells, 11 mice. Data are presented as mean ± s.e.m.

We also tested for the contribution of GluN2B NMDAR and also of mGluR5 in maintaining PPR variance. Bath applying Ro25-6981 (10 µM), a GluN2B-specific NMDAR antagonist (Fischer et al., 1997), and MPEP (25 µM), an mGluR5-specific antagonist (Gasparini et al., 1999), had little effect on PPR variance (Figure 1—figure supplement 2A-C; Ro256981 p = 0.362, MPEP p = 0.349). MPEP, however, significantly increased the mean PPR (Figure 1—figure supplement 2C; p = 0.033), which is consistent with a reported role for mGluR5 in presynaptic strength regulation (Panatier et al., 2011).

We next examined whether the changes in PPR elicited by the extracellularly applied NMDAR inhibitors accompanied changes in the EPSC amplitude and the coefficient of variation (CV) of EPSC amplitude, the latter as an additional measure of the change in presynaptic release probability (Malinow and Tsien, 1990; Larkman et al., 1992). On average, bath applied MK801 did not substantially alter the amplitude of the first EPSC to the pair of stimuli relative to control slices, despite a small tendency for an increase with a time delay (Figure 1I; p = 0.657). MK801 also had little effect on CV–2 of EPSC amplitudes (Figure 1—figure supplement 3B; p = 0.991), although the responses of individual inputs to MK801, albeit small in magnitude, were positively correlated to CV–2 (Figure 1—figure supplement 3D; Pearson’s r = 0.420, p = 0.026) and negatively correlated to PPR (Figure 1—figure supplement 3H; Pearson’s r = –0.474, p = 0.011). These data are consistent with the possibility that the observed reduction in PPR diversity by MK801 application is associated with normalization of presynaptic release probability.

In contrast to MK801, AP5 significantly increased EPSC amplitudes within 10 min of application (Figure 1J; p = 0.026), and the increase in CV–2 (Figure 1—figure supplement 3B; AP5 p = 0.003) suggested that the change in EPSC amplitude involved a presynaptic change. Notably, Ro25-6981 that had no effect on PPR variance, did produce a significant increase in CV–2 (Figure 1—figure supplement 2H; CV–2 p = 0.002), and produced a small but non-significant increase in EPSC amplitudes (Figure 1—figure supplement 2E; EPSC p = 0.261), which was consistent with the observed increase in PPR (above). None of the NMDAR antagonists caused substantial changes in the amplitude or frequency of spontaneous EPSCs (sEPSCs) or evoked EPSC waveforms (Figure 1—figure supplement 4).

We next sought to confirm the NMDAR-dependent modulation of presynaptic strength diversity as described in our previous study by comparing PPRs across two independent Schafer collateral inputs that converge onto a target CA1 pyramidal neuron (Letellier et al., 2016), and to observe the time course of PPR population variance change by NMDAR antagonists. In this method, the absolute difference of PPRs between the two inputs (PPR disparity) is taken as a proxy for presynaptic strength variability, with lower PPR disparity indicating lower PPR variability and vice versa (Figure 1—figure supplement 5A,B). Note that graphs of the PPR difference contain a regression-to-the-mean component, which is however effectively cancelled out when making direct graph comparisons, within the same parametric space. Consistent with our analysis thus far, bath application of MK801 and AP5 reduced PPR disparity (Figure 1—figure supplement 5D,E; MK801 p = 0.001, AP5 p = 0.004), while Ro25-6981 and MPEP did not (Figure 1—figure supplement 5G,H; Ro25-6981 p = 0.670, MPEP p = 0.263).

Collectively, these findings support the idea that NMDARs play a role in regulating presynaptic strengths to broaden the variability of PPR without appreciably impacting the mean PPR. Moreover, such an involvement of NMDARs in presynaptic regulation is distinct from the presynaptic regulation by mGluR5 that potentiates synaptic strength across a synapse population.

Astrocyte NMDARs mediate the effects of NMDAR antagonists on PPR diversity

To test the extent to which bath applied NMDAR antagonists affected synaptic transmission by targeting astrocyte NMDARs, we knocked down in astrocytes, the Grin1 gene that encodes GluN1, a requisite subunit for the cell surface expression of NMDARs (Fukaya et al., 2003; Abe et al., 2004). AAV carrying either mCherry-tagged nlsCre (Cre) or a control nls-mCherry lacking Cre or in some experiments control EGFP lacking Cre (Control), all of which were expressed under the GFAP104 promoter, was injected bilaterally into the dorsal hippocampus of adult Grin1 floxed mice (Figure 2A; see Materials and Methods section and E-phys statistics report for details)(Letellier et al., 2016). We used low titer AAVs (~0.4–4⨯1010 genome copies/injection) to avoid potential reactive astrocytosis, and although the extent was modest, we obtained a highly specific mCherry expression in astrocytes throughout the area CA1 with little expression in neurons (Figure 2B: Cre, 41.1% ± 2.4% of GFAP+ ve cells [n = 3,529], 0.3% ± 0.1% of NeuN+ ve cells [n = 1,150] from 5 mice; Control, 40.7% ± 1.9% of GFAP+ ve cells [n = 3,309], 0.5% ± 0.1% of NeuN+ ve cells [n = 1,217] from 5 mice). The efficacy of GluN1 knock-down in astrocytes was assessed electrophysiologically by patch-clamping astrocytes and monitoring slow depolarizing responses elicited by puff applying NMDA and glycine (1 mM each) (Figure 2—figure supplement 1) which were mediated by NMDARs but were also contributed in part by voltage-gated calcium channels in astrocytes (Letellier et al., 2016). Astrocytes that expressed Cre as identified by the mCherry fluorescence in stratum radiatum (SR) where Schaffer collateral synapses were present, showed depolarizing responses to NMDA-glycine puff that were significantly decreased compared to controls (Figure 2—figure supplement 1A; P = 0.006). Given that mCherry-positive astrocytes were present throughout the CA1, we also monitored NMDA-glycine puff responses in astrocytes in stratum oriens (SO) and stratum lacunosum moleculare (SLM). In contrast to mCherry-positive astrocytes in SR, mCherry-labeled astrocytes in SO or SLM showed depolarizing responses to NMDA-glycine puff that were not different between Cre and Control slices (Figure 2—figure supplement 1B, C; SO p = 0.247; SLM p = 0.268), despite the fact that viral transduction efficiencies were comparable amongst astrocytes across the three layers (SR: Cre 39.8% ± 2.4%, Control 40.2% ± 2.0%; SO: Cre 44.8% ± 3.0%, Control 39.3% ± 5.9%; SLM: Cre 39.1% ± 4.9%, Control 40.0% ± 4.0%). The input resistance of patched astrocytes was not altered by Cre expression in any layer (Figure 2—figure supplement 1D). Notably, NMDA-glycine responses in SLM were substantially smaller than those in SR or SO (P = 0.001, one way ANOVA, Bonferroni post-hoc tests). Nevertheless, AP5 bath application blocked NMDA-glycine puff-induced depolarizing responses in astrocytes in all three layers. This observation suggests that NMDARs are broadly expressed across hippocampal CA1 astrocytes. Moreover, the differential sensitivity of the slow depolarizing responses to astrocyte GluN1 knock-down between layers supports the possibility that the cumulative actions of signaling downstream to NMDAR activation including the contribution of voltage-gated conductances (Serrano et al., 2008; Letellier et al., 2016; Shih et al., 2013) could be heterogeneous across layers. We will return to this layer-specific differences later.

Figure 2 with 2 supplements see all
Astrocyte NMDARs mediate the antagonist-induced decrease in synaptic strength diversity.

(A) Astrocyte GluN1 was conditionally knocked down by injecting AAV-GFAP104-nls-mCherry-Cre (Cre), or as a control, AAV-GFAP104-nls-mCherry (Control), to the dorsal hippocampus of adult Grin1flx/flx mice. (B) Representative image of hippocampal area CA1 immunolabeled for GFAP (green) and mCherry (red) and counterstained with DAPI (blue), 21 days after virus injection. SO, stratum oriens; SPy, stratum pyramidale; SR, stratum radiatum; SLM, stratum lacunosum moleculare; MOL, molecular layer; GCL, granule cell layer. Right: magnified view in SR showing restricted expression of mCherry to GFAP-positive astrocytes (arrowheads). Scale bar, 20 µm. (C) Recording scheme: CA1 pyramidal neurons were patched in acute slices prepared 21 days after Control or Cre AAV infection. EPSCs were elicited by placing the stimulating electrode in the SR. Patch pipette contained MK801 (iMK801) for experiments in E-H. (D) Left: Averaged EPSC traces from representative recordings in slices from Control (top) or Cre (bottom) virus-infected mice. Individual traces are shown in grey. Right: Histograms of PPRs recorded from control (top) and Cre slices (bottom) were fit with single Gaussians. The p values were obtained for the population mean (x-) (two-tailed t-test) and variance (δ2) (one-tailed f-test for equal variances) comparing PPR distributions from control (n = 72 inputs, 43 cells, 14 mice) and Cre (n = 74 inputs, 46 cells, 12 mice) slices. (E,F) Left: average EPSC traces in baseline (grey) and after drug treatment (color) from a representative experiment. Right: PPR histograms (top) and ΔPPR vs. baseline PPR plots (bottom) in baseline and after bath applying AP5 (50 µM) to control (E) and Cre (F) slices. The p values for the population mean (x-) and variance (δ2) are as in D, comparing PPRs before and after the drug treatment. Linear fits and Pearson’s correlation coefficients (r) and p values are shown in scatter plots. (G,H) Plots of normalized EPSC amplitudes before and during the application of AP5 to control (G) and Cre-infected (H) slices (shaded area); n is the number of inputs examined. Right: summary bar graph. **p = 0.005, Mann-Whitney U-test. Control+ AP5 = 22 inputs, 12 cells, 5 mice; Cre+ AP5 = 24 inputs, 14 cells, 5 mice. Data are presented as mean ± s.e.m.

Having detected GluN1-dependent functional NMDAR activity in SR CA1 astrocytes, we next asked whether the GluN1 knock-down also affected synaptic transmission of Schaffer collateral synapses similarly to the bath applied NMDAR antagonists. Baseline PPR showed a significantly reduced PPR variance in Cre-infected slices compared to control slices without a change in the mean PPR (Figure 2C and D; δ2 P = 0.033; x-P = 0.725). Analysis of the relative PPR from two independent Schaffer collateral inputs also revealed lower PPR disparity in Cre injected slices compared to control slices (Figure 2—figure supplement 2A; p = 0.009). These observations are consistent with a reduced PPR variance upon compromising astrocyte NMDAR signaling as observed with bath applied NMDAR inhibitors. The amplitude and frequency of spontaneous EPSCs and the waveform of evoked EPSCs were unaltered by astrocyte-specific GluN1 knock-down (Figure 2—figure supplement 2B,C).

We next determined whether astrocyte GluN1 knock-down could occlude the effects of bath applied AP5 on PPR variance, by recording from postsynaptic CA1 neurons infused with MK801 in Control and Cre-infected slices. Similar to naive slices, in Control slices, AP5 decreased the population variance of PPRs by strengthening some synapses and weakening others (Figure 2E; Control + AP5 p = 0.017). In addition, AP5 caused also a variable increase in EPSC amplitudes (Figure 2G; AP5 p = 0.005) that occurred without a concomitant change in the amplitude or the frequency of spontaneous EPSCs (Figure 2—figure supplement 2D,E). In Cre slices, however, the decrease in PPR variance by AP5 was strongly attenuated, despite the modest extent of virus infection in Cre slices (Figure 2F; Cre + AP5 p = 0.789). The analysis of PPR disparity across two inputs also showed substantially decreased inhibitory effect of AP5 in Cre slices relative to controls (Figure 2—figure supplement 2H,I; Control + AP5 p = 0.013, Cre + AP5 p = 0.315). Curiously, the small increase in EPSC amplitudes observed upon applying AP5 was also attenuated in Cre slices (Figure 2H). This suggests that although the effect of NMDAR antagonists on PPR diversity and EPSC amplitudes are likely to target distinct compartments – presynaptic and postsynaptic – both mechanisms may involve astrocyte NMDARs. Together, these observations indicate that astrocyte NMDARs are the major mediators of the synaptic effects of acutely blocking NMDARs in our experimental conditions, and further support the view that astrocyte NMDARs help maintain the broad variability of presynaptic strengths across a synapse population.

Modelling of release probability variability reveals its impact on synaptic plasticity

In order to gain an insight into the physiological role for the broad release probability distribution, we sought to assess the impact of reducing the presynaptic strength variability on a synaptic learning rule by constructing a mathematical model. We first modelled release probability variations based on gamma distributions (Buzsáki and Mizuseki, 2014; Branco and Staras, 2009; Murthy et al., 1997). To achieve this, we experimentally obtained estimates of release probability using the styryl dye, FM1-43 in synaptic networks of hippocampal pyramidal neurons co-cultured with astrocytes. Briefly, neurons were extracellularly stimulated with 40 action potentials at 20 Hz in the presence of FM1-43 to label the readily releasable vesicle pool, a parameter related to release probability (Rosenmund and Stevens, 1996; Letellier et al., 2019). Two sets of data were obtained: one in control condition containing 10 μM CNQX to prevent recurrent activity (‘control’), and another in the additional presence of 50 μM AP5 (‘AP5’). Because the block of synaptic AMPARs by CNQX would compromise the activation of neuronal NMDARs, we reasoned that any changes to the FM1-43 signal in the AP5 condition (i.e. CNQX + AP5) relative to the control could suggest the contribution of astrocyte NMDARs to the readily releasable pool size. The gamma distributions that best fit the FM1-43 signal distributions in control and AP5 groups were then obtained to model the release probability distributions in the simulations; the optimal parameters for the gamma distributions were determined by maximizing the likelihood function for each case (Figure 3—figure supplement 1; see Materials and methods). Notably, the gamma distribution for the AP5 condition showed a smaller variance than that of the control condition, which was reminiscent of the effect of bath applied AP5 on PPR distribution. To facilitate the cross-check of whether the reduction in variance per se was an important factor affecting the synaptic learning rule in the simulations, we also considered a gamma distribution that best fit the FM1-43 signal distribution in the AP5 condition by fixing the mean of the gamma distribution to remain the same as for the control condition (‘AP5-with-control mean’; Figure 3—figure supplement 1B).

We next asked whether the influence of release probability variance on the efficacy of long-term synaptic plasticity could be observed in a computational model by simulating spike-timing dependent plasticity (STDP) using two leaky integrate-and-fire (LIF) neurons with a synaptic connection (Figure 3A, see Materials and methods). The presynaptic neuron received a spike train of magnitude A1 = 500 pA, while the postsynaptic neuron received a spike train of variable magnitude A2. Pairing 10 spikes at 20 Hz with a time difference between spike trains of Δt resulted in LTP or LTD, with positive Δt values producing LTP and negative Δt values resulting in LTD (Figure 3B). Simulations using the decreased release probability variance of the AP5 condition showed little change in LTD whereas LTP was compromised compared to the control condition. Specifically, when the efficacy of synaptic weight change was compared as a function of the amplitude of the postsynaptic current injection A2, a less variable release probability represented by the AP5 condition required higher levels of current injection to trigger LTP compared to a broad release probability distribution represented by the control condition (Figure 3B, left versus middle panel). The effect of narrowed release probability variance in increasing the threshold for eliciting LTP persisted also for the AP5-with-control mean group (Figure 3B, right panel). A further comparison was made by plotting the averages of the maximum change of synaptic weight for different Δt when the A2 was between 180 pA and 200 pA for the three conditions (Figure 3C). The analysis clearly revealed that the potentiation was more sensitive for smaller input current impulses. In effect, the results showed that the control condition exhibited a higher propensity to undergo LTP compared to the two AP5 groups representing conditions of reduced release probability variance.

Figure 3 with 2 supplements see all
Synaptic neurotransmitter release probability diversity impacts long-term synaptic plasticity.

(A) Illustration of the model used in the simulation. There are two leaky integrate-and-fire neurons with a synaptic connection. Each neuron receives a current injection whose magnitude is A1 for neuron 1 and A2 for neuron 2. The input spike trains for neuron 1 and neuron 2 are misaligned by Δt. A1 is set at 500 pA which is large enough to trigger neuron 1 to spike, while A2 is varied to test the sensitivity of STDP between neurons. The release probability of neuron 1 [subject to alteration by the neighboring astrocyte(s)] is modelled by different gamma distribution for three different conditions: ‘control’, ‘AP5’ representing narrowed release probability distribution, and ‘AP5-with-control mean’ in which the best fit distribution for the AP5 case has been obtained by fixing the mean to remain the same as the mean of the control distribution. (B) Summary of simulation illustrating the efficacy with which long-term synaptic plasticity is triggered as a function of the amplitude of postsynaptic current injection A2 . Changes in synaptic weight w(t) after giving 10 spike pairs at 20 Hz in which the current injection to the presynaptic neuron is 500 pA and the postsynaptic neuron is A2, in control (left), in AP5 (middle), and in AP5-with-control mean (right). The current injection sequences to the presynaptic and the postsynaptic neurons are separated by a time difference Δt. Color intensity shows the degree of potentiation (red) and depression (blue) of synaptic weight changes observed for variable amount of A2 injected to the postsynaptic neuron at each Δt for the three different conditions represented by each panel. Horizontal green dashed line shows the minimum amount of current injection to the postsynaptic neuron A2 that is required to trigger long-term potentiation (LTP) in the control condition, and similarly, the small arrows in each panel indicate the minimum amount of current injection A2 that evokes LTP in control (left), in AP5 (middle), and in AP5-with-control mean (right). Gray-shaded area is the range of A2 used to calculate the average of the maximum change in w(t) among Δt, which is shown in (C). For release probability distributions with the same mean, a smaller amount of current injection is needed to trigger LTP if the release probability distribution has a wider variance. (C) The averages of the maximum change in w(t) in the three different conditions.

We also explored the impact of narrowing the release probability variance under a more general STDP scenario by examining synaptic weight changes elicited in a pair of neurons receiving Poisson spike trains (Figure 4A). Specifically, the synaptic weights obtained in response to the same Poisson sequences in the control condition compared to the AP5 condition for independent simulations showed that the extent of potentiation was consistently smaller in the AP5 group (Figure 4B, left panel). Similarly, the extent of depression was also found decreased in the AP5 group (Figure 4B, left panel). These findings suggested that the reduced release probability variance could reshape the expression of long-term plasticity. Whether the reduced efficacy of plasticity expression could be attributed to the change in release probability variance per se was confirmed by testing also the AP5-with-control mean condition (Figure 4B, right panel), which showed attenuation of both LTP and LTD. When the absolute average difference in the extent of LTP or LTD relative to control was compared between the two AP5 conditions, reducing the variance of the release probability distribution without a change in the mean had a stronger effect in compromising LTP than LTD, while in the AP5 condition, both potentiation and depression were similarly affected (Figure 4C). Collectively, the results of the simulations suggest that a broader distribution of release probability could serve to promote the expression of long-term plasticity.

Synaptic neurotransmitter release probability diversity impacts long-term synaptic plasticity triggered by Poisson spike input series.

(A) Illustration of the model used in the simulation. Two leaky integrate-and-fire neurons with a synaptic connection received independent Poisson spike trains. Similarly to Figure 3, the release probability of neuron 1 [subject to alteration by the neighboring astrocyte(s)] is modelled by three different gamma distributions representing control, AP5, and AP5-with-control mean conditions. (B) Summary of simulation. Major vertical axis (left, blue) shows the relative differences in the final synaptic weights in AP5 (left plot) or AP5-with-control mean (right plot) compared to the control condition. The relative differences (blue dots) are plotted against the final synaptic weight change in control condition (horizontal axis). The binned data are shown as green bars (minor vertical axis: right, green). The plots indicate that spike inputs that promote potentiation under control condition tend to favor weaker potentiation in either AP5 or AP5-with-control mean, and spike inputs that promote depression under control condition tend to favor weaker depression in either AP5 or AP5-with-control mean. (C) The reduction of long-term plasticity in AP5 and AP5-with-control mean representing situations of narrowed release probability variance, relative to the control condition. The reduction of plasticity is defined by the mean of the differences between the final weights in NMDAR-blocked conditions and the control condition with a sign correction since long-term potentiation and long-term depression change weights in opposite directions.

Hippocampal astrocytes express GluN2C NMDARs

Our results thus far point to astrocyte NMDARs, and in particular, the involvement of the obligatory subunit GluN1 in regulating the variability of presynaptic strengths. In order to obtain direct molecular evidence for the expression of NMDAR in hippocampal astrocytes and to clarify the relevant NMDAR subtype, we performed single cell RT-PCR to compare the expression of Grin1, Grin2a, Grin2b, and Grin2c mRNAs in CA1 astrocytes across SO, SR and SLM layers and in CA1 pyramidal neurons (Figure 5A). Acute hippocampal slices were prepared from adult mice as used for electrophysiology experiments, and after labelling astrocytes with sulforhodamine101 (Nimmerjahn et al., 2004), RNA from single cells was extracted by patch-clamping. All putative astrocytes had low input resistances (SR 11.20 ± 0.36 MΩ; SO 13.24 ± 0.43 MΩ; SLM 10.60 ± 0.33 MΩ; n = 39 cells from 9 mice sampled for each layer), lacked action potentials, and displayed linear current-voltage relationships (Figure 5A and B). CA1 pyramidal neurons expressed high levels of Grin1, Grin2a, and Grin2b subunit mRNAs but expressed substantially low levels of Grin2c mRNA. In contrast, astrocytes in all three layers showed robust expression of Grin2c mRNA. Moreover, astrocytes showed minimal expression of Grin2a and Grin2b mRNAs, and the Grin1 mRNA expression in astrocytes was also substantially low compared to neurons (Figure 5C). Grin2d mRNA in CA1 pyramidal neurons and astrocytes was also tested, and it was undetectable in RNA extracted from patch clamping both cell types while the probe itself could detect Grin2d mRNA in brain tissue extracts (data not shown). Inability to detect Grin2d mRNA in astrocytes is consistent with previous reports of single cell RNA-seq analysis (Khakh Lab database; also see Alsaad et al., 2019). We also examined the levels of expression of NMDAR subunits in CA3 pyramidal neurons, and the pattern of subunit expression mirrored the pattern observed for CA1 pyramidal neurons (data not shown).

Adult mouse hippocampal astrocytes express GluN2C-NMDA receptors.

(A) Top: patch RT-PCR strategy. Bottom: representative voltage responses to current steps in CA1 pyramidal neuron (far left, black), and current responses to voltage steps in astrocytes in stratum radiatum (SR, green), stratum oriens (SO, yellow), and stratum lacunosum moleculare (SLM, blue). Scale bars: neuron, 40 mV, 500 ms; astrocytes, 6 nA, 200 ms. (B) Summary of I-V curves of astrocytes obtained before collecting RNA. N = 39 cells, 36 slices, 9 mice for each layer. (C) Summary of RT-PCR analysis for mRNA encoding indicated NMDAR subunits: Grin1 (1), Grin2a (2A), Grin2b (2B), and Grin2c (2C). Control samples (no patch) were obtained by inserting the electrode into the slice without patching cells. Three technical replicates were performed for each condition. (D) Western blots of co-immunoprecipitations performed using adult mouse hippocampal extracts with the indicated antibodies where anti-Cre antibody is used as a negative control. The blots are probed for GluN2A/2B (top row), GluN2C (middle row) or GluN1 (bottom row). (E) Representative brain sections of Grin2ciCre/+ mice crossed to a tdTomato reporter line (Ai9), showing hippocampal area CA1 immunolabelled for GFAP, tdTomato and NeuN; scale bars, 160 μm (top), 25 μm (bottom). (F) Quantification of % cells that are positive for tdTomato amongst the counts of NeuN-labelled cells in stratum pyramidale (left, total n = 3155 cells) and SR (middle, total n = 619 cells), and GFAP-labelled cells (right, total n = 3149 cells). N = 10 fields of view of area CA1 from individual hippocampal sections (n = 10) obtained from 5 mice. Data are presented as mean ± s.e.m.

The expression of GluN2C subunit protein in the hippocampus was confirmed by immunoprecipitation of mouse hippocampal extracts followed by Western blotting, using an antibody against the Cre recombinase that is not expressed in wild type mice, as a negative control (Figure 5D). For characterizing the cellular expression pattern of GluN2C in the hippocampus, we were unable to identify an antibody against GluN2C that was suitable for immunofluorescence labelling experiments. Therefore, in order to visualize the cells that expressed GluN2C, we used a GluN2C mutant mouse line carrying an insertion of a codon-improved Cre recombinase immediately downstream of the translation initiation site in the Grin2c gene (Miyazaki et al., 2012). Grin2ciCre/+ mice were crossed with a Cre reporter line (Ai9: Madisen et al., 2010) that expressed tdTomato upon Cre-mediated recombination. The hippocampus of the offspring showed robust tdTomato fluorescence in astrocytes identified by GFAP labelling (81.6% ± 5.7% of GFAP+ ve cells; Figure 5E and F), which was consistent with the high expression level of GluN2C mRNA in astrocytes (Figure 5C). Moreover, some interneurons in SR identified by the NeuN labeling also expressed tdTomato (3.6% ± 1.6% of NeuN+ ve cells in SR, Figure 5E and F), which was in agreement with previous reports (Ravikrishnan et al., 2018; Gupta et al., 2016). Surprisingly, the pyramidal cell layer also showed sporadic tdTomato-labeled neuronal cell bodies, although such cells represented a limited proportion of pyramidal neurons (5.8% ± 1.0% of NeuN+ ve cells in stratum pyramidale, Figure 5E and F). The RT-PCR, biochemical, and immunohistochemistry experiments collectively provide evidence in support of GluN2C-containing NMDAR as a major NMDAR type expressed in hippocampal CA1 astrocytes.

Inhibition of GluN2C NMDARs reduces PPR variability in a manner sensitive to astrocyte GluN1 expression

Given the prominent expression of GluN2C in astrocytes, we wondered if the regulation of PPR could be attributed to GluN2C NMDARs. To test such a possibility, we examined whether pharmacological inhibition of GluN2C NMDARs could mimic the effects of bath applied AP5 and MK801 on PPR variance using QNZ46 (25 µM), an NMDAR antagonist specific for GluN2C/D (Hansen and Traynelis, 2011). Similarly to MK801 and APV, QNZ46 reduced the population variance of PPRs without altering the mean PPR (Figure 6A; δ2 P = 0.008; x-P = 0.312). Again we observed the negative correlation between the change in PPR and the baseline PPR (Figure 6A, bottom), with a linear fit to the data intercepting the x-axis near the baseline mean PPR (X intercept = 2.07). Additionally, in experiments monitoring two independent Schaffer collateral inputs, bath application of QNZ46 decreased the PPR disparity as found for AP5 and MK801 (Figure 1—figure supplement 5F; p = 0.005). These observations suggest that GluN2C/D-containing NMDARs contribute to maintaining the broad PPR diversity. Given the lack of detectable expression of GluN2D in hippocampal CA1 cells, it is likely that NMDARs containing GluN2C mediate the observed effects of QNZ46 in modulating presynaptic strength to decrease the overall range of PPR variability.

Inhibiting GluN2C NMDARs reduces PPR variability in a manner dependent on astrocyte expression of GluN1.

(A–C) Left: representative EPSC traces (average of 20 sweeps) to pairs of pulses (Δt = 50 ms) applied to Schaffer collateral axons in baseline and after wash-on of QNZ46 (25 µM). Right: PPR histograms (top) and ΔPPR vs. baseline PPR plots (bottom) before and after bath applying QNZ46 to control uninfected (A), Control (B) and Cre (C) slices. The p values were obtained for the population mean (x-) (paired sample t-test) and variance (δ2) (one-tailed f-test for equal variances) comparing PPRs before and after the drug treatment. Linear fits and Pearson’s correlation coefficients (r) and p values are shown in scatter plots. (D–F) Plots of EPSC amplitudes (normalized to the baseline average) before and during the application of QNZ46 to control uninfected (D), Control (E) and Cre-infected (F) slices (shaded area) where n is the number of inputs examined for each experiment. Baseline and experimental periods are indicated (black bars). Box plots show the summary of normalized EPSC amplitudes during the QNZ46 application. *p < 0.05, **p = 0.005, Mann-Whitney U-test was used to compare the control to the drug conditions. No virus infection+ QNZ46 = 21 inputs, 11 cells, 7 mice; Control+ QNZ46 = 24 inputs, 12 cells, 5 mice; Cre+ QNZ46 = 20 inputs, 12 cells, 6 mice. Data are presented as mean ± s.e.m.

When EPSC amplitudes were examined, QNZ46 bath application caused a significant increase, reminiscent of the changes observed upon AP5 and MK801 application (Figure 6D; p = 0.014). Moreover, similarly to AP5, QNZ46 significantly increased CV–2 of EPSC amplitudes (Figure 1—figure supplement 3B; p = 0.009), which was suggestive of presynaptic alterations.

Next we asked whether QNZ46 acted on astrocyte NMDARs to control PPR variability. To address this point, we again tested for occlusion using astrocyte GluN1 knock-down slices. QNZ46 was bath applied to Cre and Control AAV-infected slices while recording EPSCs from CA1 neurons infused with MK801. In control slices QNZ46 decreased the PPR variance (Figure 6B; p = 0.065) and caused a variable increase in EPSC amplitudes (Figure 6E; p = 0.005) without a concomitant change in the amplitude or the frequency of spontaneous EPSCs (Figure 2—figure supplement 2F). Crucially, as observed for AP5, QNZ46 was no longer effective in producing a significant change in the PPR variance in Cre slices (Figure 6C; p = 0.649). When PPR disparity across two inputs was monitored, QNZ46 decreased the PPR disparity in a manner that was sensitive to astrocyte GluN1 knock-down, which also supported the involvement for GluN2C-NMDARs in regulating presynaptic strength diversity (Figure 2—figure supplement 2J,K; Control + QNZ46 p = 0.031; Cre + QNZ46 p = 0.126). Moreover, similarly to AP5, QNZ46-dependent increase of EPSC amplitudes was attenuated also in Cre slices (Figure 6F).

Together, these observations indicate that GluN2C NMDARs expressed in astrocytes function to maintain the broad variability of presynaptic strengths across a synapse population. While GluN2C NMDARs also play a role in regulating EPSC amplitudes, this is likely to involve a mechanism that is distinct from the presynaptic regulation but is engaged in parallel to target postsynaptic processes.

Layer-specific regulation of synaptic strength by astrocyte NMDARs

Astrocytes are a highly heterogeneous cell type (Zhang and Barres, 2010; Khakh and Sofroniew, 2015) that influence synaptic transmission in a synapse- and circuit-specific manner (Martin-Fernandez et al., 2017; Schwarz et al., 2017; Martín et al., 2015; Lanjakornsiripan et al., 2018). Our RT-PCR analysis of NMDAR subtype expression showed relatively high expression of GluN2C mRNA across SR, SO, and SLM layers in CA1 astrocytes compared to CA1 pyramidal neurons. Nevertheless, despite the relatively even expression of GluN2C across layers, our experiments monitoring NMDA-glycine puff application responses in CA1 astrocytes revealed layer-specific differences in the GluN1-dependent component of the slow depolarizing responses triggered by the NMDA-glycine puff, with SR astrocytes showing the highest sensitivity to the astrocyte GluN1 knock-down (Figure 2—figure supplement 1). This raised the possibility that astrocyte NMDAR-dependent modulation of synaptic inputs to CA1 pyramidal neurons whose dendrites span across SR, SO, and SLM, might also differ across layers.

To test such possibility of layer-specific regulation, we compared astrocyte NMDAR-dependence of synaptic transmission in CA1 neurons across three layers in slices from astrocyte GluN1 knock-down and control mice. In contrast to the specific decrease in the PPR variance observed in Cre slices relative to Control slices for the SR input (Figure 2D), neither the PPR variance nor its mean differed between Cre and Control slices for the SO and the SLM inputs (Figure 7A–D; SO: δ2 P = 0.303, x-P = 0.402; SLM: δ2 P = 0.624, x-P = 0.161). We also examined the layer-specificity of PPR diversity regulation using a pharmacological approach in slices from wild-type mice. The sensitivity of PPRs to bath applied NMDAR antagonists in CA1 pyramidal neurons intracellularly perfused with MK801, was monitored in response to pairwise stimulation of SO and SLM inputs as had been done for the SR input. Neither MK801 nor AP5 had any appreciable effect on PPR variance in SO (Figure 7E and G: MK801, p = 0.107; AP5 p = 0.472) and SLM (Figure 7I and K: MK801, p = 0.920; AP5, p = 0.741). MK801 modestly increased EPSC amplitudes at some inputs in SO (Figure 7F: p = 0.783) but it had no effect in SLM (Figure 7J: p = 0.915). Moreover, AP5 did not potentiate EPSC amplitudes as observed for the SR input, but instead it significantly depressed EPSC amplitudes in SO (Figure 7H: p = 0.028) while it caused no appreciable change in SLM (Figure 7L: p = 0.317). Collectively, these results suggest that regulation of PPR diversity by astrocyte NMDARs in CA1 pyramidal neurons is specific to the SR inputs.

Figure 7 with 1 supplement see all
Interfering with astrocyte NMDARs has little effect on synaptic strength diversity in the stratum oriens (SO) and the stratum lacunosum moleculare (SLM).

(A) Left: recording scheme. Right: confocal image from SO in CA1 showing nls-mCherry-Cre-positive astrocytes labelled with GFAP and DAPI. Scale bar, 20 µm. (B) Average (color) and individual (grey) EPSC sweeps from representative recordings by stimulating SO of Control and Cre infected slices from Grin1flx/flx mice. PPR histograms from Control (top) and Cre slices (bottom) fit with single Gaussians. [p values: comparisons of population mean (x-) (Mann-Whitney test) and variance (δ2) (one-tailed f-test for equal variances) between Control and Cre.] Control, n = 50 inputs, 25 cells, 7 mice; Cre, n = 50 inputs, 25 cells, 7 mice. (C,D) Same analysis as in A,B for SLM. Control, n = 67 inputs, 47 cells, 12 mice; Cre, n = 71 inputs, 49 cells, 12 mice. (E,G) Representative EPSC traces and PPR histograms of from SO stimulation in baseline and after applying MK801 (E) or AP5 (G). (F,H) Normalized EPSC amplitudes before and after applying MK801 (F) or AP5 (H) (shaded area) in SO. Right, summary bar graph. MK801, n = 24 inputs, 12 cells, 6 mice; AP5, n = 20 inputs, 10 cells, 7 mice. * p < 0.05 Mann-Whitney U-test. (I,K) Same analysis as in E,G for PPRs elicited in SLM in control and MK801 (I) or AP5 (K). (J,L) Same analysis as in F,H with MK801 (J) or AP5 application (L). MK801: n = 26 inputs, 13 cells, 8 mice; AP5, n = 24 inputs, 12 cells, 6 mice. Data are presented as mean ± s.e.m.

As a further test of the layer specificity of astrocyte NMDAR-dependent regulation of PPR variability, we assessed PPR disparity by performing two independent pathway analysis in each layer in response to bath applied NMDAR antagonists. Again, in contrast to SR input (Figure 1—figure supplement 5D, E), neither MK801 nor AP5 depressed the PPR disparity of the SO and the SLM inputs (Figure 7—figure supplement 1). These observations are consistent with the role for astrocyte NMDARs, and particularly GluN2C NMDARs, for maintaining the diversity of PPR specifically of SR inputs to CA1 pyramidal neurons.

Discussion

Broad heterogeneity in the efficacy of synaptic transmission is a fundamental feature of small glutamatergic synapses in the mammalian brain (Dobrunz and Stevens, 1997; Atwood and Karunanithi, 2002; Branco and Staras, 2009). A variety of presynaptic parameters contribute to the observed variability, such as the abundance, subtype, and location of presynaptic calcium channels (Thalhammer et al., 2017; Brenowitz and Regehr, 2007; Éltes et al., 2017), their positioning with respect to the synaptic vesicles, the active zone size and the number of docked vesicles, the state of vesicle fusion machinery (Park et al., 2012; Marra et al., 2012; Fulterer et al., 2018; Glebov et al., 2017; Holderith et al., 2012), as well as the coupling to neuromodulatory signals (Burke et al., 2018), and the synapse’s recent history of synaptic plasticity. These determinants of presynaptic release efficacy are influenced by target-specific signals (Éltes et al., 2017; Branco et al., 2008; Reyes et al., 1998; Markram et al., 1998) and are further subject to activity-dependent modulation (Thalhammer et al., 2017; Goda and Stevens, 1998; Burke et al., 2018). Although the basis for presynaptic release probability regulation at individual synapses have been intensively studied, whether and how basal release probability variability is collectively controlled across a synapse population is poorly understood, despite its implications in information processing and memory storage (Barbour et al., 2007; Buzsáki and Mizuseki, 2014; Bromer et al., 2018; Rotman and Klyachko, 2013). Our study highlights the novel contribution of astrocyte GluN2C NMDAR signaling in broadening the range of basal release efficacy of Schaffer collateral synapses by effectively maintaining strong synapses stronger and weaker synapses weaker without an overt effect on the mean synaptic strength.

The present study took advantage of paired-pulse response as a measure of presynaptic efficacy. While PPR – a form of short-term plasticity – is inversely related to presynaptic release probability (Dobrunz and Stevens, 1997), it does not necessarily always represent changes in presynaptic release across all conditions. For example, PPR changes can arise from a postsynaptic mechanism involving changes in the extent accumulation of desensitized AMPA receptors at the postsynaptic density (Heine et al., 2008; Frischknecht et al., 2009). Therefore, in some experiments, presumed changes in release probability as monitored by PPR were further corroborated by the correlated changes in CV–2 (Malinow and Tsien, 1990; Larkman et al., 1992). This study extends the concept of NMDAR-mediated glial control of PPR (Letellier et al., 2016) to include populations of synapses within defined hippocampal subregions in the healthy adult mouse brain. Sampling a small number of synapses by a relatively weak stimulation intensity (i.e. mean EPSC amplitude of ~60 ± 6 pA to Schaffer collateral stimulation in control condition) consistently revealed a component of PPR distribution that was regulated by astrocyte NMDARs. Notably, such modulation could be masked when sampling a large synapse population that yields stable ensemble responses (Lines et al., 2017). Altogether, results from (i) pharmacological experiments using subtype-specific NMDAR antagonists, which allowed monitoring the effects of NMDAR inhibition in the same synapse population, (ii) the genetic interference specifically of astrocyte NMDARs, and (iii) the cellular expression analysis of NMDAR subunits, collectively point to GluN2C NMDAR as the major astrocyte receptor that regulates synaptic strength variability of Schaffer collateral inputs. The use of methods that allow for a more direct estimate of release probability in hippocampal CA1 neurons as have been reported recently (e.g. Grillo et al., 2018; Jensen et al., 2021) will be informative in further pursuing the underlying mechanisms.

Functional significance of the broad distribution of presynaptic strengths

What might be an advantage in maintaining a highly variable presynaptic efficacy across a synapse population of a given input type? Our mathematical modeling and simulation data indicate that the width of release probability distribution can bias the outcome of activity-dependent synaptic plasticity, and in a generalized spiking neuron model, a larger release probability variance promoted LTP and LTD. Notably, several studies have implicated astrocyte signaling in LTD in hippocampal CA3 to CA1 synapses (Chen et al., 2013; Andrade-Talavera et al., 2016; Navarrete et al., 2019; Pinto-Duarte et al., 2019), where spike timing-dependent LTD in slices from young mice is sensitive to inhibitors of GluN2C/D, although the source of GluN2C/D remains to be determined (Andrade-Talavera et al., 2016). Astrocyte GluN2C NMDAR-dependent maintenance of variable basal presynaptic strengths of CA3 to CA1 synapses that we have identified here could therefore be linked to astrocyte signaling that promotes LTP and LTD, which in turn, are not only important for learning and memory but also for network stability (Collingridge et al., 2010; Royer and Paré, 2003; Zenke and Gerstner, 2017). Curiously, this GluN2C NMDAR-dependent broadening of the basal presynaptic efficacy in CA1 neurons is confined to the SR inputs and not observed for SO or SLM inputs. This suggests that for dendritic computations in CA1 pyramidal neurons, the broad variability of synaptic strengths at CA3-CA1 synapses is more crucial compared to the synaptic strength variability of basal or apical tuft inputs. Mice deficient in GluN2C, while mostly normal in their behavior, show deficits in acquisition of conditioned fear and working memory and changes in neuronal oscillations (Hillman et al., 2011; Mao et al., 2020). Some population of interneurons express GluN2C, however (Gupta et al., 2016; Ravikrishnan et al., 2018), and this confounds the interpretation of the observed effects solely to deficits in astrocyte GluN2C. It would be of interest to determine in the future whether there is a learning performance deficit in mice specifically deficient in astrocyte GluN2C or GluN1 in the hippocampal CA1 subfield.

The mechanism by which astrocyte GluN2C NMDARs are coupled to the changes in presynaptic efficacy remains to be clarified. The narrowing of the range of PPR upon astrocyte NMDAR inhibition suggests a capacity for astrocytes to impose dual modulatory actions downstream of astrocyte GluN2C NMDARs, in one possibility, involving the release of two types of gliotransmitters with one potentiating and another depressing synaptic transmission (Schwarz et al., 2017; Covelo and Araque, 2018). In some cases, the release of a single molecule may also suffice: astrocyte-mediated release of ATP, which is converted to adenosine by extracellular ATPases, has been implicated in the bi-directional modulation of presynaptic efficacy (Panatier et al., 2011; Pascual et al., 2005; Zhang et al., 2003; Tan et al., 2017). The effects of adenosine depend in part on the presynaptic A2A or A1 receptors that either enhance or suppress presynaptic function, respectively (Panatier et al., 2011; Tan et al., 2017). Importantly, a potential mechanism that expands the range of presynaptic efficacy would favor potentiation of strong synapses and depression of weak synapses. At the neuromuscular junction, where glial cells mediate synaptic competition involving glial purinergic signaling, it has been shown that a stronger synaptic input is preferentially potentiated further by the activation of presynaptic A2A receptors while the weaker one remains unchanged or slightly depressed (Darabid et al., 2018). Such a mechanism could provide a framework for deciphering the actions of GluN2C NMDARs, where for example, the relative abundance of A2A or A1 receptors at individual presynaptic boutons could determine the polarity of presynaptic efficacy change upon accumulation of extracellular adenosine by the astrocyte GluN2C NMDAR activity, provided that the relative abundance and/or the type of adenosine receptors at individual boutons show correlation to basal release probability. In another scenario, astrocyte GluN2C receptors may influence the release of other gliotransmitters such as glutamate (Jourdain et al., 2007) to target presynaptic glutamate receptors and/or GluN2C receptor signaling may influence astrocyte-mediated K+ clearance to locally shape presynaptic action potential waveforms (Cui et al., 2018) to in turn affect presynaptic efficacy. Further understanding of such mechanisms is warranted to identify the basis for how the relative differences in synaptic strengths are promoted to facilitate broadening of the range of presynaptic efficacy.

Astrocyte NMDAR subunit mRNAs and implications for layer-specific synapse modulation

Previous transcriptome analysis of the major cell types in mouse cerebral cortex have suggested that the GluN2C NMDAR subunit mRNA is one of the highly enriched transcripts in astrocytes, whose level can be up to 70-fold of the level found in neurons (Zhang et al., 2014). Our single-cell RT-PCR analyses also show robust expression of GluN2C mRNA in astrocytes across the three CA1 layers in contrast to GluN2C mRNA expression in pyramidal neurons. Curiously, despite the high expression of GluN2C mRNA in astrocytes, only low levels of GluN1 mRNA is detected in astrocytes. This finding is unexpected given that GluN1 subunit is required for the surface expression of functional NMDARs (Fukaya et al., 2003; Abe et al., 2004). Moreover, the observed occlusion of QNZ46 effects on synaptic transmission by astrocyte-specific knock-down of GluN1 also support the presence of functional heteromeric GluN1/GluN2C NMDARs in astrocytes. The whole-cell patch clamp method we used to collect RNAs is biased towards sampling of transcripts that are abundant in the cell body. Given that astrocyte processes are numerous and thin, the discordance between the detected GluN1 and GluN2C mRNA levels could be explained if the mRNAs are differentially localized, with GluN1 mRNA being preferentially targeted to processes compared to the cell body of astrocytes. Such a proposal is consistent with a recent study reporting of GluN1 mRNA in astrocyte processes that is locally translated (Sakers et al., 2017). The precise intracellular localization of NMDAR subunit mRNAs in astrocytes remains to be determined.

The present study revealed synapse regulation by the astrocyte GluN2C NMDARs that is confined to the SR input although GluN2C mRNA is expressed broadly across CA1 astrocyte layers. Therefore, the layer-specificity of synaptic modulation could potentially arise from features of GluN2C NMDAR assembly and trafficking and/or signaling that is unique to SR astrocytes over SO and SLM astrocytes. In support of layer-specific differences in astrocyte NMDAR signaling, the slow astrocyte membrane depolarization triggered by puff applied NMDA-glycine shows significant dependence on astrocyte NMDAR only in SR and not in SO nor in SLM (Figure 2—figure supplement 1). Additionally, differences in the properties of presynaptic inputs to CA1 pyramidal neuron dendrites across layers (e.g. Schroeder et al., 2018) could also contribute to layer-specific synaptic modulation by astrocyte GluN2C NMDARs.

The GluN2C subunit forms diheteromeric receptor complexes with the obligate GluN1 subunit and triheteromeric receptor complexes with GluN1 and GluN2A (Paoletti et al., 2013; Hansen et al., 2018). The presence of GluN2C confers NMDAR properties that are distinct from NMDARs containing GluN2A or GluN2B which are abundant in neurons. For example, GluN2C-containing NMDARs show reduced channel open probability, increased glutamate sensitivity, slow receptor deactivation, and a decreased sensitivity to Mg2+ block (Paoletti et al., 2013; Hansen et al., 2018). Weak Mg2+ binding would enable open-pore blockers such as MK801, to nonetheless rapidly exert their inhibitory action. Such reduced Mg2+ sensitivity of astrocyte GluN2C NMDARs could have contributed to the rapid effect observed for MK801 in normalizing presynaptic strengths and reducing the PPR disparity. Moreover, the low EC50 of GluN2C-containing NMDAR activation to glutamate (Hansen et al., 2018) suggests that they may be well suited for detecting synaptic release events at perisynaptic astrocyte processes that can be at some distance from the active zone. Several drugs that target NMDARs have been in clinical use, such as ketamine as an anesthetic and treatment for depression (Krystal et al., 2019; Williams and Schatzberg, 2016) and memantine for the treatment of moderate to severe dementia in Alzheimer’s disease (Graham et al., 2017). Under physiological conditions, GluN2C/GluN2D-containing NMDARs display up to 10-fold higher sensitivity to ketamine and memantine in comparison to GluN2A/GluN2B-containing NMDARs that are highly expressed in neurons (Kotermanski and Johnson, 2009; Hansen et al., 2017). Therefore, although therapeutics targeting NMDARs to date have largely focused on neuronal NMDARs, it would be crucial to consider also the consequences of interfering with GluN2C NMDARs that are enriched in astrocytes, which is underscored by the increasing recognition of the involvement of astrocytes in a variety of neurological disorders (Zuchero and Barres, 2015; Chung et al., 2015).

Materials and methods

Slice preparation and whole cell recordings

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Mice (P60-120) were deeply anesthetized with isoflurane and transcardially perfused with ice-cold cutting aCSF containing (in mM) 93 N-methyl D-glucamine, 2.5 KCl, 1.2 NaH2PO4, 30 NaHCO3, 20 HEPES, 20 glucose, 5 Na ascorbate, 2 thiourea, 3 sodium pyruvate, 12 N-acetyl L-cysteine, 10 MgSO4, 0.5 CaCl2, pH adjusted to 7.4 with HCl and bubbled with 95 % O2/5 % CO2. Brains were extracted and 350 μm transverse hippocampal slices were cut in ice-cold cutting aCSF on a Leica VT1200 vibrating microtome. Slices were incubated for 12 min in cutting aCSF warmed to 34 °C, then placed in holding solution containing (in mM) 81.2 NaCl, 2.5 KCl, 1.2 NaH2PO4, 30 NaHCO3, 20 HEPES, 20 D-glucose, 5 Na ascorbate, 2 thiourea, 3 sodium pyruvate, 12 N-acetyl L-cysteine, 2 MgSO4, 2 CaCl2, pH 7.4, bubbled with 95 % O2/5 % CO2 for up to 8 h. In some cases, slices were incubated in 50 µM sulphorhodamine for 30 min at 34 °C to identify astrocytes for RNA extraction.

Whole-cell patch clamp recordings were obtained from CA1 pyramidal neurons or astrocytes using an Olympus BX51W1 microscope equipped with IR-DIC optics and a motorized stage. Images were captured with a digital camera (Andor, iXon3) and imaged using MetaMorph software (Molecular Devices). Voltage clamp and current clamp experiments were carried out using Multiclamp 700B amplifiers (Molecular Devices) and up to four micromanipulators (SM5, Luigs and Neumann). Patch pipettes (tip resistance 2–4 MΩ for neurons, 4–6 MΩ for astrocytes) were pulled using a vertical 2-stage puller (PC-10 Narishige). Slices were constantly perfused with recording aCSF containing (in mM) 119 NaCl, 2.5 KCl, 1.3 NaH2PO4, 26 NaHCO3, 1 MgCl2, 2 CaCl2, 20 D-glucose and 0.5 Na ascorbate pH 7.4, bubbled with 95 % O2/5 % CO2 and maintained at 32–34 °C using an in-line heater (Harvard Instruments). Picrotoxin (100 µM) was added to the recording aCSF to block GABAA-receptors and isolate glutamatergic synaptic transmission. Internal pipette solution contained (in mM) 130 CsMeSO3, 8 NaCl, 4 Mg-ATP, 0.3 Na-GTP, 0.5 EGTA, 10 HEPES, pH 7.3, 290–295 mOsm for neuron recordings, and 130 K gluconate, 10 HEPES, 4 MgCl2, 4 Na2-ATP, 0.4 Na3-GTP, 10 Na-phosphocreatine, pH 7.3, 290 mOsm for astrocyte recordings. 50 µM AlexaFluor488 or AlexaFluor594 hydrazide (ThermoFisher Scientific) was included in the pipette solution to verify cell identity and to position stimulating and puff pipettes. Neurons were voltage clamped at –70 mV and series resistance (Rs) was monitored throughout all recordings and left uncompensated. Experiments were discarded if Rs <30 MΩ and/or changed by >20%. Average Rs was 25.24 ± 0.27 MΩ (n = 298 cells).

Recordings of synaptic transmission

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For all experiments using NMDAR antagonists, 1 mM MK801 was included in the patch pipette solution. Internal solution was allowed to equilibrate into the cell for 10–15 min and inputs were stimulated at a low frequency (0.1 Hz) with pairs of pulses at least 45 times before beginning the experiment to pre-block postsynaptic NMDAR receptors. Effective inhibition of NMDAR currents by internal MK801 was confirmed in a separate set of experiments (Figure 1—figure supplement 1). Pipettes were tip filled with ~0.2 µl of internal solution lacking MK801 to avoid its leakage to the extracellular milieu prior to seal formation. Small bundles of axons in the stratum radiatum, oriens, or lacunosum moleulare were stimulated using AgCl bipolar electrodes in theta-glass pipettes (tip diameter ~2–3 µm) filled with recording aCSF, connected to a stimulus isolation unit (A360, WPI). These bundles are identified throughout as ‘input’. Stimulation strengths varied between ~50 and 500 µA and were adjusted to obtain EPSCs of approximately 50–100 pA. Up to three independent inputs were sampled during a single experiment, although a maximum of two independent inputs were activated per input pathway. When multiple stimulation electrodes were used they were positioned on opposite sides of the neuron and/or in different input pathways (i.e. SR and SLM or SR and SO). When sampling two inputs in a single pathway, the independence of the two inputs was confirmed by a cross-paired-pulse stimulation paradigm (Otani and Connor, 1996; Scimemi et al., 2004); after stimulating one of the two inputs the other input was stimulated in quick succession (50 ms inter-pulse interval). If facilitation or depression was observed in the second pulse, then the position of stimulation electrode was changed and independence of the two inputs was re-assessed. During the experiment, EPSCs from each pathway were sampled with paired pulses every 30 s. Inputs from separate pathways were stimulated at least 5 s apart. EPSCs were sampled over a 10 min baseline period (i.e. 20 sweeps) before the perfusion of drugs and continued for at least an additional 20 min. EPSC amplitudes were averaged over 20 pre-drug baseline sweeps, and 20 post-drug sweeps. The change in EPSCs (ΔEPSCs) was calculated as the ratio of the average post-drug amplitude to the average baseline amplitude. PPRs were calculated based on the average EPSCs of 5 sweep bins (i.e. 2.5 min). Baseline and post-drug averages were calculated as the average of four bins (i.e. over 10 min) before and after drug application, respectively. Changes in PPR (ΔPPR) were calculated as the difference between the drug application value and the baseline value. Values for coefficient of variation (CV) were obtained from 20 baseline, and 20 post-drug sweeps. The change in CV–2 (ΔCV–2) was calculated as the ratio of post-drug CV–2 to pre-drug CV–2. EPSCs and sEPSCs amplitudes in the absence of drug application were stable over the duration of the experiment, and sEPSC amplitudes were stable across all drug conditions tested (Figure 1—figure supplement 3, Figure 2—figure supplement 1), suggesting that run-down (i.e. non-stationarities) will not substantially influence the outcome of the CV–2 analysis. All EPSC amplitude, rise-time, and decay measurements were performed in Clampfit 10.6 software. sEPSCs were identified as events outside a 50 ms window following the second stimulation pulse for each pathway using the template matching algorithm in Clampfit 10.6.

Astrocyte recordings and NMDA/glycine puff

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Astrocytes were identified in acute slices based on mCherry or sulphorhodamine fluorescence, or on their appearance under IR-DIC observation (small, circular cell bodies in the neuropil). Astrocyte identity was always confirmed by their passive electrical properties (linear I-V relationship), low input resistance ( < 20 MΩ), and low resting membrane potential (< –75 mV), as well as post-hoc labeling by Alexa dyes included in the patch pipette. Recording aCSF contained picrotoxin (100 µM), tetrodotoxin (0.5 µM), and CNQX (10 µM) to reduce network excitability associated with the application of iGluR agonists. Patch pipettes (Rt = 4–6 MΩ) were used to locally deliver recording aCSF solution containing 1 mM NMDA and 1 mM glycine. Puff pipettes were placed approximately 50 µm from the patched cell and puff pressure (3 psi, 100 ms duration) was controlled with a Picospritzer III (Parker Hannifin) connected to N2 gas.

Whole-cell patch RNA extraction from astrocytes and neurons

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Whole-cell patch clamp recordings from astrocytes and neurons were performed using pipettes containing (in mM) 130 K gluconate, 10 HEPES, 4 MgCl2, 4 Na2-ATP, 0.4 Na3-GTP, 10 Na-phosphocreatine, 1 U/µl RNAase inhibitor, 50 µM AlexaFluor488, pH 7.3, 290 mOsm. Pipettes were tip filled (~0.2 µl) with the same internal solution but lacking RNAase inhibitor in order to facilitate obtaining of GΩ seals. After determining the electrical properties of the patched cell to confirm its identity, RNA was extracted using a previously published protocol (Fuzik et al., 2016). The cell was held at –5 mV, and repetitively depolarized to +20 mV for 5 ms at 100 Hz for ~5 min while light negative pressure was applied to the pipette. The AlexaFluor fluorescence signal was visualized to confirm the extraction of cell cytoplasm.

Single-cell quantitative PCR

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Individual patched cells were processed following the provider´s recommendation for the Single Cell-to-CT qRT-PCR Kit (ThermoFisher Scientific). cDNAs for Grin1, Grin2a, Grin2b, Grin2c, Grin2d, and Rn28s1 were quantified by TAQMAN system using the following probes. The provider’s recommended pre-amplification step was performed for all the genes except for Rn28s1.

  • Grin1: fw 5’GACCGCTTCAGTCCCTTTGG 3’, rv 5’ CACCTTCCCCAATGCCAGAG 3’, probe 5’ AGCAGGACGCCCCAGGAAAACCAC 3’ (MGBNFQ, 6FAM)

  • Grin2a: fw 5’ AGACCCCGCTACACACTCTG 3’, rv 5’TTGCCCACCTTTTCCCATTCC 3’, probe 5’AGCACGATCACCACAAGCCTGGGG 3’ (MGBNFQ, 6FAM)

  • Grin2b: fw 5’GGCATGATTGGTGAGGTGGTC 3’, rv 5’GGCTCTAAGAAGGCAGAAGGTG 3’, probe 5’ATTGCTGCGTGATACCATGACACTGATGCC 3’ (MGBNFQ, 6FAM)

  • Grin2c: fw 5’GGAGGCTTTCTACAGGCATCTG 3’, rv 5’ATACTTCATGTACAGGACCCCATG 3’, probe 5’TCCCACCGTCCCACCATCTCCCAG 3’ (MGBNFQ, 6FAM)

  • Grin2d: fw 5’TCAGCGACCGGAAGTTCCAG 3’, rv 5’TCCCTGCCTTGAGCTGAGTG 3’, probe 5’ TCCTCCACTCTTGGCTGGTTGTATCGCA 3’(MGBNFQ, 6FAM)

  • Rn28s1: fw 5’CCTACCTACTATCCAGCGAAACC 3’, rev 5’AGCTCAACAGGGTCTTCTTTCC 3’, probe 5’ CTGATTCCGCCAAGCCCGTTCCCT 3’ (MGBNFQ, VIC)

RNA levels were normalized by the quantity of Rn28s1, and standard curves were prepared for estimating the quantity (in femtograms) of the targeted RNAs after pre-amplification. For making the standard curves, total RNA from mouse brain hippocampal tissue was extracted using TRIZOL reagent. Target cDNAs were amplified by regular PCR, and amplicons were purified, quantity of cDNA was measured and submitted to serial dilutions, used for standard curves in TAQMAN system, in parallel to the single cell derived cDNA samples.

Intracranial AAV injections

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Recombinant AAV vectors were targeted to the dorsal CA1 hippocampus of adult (P60-P90) Grin1flx/flx mice as previously described (Letellier et al., 2016; Cetin et al., 2007). Bilateral injections (~600 nl/hemisphere at a rate of 200 nl/min) of AAV9.2-GFAP104-nls-mCherry-Cre (6.67⨯1013 genome copies/ml), AAV9.2-GFAP104-nls-mCherry (i.e. Control; 6.86⨯1013 genome copies/ml), AAVDJ8-GFAP104-nls-mCherry-Cre (5.95⨯1013 genome copies/ml), or AAVDJ8-GFAP104-eGFP (also denoted Control for simplicity; 3.98⨯1013 genome copies/ml) were made using the following coordinates: X (posterior from Bregma) – 1.4 mm, Y (lateral from sagittal suture) ± 1.4 mm, Z (ventral from pia) – 2.1 mm. Viruses were expressed for at least 21 days (max post-surgical duration of 40 days) before mice were used in physiology or imaging experiments. Comparisons were always made between Cre and Control (Cre-lacking) virally infected slices from animals that received the same AAV serotype. While most experiments used the AAV9.2 viruses, the following data used the AAVDJ8: Figures 2 and 7, Figure 2—figure supplement 2A, B; see E-phys statistics report for details.

Immunohistochemistry

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Slices prepared from Grin1flx/flx mice expressing GFAP104-nls-mCherry-Cre were fixed in 4 % paraformaldehyde (PFA) in 0.1 M phosphate buffer solution (PB) for 1–2 hr, then washed in PB and stored overnight. Alternatively, mice brain tissue was fixed with 4 % PFA in PB (pH7.5) by transcardial perfusion, followed by further overnight incubation of the removed brain. Fixed brains were cryosectioned to a thickness of 40 μm. Slices were permeabilized with 0.3 % Triton X-100 and blocked in 10 % goat serum, then incubated in primary antibody overnight at 4 °C. Slices were washed and incubated in secondary antibodies for 1–2 hr at room temperature. Slices were then washed and mounted in ProLong antifade mounting medium (ThermoFisher Scientific) containing DAPI (1:1000, ThermoFisher Scientific), and visualized on a Zeiss 780, or an Olympus FV1200 or FV3000 confocal microscope. The primary antibodies used were chicken anti-RFP (1: 1000, Rockland #600-901-379), mouse anti-GFAP (1:1000, Synaptic Systems #173011), rabbit anti-GFAP (1:500, Abcam ab48050), rabbit anti-NeuN (1:500, Abcam #ab177487), and guinea pig anti-GluN1 (1:100, Alamone Labs AGP-046). The secondary antibodies used were AlexaFluor555 goat anti-chicken (1:1000, Invitrogen #A32932), AlexaFluor633 goat anti-mouse (1:1000, Invitrogen #A21052), AlexaFluor488 goat anti-rabbit (1:1000, Invitrogen #A11034), and AlexaFluor647 goat anti-guinea pig (1:200, Invitrogen A21450).

NMDA receptor immunoprecipitation from mouse brain

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Mouse hippocampal tissues were dissociated using a glass dounce homogenizer in a 50 mM Tris-HCl, pH 9.0 buffer containing protease inhibitors (10% w/v, cOmplete, Merck). Subsequently, 1 % (w/v) sodium deoxycholate was added and incubated for 30 min at 37 °C with mild shaking to solubilize the tissue. Samples were centrifuged at 100,000 rpm at 4 °C for 1 hr. Supernatant was collected, protein concentration was measured by the BCA assay, and stored at –80 °C for later analysis or diluted fivefold in a 50 mM Tris-HCl pH 7.5 buffer containing 0.1 % of Triton X-100 for co-immunoprecipitation experiments.

To test for co-immunoprecipitation, a mix of Sepharose Fast-Flow protein A and protein G beads was prepared. For each antibody reaction, 40 μl of the mixed resin was incubated with 5 μg of antibody. After at least 2 hr of incubation at 4 °C excess antibody was removed by washing, and 2 mg of protein extract was added to each sample containing the resin beads bound by the antibody and incubated overnight at 4 °C. Resins were washed three times with 10 volumes of 50 mM Tris-HCl pH 7.5 with 0.1 % Triton X-100. Supernatant was carefully removed, and resins were suspended in 2 x SDS-PAGE protein loading buffer containing DTT. The unbound protein extract was treated for a second overnight incubation at 4 °C with a freshly prepared antibody-bound resin under identical conditions as the first overnight incubation, to ensure effective pull-down of soluble NMDA receptor content from the extract. Co-immunoprecipitated proteins from the two rounds of incubation were pooled together.

Protein samples (inputs and co-immunoprecipitated proteins) were heat denatured for 2 min at 95 °C and subjected to 6 % SDS-PAGE separation followed by western blot. Antibodies used were: mouse anti-NR1 (Synaptic Systems #114011), rabbit anti-GluN2A/2B (Synaptic Systems #244003), rabbit anti-GluN2C (generously provided by Dr. Masahiko Watanabe or purchased from Frontier Institute #GLURE3C-RB-AF270), and mouse anti-Cre (Merck Millipore clone 2D8).

FM1-43 experiments for estimating the release probability distributions used for modeling

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Release probability distributions for mathematical modelling were obtained by measurements of readily releasable pool using the styryl dye FM1-43 (Invitrogen, Fisher-Scientific) in rat primary hippocampal neurons co-cultured with astrocytes as described previously (Goda and Stevens, 1998; Letellier et al., 2019). Images were captured on an inverted Olympus IX71 microscope equipped with an EMCCD camera (Andor Technology, Oxford Instruments) controlled by Metamorph software (Molecular Devices). The extracellular solution consisted of (in mM) 137 NaCl, 5 KCl, 10 D-Glucose, 5 HEPES pH 7.3, 2 CaCl2, 2 MgCl2, 0.01 CNQX, 0.1 picrotoxin at 300 mOsm. Briefly, neurons (used at DIV11-14) were stimulated by a pair of field electrodes (positioned ~10 mm apart) using 40 action potentials at 20 Hz in the presence of 10 μM FM1-43 and for an additional minute in FM1-43 but without stimulation to allow for completion of endocytosis. Subsequently, cells were washed in extracellular solution containing 1 mM Advasep-7 (Biotium) for 1 min to facilitate dye removal, and then the wash was continued for a total period of 10 min. Images were acquired before and after the unloading stimulation, which was 600 action potentials at 20 Hz. The signal remaining was taken as background. The images were analyzed by OpenView software (Kaufman et al., 2012) kindly provided by Dr. Noam Ziv.

Mathematical model for the numerical investigation

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We used the synapse model based on the Tsodyks-Pawelzik-Markram model (Tsodyks et al., 1998) and the leaky integrate-and-fire neuron model to reproduce ratios of peaks of EPSC waveforms with a 20 Hz spike input. The synaptic dynamics is modeled by [1]:

(1) dx0(t)dt=x2(t)τrecu0(tsp+)x0(tsp)δ(ttsp)
(2) dx1(t)dt =x1(t)τin+u0(tsp+)x0(tsp)δ(ttsp)
(3) dx2(t)dt=x1(t)τinx2(t)τrec
(4) du0(t)dt=u0(t)τf+USE(1u0(tsp))δ(ttsp)

Here, x0 is the portion of available neurotransmitters, x1(t) is the portion of neurotransmitters released by pre-synaptic spikes, x2(t) is the portion of neurotransmitters being recovered, u0(t) is the utilization of available neurotransmitters after each spike. τin is the timescale of neurotransmitter release. τrec is the recovery timescale. τf is the timescale of synaptic facilitation. USE is the initial release probability without the influence of synaptic facilitation. In this model, the dynamical variables are xi with x0+x1+x2=1. USE is a number determined by a random number drawn from a gamma distribution. Readily releasable pool size, which is monitored by the FM1-43 labeling as a proxy of release probability at individual synapses, is fitted with a gamma distribution (c.f. Murthy et al., 1997):

(5) P(z;k,θ)=1Γ(k)θkzk1ezθ

where z is the FM1-43 signal, k is the shape parameter and θ is the scale parameter. The release probability USE is then given by USE=z140.0 , by normalizing z that is proportional to release probability by an arbitrarily chosen maximal value of FM1-43 signal, which is at most140 in our measurements (Figure 3—figure supplement 1). The FM1-43 signal distributions in control and AP5 conditions are modeled by different shapes of the distribution. The fits and observed measurements of the FM1-43 distributions are presented in Figure 3—figure supplement 1. The best fit parameters for different conditions are k,θ=1.40,26.5 for control, k,θ=3.12,10.1 for AP5, and k,θ=2.94,12.6 for AP5 when the mean is fixed to stay the same as for the control (‘AP5 with control mean’).

In the simulations, the model is based on leaky integrate-and-fire (LIF) neurons. There is a synaptic connection between neurons. The membrane potential Vm of a LIF neuron is given by

(6) CmdVmdt=(VmEL)Rm+Istim+IE

where Cm is the membrane capacitance, R is the membrane resistance, Istim is the input current and IE is the excitatory current triggered by an excitatory input. In this study, we set Cm=150 pF and Rm=100 MΩ, with the time constant τ=15 ms for the neurons. Additionally, the following values are used: the threshold potential Vthre=-50.0 mV, reversal potential EL= -65.0 mV, and refractory time τref=2 ms.

The excitatory current IE is given by

(7) IE=gE(EEVm)

where gE is the conductance and EE=0 mV is the reversal potential of the excitatory response. The dynamics of conductance is given by

(8) τsyn,EdgEdt=gE+w(tsp)u0(tsp+)x0(tsp)δ(ttsp)

where τsyn,E is the time constant of the excitatory synaptic response and wt is the weight of the connection. Here we set τsyn,E to be 3 ms, which is approximately the weighted average decay time constant of AMPA receptors on dendrite and soma of hippocampal CA1 pyramidal neurons (Spruston et al., 1995). With this setting, parameters τf , τrec, and w0 can be determined by fitting both EPSC peaks and PPR variance observed in the experiment (Figure 3—figure supplement 2).

The spike-time-dependent plasticity (STDP) used to modify wt follows the rule used by Rubin et al., 2001:

Δw(t)={λ[wmaxw(t)] exp[(tpretpost)τ+],  if tpost>tpreλw(t) exp[(tposttpre)τ],  if tpost>tpre

where wmax=2w0 , λ=0.1, and τ+=τ-=20ms. tpre and tpost are spike times of presynaptic and postsynaptic neuron respectively.

Statistics

All statistical analyses were performed using OriginPro software (OriginLab Corp.). Datasets were tested for normality using the Shapiro Wilk test. When the criteria for normality was achieved, differences of mean values were examined using paired or unpaired Student’s two-tailed t-tests or one-way ANOVAs. When the criteria for normality was not achieved, differences in mean values were examined using Mann-Whitney or Kruskal Wallis tests. Normalized mean values obtained from recordings (i.e. normalized EPSCs) were compared to values obtained at the same time point in control experiments using Mann-Whitney tests. Variance of PPR distributions were examined using one-tailed f-tests for equal variances or Levene’s test. Box plots represent median and quartile values, whiskers represent maximum and minimum values that are not outliers. p Values are indicated throughout or indicated by * if p < 0.05. Sample sizes for electrophysiology experiments were determined based on PPR disparity obtained in pilot studies and experimental data outlined in Letellier et al., 2016. A power analysis was performed using a β value of 0.8 and an α value of 0.05. Standard deviations for PPR disparity data were determined to be ~0.35, and the expected effect size is ~45 %.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
strain, strain background (Mouse)C57Bl/6 JNihon SLCC57Bl/6JJmsSlc
strain, strain background (Rat)Sprague Dawley ratNihon SLCSlc:SDTime pregnant (pups were used at P0-P3 to harvest tissue for culture)
genetic reagent (mouse)Grin1flx/flxMcHugh Lab (RIKEN CBS)B6.129S4-Grin1tm2Stl/J Jackson Labs 005246(Tsien et al. Cell 87, 1327–1338, 1996)
genetic reagent (mouse)Ai9Itohara Lab (RIKEN CBS)B6.Cg-Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J Jackson Labs 007909(Madisen et al. Nat Neurosci 13, 133–140, 2010)
genetic reagent (mouse)Grin2ciCre/+Sakimura Lab (Niigata U)NA(Miyazaki et al., J Neurosci 32, 1311–1328, 2012)
cell line (Homo-sapiens)293FT (derived from embryonic kidney)ThermoFisher ScientificCAT# R70007Used in AAV production; authenticated by STR anaylsis using GenePrint10 System (Promega)(Further details will be provided by the lead contact upon request.)
antibodyAnti-RFP (Chiken polyclonal)RocklandCAT# 600-901-379(IF 1:1000)
antibodyAnti-GFAP (Mouse monoclonal)Synaptic SystemsCAT# 173011 RRID: AB_2232308(IF 1:1000)
antibodyAnti-GFAP (Rabbit polyoclonal)AbcamCAT# ab48050 RRID: AB_941765(IF 1:500)
antibodyAnti-NeuN (Recombiinant rabbit monoclonal)AbcamCAT# ab177487 RRID: AB_2532109(IF 1:500)
antibodyAnti-GluN1 (Guinea pig polyclonal)Alamone LabsCAT# AGP-046(IF 1:100)
antibodyAnti-Chicken AlexaFluor-555 (Goat polyclona)InvitrogenCAT# A32932(IF 1:1000)
antibodyAnti-Mouse AlexaFluor 633 (Goat polyclona)InvitrogenCAT# A21052 RRID: AB_2535719(IF 1:1000)
antibodyAnti-Rabbit AlexaFluor 488 (Goat polyclona)InvitrogenCAT# A11034(IF 1:1000)
antibodyAnti-Guinea pig AlexaFluor 647 (Goat polyclonal)InvitrogenCAT# A21450 RRID: AB_141882(IF 1:200)
antibodyAnti-GluN1 (Mouse monoclonal)Synaptic SystemsCAT# 114011 RRID: AB_887750(WB 1:1000) (IP 5 μg / 40 μl ProteinA/G resin)
antibodyAnti-GluN2A/B (Rabbit polyclonal)Synaptic SystemsCAT# 244003 RRID: AB_10804284(WB 1:1000) (IP 5 μg / 40 μl ProteinA/G resin)
antibodyAnti-GluN2C (Rabbit polyclonal)Frontier Institute Co.CAT# GLURE3C-RB-AF270 RRID: AB_2571763(WB 1:200) (IP 5 μg / 40 μl ProteinA/G resin)
antibodyAnti-Cre, clone 2D8 (Mouse monoclonal)Merck Milliporemouse anti-Cre (Merck Millipore clone 2D8)(IP 5 μg / 40 μl ProteinA/G resin)
Biological sample (AAV)AAV9.2 GFAP104-nls-mCherry-CreThis paperNAFurther information will be provided by the Lead Contact upon request.
Biological sample (AAV)AAV9.2-GFAP104-nls-mCherryThis paperNAFurther information will be provided by the Lead Contact upon request.
Biological sample (AAV)AAVDJ8-GFAP104-nls-mCherry-CreGoda LabNA(Letellier et al., PNAS 2016)
Biological sample (AAV)AAVDJ8-GFAP104-eGFPGoda LabNA(Letellier et al., PNAS 2016)
sequence-based reagentGrin1 fwThis paperPCR primersGACCGCTTCAGTCCCTTTGG
sequence-based reagentGrin1 rvThis paperPCR primersCACCTTCCCCAATGCCAGAG
sequence-based reagentGrin2a fwThis paperPCR primersAGACCCCGCTACACACTCTG
sequence-based reagentGrin2a rvThis paperPCR primersTTGCCCACCTTTTCCCATTCC
sequence-based reagentGrin2b fwThis paperPCR primersGGCATGATTGGTGAGGTGGTC
sequence-based reagentGrin2b rvThis paperPCR primersGGCTCTAAGAAGGCAGAAGGTG
sequence-based reagentGrin2c fwThis paperPCR primersGGAGGCTTTCTACAGGCATCTG
sequence-based reagentGrin2c rvThis paperPCR primersATACTTCATGTACAGGACCCCATG
sequence-based reagentGrin2d fwThis paperPCR primersTCAGCGACCGGAAGTTCCAG
sequence-based reagentGrin2d rvThis paperPCR primersTCCCTGCCTTGAGCTGAGTG
sequence-based reagentRn28s1 fwThis paperPCR primersCCTACCTACTATCCAGCGAAACC
sequence-based reagentRn28s1 rvThis paperPCR primersAGCTCAACAGGGTCTTCTTTCC
commercial assay or kitSingle Cell-to-CT qRT-PCR KitInvitrogenCAT# 4458236
commercial assay or kitBCA Protein Assay KitThermoFisher ScientificCAT# 23,227
commercial assay or kitTaqMan Universal PCR Master MixRoche Applied BiosystemsCAT# 4304437
chemical compound, drugAlexaFluor 488ThermoFisher ScientificCAT# A10436
chemical compound, drugAlexaFluor 594ThermoFisher ScientificCAT# A10438
chemical compound, drugNMDATocris BioscienceCAT# 0114
chemical compound, drug(+)-MK 801 maleateTocris BioscienceCAT# 0924
chemical compound, drugD-AP5Tocris BioscienceCAT# 0106
chemical compound, drugQNZ46Tocris BioscienceCAT# 4,810
chemical compound, drugR025-6981Tocris BioscienceCAT# 1,594
chemical compound, drugMPEPTocris BioscienceCAT# 1,212
chemical compound, drugPicrotoxinSigma AldrichCAT# P1675
software, algorithmMetamorphMolecular DevicesRRID: SCR_002368
software, algorithmpClampMolecular DevicesRRID: SCR_011323
software, algorithmImageJNIHhttps://imagej.nih.gov/ij/
software, algorithmOpenViewZiv Lab (Technion)NAKindly provided by Noam Ziv for use in the analysis of FM data (Kaufman et al., PLoSOne 7:e40980, 2012)
otherDAPI stainSigma AldrichD9564(1 µg/mL)
otherFM1-43 DyeInvitrogenCAT# T35356(10 µM)
otherADVASEP-7-Sigma AldrichCAT# A3723(1 mM)
otherTRIzol ReagentSigma AldrichCAT# T9424
othercOmplete protease inhibitorsMerck (Roche Diagnostics GmbH)CAT# 11873580001
otherEMCCD cameraAndor TechnologyiXon
otherInverted Fluorescence microscopeOlympusIX71
otherUpright Fluorescence microscopeOlympusIX51WI
otherConfocal Laser Scanning MicroscopeOlympusFV3000

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 3 and 4.

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Decision letter

  1. John A Gray
    Reviewing Editor; University of California, Davis, United States
  2. Lu Chen
    Senior Editor; Stanford University, United States

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

Decision letter after peer review:

Thank you for submitting your article "Astrocyte GluN2C NMDA receptors control basal synaptic strengths of hippocampal CA1 pyramidal neurons in the stratum radiatum" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Lu Chen as the Senior Editor. The other reviewers have opted to remain anonymous.

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

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 simply require clarification, minor figure edits, or additional discussion. Key revisions are related to clarifying the physiological relevance of the primary findings:

1) Addressing reviewer 2's comment #7: "A clearer hypothesis on how a NMDAR-mediated molecular signal could both reduce and increase release probability depending on its current state, or otherwise just shift synaptic Pr randomly up or down, needs to be put forward. The authors are encouraged to find some material evidence for this type of phenomenon in the literature."

2) Each reviewer had concerns regarding the modeling as presented and are in agreement that significant revision of the modeling is required. The modeling and simulations are important for providing some physiological framework for the counterintuitive result of a change in the variance but not the mean of the presynaptic release probability. The authors should focus on reviewer 2's comment #8, and reviewer 3's concerns regarding the modeling.

Reviewer #1:

In the current manuscript, Chipman et al. present evidence that GluN2C-containing NMDA receptors in hippocampal astrocytes tune synaptic strengths in CA1 pyramidal neurons by helping to maintain a wide distribution of presynaptic release probabilities. This work was an extension of the lab's previous work (Letellier et al. 2016) where they first showed that astrocytic calcium and NMDA receptors regulate presynaptic release probability. Here they add genetic data supporting these earlier findings and provide a model for how a narrowing of the distribution of presynaptic release probability might affect synaptic plasticity. The go on to provide strong evidence that astrocytic NMDA receptors largely contain the GluN2C subunit and provide evidence that these GluN2C-containing receptors are involved in the effect on presynaptic release probability. The conclusions of this paper are mostly supported by the data and the combination of pharmacological and genetic approaches are a major strength of this manuscript.

There are some concerns about the selectivity of some of the manipulations allowing the possibility that neuronal NMDA receptors may be involved. Further, not much new insight is provided into the mechanisms for how astrocytes might regulate the variation of presynaptic release probability, leaving this as a more descriptive work. Also, while the modeling is interesting, it was not tested experimentally using the findings presented.

Comments for the authors:

1) The combination of pharmacological and genetic approaches to blocking astrocytic NMDA receptors and GluN2C-containing receptors is a major strength of this manuscript. However, there are a few reasons to question selectivity of these manipulations on astrocytes.

First, following Cre-mediated GluN1 deletion in astrocytes a complete loss of response to the puff of NMDA/glycine would be expected, but the response was only reduced by about half (though completely eliminated by AP5) (Figure 2—figure supplement 1) raising concerns about the effectiveness of the genetic deletion.

Second, they utilize intracellular MK801 in the patch pipette to block postsynaptic NMDA receptors. It has recently been shown (Sun et al. Neuropharmacology 2018) that intracellular MK801 incompletely inhibits NMDA receptors due to an ~30,000-fold lower affinity compared to extracellular application. Indeed, they see some remaining current in Figure 1—figure supplement 1.

Furthermore, the conclusions of this paper would be better supported by a set of experiments increasing GluN2C activity rather than just reducing – perhaps with the positive allosteric modulator CIQ (Mullasseril et al. Nature Communications, 2010), or by a GluN2C replacement approach. Indeed, the statement on lines 182-193, that "these findings support the idea NMDARs play a role in bi-directionally regulating presynaptic strengths to broaden the variability of PPR" is not fully supported.

2) The modeling is interesting, but its value is limited as it was not tested experimentally using the most selective manipulations they have identified (e.g. GluN2C blockade/deletion)

3) This work remains quite descriptive without much progress in the mechanism of astrocytic control of presynaptic release probability. The authors discuss some possible mechanism, including through astrocyte-mediated release of ATP and action at adenosine receptors, release of other putative gliotransmitters, or modulation of local K+ dynamics. Observing GluN2C mediated effects on any of these processes would strengthen the conclusions.

Reviewer #2:

The present work deals with a timely and potentially important subject, the role of astroglial NMDA receptors in regulating release efficacy at local excitatory synapses. The authors combine refined electrophysiology methods with pharmacological dissection, genetic targeting, and theoretical simulations to conclude that signals mediated by astroglial NDMARs help widen the range of release probability values among CA3-CA1 synapses in the hippocampus. Modelling suggests that such widening should be conducive to the expression of synaptic plasticity. There are several issues, which the authors may want to address.

1. Whilst changes in PPR are normally inversely correlated with changes in Pr, it does not mean that PPR represents faithfully Pr across various connections, in various conditions. Slice condition, afferent excitability, retrograde messaging or network influences post-stimulus, etc. can affect PPR without affecting Pr. This has to be at least discussed, in the context.

2. Figure 1: One has to dig into the Methods to understand how PPR values were sampled. It would help the reader to mention the sampling design (number of slices / independent inputs) in the figures or in the main text. Methods: it seems that the 'cross-paired-pulse stimulation paradigm' to gauge input independence was introduced by Scimemi et al. (2004 J Neurosci).

3. The classical 'regression to the mean' phenomenon means that the random variable once sampled close to the top of its range will tend to show smaller values upon next sampling, and vice versa. This is the origin of the regression lines shown as delta-PPR plots, throughout the paper. This type of regression depends on multiple, poorly controlled concomitants, so its use for statistical inference is highly ambiguous. The authors are encouraged to focus on direct comparisons of the PPR dispersion values using appropriate, possibly non-parametric statistical tests (since some of their histograms do not look Gaussian).

4. After the NMDAR blockade in one postsynaptic cell, all other neurons will still operate their NMDARs. Their subsequent blockade might involve changes in retrograde messaging, the dynamics of extracellular potassium, etc. This has to be addressed or at least discussed.

5. Figure 3: Direct readouts of Pr and/or PPR at excitatory synapses along both apical and basal dendrites of CA1 pyramidal cells have recently been reported (Grillo et al., Nat Neurosci 2018; Jensen et al. eLife 2021). It would surely make sense to either adopt or at least discuss those most recent data sets in the context of the present experiments and simulations.

6. Figure 3: LTP induction by its virtue refers to a sub-population of potentiated synapses (rather than all available synapses), which is supposed to be followed by a re-distribution of synaptic weights, to avoid runaway excitation. Whether this notion is compatible with the present simulation paradigm needs explaining.

7. A clearer hypothesis on how a NMDAR-mediated molecular signal could both reduce and increase release probability depending on its current state, or otherwise just shift synaptic Pr randomly up or down, needs to be put forward. The authors are encouraged to find some material evidence for this type of phenomenon in the literature.

8. The modeling appears unnecessarily complicated yet narrow in scope. In their model, BCM plasticity reflects STDP but with uncorrelated, Poisson-process pre and postsynaptic spiking at 20 Hz, which is hardly universal. This type of models deals with a 'fractional' neurotransmitter release as a representation of Pr, and whether this could be readily combined with explicit Pr distributions to reflect reality is not clear. Perhaps a more transparent and robust, 'threshold-type' plasticity approach, such as HFS or theta-burst LTP would be helpful. Regarding the model setting per se, another recent model (https://elifesciences.org/articles/62588) gave experimentally-probed stochastic synapses with distributed Pr that are scattered along realistic CA1 pyramid dendrites. This model might be considered as the context is pretty similar.

Reviewer #3:

Chipman et al. conducted a battery of experiments to demonstrate that astrocytes in the CA1 area of the mouse hippocampus express NMDA-type glutamate receptors (NMDARs) containing the subunits GluN1 and GluN2C, and that signaling through these receptors influences presynaptic neurotransmitter release probability at excitatory synapses onto CA1 pyramidal neurons in the stratum radiatum region of CA1. In acute brain slices, the authors electrically stimulated presynaptic axons and recorded intracellular postsynaptic currents to monitor changes in the mean and variance of presynaptic release probability under various conditions. To isolate the effects of NMDARs not expressed in the recorded cell, the authors applied a use-dependent blocker of NMDARs intracellularly to recorded CA1 pyramidal neurons and stimulated afferent fibers until all synaptic NMDARs were silenced. On that background, application of extracellular blockers of NMDARs decreased the variance, but not the mean, of the distribution of presynaptic release probabilities measured across stimulation pathways, cells, and animals. These effects were occluded when GluN1-containing NMDARs were genetically removed specifically from hippocampal astrocytes. The authors then used single-cell RT-PCR to show that the NMDAR subunit GluN2C is expressed at higher levels in astrocytes than pyramidal neurons. A selective antagonist of GluN2C-containing NMDARs also increased the variance of presynaptic release probability in pyramidal cells, but not when GluN1-containing NMDARs were genetically removed specifically from hippocampal astrocytes. This paper replicates, corroborates, and extends previous findings from a prior publication, Letellier et al., PNAS, 2016. The rigor of these new experiments provides important additional support for an intriguing model of glial-neuronal interactions that highlights new questions regarding possible mechanisms.

To explore the functional consequences of the observed specific effect on the variance, but not the mean, of presynaptic release probability, the authors simulated models of long-term synaptic plasticity and suggested that the variance of release probability affects changes in synaptic weight, and by implication, memory storage. This paper could, in principle, be enhanced by the inclusion of this type of modeling. However, in the current form, the modeling component is incompletely motivated, insufficiently described and interpreted, and may suffer from technical errors.

Comments for the authors:

Please find below comments, questions, constructive criticism, and suggestions to potentially improve the manuscript and enhance its clarity, interpretability, and impact for a broad readership.

Major commentary on Figure 3:

Ideally a model of synaptic transmission with use-dependent release probability would first be fit to and compared to the experimental data, and then used to explore the implications of the experimental findings. It is indeed a counter-intuitive result that signaling in hippocampal astrocytes alters the variance, but not the mean, of the distribution of presynaptic release probabilities at excitatory synapses onto CA1 pyramidal neurons, and it is not obvious how this would influence information processing. This is a great opportunity to use modeling to gain insight into how such a change would impact circuit function and/or memory storage. If done well, it could potentially greatly elevate the impact of the paper. However, in current form, the text does not sufficiently motivate the use of the specific plasticity models chosen, the presentation of the modeling data is confusing and hard to relate to the experimental results, and there appears to be a technical error that confounds the results. Please find below suggestions to improve this component of the paper.

1) In the experiments presented in Figures 1B – D, a small number of fibers are stimulated, and the ratio of responses to a 50 ms paired-pulse stimulation is measured. Each measurement reflects the compound effect of multiple synapses that themselves might have different initial release probabilities (Pr), and different degrees of facilitation or depression. Across many stimulation pathways, cells, and animals, a distribution of these paired-pulse ratios (PPR) is obtained and analyzed. These distributions appear to be fit by a normal distribution. However, in Figure 3A, a population of synapses is simulated, with their individual release probabilities sampled from a log-normal distribution. The first step should be to select random groups of these synapses with release probabilities sampled from the modeled distribution, probe them with a paired-pulse protocol, and verify that indeed a normal distribution of PPRs are obtained that recapitulate the experimental data in Figure 1B. Then, an appropriate distribution of release probabilities with a lower variance can be calibrated to simulate the AP5 condition. Again a paired-pulse protocol should be used to verify that the resulting PPR distribution is normal, has the same mean as the control condition, but a lower variance recapitulating the data shown in Figure 1D.

Currently, the way that the authors have constructed their release probability distributions, there is a parameter Umax that sets the value of USE where the distribution is maximal. A Control distribution with large variance (σmax) and a given Umax can be compared to an AP5 distribution with lower variance (σmax / 2), but the same Umax. However, the mean USE sampled from these two distributions with the same Umax are not equal. The mean USE sampled from the Control log-normal distribution is higher than the mean USE sampled from the AP5 distribution. So the authors have not succeeded in modeling the situation where AP5 changes only the variance, but not the mean Pr. As an aside, the definition of "SE" in USE is never provided in the paper.

2) The Equations 5, 6, and 7 must contain an error. I was not able to reproduce exactly the distributions shown in Figure 1A with Umax = 0.1. Also the value of the "a" term in Equation 6 is not provided.

3) Currently, the choice to test the BCM plasticity model is not motivated at all in the text. On the surface, this is an odd choice of model to test because it has both a Hebbian component that depends on coincidence of pre- and post-synaptic activity, but also a homeostatic, meta-plastic component that adjusts the transition point from potentiation to depression depending on the recent history of post-synaptic activation. While most implementations of the BCM model assume an effective presynaptic release probability of 1, here the authors would like to investigate how a dynamic release probability affects the changes in synaptic weight produced by this plasticity model. So the first test should be to compare what happens when the stimulated synapses with the same initial weight have a low USE vs. a high USE. Traces for all relevant model intermediates over time should be shown: presynaptic rate r(t), successful release event rate x(t), post-synaptic current I(t), dynamic plasticity threshold Theta(t), and the synaptic weight w(t). My intuition is that synapses with a lower USE will result in a lower mean x(t), resulting in a lower mean I(t), and initially favor synaptic depression. However, over time, this will cause Theta(t) to decrease, causing potentiation to be preferred over depression. It is possible that, given enough time to equilibrate, the final target weight will be identical for low USE vs. high USE, as long as the BCM rule is given enough time to equilibrate.

Once it has been established how the BCM rule is sensitive to USE, then the behavior of the learning rule can be probed with a random group of synapses with variable USE sampled from an appropriate distribution. However, I suspect that as long as the mean USE is truly equal between two distributions, then the average equilibrium weight across the sample of synapses will be equal, regardless of the variance of the U distribution. The changes at low and high USE will cancel each other out. It is critical that the authors ensure that they are comparing distributions with equal mean USE. If there truly is an effect of the variance of the distribution separately from the mean, then the authors are advised to provide substantially more interpretation and discussion of how or why this is the case. It is not sufficient to simply show the modeling results and state that variance matters without explaining the mechanism.

4) In Figure 3B, the value of alpha is varied, but this parameter is not sufficiently described in the text, figure legend, or Methods. This should not be used as a parameter to shift the balance between potentiation and depression. Instead, it should be the Pr (USE) that determines x(t) and I(t), and then it is the relative value of I(t) and Theta(t) determines the balance between potentiation and depression. An alternative could be to compare different initial values of Theta(t), though this will quickly equilibrate to differences in the mean I(t) that result from differences in USE.

5) In Figure 3B, the sigma symbol with a subscript of 0.1 is used in the figure label but not anywhere in the text, figure legend, or Methods. I believe this is meant to indicate the value of σmax when Umax = 0.1. This should be made explicit in the text.

6) In Figure 3C, what variable was changed to generate different values for the absolute changes in synaptic weight in the Control condition (the x-axis)? Was it the value of "alpha"? This was not explained sufficiently. This plot does not have an obvious interpretation, and one is not provided in the text. It does not help the reader understand how the variance of the USE distribution would cause this particular change in long-term plasticity.

7) With regards to the use of an STDP model in Figure 3D – again, there is not sufficient description the text to understand how this model is implemented. Why is the x-axis labeled "Δt / ms"? The use of the variable postsynaptic current A2, or why the y-axis is labeled "A2 / ms" is not clear at all.

A classical phenomenological STDP model would stimulate presynaptic spikes at defined times relative to postsynaptic spikes, and update the synaptic weight(s) accordingly. Inclusion of a dynamic release probability would simply cause some fraction of the presynaptic spikes to be failures and therefore to not be paired with a postsynaptic spike within the plasticity window, and therefore not result in any change in weight. Again my intuition is that for a synapse with a low Pr (USE), it will take more spike pairings over a longer time to equilibrate compared to a synapse with a high Pr. So the first test should be to compare the final weights of synapses with low USE to those of synapses with high USE after a period of Poisson pre- and post-synaptic spiking. However, it is again not clear that changes in the variance of the USE distribution in the absence of any changes in the mean would have any impact on the relative degree of potentiation or depression.

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

Thank you for resubmitting your work entitled "Astrocyte GluN2C NMDA receptors control basal synaptic strengths of hippocampal CA1 pyramidal neurons in the stratum radiatum" for further consideration by eLife. Your revised article has been evaluated by Lu Chen as the Senior Editor and John Gray as the Reviewing Editor in consultation with the original peer reviewers.

The authors have made a significant effort in addressing most of the concerned expressed by the reviewers and the manuscript has been improved, but there are some remaining issues that need to be addressed, as outlined below:

1) At the suggestion of the reviewers, the authors have revised their modeling of the potential impact of changing Pr distribution (but not mean) on synaptic plasticity. Appropriately, they removed the BCM modeling and focused on STDP and provided experimentally collected estimates of Pr using FM1-43 imaging of the RRP from hippocampal neurons co-cultured with astrocytes. However, the reviewers found the text related to Figure 3 to be confusing and poorly described. Specifically, it took some discussion for all the reviewers to understand how Figure 3 actually tests the impact of Pr distribution based on the A2 injection amplitude. That is, if you have two distributions of Pr with the same mean, but one has a wide variance and one has a narrow variance – if you set a threshold above the mean, the probability that the threshold will be crossed is higher for the distribution with wider variance. This needs to be explicitly described and explained by the authors in the manuscript text and in the figure legend. Figure revisions are not necessary, but could also benefit the readers if there is improved clarity.

2) The authors are also urged to mention explicitly that their delta PPR graphs contain a regression-to-the-mean component which is however effectively cancelled out when making direct graph comparisons, within the same parametric space

https://doi.org/10.7554/eLife.70818.sa1

Author response

Reviewer #1:

[…] There are some concerns about the selectivity of some of the manipulations allowing the possibility that neuronal NMDA receptors may be involved. Further, not much new insight is provided into the mechanisms for how astrocytes might regulate the variation of presynaptic release probability, leaving this as a more descriptive work. Also, while the modeling is interesting, it was not tested experimentally using the findings presented.

Comments for the authors:

1) The combination of pharmacological and genetic approaches to blocking astrocytic NMDA receptors and GluN2C-containing receptors is a major strength of this manuscript. However, there are a few reasons to question selectivity of these manipulations on astrocytes.

First, following Cre-mediated GluN1 deletion in astrocytes a complete loss of response to the puff of NMDA/glycine would be expected, but the response was only reduced by about half (though completely eliminated by AP5) (Figure 2—figure supplement 1) raising concerns about the effectiveness of the genetic deletion.

The NMDA/glycine puff is expected to activate nearby neuronal NMDARs that are not targeted by the Cre-mediated knockdown. Postsynaptic NMDA receptor activation has been shown to be the major source of K+ efflux (Shin et al., Cell Rep 2013), which will alter the local extracellular ionic milieu, and in turn, impact astrocyte ionic currents. In our prior work (Letellier et al., 2016), we demonstrated that the slow depolarization of astrocyte membrane potential induced by NMDA/glycine puff could be partially blocked by extracellular application of nifedipine or nimodipine, and intracellular loading into astrocytes of QX-314 or D-890, an L-VGCC antagonist. Therefore, other voltage-gated currents, including calcium channels contribute to the NMDA/glycine puff responses in astrocytes. We have clarified the sentence in lines 223-227:

“The efficacy of GluN1 knock-down in astrocytes was assessed electrophysiologically by patch-clamping astrocytes and monitoring slow depolarizing responses elicited by puff applying NMDA and glycine (1mM each) (Figure 2—figure supplement 1) which were mediated by NMDARs but were also contributed in part by voltage-gated calcium channels in astrocytes (Letellier et al., 2016)”.

Please note that we have included the NMDA/glycine puff response data in the present manuscript to demonstrate the ability to detect AP5-dependent responses across astrocytes in all three layers in the CA1 subfield, which is in line with the RT-PCR results. Importantly, the data highlight the differences in sensitivity of the NMDA/glycine puff responses to astrocyte NMDAR knock-down across the three layers, which suggest that signaling downstream of NMDAR activation underlying the slow depolarization differs across the three layers, specifically for astrocytes in the stratum radiatum.

Second, they utilize intracellular MK801 in the patch pipette to block postsynaptic NMDA receptors. It has recently been shown (Sun et al. Neuropharmacology 2018) that intracellular MK801 incompletely inhibits NMDA receptors due to an ~30,000-fold lower affinity compared to extracellular application. Indeed, they see some remaining current in Figure 1—figure supplement 1.

We acknowledge that MK-801 may not always result in 100% block of postsynaptic NMDAR currents. After confirming the paucity of expression of GluN2C in pyramidal neurons and GluN2D in hippocampal extracts, and the robust expression of GluN2C in astrocytes, we find that application of GluN2C/D-specific inhibitor is sufficient to alter PPR. This points to the involvement of astrocyte GluN2C NMDAR. Moreover, the effect on PPR is occluded by the genetic knock-down of GluN1 in astrocytes. Collectively, these observations support the sufficiency of astrocyte NMDARs in affecting PPR.

Furthermore, the conclusions of this paper would be better supported by a set of experiments increasing GluN2C activity rather than just reducing – perhaps with the positive allosteric modulator CIQ (Mullasseril et al. Nature Communications, 2010), or by a GluN2C replacement approach. Indeed, the statement on lines 182-193, that "these findings support the idea NMDARs play a role in bi-directionally regulating presynaptic strengths to broaden the variability of PPR" is not fully supported.

We agree with the reviewer that expanding the pharmacological manipulation of GluN2C activity by using CIQ to enhance its activity and genetically manipulating GluN2C expression might provide additional experimental support to the present findings. Nevertheless, gain of function experiments require careful considerations. We plan to capitalize on such tools for our future work in pursuing the mechanisms underlying GluN2C-NMDAR-dependent presynaptic modulation. We have toned down the statement on lines 204-206 to read:

“Collectively, these findings support the idea that NMDARs play a role in regulating presynaptic strengths to broaden the variability of PPR without appreciably impacting the mean PPR”.

2) The modeling is interesting, but its value is limited as it was not tested experimentally using the most selective manipulations they have identified (e.g. GluN2C blockade/deletion)

3) This work remains quite descriptive without much progress in the mechanism of astrocytic control of presynaptic release probability. The authors discuss some possible mechanism, including through astrocyte-mediated release of ATP and action at adenosine receptors, release of other putative gliotransmitters, or modulation of local K+ dynamics. Observing GluN2C mediated effects on any of these processes would strengthen the conclusions.

We respectfully disagree with the reviewer that the modeling is of limited value. The modeling provides a framework to demonstrate the impact of directly altering the width of release probability variance on synaptic plasticity. The experimental test of GluN2C manipulation on synaptic plasticity involving careful characterization of the underlying mechanisms, including the determination of glial signaling and putative gliotransmitters, are aspects of the study that we will pursue in the future and are beyond the scope of the present work. Please note that we have made major changes to the modelling section in the revised manuscript as described in detail in the replies to Reviewer #3.

Reviewer #2:

The present work deals with a timely and potentially important subject, the role of astroglial NMDA receptors in regulating release efficacy at local excitatory synapses. The authors combine refined electrophysiology methods with pharmacological dissection, genetic targeting, and theoretical simulations to conclude that signals mediated by astroglial NDMARs help widen the range of release probability values among CA3-CA1 synapses in the hippocampus. Modelling suggests that such widening should be conducive to the expression of synaptic plasticity. There are several issues, which the authors may want to address.

1. Whilst changes in PPR are normally inversely correlated with changes in Pr, it does not mean that PPR represents faithfully Pr across various connections, in various conditions. Slice condition, afferent excitability, retrograde messaging or network influences post-stimulus, etc. can affect PPR without affecting Pr. This has to be at least discussed, in the context.

We fully acknowledge the fact that PPR may not necessarily always relate to Pr. We have amended the discussion as follows in lines 484-491:

“The present study took advantage of paired-pulse response as a measure of presynaptic efficacy. […] Therefore, in some experiments, presumed changes in release probability as monitored by PPR were further corroborated by the correlated changes in CV-2 (Malinow and Tsien, 1990; Larkman et al., 1992).”

2. Figure 1: One has to dig into the Methods to understand how PPR values were sampled. It would help the reader to mention the sampling design (number of slices / independent inputs) in the figures or in the main text. Methods: it seems that the 'cross-paired-pulse stimulation paradigm' to gauge input independence was introduced by Scimemi et al. (2004 J Neurosci).

Number of inputs, cells and mice are stated in the bottom of figure legend. Cross-paired pulse stimulation has been used as a standard test for input independence for quite some time (e.g. Otani and Connor, J Physiol 1996). We now cite Otani and Connor (1996) and Scimemi et al. (2004) as example references in line 669.

3. The classical 'regression to the mean' phenomenon means that the random variable once sampled close to the top of its range will tend to show smaller values upon next sampling, and vice versa. This is the origin of the regression lines shown as delta-PPR plots, throughout the paper. This type of regression depends on multiple, poorly controlled concomitants, so its use for statistical inference is highly ambiguous. The authors are encouraged to focus on direct comparisons of the PPR dispersion values using appropriate, possibly non-parametric statistical tests (since some of their histograms do not look Gaussian).

We thank the reviewer for the important point. We included the delta-PPR data in order to consider the present results relative to data in our previous study that used delta-PPR comparisons (Letellier et al., 2016). In the present work we sought to compare PPR distributions directly as such an analysis could be more informative, as the reviewer indicated. The datasets were tested for normality using the Shapiro-Wilk test. Details of statistical analyses are described in Materials and methods section, and the statistics of individual datasets are included in the excel file labelled “E-phys statistics report”.

4. After the NMDAR blockade in one postsynaptic cell, all other neurons will still operate their NMDARs. Their subsequent blockade might involve changes in retrograde messaging, the dynamics of extracellular potassium, etc. This has to be addressed or at least discussed.

We acknowledge the fact that we cannot exclude the possible contribution of NMDARs present in neurons that we are not recording from nor of NMDARs in the recorded neuron should there be incomplete block of NMDARs from intracellularly applied MK801. Please see the reply to reviewer 1, second comment of part 1.

5. Figure 3: Direct readouts of Pr and/or PPR at excitatory synapses along both apical and basal dendrites of CA1 pyramidal cells have recently been reported (Grillo et al., Nat Neurosci 2018; Jensen et al. eLife 2021). It would surely make sense to either adopt or at least discuss those most recent data sets in the context of the present experiments and simulations.

For the mathematical modelling in the revised manuscript, we experimentally obtained the Pr distribution. Our Pr estimate was based on direct measurements using the FM1-43 styryl dye, of readily releasable vesicle pool (RRP) at individual boutons formed on dendrites of hippocampal pyramidal neurons in primary culture. FM dye experiments have been carried out following standard procedures in the presence of 10 μM CNQX to block AMPA receptors so as to prevent recurrent network activity. Under such conditions, neuronal NMDARs do not experience the efficient removal of Mg block which is facilitated by membrane depolarization from AMPAR activation. In contrast, GluN2C-NMDARs have reduced Mg block compared to neuronally expressed NMDARs, and given the expression of GluN2C in hippocampal astrocytes, any effect of AP5 (added on top of CNQX), if observed, could be associated with astrocyte NMDAR inhibition. Notably, the distribution of RRP monitored in the presence of AP5 and CNQX was less variable compared to the distribution observed in CNQX alone.

With respect to Grillo et al. (2018) and Jensen et al. (2021) studies, we refer to the work in the discussion (lines 503-506):

“The use of methods that allow for a more direct estimate of release probability in hippocampal CA1 neurons as have been reported recently (e.g. Grillo et al., 2018; Jensen et al., 2021) will be informative in further pursuing the underlying mechanisms.”

6. Figure 3: LTP induction by its virtue refers to a sub-population of potentiated synapses (rather than all available synapses), which is supposed to be followed by a re-distribution of synaptic weights, to avoid runaway excitation. Whether this notion is compatible with the present simulation paradigm needs explaining.

We have not considered homeostatic compensation across space in the present simulation. Please note that in the Poisson spike model where presynaptic and postsynaptic neurons are not fixed to spike in any particular order or frequency to favor LTP or LTD induction, both forms of plasticity are observed across trials. Thus potentiation could be countered by depression and vice versa across time at minimum. Importantly, the extent expression of both LTP and LTD are compromised by reducing the variability of presynaptic release probability.

7. A clearer hypothesis on how a NMDAR-mediated molecular signal could both reduce and increase release probability depending on its current state, or otherwise just shift synaptic Pr randomly up or down, needs to be put forward. The authors are encouraged to find some material evidence for this type of phenomenon in the literature.

We apologize for the lack of clarity in our original discussion of how astrocyte NMDAR activity could expand the range of release probability. The relevant section (lines 533-560) has been reworded with an additional reference (Darabid et al., Cell Rep 2018):

“The mechanism by which astrocyte GluN2C NMDARs are coupled to the changes in presynaptic efficacy remains to be clarified. […] Further understanding of such mechanisms is warranted to identify the basis for how the relative differences in synaptic strengths are promoted to facilitate broadening of the range of presynaptic efficacy.”

8. The modeling appears unnecessarily complicated yet narrow in scope. In their model, BCM plasticity reflects STDP but with uncorrelated, Poisson-process pre and postsynaptic spiking at 20 Hz, which is hardly universal. This type of models deals with a 'fractional' neurotransmitter release as a representation of Pr, and whether this could be readily combined with explicit Pr distributions to reflect reality is not clear. Perhaps a more transparent and robust, 'threshold-type' plasticity approach, such as HFS or theta-burst LTP would be helpful. Regarding the model setting per se, another recent model (https://elifesciences.org/articles/62588) gave experimentally-probed stochastic synapses with distributed Pr that are scattered along realistic CA1 pyramid dendrites. This model might be considered as the context is pretty similar.

We have decided to forego the BCM model in the present study given the concerns raised by the reviewers, and instead, we have sought to simplify the modelling by focusing on leaky integrate-and-fire neurons under two different conditions: first, by applying the commonly used STDP paradigm, and second, for exploring a more general condition by applying Poisson spike trains with an average spike rate of 10 Hz. Moreover, we took advantage of an experimentally determined Pr distributions which were obtained under the control condition and in the presence of AP5 that yielded less variable Pr as explained above. In the improved version of the STDP modeling study, we found a clear picture that the reduction of variance in release probability can suppress the efficacy of long-term potentiation and depression.

Reviewer #3:

Chipman et al. conducted a battery of experiments to demonstrate that astrocytes in the CA1 area of the mouse hippocampus express NMDA-type glutamate receptors (NMDARs) containing the subunits GluN1 and GluN2C, and that signaling through these receptors influences presynaptic neurotransmitter release probability at excitatory synapses onto CA1 pyramidal neurons in the stratum radiatum region of CA1. In acute brain slices, the authors electrically stimulated presynaptic axons and recorded intracellular postsynaptic currents to monitor changes in the mean and variance of presynaptic release probability under various conditions. To isolate the effects of NMDARs not expressed in the recorded cell, the authors applied a use-dependent blocker of NMDARs intracellularly to recorded CA1 pyramidal neurons and stimulated afferent fibers until all synaptic NMDARs were silenced. On that background, application of extracellular blockers of NMDARs decreased the variance, but not the mean, of the distribution of presynaptic release probabilities measured across stimulation pathways, cells, and animals. These effects were occluded when GluN1-containing NMDARs were genetically removed specifically from hippocampal astrocytes. The authors then used single-cell RT-PCR to show that the NMDAR subunit GluN2C is expressed at higher levels in astrocytes than pyramidal neurons. A selective antagonist of GluN2C-containing NMDARs also increased the variance of presynaptic release probability in pyramidal cells, but not when GluN1-containing NMDARs were genetically removed specifically from hippocampal astrocytes. This paper replicates, corroborates, and extends previous findings from a prior publication, Letellier et al., PNAS, 2016. The rigor of these new experiments provides important additional support for an intriguing model of glial-neuronal interactions that highlights new questions regarding possible mechanisms.

To explore the functional consequences of the observed specific effect on the variance, but not the mean, of presynaptic release probability, the authors simulated models of long-term synaptic plasticity and suggested that the variance of release probability affects changes in synaptic weight, and by implication, memory storage. This paper could, in principle, be enhanced by the inclusion of this type of modeling. However, in the current form, the modeling component is incompletely motivated, insufficiently described and interpreted, and may suffer from technical errors.

We thank the reviewer for acknowledging that our new experiments extend our previous work and provide “important additional support for an intriguing model of glial-neuronal interactions”. The reviewer has also indicated several major concerns for the results presented in Figure 3 of the original submission. Following his/her suggestions, we have substantially revised the modelling part of the study.

Comments for the authors:

Please find below comments, questions, constructive criticism, and suggestions to potentially improve the manuscript and enhance its clarity, interpretability, and impact for a broad readership.

Major commentary on Figure 3:

Ideally a model of synaptic transmission with use-dependent release probability would first be fit to and compared to the experimental data, and then used to explore the implications of the experimental findings. It is indeed a counter-intuitive result that signaling in hippocampal astrocytes alters the variance, but not the mean, of the distribution of presynaptic release probabilities at excitatory synapses onto CA1 pyramidal neurons, and it is not obvious how this would influence information processing. This is a great opportunity to use modeling to gain insight into how such a change would impact circuit function and/or memory storage. If done well, it could potentially greatly elevate the impact of the paper. However, in current form, the text does not sufficiently motivate the use of the specific plasticity models chosen, the presentation of the modeling data is confusing and hard to relate to the experimental results, and there appears to be a technical error that confounds the results. Please find below suggestions to improve this component of the paper.

1) In the experiments presented in Figures 1B – D, a small number of fibers are stimulated, and the ratio of responses to a 50 ms paired-pulse stimulation is measured. Each measurement reflects the compound effect of multiple synapses that themselves might have different initial release probabilities (Pr), and different degrees of facilitation or depression. Across many stimulation pathways, cells, and animals, a distribution of these paired-pulse ratios (PPR) is obtained and analyzed. These distributions appear to be fit by a normal distribution. However, in Figure 3A, a population of synapses is simulated, with their individual release probabilities sampled from a log-normal distribution. The first step should be to select random groups of these synapses with release probabilities sampled from the modeled distribution, probe them with a paired-pulse protocol, and verify that indeed a normal distribution of PPRs are obtained that recapitulate the experimental data in Figure 1B. Then, an appropriate distribution of release probabilities with a lower variance can be calibrated to simulate the AP5 condition. Again a paired-pulse protocol should be used to verify that the resulting PPR distribution is normal, has the same mean as the control condition, but a lower variance recapitulating the data shown in Figure 1D.

Currently, the way that the authors have constructed their release probability distributions, there is a parameter Umax that sets the value of USE where the distribution is maximal. A Control distribution with large variance (σmax) and a given Umax can be compared to an AP5 distribution with lower variance (σmax / 2), but the same Umax. However, the mean USE sampled from these two distributions with the same Umax are not equal. The mean USE sampled from the Control log-normal distribution is higher than the mean USE sampled from the AP5 distribution. So the authors have not succeeded in modeling the situation where AP5 changes only the variance, but not the mean Pr. As an aside, the definition of "SE" in USE is never provided in the paper.

We thank the reviewer for the criticisms and helpful suggestions. We have completely revamped our approach in the numerical modeling section. One of the major changes is in the way we obtained the Pr distribution, which was based on our own experimental measurements that used a styryl dye FM1-43 to label the readily releasable pool at individual boutons in cultured hippocampal pyramidal neurons. The readily releasable pool provides a more direct estimate Pr compared to PPR. As an advantage of this approach, we could obtain the Pr distributions experimentally under both control condition and in the presence of AP5, and particularly in AP5, the distribution showed a reduced variance (Figure 3—figure supplement 1). This result was consistent with acute slice experiments presented. Given that FM1-43 experiments were performed under conditions that did not favor the activation of neuronal NMDARs (i.e. in the continuous presence of CNQX), the effect of AP5 suggested the consequences of blocking astrocyte NMDARs. Furthermore, in our prior study, co-cultures of hippocampal neurons and astrocytes preserved aspects of astrocyte NMDAR signaling-dependent regulation of PPR (Letellier et al., 2016). Therefore, collectively, the FM1-43 data support the main conclusions and provide experimental Pr distributions for both the control and the AP5 cases. In the revised manuscript, we obtained gamma distribution fits to the FM1-43 experimental data, which were then used to perform numerical simulations. Following the suggestions, the simulations have been tested for the fits to experimentally obtained EPSC peaks, and simulated PPR has been compared to data reported in Figure 1B-D in control and AP5 (Figure 3—figure supplement 2).

2) The Equations 5, 6, and 7 must contain an error. I was not able to reproduce exactly the distributions shown in Figure 1A with Umax = 0.1. Also the value of the "a" term in Equation 6 is not provided.

In the revised manuscript, we have used the gamma distributions for simulations, and therefore the equations referred to are not included. The parameters for the gamma distributions are obtained from fitting the FM1-43 signal as explained above, and the values are stated in the legend of Figure 3—figure supplement 1 and in Materials and methods.

3) Currently, the choice to test the BCM plasticity model is not motivated at all in the text. On the surface, this is an odd choice of model to test because it has both a Hebbian component that depends on coincidence of pre- and post-synaptic activity, but also a homeostatic, meta-plastic component that adjusts the transition point from potentiation to depression depending on the recent history of post-synaptic activation. While most implementations of the BCM model assume an effective presynaptic release probability of 1, here the authors would like to investigate how a dynamic release probability affects the changes in synaptic weight produced by this plasticity model. So the first test should be to compare what happens when the stimulated synapses with the same initial weight have a low USE vs. a high USE. Traces for all relevant model intermediates over time should be shown: presynaptic rate r(t), successful release event rate x(t), post-synaptic current I(t), dynamic plasticity threshold Theta(t), and the synaptic weight w(t). My intuition is that synapses with a lower USE will result in a lower mean x(t), resulting in a lower mean I(t), and initially favor synaptic depression. However, over time, this will cause Theta(t) to decrease, causing potentiation to be preferred over depression. It is possible that, given enough time to equilibrate, the final target weight will be identical for low USE vs. high USE, as long as the BCM rule is given enough time to equilibrate.

Once it has been established how the BCM rule is sensitive to USE, then the behavior of the learning rule can be probed with a random group of synapses with variable USE sampled from an appropriate distribution. However, I suspect that as long as the mean USE is truly equal between two distributions, then the average equilibrium weight across the sample of synapses will be equal, regardless of the variance of the U distribution. The changes at low and high USE will cancel each other out. It is critical that the authors ensure that they are comparing distributions with equal mean USE. If there truly is an effect of the variance of the distribution separately from the mean, then the authors are advised to provide substantially more interpretation and discussion of how or why this is the case. It is not sufficient to simply show the modeling results and state that variance matters without explaining the mechanism.

We acknowledge the issues concerning the BCM model. To present a clearer simulation study we removed the BCM model in the revised manuscript. We focused on the model of spiking neurons with STDP to demonstrate how the range of release probability variability could affect the efficacy of STDP.

4) In Figure 3B, the value of alpha is varied, but this parameter is not sufficiently described in the text, figure legend, or Methods. This should not be used as a parameter to shift the balance between potentiation and depression. Instead, it should be the Pr (USE) that determines x(t) and I(t), and then it is the relative value of I(t) and Theta(t) determines the balance between potentiation and depression. An alternative could be to compare different initial values of Theta(t), though this will quickly equilibrate to differences in the mean I(t) that result from differences in USE.

As the BCM model is removed, original Figure 3B is no longer shown. However, to address the comments in general, we have included more descriptions of the model. Also, as explained above, we have included the FM1-43 data and the corresponding fits to justify the parameters of the gamma distributions used in simulations.

5) In Figure 3B, the sigma symbol with a subscript of 0.1 is used in the figure label but not anywhere in the text, figure legend, or Methods. I believe this is meant to indicate the value of σmax when Umax = 0.1. This should be made explicit in the text.

Original Figure 3B has been deleted.

6) In Figure 3C, what variable was changed to generate different values for the absolute changes in synaptic weight in the Control condition (the x-axis)? Was it the value of "alpha"? This was not explained sufficiently. This plot does not have an obvious interpretation, and one is not provided in the text. It does not help the reader understand how the variance of the USE distribution would cause this particular change in long-term plasticity.

Original Figure 3C has been deleted.

7) With regards to the use of an STDP model in Figure 3D – again, there is not sufficient description the text to understand how this model is implemented. Why is the x-axis labeled "Δt / ms"? The use of the variable postsynaptic current A2, or why the y-axis is labeled "A2 / ms" is not clear at all.

A classical phenomenological STDP model would stimulate presynaptic spikes at defined times relative to postsynaptic spikes, and update the synaptic weight(s) accordingly. Inclusion of a dynamic release probability would simply cause some fraction of the presynaptic spikes to be failures and therefore to not be paired with a postsynaptic spike within the plasticity window, and therefore not result in any change in weight. Again my intuition is that for a synapse with a low Pr (USE), it will take more spike pairings over a longer time to equilibrate compared to a synapse with a high Pr. So the first test should be to compare the final weights of synapses with low USE to those of synapses with high USE after a period of Poisson pre- and post-synaptic spiking. However, it is again not clear that changes in the variance of the USE distribution in the absence of any changes in the mean would have any impact on the relative degree of potentiation or depression.

Figure 3D in the original submission is now presented as Figure 3B in the revision. We apologize for the typo in the vertical axis of the previous version, which has now been corrected. This plot shows the resulting synaptic weight after spike pairing, with different postsynaptic current injection (A2) and spike-pairing time difference (Δt) with the aim to demonstrate how the change in Pr distribution can impact the sensitivity of STDP expression. As pointed out by the reviewer, we acknowledge that there could be some chance that the presynaptic spike has no effect due to the small initial release probability. Here we present the mean of 10,000 simulations, and the result shows the differences in the general tendency of undergoing potentiation associated with Pr distributions representing three different conditions: control, AP5, and AP5 condition in which the mean is constrained to the mean of the control Pr distribution. The third condition has been implemented to address the concerns related to the contribution of the mean of distribution. We have made extensive changes to the text to clarify the model implementations shown in Figures 3 and 4 with added descriptions. Specifically with respect to Figure 3B, in order to highlight the difference between three conditions shown, we now include a bar plot (new Figure 3C) which shows the average maximum change of synaptic weight when the extra input to the post-synaptic neuron is small. We feel that these changes have substantially clarified the key consequences of altering the Pr variance on the expression of plasticity.

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

The authors have made a significant effort in addressing most of the concerned expressed by the reviewers and the manuscript has been improved, but there are some remaining issues that need to be addressed, as outlined below:

1) At the suggestion of the reviewers, the authors have revised their modeling of the potential impact of changing Pr distribution (but not mean) on synaptic plasticity. Appropriately, they removed the BCM modeling and focused on STDP and provided experimentally collected estimates of Pr using FM1-43 imaging of the RRP from hippocampal neurons co-cultured with astrocytes. However, the reviewers found the text related to Figure 3 to be confusing and poorly described. Specifically, it took some discussion for all the reviewers to understand how Figure 3 actually tests the impact of Pr distribution based on the A2 injection amplitude. That is, if you have two distributions of Pr with the same mean, but one has a wide variance and one has a narrow variance – if you set a threshold above the mean, the probability that the threshold will be crossed is higher for the distribution with wider variance. This needs to be explicitly described and explained by the authors in the manuscript text and in the figure legend. Figure revisions are not necessary, but could also benefit the readers if there is improved clarity.

Additional explanations to clarify Figure 3B have been added in the main text and in the legend. Moreover, color coding used in Figure 3B has been adjusted to improve clarity. Please note that a finer color gradation scheme allowed for visualization of small synaptic weight changes that were not previously visible. For this reason, relative to the previous Figure 3B, there are very small shifts in the position of the arrows, although the shifts do not affect the conclusions.

2) The authors are also urged to mention explicitly that their delta PPR graphs contain a regression-to-the-mean component which is however effectively cancelled out when making direct graph comparisons, within the same parametric space

The statement regarding delta PPR plots has been added to the Results section when we first introduce the delta PPR plots.

https://doi.org/10.7554/eLife.70818.sa2

Article and author information

Author details

  1. Peter H Chipman

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Present address
    Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Chi Chung Alan Fung

    Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Alejandra Pazo Fernandez
    Competing interests
    No competing interests declared
  3. Alejandra Pazo Fernandez

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Visualization
    Contributed equally with
    Chi Chung Alan Fung
    Competing interests
    No competing interests declared
  4. Abhilash Sawant

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Present address
    Department of Neuroscience, University of Wisconsin-Madison, Madison, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Visualization
    Competing interests
    No competing interests declared
  5. Angelo Tedoldi

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Contribution
    Data curation, Investigation, Methodology, Visualization
    Competing interests
    No competing interests declared
  6. Atsushi Kawai

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  7. Sunita Ghimire Gautam

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Present address
    Shikhar Biotech Pvt. Ltd, Lalitpur, Nepal
    Contribution
    Investigation, Methodology, Resources, Validation
    Competing interests
    No competing interests declared
  8. Mizuki Kurosawa

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Contribution
    Investigation, Methodology, Resources
    Competing interests
    No competing interests declared
  9. Manabu Abe

    Department of Animal Model Development, Brain Research Institute, Niigata University, Niigata, Japan
    Contribution
    Resources
    Competing interests
    No competing interests declared
  10. Kenji Sakimura

    Department of Animal Model Development, Brain Research Institute, Niigata University, Niigata, Japan
    Contribution
    Resources
    Competing interests
    No competing interests declared
  11. Tomoki Fukai

    Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Japan
    Contribution
    Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Yukiko Goda

    RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
    Contribution
    Conceptualization, Funding acquisition, Investigation, , Project administration, Writing – original draft, Writing – review and editing
    For correspondence
    yukiko.goda@riken.jp
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0352-9498

Funding

Japan Society for the Promotion of Science (Overseas Research Fellow (P14760))

  • Peter H Chipman

Japan Society for the Promotion of Science (Core-to-Core Program (JPJSCCA20170008))

  • Yukiko Goda

MEXT Grants in Aid for Scientific Research (15H04280)

  • Yukiko Goda

MEXT Grants in Aid for Scientific Research (18H05213)

  • Tomoki Fukai

RIKEN Center for Brain Science

  • Yukiko Goda

Uehara Memorial Foundation

  • Yukiko Goda

Japan AMED Brain/MINDS

  • Yukiko Goda

MEXT Grants in Aid for Scientific Research (19K16885)

  • Chi Chung Alan Fung

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

Acknowledgements

We thank all members of the Goda laboratory for providing valuable and ongoing feedback throughout the course of this study. We thank Tatjana Tchumatchenko, Thomas Chater and Toru Shinoe for comments on an earlier version of the manuscript, Noam Ziv for kindly sharing the custom written OpenView software, Yun Kyung Park for help with RNA collection for RT-PCR experiments, Toru Shinoe for pilot electrophysiology experiments, Tom McHugh and Shigeyoshi Itohara for kindly providing Grin1flx/flx mice and Ai9 mice, respectively, and Masahiko Watanabe for generously providing GluN2C antibodies. PC was an Overseas Research Fellow of the Japan Society for the Promotion of Science (P14760). This work was supported by the RIKEN Center for Brain Science, the Uehara Memorial Foundation, JSPS Core-to-Core Program (JPJSCCA20170008), Grants-in-Aid for Scientific Research (15H04280, YG; 18H05213, TF; 19K16885, CCAF) from the MEXT, and the Brain/MINDS from the Japan AMED.

Ethics

All animal experiments were approved by the RIKEN Animal Experiments Committee and performed in accordance with the RIKEN rules and guidelines. [Animal Experiment Plan Approval no. W2021-2-015(2)].

Senior Editor

  1. Lu Chen, Stanford University, United States

Reviewing Editor

  1. John A Gray, University of California, Davis, United States

Publication history

  1. Preprint posted: May 29, 2021 (view preprint)
  2. Received: May 31, 2021
  3. Accepted: October 22, 2021
  4. Accepted Manuscript published: October 25, 2021 (version 1)
  5. Version of Record published: November 16, 2021 (version 2)

Copyright

© 2021, Chipman 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.

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