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Predominantly linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons

  1. Attila Ozsvár
  2. Gergely Komlósi
  3. Gáspár Oláh
  4. Judith Baka
  5. Gábor Molnár
  6. Gábor Tamás  Is a corresponding author
  1. MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
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Cite this article as: eLife 2021;10:e65634 doi: 10.7554/eLife.65634

Abstract

Summation of ionotropic receptor-mediated responses is critical in neuronal computation by shaping input-output characteristics of neurons. However, arithmetics of summation for metabotropic signals are not known. We characterized the combined ionotropic and metabotropic output of neocortical neurogliaform cells (NGFCs) using electrophysiological and anatomical methods in the rat cerebral cortex. These experiments revealed that GABA receptors are activated outside release sites and confirmed coactivation of putative NGFCs in superficial cortical layers in vivo. Triple recordings from presynaptic NGFCs converging to a postsynaptic neuron revealed sublinear summation of ionotropic GABAA responses and linear summation of metabotropic GABAB responses. Based on a model combining properties of volume transmission and distributions of all NGFC axon terminals, we predict that in 83% of cases one or two NGFCs can provide input to a point in the neuropil. We suggest that interactions of metabotropic GABAergic responses remain linear even if most superficial layer interneurons specialized to recruit GABAB receptors are simultaneously active.

Introduction

Each neuron in the cerebral cortex receives thousands of excitatory synaptic inputs that drive action potential (AP) output. The efficacy and timing of excitation is effectively governed by GABAergic inhibitory inputs that arrive with spatiotemporal precision onto different subcellular domains. Synchronization of GABAergic inputs appears to be crucial in structuring cellular and network excitation and behaviorally relevant rhythmic population activity (Klausberger and Somogyi, 2008). Diverse subpopulations of GABAergic neurons contribute to network mechanisms at different temporal windows and synchronized cells of particular interneuron types appear to fire in a stereotyped fashion (Klausberger and Somogyi, 2008). In general, this frequently results in coactivation of similar (and asynchronization of dissimilar) GABAergic inputs arriving to target neurons (Jang et al., 2020; Karnani et al., 2016; Kvitsiani et al., 2013), which leads to postsynaptic summation of GABAergic responses synchronously activated by presynaptic cells of the same type. Most GABAergic cell types exert inhibitory control through ionotropic GABAA receptors allowing Cl ions to pass rapidly through the membrane (Barker and Ransom, 1978) and depending on the magnitude of GABA release and/or the number of synchronously active presynaptic interneurons, synaptic and extrasynaptic GABAA receptors could be recruited. The integration of ionotropic inhibitory signals on the surface of target cell dendrites is temporally precise and spatially specific (Bloss et al., 2016; Klausberger, 2009; Müller et al., 2012). Summation of ionotropic receptor-mediated responses are extensively studied in the neocortex and predominantly characterized by nonlinear rules of interaction (Jadi et al., 2012; Koch et al., 1983; London and Häusser, 2005; Qian and Sejnowski, 1990; Silver, 2010). In addition to GABAA receptors, metabotropic GABAB receptor activation can occur during synchronized and/or long-lasting activation of GABAergic inputs (Dutar and Nicoll, 1988; Isaacson et al., 1993; Mody et al., 1994; Scanziani, 2000; Thomson and Destexhe, 1999).

Among the various interneuron subtypes identified in the neocortex neurogliaform cells (NGFCs) form a large subset of interneuron population (Ascoli et al., 2008; Markram et al., 2004; Schuman et al., 2019). Compared to other interneuron subtypes, NGFCs form dense axonal arborization with an unusually high presynaptic bouton density that is highly interconnected with other neighboring neurons. NGFCs are known to be especially effective in recruiting metabotropic GABAB receptors in addition to ionotropic GABAA receptors by sporadic firing using single cell triggered volume transmission in the microcircuit (Oláh et al., 2009; Tamás et al., 2003).

GABA binding to GABAB receptors catalyzes GDP/GTP exchange at the Gα subunit and the separation of Gβγ (Bettler et al., 2004). The Gβγ subunits – as membrane-anchored proteins – locally diffuse in the plasma membrane and up to four Gβγ subunits bind cooperatively to G-protein gated inward rectifier potassium (GIRK) channels and trigger a channel opening that drives the membrane potential toward the K+ reverse potential (Dascal, 1997; Inanobe and Kurachi, 2014; Stanfield et al., 2002; Wang et al., 2016; Wickman and Clapham, 1995). Activation of GABAB receptors by NGFCs controls the firing of dendritic spikes in the distal dendritic domain in pyramidal cells (PCs) (Larkum et al., 1999; Palmer et al., 2012a; Wozny and Williams, 2011) and activity in the prefrontal cortex is effectively controlled by the strong feed-forward GABAB inhibition mediated by NGFCs (Jackson et al., 2018). Moreover, GABAB receptors contribute to termination of persistent cortical activity (Craig et al., 2013) and slow inhibition contributes to theta oscillations in the hippocampus (Capogna and Pearce, 2011).

Relative to the summation of ionotropic responses, postsynaptic summation properties of metabotropic receptors are unexplored and to date, there has been no experimental analysis of how neurons integrate electric signals that are linked to inhibitory metabotropic receptors. We set out to test the summation of metabotropic receptor-mediated postsynaptic responses by direct measurements of convergent inputs arriving from simultaneously active NGFCs and to characterize the likelihood and arithmetics of metabotropic receptor interactions in a model of population output by incorporating experimentally determined functional and structural synaptic properties of NGFCs.

Results

Quantal and structural characteristics of GABAergic connections established by individual NGFCs

NGFCs are capable of activating postsynaptic receptors in the vicinity of their presynaptic boutons via volume transmission (Oláh et al., 2009). To gain insight into the possible effective radius of volume transmission, we characterized properties of NGFC-PC connections. In vitro simultaneous dual whole-cell patch clamp recordings were carried out on synaptically connected L1 NGFC to L2/3 PC pairs in brain slices from the somatosensory cortex of juvenile male Wistar rats. Pre- and postsynaptic cells were chosen based on their characteristic passive membrane and firing properties (Figure 1A) and recorded neurons were filled with biocytin, allowing post hoc anatomical reconstruction of recorded cells and estimation of putative synaptic release sites (Figure 1B,H). Single APs triggered in NGFCs elicit biphasic GABAA and GABAB receptor-mediated responses on the target neurons (Tamás et al., 2003). To determine the number of functional release sites (Nfrs), we recorded induced pluripotent stem cells (IPSCs) under different release probability by varying extracellular Ca2+ and Mg2+ concentrations (Figure 1C,F). NGFC evoked inhibitory postsynaptic potentials (IPSPs) show robust use-dependent synaptic depression, therefore we limited the intervals of AP triggered in NGFCs to 1 min (Karayannis et al., 2010; Tamás et al., 2003). We collected a dataset of n = 8 L1 NGFC to L2/3 PC pairs with an average of 65.5 ± 5.26 trials per pair and 32.75 ± 4.16 trials for a given Mg2+/Ca2+ concentration per conditions. The limited number of trials due to the use-dependent synaptic depression of NGFCs restricted our approach to Bayesian quantal analysis (BQA) previously shown to be robust for the estimation of quantal parameters (Bhumbra and Beato, 2013). As expected, IPSC peak amplitudes were modulated by elevated (3 mM Ca2+ and 1.5 mM Mg2+; mean amplitude: 32.7 ± 22.17 pA; rise time: 6.47 ± 1.34 ms; decay time: 12.27 ± 2.42 ms) and reduced (1.5 mM Ca2+ and 3 mM Mg2+; mean amplitude: 13.58 ± 6.95 pA; rise time: 7.64 ± 3.53 ms; decay time: 11.79 ± 3.21 ms or 2 mM Ca2+ and 2 mM Mg2+; mean amplitude: 12.47 ± 10.9 pA; rise time: 6.95 ± 1.52 ms; decay time: 15.5 ± 10.54 ms) extracellular Ca2+ concentrations consistent with the decline in release probability (Figure 1D). Distributions of IPSC amplitudes detected in paired recordings were in good agreement with the estimated quantal amplitude distribution derived from the BQA (Figure 1E). According to BQA, the estimated mean Nfrs was 10.96 ± 8.1 with a mean quantal size (q) of 3.93 ± 1.21 pA (Figure 1G). We performed n=4 experiments in which the use of low extracellular Ca2+ reduced release probability to a level at which postsynaptic uniqantal events appeared in response to NGFC activation. We evaluated our uniquantal event detection dataset by measuring multiple parameters of each event. We measured the slope of fitted line on the initial phase of events/failures, amplitude (averaged maximum), and the area of events. Using K-means cluster analysis on the three parameters, we managed to separate the events into three groups having failures, uniquantal and multiquantal responses as separate groups. Having separated uniquantal events this way, we found that averaged quantal amplitude was 4.59±0.73 pA (n=4) which is statistically not different (p=0.68, Mann-Whitney test) from quantal amplitude measured with BQA. (Figure 1—figure supplement 1). Full reconstruction of functionally connected NGFC-PC pairs (n = 6) allowed comparisons of the Nfrs estimated by BQA and the number of putative release sites by counting the number of presynaptic boutons located within increasing radial distances measured from postsynaptic dendrites (Figure 1H). Previous experiments showed that direct synaptic junctions are not required for functional NGFC to PC connections (Oláh et al., 2009) and GABA reaches receptors up to 3 µm from the release site (Farrant and Nusser, 2005; Overstreet-Wadiche and McBain, 2015; Overstreet et al., 2000). In agreement with earlier observations (Oláh et al., 2009), direct appositions were not observed in most NGFC to PC pairs and the number of NGFC axonal boutons potentially involved in eliciting postsynaptic responses increased by systematically increasing the radial distance from the dendrites of PCs. Projecting the range of BQA-derived Nfrs estimates over the number of NGFC boutons putatively involved in transmission for the same connections (Figure 1H, red lines) suggests an effective range of 0.86–1.75 µm for nonsynaptic volume transmission from NGFCs to PCs supporting previous reports on distances covered by extrasynaptic GABAergic communication (Farrant and Nusser, 2005; Overstreet-Wadiche and McBain, 2015; Overstreet et al., 2000). Moreover, we detected linear correlation (r = 0.863, p = 0.027) between BQA-derived Nfrs and the number of NGFC boutons putatively involved in transmission at radial distances <1.5 µm from PC dendrites; decreasing or increasing the distance resulted in the loss of correlation (Figure 1I).

Figure 1 with 1 supplement see all
Quantal and structural characteristics of GABAergic connections established by individual neurogliaform cells (NGFCs).

(A–E) Quantal and structural properties of a neurogliaform to pyramidal cell (PC) connection. (A) Firing patterns of the presynaptic L1 NGFC (blue) and postsynaptic L2/3 PC (black). (B) Three-dimensional anatomical reconstruction of a recorded L1 NGFC (soma and axon blue) and L2/3 PC (soma and dendrites black, axon gray). Horizontal line indicates the border of layer 1 (I) and layer 2 (II). (C) Presynaptic action potentials of the L1 NGFC (top, blue) elicited of unitary induced pluripotent stem cells (IPSCs) in the postsynaptic L2/3 PC at −50 mV holding potential in different Ca2+ concentrations (middle, dark gray: 3 mM Ca2+, 1.5 mM Mg2+, light gray: 2 mM Ca2+, 2 mM Mg2+). Bottom: Representative consecutive traces of elicited unitary IPSCs. Asterisk marks synaptic transmission failure. (D) Single IPSC peak amplitudes recorded in high (3 mM Ca2+, 1.5 mM Mg2+, dark gray) and low release probability conditions (2 mM Ca2+, 2 mM Mg2+, light gray), respectively. (E) Distribution of IPSC peak amplitudes in 3 mM Ca2+, 1.5 mM Mg2+ (left) and 2 mM Ca2+, 2 mM Mg2+ (right), with projected binomial fits (blue). (F) Estimated release probability values in different experimental conditions (n = 8). (G) Estimated quantal size (3.93 ± 1.22 pA) and number of functional release sites (Nfrs; 10.96 ± 8.1) derived from Bayesian quantal analysis in each experiment (n = 8). (H) Number of NGFC boutons in the proximity of postsynaptic PC dendrites from anatomical reconstructions of connected NGFC to PC pairs (n = 8; gray, individual pairs; blue, average, and SD). For comparison, red lines indicate mean ± SD of Nfrs shown on panel F corresponding to distances between presynaptic NGFC boutons and PC dendrites. (I) Number of NGFC boutons counted at increasing distances from PC dendrites in NGFC to PC pairs. Correlation to Nfrs in the same pairs is best when counting boutons closer than 1.5 µm from PC dendrites.

Structural characteristics of GABAergic connections established by the population of layer 1 NGFCs

To have a better idea about how does the volume transmission radius potentially affect the fraction of converging outputs of L1 NGFC population to the same space, we developed a model to assess the overall output of NGFCs located in the supragranular layers of the neocortex. Unitary volume transmission by NGFCs is limited to their extremely dense axonal arborization (Oláh et al., 2009; Rózsa et al., 2017) Therefore, we determined the three-dimensional (3D) distribution of axon lengths of individual NGFCs with Sholl analysis (Figure 2A). By superimposing individual NGFC reconstructions centered by their somata (n = 16; Figure 2C), a representative distribution of axons was calculated as a function of distance from the soma (Figure 2D). We also assessed the distance between axonal boutons (n = 1456) along reconstructed axons of NGFCs (n = 6) and found that interbouton distances were 3.36 ± 2.54 µm (Figure 2—figure supplement 1). Next, we developed an algorithm that generates model NGFCs (n = 52) by growing axon arborizations similar (p = 0.99, two-sided K-S test, Figure 2D) to the population of the experimentally reconstructed representative distribution of NGFC axons (n = 16) using interactions of segment lengths, branch point locations, and segment orientations while keeping the density of axonal boutons along axon segments (Figure 2B,C). In order to achieve a relatively complete representation of all NGFC axon terminals in a model at the population level, we performed immunhistochemical labeling of α-actinin2, which is known to label the overwhelming majority of supragranular NGFCs in the neocortex (Uematsu et al., 2008). Somata immunoreactive for α-actinin2 in superficial cortical layers showed distribution along the axis perpendicular to the surface of the cortex with a peak at ~50–150 µm distance from the pia mater (Figure 2E). According to this radial distribution and with no apparent tendency along the horizontal axis, we placed NGFC somatas in a 354 × 354 × 140 µm3 volume to create a realistic spatial model of L1 NGFC population (Figure 2F). 3D pairwise shortest distances between α-actinin2 + somata (n = 152) and distances between somata placed into the model space (n = 374) were similar (p = 0.51, two-sided K-S test, Figure 2G). We then used the axon growing algorithm detailed above from each soma position to model a population-wide distribution of NGFC axonal release sites. Quantal and structural properties of NGFC to PC connections shown above suggest a volume transmission distance of ~1.5 µm from potential sites of release (Figure 1H,I), thus we mapped the coverage of surrounding tissue with GABA simultaneously originating from all NGFC terminals with a 1.5 µm of transmitter diffusion in the model. Using these conditions in simulations (n = 36), less than eight NGFC axonal terminals contributed as effective GABA sources at any location in the superficial neocortex (Figure 2H). Moreover, these boutons originated from a limited number of presynaptic NGFCs; when considering the extreme case of population-level cooperativity, that is, when all putative NGFCs were active, most frequently a single NGFC release site serves as a GABA source (67.7 ± 7%) and potential interactions between two, three, or more different NGFCs take place in limited occasions (15.34 ± 2.1%, 8.5 ± 2.6%, and 8.45 ± 3.16%, respectively). The outcome of these simulations is consistent with earlier results suggesting that single cell-driven volume transmission covers only the close proximity of NGFCs (Oláh et al., 2009) but also indicates potential interactions between a restricted number of neighboring NGFCs.

Figure 2 with 1 supplement see all
Structural characteristics of collective GABAergic output formed by the population of layer 1 neurogliaform cells (NGFCs).

(A) Sholl analysis on the axonal arborization of an individual NGFC. Inset, axonal lengths measured in concentric shells of increasing radius (step, 10 µm). (B) Three-dimensional arborization of a model generated axon. (C) Superimposition of three-dimensionally reconstructed axonal arborizations of NGFCs (n = 16, blue) and the computer generated model NGFCs (n = 16, red) aligned at the center of somata. (D) Comparison of manually reconstructed axonal arborizations of NGFCs (n = 16; blue, mean; light blue, SD) and model generated axons (n = 52; red, mean; light red, SD). (E) Left: α-Actinin2 immunohistochemistry in supragranular layers of the neocortex (scale bar, 100 µm). Right: Distribution of α-actinin2 immunopositive somata. (F) Top: Three-dimensional model of NGFCs somata, axonal arborizations, and bouton distributions in a 354 × 354 × 140 µm3 volume. Bottom: heat map showing the number of axonal boutons interacting at distances of <1.5 µm. (G) Distribution of the shortest distance between somata in the model (red) and in α-actinin2 immunohistochemistry experiments (blue). (H) Percentage distribution of the number of interacting boutons within 1.5 µm distance from each NGFC.

Coactivation of putative NGFCs in L1 somatosensory cortex in vivo

Transcallosal fibers establish interhemispheric inhibition that operates via GABAB receptor activation located on apical dendrites (Palmer et al., 2012b) and it has been suggested that this massive GABAB receptor recruitment in the superficial layers includes the activation of NGFCs (Palmer et al., 2012b). To assess the fraction of synchronously active putative NGFCs under close to physiological conditions, we applied in vivo two-photon Ca2+ imaging. We monitored the activity of L1 neurons bulk-loaded with calcium indicator Oregon Green BAPTA-1-AM (OGB-1-AM) (Figure 3A) during hindlimb stimulation, which results in the activation of transcallosal inputs in L1 of the somatosensory cortex of urethane-anesthetized rats (n = 5). Stimulation of the ipsilateral hindlimb (200 mA, 10 ms) evoked Ca2+ signals in a subpopulation of neurons in L1 (n = 114 neurons; n = 46 versus 68 responsive versus non-responsive neurons, respectively; data pooled from six animals; Figure 3B and C). On average, 38.2 ± 5.2% of the L1 neurons were active following ipsilateral hindlimb stimulation, which is remarkably similar to the proportion found earlier (Palmer et al., 2012bFigure 3E). To further identify L1 neurons active during hindlimb stimulation, we performed immunohistochemistry for the actin-binding protein α-actinin2 (Uematsu et al., 2008Figure 3D and F) using the same cortical area of L1 on which two-photon imaging was performed previously. Mapping α-actinin2 positive cells among in vivo two-photon Ca2+ imaging monitored interneurons (Figure 3D) showed that closely located presumed NGFCs were active (on average 216.09 ± 77.93 µm soma to soma distance from two experiments, Figure 3—figure supplement 1) allowing summation of outputs by presumptive NGFCs. Cross-examination of neurons responsive/non-responsive to hindlimb stimulation versus neurons immunopositive/negative for α-actinin2 revealed that the majority of the active neurons were α-actinin2 positive (10 out of 15 neurons, 67%, n = 2 animals) and the majority of inactive neurons were α-actinin2 negative (22 out of 26 neurons, 85%, Figure 3G) suggesting that a substantial fraction of L1 NGFCs are activated during hindlimb stimulation. Combination of the datasets derived from the structural analysis of GABAergic connections established by the population of layer 1 NGFCs above and the in vivo mapping of coactivated putative NGFCs suggests that summation of NGFC output is feasible and, at the same time, is dominated by GABA released from boutons of a one or two NGFCs: having approximately two-thirds of NGFCs simultaneously active further suppresses the potentially synchronous contribution of three or more NGFCs (8.5% and 8.45%, see above) already constrained by structural properties in a point of the cortex.

Figure 3 with 1 supplement see all
Coactivation of neurogliaform cell population in L1 somatosensory cortex in vivo.

(A) Experimental setup. Head-fixed anesthetized rats were placed under a two-photon microscope having a cranial window above the hindlimb somatosensory cortex. OGB-1 AM and SR 101 were injected into L1. Ipsilateral hindlimb stimulation was performed with an electric stimulator. (B) Two-photon image of neurons that were labeled with OGB-1 in L1. SR 101 labeled astrocytes. Right: ΔF/F changes of Ca2+ signals (gray: individual traces; black: mean of 10 consecutive traces) during series of ipsilateral stimulation (black arrowhead). Traces correspond to the marked cells. (C) Time-series heat map of 44 L1 interneurons evoked ΔF/F changes in Ca2+ signals during ipsilateral hindlimb stimulation in a single experiment (black arrowhead). (D) Scatter plot showing the spatial location of L1 interneuron somata in a single experiment. Colors are corresponding to the responsiveness shapes to the molecular identity (not responsive and α-actinin2−, blue dots; responsive and α-actinin2−, green dots; responsive and α-actinin2+, green triangles). (E) Stack columns show the fraction of responsive versus not responsive cells in different experiments (n = 5 animals). Far-right columns show the mean value. (F) In vivo two-photon image showing imaged neurons. To the right, confocal images from the same area show immunohistochemical detection of α-actinin2+ neurons (arrowhead). α-Actinin2− cells were visualized by exclusive DAPI (4′,6-diamidino-2-phenylindole) staining (arrow). Scale bar, 20 μm. (G) Stack columns show the proportion of α-actinin2 immunoreactivity among responsive versus not responsive cells (n = 2 animals). Far-right columns show the mean value.

Summation of convergent, unitary IPSPs elicited by NGFC

Our in vivo measurements above corroborate earlier results (Palmer et al., 2012b) on widespread simultaneous activation of putative L1 NGFCs in response to transcallosal inputs. To directly measure the summation of converging inputs from superficial NGFCs, in vitro simultaneous triple recordings were performed from two presynaptic NGFCs and a target PC (n = 4, Figure 4A). First, we measured the amplitude of unitary IPSPs (n = 8) elicited by single L1 NGFCs in the target L2/3 PC and found that smaller and bigger inputs in a triplet were −1.68 ± 1.51 and −2.19 ± 1.33 mV (rise time: 4.66 ± 2 ms; half-width: 19.29 ± 5.76 ms; decay time: 8.94 ± 2.91 ms), respectively. Next we activated the two L1 NGFC inputs synchronously (0.17 ± 0.05 ms) and such coactivation resulted in moderately sublinear summation of convergent IPSPs (maximal nonlinearity, −9.1 ± 4.3%) measured as the difference of calculated (−3.81 ± 2.76 mV) and experimentally determined (−3.57 ± 2.55 mV) sums of convergent single inputs (n = 4, Figure 4B). These results are in line with experiments showing moderately sublinear interactions between identified, single cell evoked fast IPSPs (Tamás et al., 2002). Interestingly, the time course of sublinearity followed the fast, presumably GABAA receptor-mediated part of the unitary and summated IPSPs (Figure 4C) suggesting that ionotropic and metabotropic GABAergic components of the same input combinations might follow different rules of summation. To test the interaction of unitary GABAB receptor-mediated IPSPs directly, we repeated the experiments above with the application of the GABAA receptor antagonist gabazine (10 µM). Pharmacological experiments on the output of NGFCs are very challenging due to the extreme sensitivity of NGFC triggered IPSPs to presynaptic firing frequency (Capogna, 2011; Tamás et al., 2003) forcing us to collect the data in a different set of triple recordings (n = 8, Figure 4D). As expected (Tamás et al., 2003) unitary, gabazine-insensitive, slow IPSPs had onset latencies, rise times, and half-widths similar to GABAB receptor-mediated responses (49.42 ± 5.8, 86.95 ± 8.82, and 252.27 ± 36.92 ms, respectively, n = 16, Figure 4E). Peak amplitudes of converging smaller and bigger slow IPSPs were −0.66±0.22 and −0.94±0.37 mV, respectively. Synchronous activation of two presynaptic L1 NGFC converging onto the same L2/3 PC resulted in linear (−1.6 ± 6.6%) summation of slow IPSPs as peak amplitudes of calculated sums of individual inputs versus experimentally recorded compound responses were −1.58 ± 0.53 and −1.60 ± 0.55 mV, respectively (Figure 4F). Taken together, our triple recordings in gabazine versus control conditions suggest significantly different (p = 0.021, two-sided MW U-test) linear interactions between slow, GABAB IPSPs as opposed to sublinearly summating fast, GABAA IPSPs elicited by the same presynaptic interneuron population.

Summation of convergent, unitary inhibitory postsynaptic potentials (IPSPs) elicited by neurogliaform cells (NGFCs).

(A) Schematic experimental setup of triplet recordings. Firing pattern of two presynaptic NGFCs (light blue and blue) and a postsynaptic pyramidal cell (PC) (black). (B) Action potential triggered under control conditions in the NGFCs individually (1, 2) or synchronously (1 and 2) elicited unitary (1, 2) and convergent (1 and 2) IPSPs in the postsynaptic PC. Below, the time course of the difference between the measured (1 and 2) and calculated (1 + 2) sums of convergent IPSPs. (C) The linearity of response summation in a single experiment (top) and on populations of convergent NGFC triggered IPSPs (bottom) recorded in control conditions (n = 4) (dark green, population average; light green, SD). (D) Same as experimental setup as (A) on a different set of cells but in the presence of GABAA receptor antagonist, gabazine. (E) Identical stimulation protocol as (B), note the disappearance of the difference between the measured (1 and 2) and calculated (1 + 2) sums of convergent IPSPs. (F) Same as (C), but under blocking GABAA receptors with gabazine (n = 8) (dark green, population average; light green, SD).

Integration of GABAB receptor-mediated responses are not affected by HCN channel and GABA reuptake

The predominant target area of the superficial NGFCs, the distal apical dendritic membrane of PCs, express voltage-dependent hyperpolarization-activated cyclic nucleotide-gated channel 1 (HCN1) known to attenuate dendritic signals (Berger et al., 2001; Kalmbach et al., 2018; Lörincz et al., 2002; Robinson and Siegelbaum, 2003; Sheets et al., 2011). To investigate whether HCN1 channels contribute to mechanisms of interactions between GABAB receptor-mediated postsynaptic responses, we performed experiments on NGFC-to-PC pairs and evoked one to four APs in a single presynaptic NGFC at 100 Hz. The high stimulus frequency allows to induce GABA release with up to four APs and still remain within the time window where the presynaptic GABAB receptors are not yet activated and unable to inhibit voltage-dependent Ca2+ channels (Chen and van den Pol, 1998). This ensures that short-term plasticity does not have a presynaptic effect that could hinder the release of GABA by later APs (Karayannis et al., 2010). This experimental configuration mimics the extreme conditions when multiple presynaptic release sites converge in a tight space and creating excessive GABAB receptor-mediated inhibition (Figure 5A). Triggering a single spike in the presence of gabazine (10 µM) did not saturate postsynaptic GABAB receptors since the postsynaptic response induced by two spikes was proportional to the arithmetic sum of unitary postsynaptic responses (experimental sums: −1.25 mV calculated sums: −1.26 mV; unitary IPSP rise time: 78.69 ± 27.28 ms; decay time: 56.27 ± 15.88 ms), apparently showing linear summation properties similar to triple recordings testing summation convergent inputs above. However, further increase in the number of evoked APs to 3 and 4 introduced sublinearity to summation (n = 6, 1AP: −0.63 ± 0.50 mV, 2APs: −1.25 ± 1.06 mV, 3APs: −1.53 ± 0.84 mV, 4APs: −1.61 ± 1.09 mV, Figure 5B; normalized values: 2APs: 2.00 ± 1.08, 3APs: 2.34 ± 1.16, 4APs: 3.17 ± 1.26, Figure 5C). Importantly, recordings in the presence of HCN1 channel blocker, ZD7288 (10 µM) showed summation properties similar to control, summation was linear with two APs and changed to slightly sublinear upon the 3rd to 4th spike (n = 5, 1AP: −0.82 ± 0.63 mV, 2APs: −1.59 ± 0.76 mV, 3APs: −1.66 ± 0.72 mV, 4APs: −1.90 ± 1.07 mV, unitary IPSP rise time: 97.7 ± 29.05 ms and decay time: 103.57 ± 48.94 ms; Figure 5B; normalized values and its comparison to control: 2APs: 2.06 ± 1.06, p = 0.983; 3APs: 1.99 ± 1.17, p = 0.362; 4APs: 2.56 ± 1.6, p = 0.336; two-sided MW U-test, Figure 5C). These experiments suggest that when a physiologically probable number of NGFCs are simultaneously active, HCN1 channels are locally not recruited to interfere with the summation of GABAB receptor-mediated responses.

Integration of GABAB receptor-mediated responses are not affected by HCN channel and GABA reuptake.

(A) Schematic experimental setup of paired recordings. Bursts of up to four action potentials (APs) were elicited in neurogliaform cells (NGFCs) at 100 Hz in the presence of gabazine. (B) NGFC to pyramidal cell (PC) paired recordings showed similar linear GABAB receptor-mediated summation under control conditions. Top: Individual traces showing inhibitory postsynaptic potential (IPSP) kinetics upon AP burst protocol (vertical lines indicating the triggered APs) during control (green traces, n = 6), in presence of hyperpolarization-activated cation (HCN) channel blocker ZD7288 (red traces, n = 5) or GABA reuptake blocker NO-711 (blue traces, n = 6). Bottom: Traces show measured IPSP from two consecutive presynaptic stimulation (measured) and the arithmetic sum of two unitary IPSP (calculated). (C) Summary of normalized IPSP peak amplitudes. Compare to control conditions (2APs: 2.00 ± 1.08; 3APs: 2.34 ± 1.16; 4APs: 3.17 ± 1.26), summation properties of GABAB mediated unitary IPSPs are neither affected by application of ZD7288 (2APs: 2.06 ± 1.06, p = 0.983; 3APs: 1.99 ± 1.17, p = 0.362; 4APs: 2.56 ± 1.6, p = 0.336; two-sided MW U-test) nor NO-711 (2APs: 2.06 ± 1.17, p = 0.853; 3APs: 2.36 ± 0.31, p = 0.645; 4APs: 2.9 7± 1.54, p = 0.515; two-sided MW U-test). Dashed line indicates the linearity.

Previous experiments suggested that a single AP in an NGFC is able to fill the surrounding extracellular space with an effective concentration of GABA (Oláh et al., 2009) and, in turn, extracellular GABA concentration producing GABAB receptor activation is tightly regulated via GABA transporters (GAT-1) (Gonzalez-Burgos et al., 2009; Isaacson et al., 1993; Rózsa et al., 2017; Szabadics et al., 2007). Therefore, we tested whether GAT-1 activity affects the summation of GABAB receptor-mediated responses potentially limiting the number of GABAB receptors reached by GABA released by NGFCs. Selective blockade of GAT-1 with NO-711 (10 µM) increased the amplitude of GABAB receptor-mediated IPSP; however, it did not influence summation properties (n = 6, 1AP: −1.11 ± 0.62 mV, 2APs: −2.28 ± 1.07 mV, 3APs: −3.1 ± 0.40 mV, 4APs: −3.54 ± 1.59 mV, unitary IPSP rise time: 118.85 ± 37.58 ms; decay time: 80.54 ± 40.94 ms; Figure 5B; normalized values and its comparison to control: 2APs: 2.06 ± 1.17, p = 0.853; 3APs: 2.36 ± 0.31, p = 0.645; 4APs: 2.97 ± 1.54, p = 0.515; two-sided MW U-test, Figure 5C). Accordingly, interactions between an in vivo realistic number of simultaneously active NGFCs lead to linear GABAB response summation even if increased concentration of GABA is present in the extracellular space.

Subcellular localization of GABAB receptor-GIRK channel complex determines summation properties

High-resolution quantitative electron microscopy showed that GABAB receptors and GIRK channels are segregated on dendritic shafts; however, receptor-channel complexes colocalize on dendritic spines (Kulik et al., 2006). Theoretical studies suggest that the distance between the receptor and effector limits the recruitment of effector molecules to the vicinity of receptors (Brinkerhoff et al., 2008; Kulik et al., 2006), thus we asked if summation properties were influenced by the relative location of of GABAB receptors and GIRK channels when several presynaptic inputs converge. To this end, we constructed a simulation environment based on a previously published 3D reconstruction of a postsynaptic dendritic segment (Edwards et al., 2014) targeted by realistically positioned release sites of NGFCs (Figure 6A and B). Molecular interactions in this spatially realistic system were modeled using Monte Carlo algorithms to simulate movements and reactions of molecules (Kerr et al., 2008). Membranes of the postsynaptic dendritic segment were populated (see Materials and methods, Figure 6—figure supplement 1) with GABAB receptors and GIRK channels according to compartment-dependent data from SDS-digested freeze-fracture replica immunolabeling (Kulik et al., 2006Figure 6C). Neurotransmitter diffusion in the brain is influenced by tissue tortuosity and the fraction of extracellular space in total tissue volume (Syková and Nicholson, 2008), thus we simulated realistic molecular diffusion in tortuous extracellular space (Tao et al., 2005) (see Materials and methods). The number and position of NGFC presynaptic boutons around the postsynaptic dendritic segments in the model were used according to structural characteristics of GABAergic connections established by individual NGFCs (n = 4 boutons 1.2 ± 0.7 µm from the dendrite; Figure 1I,HFigure 6—figure supplement 2) and according to the bouton density determined for the overall output of NGFC population (Figure 2F). Previous work suggests that a single AP in an NGFC generates GABA concentrations of 1–60 µM lasting for 20–200 ms (Karayannis et al., 2010). In our model, the amount of released GABA was consistent from release event to release event and dropped off exponentially with distance from the synapse ([GABA]: 0.0 μm, 1 mM; 0.5 μm, 60 μM; 2.0 μm, 1 μM; Figure 6—figure supplement 3, Supplementary file 1, 2). The GABA exposure time was 114.87 ± 2.1 ms with decay time constants of 11.52 ± 0.14 ms. Our modeling trials show that single AP triggered GABA release can activate a total of 5.82 ± 2.43 GABAB receptors (2.81 ± 1.55 on spine, 3.01 ± 1.71 on the shaft). Furthermore, activation of GABAB receptors triggers intracellular mechanisms and the initial GDP/GTP exchange at the Gα subunit separates the G-protein heterotrimeric protein and produces Gβγ subunits (peak number of Gβγ subunits for single AP: 338.54 ± 138.75). Lateral membrane diffusion of Gβγ subunits lead to the activation of 3.66 ± 2.17 GIRK channels in total (2.47 ± 1.88 on spine, 1.17 ± 1.26 on the shaft) in response to single AP. Next, consecutive GABA releases were induced with 10 ms delays to replicate the 100 Hz stimulation protocol used in the experiments above (Figure 5A). The increased GABA concentration from two sequential stimuli raised the number of active GABAB receptors to 11.29 ± 3.48 (5.57 ± 2.36 on spine, 5.72 ± 2.52 on the shaft). Three and four consecutive releases activated a total of 16.19 ± 3.88 and 20.99 ± 4.99 GABAB receptors, respectively (7.96 ± 2.74 on spine, 8.23 ± 2.97 on the shaft and 10.62 ± 3.28 on spine, 10.37 ± 3.53 on the shaft, respectively). When modeling consecutive GABA releases, massive amount of Gβγ subunits were produced together with a decline in relative production efficacy per APs, possibly due to the limited number of G-proteins serving as a substrate in the vicinity of active receptor clusters (peak number of Gβγ subunits for 2AP: 612.10 ± 171.95; 3AP: 857.78±194.14; 4AP: 1081.81 ± 229.57). Two consecutive APs resulted in the activation of 6.98 ± 3.29 GIRK channels (2.13 ± 1.63 on dendritic shaft and 4.85 ± 2.65 on spine) in the simulations. Importantly, this number of activated GIRK channels in response to two APs was close to the arithmetic sum of the number of GIRK channels activated by two single AP responses (−4.87% in total, −1.86% on spines and −9.86% on the shaft; Figures 4E and 5A). Further increase in the GABA exposure proportional to three and four APs leads to the activation of 10.39 and 12.89 GIRK channels, respectively (7.01 ± 3.11 and 8.68 ± 3.46 on spines and 3.37 ± 2.08 and 4.21 ±2. 48 on the shaft, respectively). These numbers of GIRK channels corresponded to −5.68% and −13.58% of the arithmetic sum of GIRK channels activated by three and four single AP responses (−5.71% and 13.82% on spines and −4.15% and −11.16% on the shaft). The increased linearity of total GIRK channel activation relative to experimental results could be a result of several unknown properties of the cascade linking GABAB receptors to GIRK channels and/or due to vesicle depletion in terminals of NGFCs during multiple rapid release of GABA not incorporated into our model.

Figure 6 with 3 supplements see all
Subcellular localization of GABAB receptor-G-protein gated inward rectifier potassium (GIRK) channel complex determines summation properties.

(A) Visualization of the complete MCell-based model in the course of GABA release. (B) Magnified view of the model. (C) Distribution of GABAB receptors and GIRK channel clusters on the dendritic membrane in the model (gray bars: dendritic spine; black bars: dendritic shaft). (D) Overview of the molecular interactions during increasing GABA release. Top to bottom: NGFC output simulated by releasing GABA (red) in the extracellular space proportional to one to four action potential (AP) stimulation. Below, the total number of produced Gβγ subunits (brown) by activated GABAB receptors (green) located on the dendritic shaft and spine. After lateral diffusion in the plasma membrane, Gβγ subunits bind to GIRK channels (blue). (E) Boxplot of GABA, Gβγ subunits, GABAB receptors, and GIRK channels quantity normalized to 1AP (GABA: 2APs: 1.67 ± 0.004, 3APs: 2.12 ± 0.005, 4APs: 2.42 ± 0.006, Gβγ subunits: 2APs: 1.85 ± 0.51, 3APs: 2.53 ± 0.57, 4APs: 3.2 ± 0.68; GABAB receptor shaft: 2APs: 1.91 ± 0.84, 3APs: 2.74 ± 0.99, 4APs: 3.45 ± 1.18; GABAB receptor spine: 2APs: 1.98 ± 0.84, 3APs: 2.84 ± 0.98, 4APs: 3.78 ± 1.17; GIRK channel shaft: 2APs: 1.8 ± 1.37, 3APs: 2.84 ± 1.75, 4AP: 3.55 ± 2.09; GIRK channel spine: 2APs: 1.96±1.07, 3APs: 2.84 ± 1.26, 4APs: 3.52 ± 1.4). Square indicates the mean, line shows the median inside the boxplot. (F) Quantification of the signaling effectiveness on the shaft and spine region of the model dendrite during increasing GABA release (1AP: shaft: 0.39 ± 0.06, spine: 0.82 ± 0.21, p < 0.005, n = 1164, two-sided MW U-test; 2AP: shaft: 0.35 ± 0.086, spine: 0.84 ± 0.19, p < 0.005, n = 534, two-sided MW U-test; 3AP: shaft: ± 0.41 ± 0.036, spine: 0.85 ± 0.15, p < 0.005, n = 1871, two-sided MW U-test; 4AP: shaft: 0.39 ± 0.05, spine: 0.81 ± 0.01, p < 0.005, n = 709, two-sided MW U-test). Square indicates the mean, line shows the median inside the boxplot.

GABAB receptor and GIRK channel complexes located in particular subcellular compartments appeared to have different effectiveness of recruiting GABAB receptors and active GIRK channels in our simulations (Figure 6D). We observed different numbers of GABAB receptors activated on the shaft and spine (normalized values to 1AP: 2APs: shaft: 1.91 ± 0.84, spine: 1.98 ± 0.84, n = 534, p = 0.009; 3APs: shaft: 2.74 ± 0.99, spine: 2.84 ± 0.98, n = 1871, p = 0.173; 3APs: shaft: 3.45 ± 1.18, spine: 3.78 ± 1.17, n = 709, p< 0.005, two-sided MW U-test, Figure 6E). The recruitment of GIRK channels was more effective on spines compared to shafts when triggering two APs (normalized values to 1AP: shaft: 1.8±1.37, spine: 1.96±1.07, n=534, p<0.005); the trend was similar in response to three and four APs, but results were not significant (normalized values to 1AP: 3AP: shaft: 2.84±1.75, spine: 2.84 ± 1.26, n = 1871, p = 0.109; 4AP: shaft: 3.55 ± 2.09, spine: 3.52 ± 1.4, n = 709, p = 0.216, two-sided MW U-test, Figure 6E). The compartment-specific effectiveness of signaling as the ratio of activated GIRK channels and active GABAB receptors (Figure 6F) shows that spines represent the preferred site of action corroborating earlier suggestions (Qian and Sejnowski, 1990).

Discussion

The unique inhibitory communication via volume transmission separates NGFC interneurons from other interneuron classes in the neocortex. Numerous observations support the idea of volume transmission (Overstreet-Wadiche and McBain, 2015). (1) NGFC activation generates an unusually prolonged inhibition on the postsynaptic cell (Karayannis et al., 2010; Mańko et al., 2012; Oláh et al., 2009; Szabadics et al., 2007). (2) Released GABA acts on synaptic and extrasynaptic GABA receptors (Karayannis et al., 2010; Oláh et al., 2009; Price et al., 2005; Tamás et al., 2003), (3) as well as on nearby presynaptic terminals (Oláh et al., 2009). (4) NGFCs show a very high rate of functional coupling between the neighboring neurons (Jiang et al., 2015; Oláh et al., 2009). (5) Ultrastructural observations showed the lack of clearly defined postsynaptic elements in the apposition of the NGFC boutons (Mańko et al., 2012; Oláh et al., 2009; Vida et al., 1998). (6) NGFCs act on astrocytes within the reach of their axonal arborization through nonsynaptic coupling (Rózsa et al., 2017). The distance of effective operation through NGFC-driven volume transmission, however, is not clear. Here, we used functional and structural characterization of NGFC-PC inhibitory connections and suggest that GABA released from NGFC axonal terminals activates GABA receptors up to about ~1.8 µm, a result remarkably similar to previous estimations for the range of extrasynaptic action of synaptically released GABA (Farrant and Nusser, 2005; Overstreet-Wadiche and McBain, 2015; Overstreet et al., 2000). Given that our distance estimates are not based on ultrastructural evidence and we cannot exclude that fraction of anatomically defined boutons were not functional, the accuracy of our prediction for the range of volume transmission is limited. Our experiments also shed light to some quantal properties of NGFC’s GABA release. These experiments are constrained by the robust use-dependent depression mediated by NGFCs (Karayannis et al., 2010; Tamás et al., 2003), therefore implementation of multiple probability fluctuation analysis (Silver, 2003), the gold standard for quantal analysis, was not feasible and BQA (Bhumbra and Beato, 2013) was needed as an alternative. The revealed linear correlation between BQA-derived Nfrs and the number of NGFC boutons putatively involved in transmission is compatible with the release of a single docked vesicle from individual NGFC boutons. However, our light microscopic methods applied in this study are insufficient for definitive claims concerning single or multivesicular release from neurogliaform boutons, especially in the light of studies highlighting the inaccuracy of comparing the Nfrs with the anatomical synaptic contacts detected by light microscopy (Oláh et al., 2009, Molnár et al., 2016, Holler et al., 2021).

The functional distance of volume transmission is particularly important for the characterization of interactions between NGFCs and for understanding the population output of NGFCs. Realistic representation of an entire subpopulation of neurons is considered essential for the interpretation of network functions (Karnani et al., 2014; Markram et al., 2015) and pioneering full-scale data-driven models were effective in deciphering emerging functions of interneuron populations (Bezaire et al., 2016). However, network diagrams addressing the function of NGFCs exclusively based on synaptic connectivity underestimate the spread of output without incorporating volume transmission by an order of magnitude (Oláh et al., 2009). Although the concept of blanket inhibition has been suggested for networks of interneuron populations having overlapping axonal arborizations and dense synaptic output (Karnani et al., 2014), our spatial model based on high-resolution reconstructions of labeled NGFCs takes the concept to its extremes and reveals an unprecedented density of release sites for a population of cortical neuron and shows that the overwhelming majority of the superficial cortical space is effectively covered by at least one NGFC. At the same time, the redundancy of the NGFC population is limited and a single cortical spatial voxel is reached by GABA released from a limited number of individual NGFCs, ~83% of space is covered by one or two NGFCs. Our relatively simple in vivo approach to gauge potential synchronous action of NGFCs gave positive results. This is in line with earlier observations suggesting widespread action of putative NGFCs in terminating persistent activity (Craig et al., 2013), or powerfully suppressing dendritic Ca2+ dynamics in L2/3 and L5 (Palmer et al., 2012a; Wozny and Williams, 2011). Strong cholinergic neuromodulation of NGFCs (Poorthuis et al., 2018) and frequent gap junctional coupling between NGFCs (Simon et al., 2005) further facilitate concerted action and are likely to play a major role in synchronizing the NGFC network (Yao et al., 2016).

When studying simultaneous action of NGFCs, our direct measurements of two converging NGFC inputs on L2/3 PC from simultaneous triple whole-cell patch clamp recordings revealed sublinear summation properties for ionotropic GABAA receptor-mediated responses. These results support classic theories on synaptic input interactions (Jadi et al., 2012; Koch et al., 1983; London and Häusser, 2005; Qian and Sejnowski, 1990; Silver, 2010) and are in line with earlier experiments measuring interactions of anatomically identified inputs converging to neighboring areas of the dendritic tree of the same postsynaptic cell (Hao et al., 2009; Tamás et al., 2002). Mechanisms of interaction between convergent inputs from NGFCs might be similar to those suggested for short-term synaptic depression of GABAA responses such as local drops in Cl driving force and membrane conductance (Huguenard and Alger, 1986; McCarren and Alger, 1985; Staley and Proctor, 1999). To our knowledge, the simultaneous triple recordings of two presynaptic NGFCs targeting the same postsynaptic PC represent the first direct experimental attempt addressing the summation of metabotropic receptor-mediated postsynaptic interactions. To date, scarce computational model studies were aimed to explore the integration properties of GABAB receptor-mediated responses and suggested a highly supralinear interaction through the amplification effect of G-protein cooperativity (Destexhe and Sejnowski, 1995). Our experimental approach indicates linear interactions between GABAB receptor-mediated responses in case the number of converging presynaptic cells corresponds to the number of NGFCs cooperating during in vivo network operations. This suggests that converging afferents that act on inhibitory metabotropic receptors in the same postsynaptic voxel show linear or slightly sublinear summation, conserving the impact of individual inputs. However, we cannot exclude the possibility that widespread synchronization across various interneuron populations might shift the summation arithmetic in a nonlinear fashion.

Intrinsic properties of postsynaptic PCs might also contribute to the regulation of summation. HCN1 channels are known to be enriched in the distal dendrites of PCs and mediate K+ cationic current activated by membrane hyperpolarization (Kalmbach et al., 2018; Lörincz et al., 2002; Robinson and Siegelbaum, 2003). Our experiments presented above show that summation properties in response to synchronized inputs from NGFCs are not significantly affected by HCN1 channels, presumably due to the relatively moderate local hyperpolarization arriving from NGFCs; again, further studies are needed to test the influence of additional interneuron classes coactivated together with NGFCs. We predict that further GABAergic activity is unlikely to change summation arithmetics based on our negative results when blocking the high-affinity plasma membrane GABA transporters concentrated in the perisynaptic and extrasynaptic areas (Melone et al., 2015) effective in modulating GABA-mediated inhibition through extrasynaptic GABA spillover (Barbour and Häusser, 1997; Hamann et al., 2002; Scanziani, 2000; Szabadics et al., 2007). Despite having similar summation arithmetics of two consecutive APs to the triple-recording configuration, it remains undefined as to what extent multiple presynaptic APs resemble synchronous activation of individual release sites. Presynaptic GABAB receptor-mediated decrease in Ca2+ is unlikely (Karayannis et al., 2010); however, depletion of the readily releasable pool of vesicles leading to synaptic depression cannot be ruled out – this may contribute to the apparent inconsistency between the simulation and the experimental data concerning summation during more than three repetitive APs, since our model does not incorporate short-term synaptic depression. As suggested by pioneering simulations on the summation of GABAB receptor-mediated signaling (Destexhe and Sejnowski, 1995), a crucial intrinsic factor in the postsynaptic cells is the molecular cascade linking GABAB receptors to the GIRK channels through G-proteins. Our experimental evidence for close to linear or slightly sublinear summation of GABAB receptor-mediated responses suggests that even if amplification through G-proteins plays a role, it is unable to overturn local membrane or K+ concentration-dependent factors promoting sublinearity (Dascal, 1997; Inanobe and Kurachi, 2014; Stanfield et al., 2002; Wickman and Clapham, 1995). Amplification of GIRK current by G-proteins could be hampered by the need of cooperative action of up to four G-protein βγ subunits to be effective in opening GIRK channels. In addition, hyperpolarization and the accompanying relatively low [Na+]i might also limit GIRK channel activation knowing that high [Na+]i promotes GIRK channel opening in depolarized cells (Wang et al., 2016). The latter scenario might promote a brain state-dependent summation of metabotropic inhibitory signals in active neuronal networks, which remains to be tested in future experiments. On the other hand, our ultrastructural model corroborates pioneering suggestions (Kulik et al., 2006; Qian and Sejnowski, 1990) that the effect of GABAB receptors is more prominent on dendritic spines compared to dendritic shafts, having approximately twice the number of activated GIRK channels per GABAB receptor on spines versus shafts. Admittedly, our simulations could not cover the extensive intracellular signaling pathways known to be influenced by GABAB receptors (Gassmann and Bettler, 2012; Padgett and Slesinger, 2010; Terunuma, 2018) and future availability of comprehensive transporter and extracellular space distributions of layer 1 would enrich the model (Hrabetova et al., 2018; Korogod et al., 2015; Pallotto et al., 2015). Nevertheless, our experiments and simulations suggest that nonsynaptic GABAergic volume transmission providing relatively homogeneous and sufficient concentrations of GABA combined with increased clustering of GABAB receptors and on spines compared to shafts governs compartment-dependent efficacy.

Taken together, our experimental results and modeling analysis suggest that a randomly chosen location in the neuropil of layer 1 is targeted by a moderate number (usually one or two) presynaptic NGFCs. In turn, there is no apparent gap in the neurogliaform coverage of layer 1, that is, most elements of the neuropil including classic postsynaptic compartments, presynaptic terminals, or non-neuronal cells are located sufficiently close to terminals of at least one NGFC and receive GABA nonsynaptically. Interestingly, when two NGFCs which share target territory are coactivated or a single NGFC has a limited number of consecutive spikes, linear arithmetics accompany GABAB receptor summation. This supports the hypothesis that the density and distribution of neocortical NGFCs and their axonal terminals combined with the effective range of GABAergic volume transmission appear optimized for a spatially ubiquitous and predominantly linear metabotropic GABAB receptor summation.

Materials and methods

Slice preparation

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Experiments were conducted to the guidelines of University of Szeged Animal Care and Use Committee (ref. no. XX/897/2018). We used young adult (19–46 days of age, (P) 23.9 ± 4.9) male Wistar rats for the electrophysiological experiments. Animals were anesthetized by inhalation of halothane, and following decapitation, 320-µm-thick coronal slices were prepared from the somatosensory cortex with a vibration blade microtome (Microm HM 650 V; Microm International GmbH, Walldorf, Germany). Slices were cut in ice-cold (4°C) cutting solution (in mM) 75 sucrose, 84 NaCl, 2.5 KCl, 1 NaH2PO4, 25 NaHCO3, 0.5 CaCl2, 4 MgSO4, 25 D(+)-glucose, saturated with 95% O2 and 5% CO2. The slices were incubated in 36°C for 30 min, subsequently the solution was changed to (in mM) 130 NaCl, 3.5 KCl, 1 NaH2PO4, 24 NaHCO3, 1 CaCl2, 3 MgSO4, 10 D(+)-glucose, saturated with 95% O2 and 5% CO2, and the slices were kept in it until experimental use. The solution used for recordings had the same composition except that the concentrations of CaCl2 and MgSO4 were 3 and 1.5 mM unless it is indicated otherwise. The micropipettes (3–5 MΩ) were filled (in mM) 126 K-gluconate, 4 KCl, 4 ATP-Mg, 0.3 GTP-Na2, 10 HEPES, 10 phosphocreatine, and eight biocytin (pH 7.25; 300 mOsm).

In vitro electrophysiology and pharmacology

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Somatic whole-cell recordings were obtained at ~37°C from simultaneously recorded triplets and doublets of NGF and PC cell visualized by infrared differential interference contrast video microscopy at depths 60–160 µm from the surface of the slice (Zeiss Axio Examiner LSM7; Carl Zeiss AG, Oberkochen, Germany), 40× water-immersion objective (1.0 NA; Carl Zeiss AG, Oberkochen, Germany) equipped with Luigs and Neumann Junior micromanipulators (Luigs and Neumann, Ratingen, Germany) and HEKA EPC 10 patch clamp amplifier (HEKA Elektronik GmbH, Lambrecht, Germany). Signals were filtered 5 kHz, digitalized at 15 kHz, and analyzed with Patchmaster software.

Presynaptic cells were stimulated with a brief suprathreshold current pulse (800 pA, 2–3 ms), derived in >60 s interval. In experiments, where two presynaptic NGFCs were stimulated simultaneously, the interval was increased >300 s. The stimulation sequence in which one or the other or both presynaptic NGFCs were stimulated was constantly altered, therefore the potential rundown effect or long-term potentiation would affect all three stimulation conditions equally. In the case of 100 Hz presynaptic burst stimulation, the interval was increased >300 s. During stimulation protocol, the order of triggering a set of one to four APs on the NGFC was randomized. The postsynaptic responses were normalized to the single AP in each individual set. During postsynaptic current-clamp recording, −50 mV holding current was set. The experiments were stopped if the series resistance (Rs) exceeded 35 MΩ or changed more than 20%. During postsynaptic voltage-clamp recordings, Rs and whole-cell capacitance were monitored continuously. The experiment was discarded if the compensated Rs change reached 20% during recording.

Pharmacological experiments were carried out on NGFC-PC pairs using ACSF with the following drugs: 10 µM SR 95531 hydrobromide (Tocris), 10 µM D-(-)−2-amino-5-phosphonopentanoic acid (D-AP5) (Tocris), 10 µM 2,3-dioxo-6-nitro-1,2,3,4-tetrahydrobenzo[f]quinoxaline-7-sulfonamide (NBQX) (Tocris), 10 µM 4-(N-ethyl-N-phenylamino)-1,2 dimethyl-6-(methylamino)pyrimidinium chloride (ZD7288) (Sigma-Aldrich), 10 µM 1-[2-[[(diphenylmethylene)imino]oxy]ethyl]-1,2,5,6-tetrahydro-3-pyridinecarboxylic acid hydrochloride hydrochloride (NO711) (Sigma-Aldrich).

We performed BQA by altering the extracellular Ca2+ and Mg2+ in two different conditions (Bhumbra and Beato, 2013). One of the conditions was to provide consistently a high release probability, in which the ACSF contained (in mM): 3 Ca2+/1.5 Mg2+. For the reduced release probability, we tested two different compositions (in mM): either 2 Ca2+/2 Mg2+ or 1.5 Ca2+/ 3 Mg2+. During BQA experiments, the ACSF solution contained the following substances: 10 µM D-AP5 (Tocris), 10 µM NBQX (Tocris). Each epoch of the BQA experiment contains a stable segment of 28 up to 42 unitary IPSCs (mean 32.75 ± 4.15). BQA experiments required at least 60 min of recording time (up to 90 min). We tested all epochs for possible long-term plasticity effect by measuring the linear correlation between IPSCs amplitude and elapsed time during the experiment, and we found no or negligible correlation (Pearson’s r values from all of the experiments [n = 8] were between −0.39 and 0.46, mean −0.01 ± 0.29).

The rise time of evoked IPSCs-Ps was determined as the time interval between the points corresponding to 10% and 90% of the peak amplitude, respectively. The peak current/voltage was determined as the maximum within a window of 2 ms duration after the presynaptic AP. The decay time constant of IPSCs/Ps were measured at 67.3%. For measuring the linearity, we measured the difference of calculated and experimentally recorded IPIS peak amplitudes from converging inputs.

Immunohistochemistry and anatomical analysis

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After electrophysiological recordings, slices were fixed in a fixative containing 4% paraformaldehyde, 15% picric acid, and 1.25% glutaraldehyde in 0.1 M phosphate buffer (PB; pH = 7.4) at 4°C for at least 12 hr. After several washes in 0.1 M PB, slices were cryoprotected in 10% then 20% sucrose solution in 0.1 M PB. Slices were frozen in liquid nitrogen then thawed in PB, embedded in 10% gelatin, and further sectioned into slices of 60 µm in thickness. Sections were incubated in a solution of conjugated avidin-biotin horseradish peroxidase (ABC; 1:100; Vector Labs) in Tris-buffered saline (TBS, pH = 7.4) at 4°C overnight. The enzyme reaction was revealed by 3’3-diaminobenzidine tetrahydrochloride (0.05%) as chromogen and 0.01% H2O2 as an oxidant. Sections were post-fixed with 1% OsO4 in 0.1 M PB. After several washes in distilled water, sections were stained in 1% uranyl acetate, dehydrated in ascending series of ethanol. Sections were infiltrated with epoxy resin (Durcupan [Sigma-Aldrich]) overnight and embedded on glass slices. 3D light microscopic reconstructions were carried out using Neurolucida system with a 100× objective.

Surgery for imaging experiments

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Experiments were conducted to the guidelines of University of Szeged Animal Care and Use Committee. Young adult (22–28 days of age, (P) 24.75 ± 2.75) male Wistar rats were initially anesthetized with halothane before urethane anesthesia (1.4 g/kg of body weight) was administrated intraperitoneally. Body temperature was maintained at 37°C with a heating pad (Supertech Instruments, Pécs, Hungary). Before surgery, dexamethasone sodium phosphate (2 mg/kg of body weight) was administrated subcutaneously, and carprofen (5 mg/kg of body weight) was administrated intraperitoneally. Anesthetized animals’ head were stabilized in a stereotaxic frame and headbars were attached to the skull with dental cement (Sun Medical, Mariyama, Japan). Circular craniotomy (3 mm diameter) was made above the primary somatosensory cortex, centered at 1.5 mm posterior and 2.2 mm lateral from the bregma with a high-speed dental drill (Jinme Dental, Foshan, China). Dura mater was carefully removed surgically. Finally, the craniotomy was filled with 1.5% agarose and covered with a coverslip to limit motion artifacts. The craniotomy was then submerged with HEPES buffered ACSF recording solution containing (in mM) 125 NaCl, 3.5 KCl, 10 HEPES, 1 MgSO4, 1 CaCl2, 0.5 D(+)-glucose, pH = 7.4.

Two-photon calcium imaging in L1

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Before covering the craniotomy with the coverslip calcium indicator, Oregon Green 488 BAPTA-1 AM (10 mM) (OGB-1 AM, Thermo Fisher Scientific) and astrocytic marker sulforhodamine 101 (1 µM) (SR101, Thermo Fisher Scientific) were pressure-injected with a glass pipette (1–2 MΩ) in L1 cortical region under the visual guide of Zeiss Axio Examiner LSM7 (Carl Zeiss AG, Oberkochen, Germany) two-photon microscope using 40× water-immersion objective (W-Plan, Carl Zeiss, Germany). Subsequently, the craniotomy was filled with agarose and covered with a coverslip. Imaging experiments were performed 1 hr after preparation. The activity of L1 interneurons was monitored during ipsilateral hindlimb electrical stimulation (Digimeter, Hertfordshire, UK, 200 mA, 10 ms). OGB-1 AM was excited at 800 nm wavelength with a femtosecond pulsing Ti:sapphire laser (Mai Tai DeepSee [Spectra-Physics, Santa Clara, CA]). In the somatosensory hindlimb region, Z-stack image series (volume size 304 × 304 × 104 µm3) were acquired. Calcium signals from interneurons were obtained within this volume in full-frame mode (256 × 100 pixel), acquired at a frequency of ~20 Hz. The Ca2+-dependent fluorescence change ΔF/F was calculated as R(t)=(F(t)−F0(t))/F0(t) based on Jia et al., 2011. The R(t) denotes the relative change of fluorescence signal, F(t) denotes the mean fluorescence of a region of interest at a certain time point, F0(t) denotes the time-dependent baseline. Cells were considered responsive if there was a measurable ΔF/F change (×3 of the standard deviation of the noise) in the averaged trace of 10 trials to ipsilateral hindlimb stimulus. Image stabilization was performed by ImageJ (Fiji) software using the Image stabilizer plugin (Li, 2008; Schneider et al., 2012). At the end of the experiments, few L1 neurons were filled with biocytin containing intracellular solution to make the immunohistochemical remapping easier.

Tissue preparation for immunohistochemistry

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After imaging experiments, rats were deeply anesthetized with ketamine and xylazine. Subsequently, perfusion was performed through the aorta, first with 0.9% saline for 1 min, then with an ice-cold fixative containing 4% paraformaldehyde in 0.1 M PB (pH = 7.4) for 15 min. The whole brain was extracted and stored in 4% paraformaldehyde for 24 hr, afterward in 0.1 M PB (pH = 7.4) until slicing. Later 60-µm-thick sections were cut from the same two-photon Ca2+ imaged brain area parallel to the pia mater and washed overnight in 0.1 M PB.

Fluorescence immunohistochemistry and remapping

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After several washes in 0.1 M PB, slices were cryoprotected with 10%, then 20% sucrose solution in 0.1M PB than frozen in liquid nitrogen. The sections were incubated for 2 hr in Alexa-488 conjugated streptavidin (1:400, Molecular Probes) solved in TBS (0.1 M; pH = 7.4) at room temperature to visualize the biocytin labeled cells. After several washes in TBS, sections were blocked in normal horse serum (NHS, 10%) made up in TBS, followed by incubation in mouse anti-α-actinin (1:20,000, Sigma-Aldrich) diluted in TBS containing 2% NHS and 0.1% Triton X-100 at room temperature for 6 hr. Following several washes in TBS, Cy3 conjugated donkey anti-mouse (1:500, Jackson ImmunoResearch) secondary antibody was used to visualize the immunoreactions. After several washes in TBS, then in 0.1 M PB, slices were counterstained with DAPI (4′,6-diamidino-2-phenylindole, Thermo Fisher Scientific). Sections were then mounted on slides in Vectashield (Vector Laboratories). Images were taken with LSM 880 confocal laser scanning microscope (Carl Zeiss AG, Oberkochen, Germany) using 40× oil-immersion objective (1.4 NA). Confocal image z-stack was tilted and panned manually to match with the in vivo two-photon z-stack, allowing to profile imaged interneurons. During this process biocytin labeled neurons were used as a reference point.

Data analysis

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Electrophysiological data were analyzed with Fitmaster (HEKA Elektronik GmbH, Lambrecht, Germany), Origin 7.5 (OriginLab Corporation, Northampton, MA), IgorPro (Wavemetrics, Portland, OR). BQA experiments were analyzed using a Python written program (Bhumbra and Beato, 2013), incorporating NumPy and SciPy packages. Two-photon calcium imaging data were acquired with ZEN 2 (Carl Zeiss AG, Oberkochen, Germany) and analyzed with MATLAB (The MathWorks, Natick, MA), using Statistical Toolbox, Image Processing Toolbox, and custom-written scripts.

MCell model construction

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The model framework was constructed in Blender v2.7. The simulation environment contained a 3D reconstruction of a dendritic structure based on a series section of electron microscopic data (available from VolRoverN program; Edwards et al., 2014) and realistically positioned release sites of NGFCs. In the simulation environment, the extracellular space was also modeled by creating an array of cubic cells containing cavities based on previous work from Tao et al., 2005. The cubic cells have 800 × 800 nm2 length, containing cavity that is 400 × 400 nm2 wide, and 340 nm deep. The cubic cells and the dendritic segment were spaced 32 nm apart. The established array of cubic compartments creates an extracellular space that provides a volume fraction and tortuosity identical to the cortical brain tissue (volume fraction = 0.2 and tortuosity = 1.6). The overall dimensions of the modeled space surrounding the ultrastructurally reconstructed dendrite were 13.28 × 13.28 × 6.592 µm3 and the total volume was 1162.55 µm3.

Simulation of GABAB receptor-GIRK channel interaction was carried out with MCell v3.4 (http://www.mcell.org) (Kerr et al., 2008). Custom Matlab scripts created the MDL (Model Description Language) file that required for MCell simulation. MCell simulated the release and diffusion of GABA, GABAB receptors, and GIRK channel interaction. First, to manage a biological like distribution for the receptors and channels, a reaction cascade was used (Figure 6—figure supplement 2). This cascade was constructed and tested in a simple simulation environment first, containing only a plane surface. At the beginning of every iteration, primary seed particles were placed on the dendritic membrane. Primary seed particles subdivided into secondary seed particles, that which then produce GABAB receptor or GIRK channel clusters. Those secondary seed particles that produce the GIRK channel clusters – which contain one to four channels – were immobile in the membrane. Meanwhile, the secondary seed particles that produce GABAB receptor clusters – which contain one to eight receptors – can diffuse laterally in the membrane. At the end, the distance was defined between the center of receptor and channel clusters by calculating the distance between each receptor and channel cluster in the 2D plane surface. Delay and the forward rate of the reaction was set to allow secondary seeds, that generate GABAB receptor clusters to diffuse to specified distance, resulting in the required GABAB receptor-GIRK channel cluster distribution as seen from Kulik et al., 2006. Optimization algorithm based on simulated annealing technique (Henderson et al., 2006; Kirkpatrick et al., 1983) was written in Matlab for approximating the optimal values for the delay and the forward rate of the reaction. Optimal values of delay and the forward rate of the reaction were set to allow secondary seeds, that generate GABAB receptor clusters to diffuse to specified distance, resulting in the required GABAB receptor-GIRK channel cluster distribution as seen in Kulik et al., 2006.

Since we were interested in the interaction between the GABAB receptors and GIRK channels, our model does not include GABAA receptors and GABA amino transporters. Previous work suggests that a single AP in the NGFC generates GABA concentration of 1–60 µM lasting for 20–200 ms (Karayannis et al., 2010), therefore in our model we used similar GABA concentration range of at 0.5–2 µm distance from the release sites.

Up to six MCell simulations were run with 1 µs time steps in parallel on PC with Intel (R) Core i7-4790 3.6 GHz CPU, 32 GB RAM. Total of 4278 iterations were simulated.

Model NGFC

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Based on the experimental data of NGFCs’ total axonal length and its distribution, a 3D model NGFC was constructed. A custom-written Matlab algorithm generates the model NGFCs.

First single NGFC 3D models were created by expanding axonal segments from a center point that was considered to be the soma of the model cell. Three parameters were used to create model cells randomly. Total axonal length was taken from previously reconstructed cells, we used an average and SD value to determine the model NGFC’s axonal length. From reconstructions, the number of branching points was the other parameter that was used as an average and SD. Finally, the orientation of the axonal segments was set, we optimized the polar angle and azimuthal angle values of the generated axonal segments in the spherical coordinate system where the origin was the soma. In each run, a Sholl analysis was done on the completed model NGFC cell, and it was compared with the experimental values of axonal distribution. The parameters were optimized to the point when statistical difference was not measurable between the model NGFCs’ and reconstructed NGFCs’ axonal distribution.

Statistics

The number of experimental recordings used in each experiment is indicated in the text. Statistical tests were performed using Origin 7.5 (OriginLab Corporation, Northampton, MA) and SPSS software (IBM, Armonk, NY). Data are represented as mean ± standard deviation (SD). Data were first subject to a Shapiro-Wilk test of normality, and based on the result to the indicated parametric and non-parametric tests. Results were considered significantly different if p<0.05.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

The following data sets were generated
    1. Ozsvár A
    2. Komlósi G
    3. Oláh Gsr
    4. Baka J
    5. Molnár Gb
    6. Tamás Gb
    (2021) Dryad Digital Repository
    Data from: Predominantly linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons.
    https://doi.org/10.5061/dryad.qv9s4mwf4

References

    1. Henderson D
    2. Jacobson SH
    3. Johnson AW
    (2006)
    Handbook of Metaheuristics
    287–319, The Theory and Practice of Simulated Annealing, Handbook of Metaheuristics, Norwell, MA, Kluwer Academic Publishers.
    1. Silver RA
    (2010) Neuronal arithmetic
    Nature Reviews Neuroscience 11:474–489.
    https://doi.org/10.1038/nrn2864

Decision letter

  1. Marco Capogna
    Reviewing Editor; University of Aarhus, Denmark
  2. John R Huguenard
    Senior Editor; Stanford University School of Medicine, United States
  3. Marco Capogna
    Reviewer; University of Aarhus, Denmark
  4. Mark P Beenhakker
    Reviewer; University of Virginia, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

The paper attempts the first experimental quantitative analysis of the distinct integration mechanisms of GABA-A and GABA-B receptor activation by γ-amino-butyric-acid (GABA) released via volume transmission from an important class of cortical interneuron type, known as neurogliaform cell. The data offer original insights into the logic of volume transmission and the subcellular distribution of postsynaptic GABA-B receptors. Therefore, this paper provides novel and important information on the role of the GABAergic system within cortical microcircuits.

Decision letter after peer review:

Thank you for submitting your article "Linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons" for consideration by eLife. Your article has been reviewed by 3 peer reviewers including Marco Capogna as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by John Huguenard as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Mark P Beenhakker (Reviewer #2).

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:

I recommend the Authors to provide a point to point reply to each comments raised by all the Reviewers. It will be essential to provide significant improvements to the following major critical point:

1) The Authors should provide convincing evidence that volume transmission satisfies the assumptions required for quantal analysis.

2) The revised version should provide a more convincing explanation of the subcellular localization of postsynaptic GABA-B receptor-GIRK channels and possibly a mechanism underlying the linear summation observed. The Authors should also consider the possibility of including GABA transporters in their model.

3) The Authors should re-work and/or re-explain the conflicting results of linear versus non-linear GABA-B responses observed after one-two or three-more action potentials, respectively.

4) The Authors should further provide further controls to confirm that for strong use-dependent depression of GABA release from NGFCs, shunting inhibition and/or activation of intrinsic postsynaptic membrane channels do not significantly alter the GABA-B receptor-mediated IPSPs summation from the activation of converging inputs.

5) The Author should improve the analysis of NGFCs activation after hindlimb stimulation in vivo, as explained in details below.

Reviewer #1 (Recommendations for the authors):

I have the following specific comments.

1) The modeling data suggest that the subcellular localization of GABA-B receptor-GIRK channels determines linear summation of GABA-B receptor responses. The recruitment of GIRK channels was found to be more effective on spines compared to shafts when the responses were evoked by two action potentials, whereas a similar summation at the dendrite spines and shafts occurred when three or four action potentials were evoked. It is not completely clear the proposed mechanism underlying this difference (results observed with two versus three-four action potentials). This aspect should be further explored with simulations or at least it should be clarified and better discussed. How these data align with the conclusion that spines represent the preferred site of action of GABA-B receptors?

2) The authors evoked unitary synaptic responses by one presynaptic NGFC with low frequency of stimulation (>60s interval), but when two presynaptic NGFCs were stimulated simultaneously then this interval was increased >300s. Why did the authors increase the interval of stimulation for this latter protocol? Was the synaptic depression stronger when two independent presynaptic cells were stimulated, suggesting a likely postsynaptic mechanism? I think it would be interesting to know more about their results here, because NGFCs-mediated synaptic depression remains a phenomenon that is repetitively observed but still requires some more mechanistic insights (Karayannis et al., 2010 suggested receptor desensitization but this is probably only one aspect of the phenomenon). Did the authors attempt to evoke presynaptic action potentials by using the cell-attached configuration of recording postsynaptic IPSPs by using whole cell perforated patch clamp or by using sharp microelectrode recordings in order to ascertain whether these alternative recording conditions would affect NGFC-mediated synaptic depression?

3) The HCN channel blocker ZD7288 did not alter GABA-B receptor-mediated summation. This interesting negative result should be complemented by some new experiments, if coronavirus emergency situation will allow to perform experiments at Szeged University, in the next weeks, a positive control result showing that ZD7288 affects the excitability of the recorded neurons.

Reviewer #2 (Recommendations for the authors):

The authors present a compelling study that aims to resolve the extent to which synaptic responses mediated by metabotropic GABA receptors (i.e. GABA-B receptors) summate. While response summation mediated by ionotropic receptors is well described, metabotropic response summation is not, thereby making the authors' exploration of the phenomenon novel and impactful. By carrying out a series of elegant and challenging experiments that are coupled with computational analyses, the authors conclude that summation of synaptic GABA-B responses is linear, unlike the sublinear summation observed with ionotropic, GABA-A receptor-mediated responses.

The manuscript is generally straightforward, even if presentation is often dense and challenging to read. Three primary issues worth considering include (1) the rather strong conclusion that GABA-B responses linearly summate, despite evidence to the contrary presented in Figure 5C, (2) additional quantification of data presented in Figure 3 to support the contention that NGFCs co-activate, and (3) how the MCell model informs the mechanisms contributing to linear response summation. These and other issues are further described below. Despite these comments, this reviewer is generally enthusiastic about the study. Through a set of very challenging experiments and sophisticated modeling approaches, the authors provide important observations on both (1) NGFC-PC interactions, and (2) GABA-B receptor mediated synaptic response dynamics.

1) The use of BQA is justified as the authors could only record a limited number of synaptic responses. On line 98, the authors indicate that BQA estimates were in good agreement with the distribution of IPSC amplitudes (Figure 1E). The authors should provide some indication why the two are in agreement (presumably because the average synaptic current aligns with the BQA-derived estimates of p, q and n?). Also, there is no description of what the q and Nfrs in Figure 1E refer to (presumably the BQA-derived values for that particular experiment?). These are fairly minor points, but asking the reader to make assumptions about the authors' intentions makes readability perhaps more challenging than necessary.

2) In Figure 2, the authors generate model NGFC axons with axonal boutons. Are the interbouton distances uniformly set at 3.35um in the model axons? If so, is doing so consistent with bouton distribution observed in real NGFCs (i.e. Figure S1)? That is, are distal boutons dispersed more closely than proximal boutons (or vice versa)? And would such spatially-dependent dispersion alter the conclusion shown in Figure 2F (bottom)?

3) Presumably, the motivation for Figure 3 is that it provides physiological context for when NGFCs might be coactive, thereby providing the context for when downstream, PC responses might summate. This is a nice addition. However, it seems that a relevant quantification/evaluation is missing from the figure. That is, the authors nicely show that hind limb stimulation evokes responses in the majority of NGFCs. But how many of these neurons are co-active, and what are their spatial relationships? Figure 3D appears to begin to address this point, but it is not clear if this plot comes from a single animal, or multiple? Also, it seems that such a plot would be most relevant for the study if it only showed α-actin 2-positive cells. In short, can one conclude that nearby, presumptive NGFCs co-activate, and is this conclusion derived from multiple animals?

4) Regarding Figure 3, what are the criteria for responsive versus non-responsive cells. In Figure 3C, are cells 1 through ~20 responsive?

5) The analysis presented in Figure 3H shows the "number of presynaptic boutons located within increasing radial distances measured from postsynaptic dendrites" (line 102). If a dendrite is long cellular compartment that winds continuously through the extracellular space (i.e. not spatially discrete), then how exactly is this measurement made? As this analysis identifies a 1.5um NGFC-PC distance as most reasonable, and this distance is used as justification in later figures, it seems that a clear description of this metric is warranted. Perhaps including a schematic describing how this measurement is made would be helpful. Moreover, isn't the distance between GABA-B receptors and release sites the more relevant measure? Are GABA-B receptors expressed uniformly on PC dendrites?

6) What is the rationale for performing the PC recordings in Figure 4 in current clamp? It seems that voltage-clamp would be more appropriate to evaluate summation?

7) The authors should provide an explanation for how linearity is calculated; presumably it's simply the difference between the observed response and the linear summation. Also, the differences between the sublinear, ionotropic responses and the linear, metabotropic responses are small. Understandably, these experiments are difficult – indeed, a real tour de force – from which the authors are attempting to derive meaningful observations. Therefore, asking for more triple recordings seems unreasonable. That said, the authors may want to consider showing all control and gabazine recordings corresponding to these experiments in a supplemental figure.

8) Why are sublinear GABA-B responses observed when driven by three or more action potentials (Figure 5C)? It is not clear why the authors do not address this observation considering that it is inconsistent with the study's overall message.

9) Line 248: The authors imply that under physiological conditions, PCs will receive, at most, two co-active NGFC inputs. That is, linear summation is the rule under physiological conditions. Is this reasonable?

10) Line 257: Can the authors please expand on the hypothesis they are testing with their MCell model? It is not immediately obvious to this reviewer why locally constrained GABA-B receptor-GIRK channel interactions would lead to linear summation of synaptic responses.

11) Line 273: The text here does not seem to best capture what is going on with the GABA concentration as a function of distance (Supp. Figure 4). It seems reasonable that the concentration of GABA within the synapse (i.e. point 0.0) is close to 1mM, and that it rapidly drops off with distance. It seems that the authors want to convey that [GABA] is 60uM at 0.5um and 1uM at 2.0um. Currently, the text seems to imply that NGFC-associated GABA transients can vary considerably from 1uM to 60uM, when in reality it seems that they want to say that the amount of released GABA is fairly consistent from release event to release event, but that it drops off exponentially with distance.

12) Line 283-312: The description of GABA-B receptor/GIRK channel activation is quantitative, a strength of the passage. However, a general summary of the observations would be helpful. Moreover, relating the simulation results back to the original motivation for the MCell model would be very helpful (i.e. the authors asked whether "linear summation was potentially a result of the locally constrained GABAB receptor – GIRK channel interaction when several presynaptic inputs converge"). It seems as if performing "experiments" on the model wherein local constraints are manipulated would begin to address this question. Why not use the model to provide some data – albeit theoretical – that begins to address their question?

13) Line 291-299: It seems that total GIRK channel activation follows a nearly linear summation from 1-4 action potentials. Is this modeling result inconsistent with the experimental result shown in Figure 5C?

Reviewer #3 (Recommendations for the authors):

1. The authors should explicitly state whether there is a significant statistical difference in the linearity of the GABAA and GABAB responses (this seems likely). Most importantly, using the same approach to experimentally address underlying mechanisms of linear GABAB summation would enhance the significance of the work. As acknowledged in the discussion, there are several alternatives that have not been ruled out and the mechanism supported by the simulation seems quite speculative.

2. The authors make a point to apply a method of quantal analysis that allows estimation of synaptic parameters with smaller datasets than conventional quantal analysis, yet the BQA method still requires more observations than is provided in the current dataset (60 observations in each of 2 data set). This is a minor concern mitigated by the fact that quantal analysis is often undertaken without the prerequisite large data sets.

More importantly, the approach of Figure 1 would more convincing if the authors validated it with another QA method, or by showing that the proposed quantal size is reasonable. One option could be recording unitary IPSCs under low Pr conditions where failures dominate and thus successes are likely quantal (optimization of the recordings would likely be required). Alternatively, the authors could address whether BQA applied to volume transmission yields the expected changes in parameters following experimental manipulations of quantal size (gabazine) or release probability.

4. It is also essential to assess GABA release during repetitive stimulation (Figure 5) in order to constrain the subsequent simulation in Figure 6 which assumes the same amount of GABA is released with each action potential (this is highly unlikely due to use-dependent depression of NGFCs). The contribution of HCN channels to summation needs additional analysis.

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

Thank you for resubmitting your work entitled "Predominantly linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons" for further consideration by eLife. Your revised article has been evaluated by John Huguenard (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

All the three Reviewers found that the revised manuscript is significantly improved and successfully addresses most of their criticism. Overall, all the Reviewers are convinced that this study provides an important contribution to the understanding of cortical neurogliaform cell signaling and integration of postsynaptic GABAB receptor-mediated responses.

However, the approach of applying quantal analysis to volume transmission still needs some clarification and improvement. There are still some concerns. First, the new data quantifying the amplitude of quantal events can be problematic, as the failures and successes are difficult to distinguish given the noise level. Second, the current analysis relies heavily on assuming that anatomically-detected axonal boutons are in fact functional release sites, and recent work highlights the inaccuracy of this method at least for mouse cortical synapses (Holler et al., Nature 2021).

The Authors should better describe the limitation of their model and to make less clear-cut conclusions throughout the manuscript. For example, they should remove the quantification of the distance of volume transmission from the releasing sites from the abstract. It would be also useful to include some predictions, such as the range of volume transmission if a reasonable fraction of anatomically-defined boutons were not functional.

https://doi.org/10.7554/eLife.65634.sa1

Author response

Essential revisions:

Reviewer #1 (Recommendations for the authors):

I have the following specific comments.

1) The modeling data suggest that the subcellular localization of GABA-B receptor-GIRK channels determines linear summation of GABA-B receptor responses. The recruitment of GIRK channels was found to be more effective on spines compared to shafts when the responses were evoked by two action potentials, whereas a similar summation at the dendrite spines and shafts occurred when three or four action potentials were evoked. It is not completely clear the proposed mechanism underlying this difference (results observed with two versus three-four action potentials). This aspect should be further explored with simulations or at least it should be clarified and better discussed. How these data align with the conclusion that spines represent the preferred site of action of GABA-B receptors?

Our suggestion supports earlier ideas proposed by the Sejnowksi laboratory on the association of spines and GABAB receptor function (Qian and Sejnowki, 1990, Destexhe et al. 1995). GABAB receptor and GIRK channel clusters localize considerably closer on membrane surface of the spines than on the shaft (Kulik et al., 2006). The distance on spines vs. shafts could lead to a distinct outcome of GABAB signaling. We admit that our model of GABAB signaling is a rather simplified reaction model that does not incorporate the many pathways of GABAB signaling: ”Admittedly, our simulations could not cover the extensive intracellular signaling pathways known to be influenced by GABAB receptors (Gassmann and Bettler, 2012; Padgett and Slesinger, 2010; Terunuma, 2018) and future availability of comprehensive transporter and extracellular space distributions of layer 1 would enrich the model (Hrabetova et al., 2018; Korogod et al., 2015; Pallotto et al., 2015).” The main aim of this model was to investigate what are the possible consequences of the domain specific localization of the receptor and channel complexes. We performed additional experiments concerning the results observed with two versus three-four action potentials, see below.

2) The authors evoked unitary synaptic responses by one presynaptic NGFC with low frequency of stimulation (>60s interval), but when two presynaptic NGFCs were stimulated simultaneously then this interval was increased >300s. Why did the authors increase the interval of stimulation for this latter protocol? Was the synaptic depression stronger when two independent presynaptic cells were stimulated, suggesting a likely postsynaptic mechanism? I think it would be interesting to know more about their results here, because NGFCs-mediated synaptic depression remains a phenomenon that is repetitively observed but still requires some more mechanistic insights (Karayannis et al., 2010 suggested receptor desensitization but this is probably only one aspect of the phenomenon). Did the authors attempt to evoke presynaptic action potentials by using the cell-attached configuration of recording postsynaptic IPSPs by using whole cell perforated patch clamp or by using sharp microelectrode recordings in order to ascertain whether these alternative recording conditions would affect NGFC-mediated synaptic depression?

We agree with the reviewer that the rapid depression seen in NGFC output is not fully understood (Capogna, 2011; Tamás et al., 2003). However, in this study, mechanisms potentially leading to use-dependent NGFC output were not addressed and the experiments were designed to avoid/minimize factors potentially leading to use-dependent output of interacting NGFCs. In triple recordings, one should consider the effect of GABA released from both presynaptic NGFCs acting not only on their own presynaptic terminals when activated alone, but also acting on terminals of the other NGFC in the vicinity through volume transmission (Oláh et al. 2009). In the triple recording experiments, we used to test convergent NGFC effects, blockade of GABAB receptors was not an option (we tested the summation of postsynaptic GABAB effects), thus we opted to increase the stimulus interval in order to minimize the effect of homosynaptic and heterosynaptic use dependent depression.

3) The HCN channel blocker ZD7288 did not alter GABA-B receptor-mediated summation. This interesting negative result should be complemented by some new experiments, if coronavirus emergency situation will allow to perform experiments at Szeged University, in the next weeks, a positive control result showing that ZD7288 affects the excitability of the recorded neurons.

We performed new experiments to measure the effect of ZD7288 on excitability with positive results. We performed a protocol of current injections with increasing steps in layer 2 pyramidal cells to measure the action potential threshold before and after 10 μM ZD7288 application (Author response image 1). We found a significant decrease of threshold potential (n=6, P=0.036) and input resistance (n=6, P=0.036).

Author response image 1
ZD7288 decreases threshold potential in pyramidal cells.

(A) Lower step current injection induce firing in pyramidal cell after application of ZD7288. (B) Threshold potential decreases significantly after ZD7288 bath application.

Reviewer #2 (Recommendations for the authors):

The authors present a compelling study that aims to resolve the extent to which synaptic responses mediated by metabotropic GABA receptors (i.e. GABA-B receptors) summate. While response summation mediated by ionotropic receptors is well described, metabotropic response summation is not, thereby making the authors' exploration of the phenomenon novel and impactful. By carrying out a series of elegant and challenging experiments that are coupled with computational analyses, the authors conclude that summation of synaptic GABA-B responses is linear, unlike the sublinear summation observed with ionotropic, GABA-A receptor-mediated responses.

The manuscript is generally straightforward, even if presentation is often dense and challenging to read. Three primary issues worth considering include (1) the rather strong conclusion that GABA-B responses linearly summate, despite evidence to the contrary presented in Figure 5C, (2) additional quantification of data presented in Figure 3 to support the contention that NGFCs co-activate, and (3) how the MCell model informs the mechanisms contributing to linear response summation. These and other issues are further described below. Despite these comments, this reviewer is generally enthusiastic about the study. Through a set of very challenging experiments and sophisticated modeling approaches, the authors provide important observations on both (1) NGFC-PC interactions, and (2) GABA-B receptor mediated synaptic response dynamics.

We appreciate the insight of the reviewer and the fair assessment of our efforts. Our main conclusion, i.e. predominantly linear summation of GABAB responses triggered by NGFCs is based not only on summation experiments shown on Figure 5, it is conjointly supported by the overall anatomical arrangement of the compound axonal structure of the population of NGFCs in the upper layers of the cortex: interaction of NGFC boutons is dominated by one or two cells with linear summation, three or more NGFCs have a relatively minor chance for potentially sublinear interaction. We emphasize this by softening the title of the manuscript (“Predominantly linear summation…”) and in additional text in the results section: “Combination of the datasets derived from the structural analyisis of GABAergic connections established by the population of layer 1 NGFCs above and the in vivo mapping of coactivated putative NGFCs suggests that summation of NGFC output is feasible and, at the same time, is dominated by GABA released from boutons of a one or two NGFCs: having approximately two-thirds of NGFCs simultaneously active further suppresses the potentially synchronous contribution of three or more NGFCs (8.5% and 8.45%, see above) already constrained by structural properties in a point of the cortex.”

1) The use of BQA is justified as the authors could only record a limited number of synaptic responses. On line 98, the authors indicate that BQA estimates were in good agreement with the distribution of IPSC amplitudes (Figure 1E). The authors should provide some indication why the two are in agreement (presumably because the average synaptic current aligns with the BQA-derived estimates of p, q and n?). Also, there is no description of what the q and Nfrs in Figure 1E refer to (presumably the BQA-derived values for that particular experiment?). These are fairly minor points, but asking the reader to make assumptions about the authors' intentions makes readability perhaps more challenging than necessary.

We agree with the reviewer that the most likely reason behind the apparent agreement between BQA estimates and the distribution of IPSC amplitudes is the alignment between average synaptic current and the BQA-derived estimates of p, q and n. This is accentuated by additional experiments we performed according to suggestion 2 of reviewer #3. Briefly, we made an effort to validate that the quantal feature is present in the neurogliaform output by measuring single vesicle release responses. We performed four experiments in which the use of low extracellular Ca2+ reduced release probability to a level at which postsynaptic uniqantal events appeared in response to neurogliaform cell activation. We found that quantal amplitude among these experiments was 4.46±0.83 pA (n=4) which is statistically not different (P=0.8, Mann-Whitney Test) from quantal amplitude measured with BQA (Figure 1—figure supplement 1). The revised text is modified accordingly.

The BQA method models the amplitude distributions to estimate the quantal parameters, and in Figure 1E shows that the modeled amplitude distribution by BQA (blue distribution histogram) is nicely in line with the experimentally collected limited amount of IPSP peak amplitudes (grey bars) in a single experiment. The q and Nfrs in Figure 1E refer to the quantal size and number of functional release sites in that particular experiment. We modified the figure legend.

2) In Figure 2, the authors generate model NGFC axons with axonal boutons. Are the interbouton distances uniformly set at 3.35um in the model axons? If so, is doing so consistent with bouton distribution observed in real NGFCs (i.e. Figure S1)? That is, are distal boutons dispersed more closely than proximal boutons (or vice versa)? And would such spatially-dependent dispersion alter the conclusion shown in Figure 2F (bottom)?

In Figure 2, the interbouton distances were set uniformly for simplicity. We did not find any correlation between interbouton intervals versus their distances from the soma. Therefore, we did not investigate how distance-dependent distribution of the NGFC boutons could alter the results.

Author response image 2
Interbouton distances along the NGFC axons.

(A) Interbouton distances versus their distances from the soma along the axonal processes. Each plot shows data from individual NGFCs. The axonal segments were randomly chosen. Grey area shows three times of the standard deviation, black line indicates the linear fit on the data. Values exceeding three times of the standard deviation were excluded from the correlation measurement. (B) Pooled data from the six NGFCs.

3) Presumably, the motivation for Figure 3 is that it provides physiological context for when NGFCs might be coactive, thereby providing the context for when downstream, PC responses might summate. This is a nice addition. However, it seems that a relevant quantification/evaluation is missing from the figure. That is, the authors nicely show that hind limb stimulation evokes responses in the majority of NGFCs. But how many of these neurons are co-active, and what are their spatial relationships? Figure 3D appears to begin to address this point, but it is not clear if this plot comes from a single animal, or multiple? Also, it seems that such a plot would be most relevant for the study if it only showed α-actin 2-positive cells. In short, can one conclude that nearby, presumptive NGFCs co-activate, and is this conclusion derived from multiple animals?

The aim of Figure 3D was to indicate that the active presumed NGFCs are located close to each other. The figure shows data from a single animal. To address the point raised by the reviewer, we revised Figure 3D and provide information about the molecular profile of the active/inactive cells. In addition, we further analyzed our in vivo dataset concerning the spatial localization of monitored interneurons (see Sup. Figure 2). The results were recorded from 4 different animals, in these experiments numerous L1 interneurons are active during the sensory stimulus as shown in the scatter plot. We calculated the shortest distances between all active cells and all ɑ-actinin2+ that were active in experiments. The data suggest that in the case of identified active ɑ-actinin2+ cells, the interneuron somats were on average 216.09 ± 77.93 µm distance from each other. Data from Figure 3 - figure supplement 1 indicates that the average axonal arborization of the NGFCs is reaching ~200-250 µm away. Taken these two datasets together, the spatial localization allows summation of outputs of neighboring NGFCs.

4) Regarding Figure 3, what are the criteria for responsive versus non-responsive cells. In Figure 3C, are cells 1 through ~20 responsive?

During the in vivo 2p Ca2+ imaging the monitored cells were considered responsive if there was a measurable ΔF/F fluorescent change (x3 of the standard deviation of the noise) in the averaged trace of 10 trials to ipsilateral hindlimb stimulus. We added the text to the methods section for clarity. In Figure 3C cells from 1 to 20 are considered to be responsive based on these criteria.

5) The analysis presented in Figure 3H shows the "number of presynaptic boutons located within increasing radial distances measured from postsynaptic dendrites" (line 102). If a dendrite is long cellular compartment that winds continuously through the extracellular space (i.e. not spatially discrete), then how exactly is this measurement made? As this analysis identifies a 1.5um NGFC-PC distance as most reasonable, and this distance is used as justification in later figures, it seems that a clear description of this metric is warranted. Perhaps including a schematic describing how this measurement is made would be helpful. Moreover, isn't the distance between GABA-B receptors and release sites the more relevant measure? Are GABA-B receptors expressed uniformly on PC dendrites?

The line 102 sentence refers to Figure 1H. The functionally connected NGFC-PC pairs from the BQA experiments were three dimensionally reconstructed using Neurolucida software and 100x objective. The NGFC boutons as possible release sites of GABA were manually marked in the proximity of the postsynaptic dendrite. In the three-dimensional space distance between the marked presynaptic release sites and the closest postsynaptic dendritic points were calculated and collected. These distances were analysed in Figure 1H and Figure 1I.

In general, in the neocortex the distal dendritic/infragranular layers show strong labeling intensities for GABAB receptors, while deeper/subgranular layers show weaker immunostaining (Fritschy et al., 1999). Similar patterns of expression can be observed along the somatodendritic axis in the CA areas of the hippocampus (Degro et al., 2015). At the subcellular level, SDS-digest freeze-fracture replica immunoelectron microscopy data on CA1 pyramidal cells show that GABAB receptors are most abundant at the perisynaptic region and extrasynaptic plasma membrane of dendritic shaft and spines (Kulik et al., 2003, Kulik et al., 2006, Degro et al., 2015). To our knowledge, there is no comprehensive study about GABAB receptor distribution on dendrites of superficial pyramidal cells in the neocortex. However, based on hippocampal data we cannot rule out the non-uniform distribution of GABAB receptors along the somatodendritic axis. We agree with the reviewer that the distance of postsynaptic GABAB receptors and presynaptic release sites would be more relevant, however, we believe that an admittedly limited model based on measurements is more useful and reproducible compared to a detailed model which is based on assumptions.

6) What is the rationale for performing the PC recordings in Figure 4 in current clamp? It seems that voltage-clamp would be more appropriate to evaluate summation?

We have chosen current clamp recordings on purpose for the summation of GABAA (Figure 4A-C) and GABAB (Figure 4D-F) responses to ensure to maintain the closest to physiological conditions knowing that driving force may alter summation of inputs (Tamas 2004). In addition, we performed new experiments of simultaneous NGFC-to-PC dual recordings with one, two, three and four spike initiation in the presynaptic NGFC (Figure 5) while holding the postsynaptic PC in voltage clamp mode. Our new results show that spike duplet activation of GABAB response measured both in current and voltage clamp measurements resulted linear summation (Author response image 3).

Author response image 3
Integration of GABAB receptor-mediated synaptic currents.

(A) Representative recording of a neurogliaform synaptic inhibition on a voltage clamped pyramidal cell. Bursts of up to four action potentials were elicited in NGFCs at 100 Hz in the presence of 1 μM gabazine and 10 μM NBQX (B) Summary of normalized IPSC peak amplitudes (left) and charge (right). (C) Pharmacological separation of neurogliaform initiated inhibitory current.

7) The authors should provide an explanation for how linearity is calculated; presumably it's simply the difference between the observed response and the linear summation. Also, the differences between the sublinear, ionotropic responses and the linear, metabotropic responses are small. Understandably, these experiments are difficult – indeed, a real tour de force – from which the authors are attempting to derive meaningful observations. Therefore, asking for more triple recordings seems unreasonable. That said, the authors may want to consider showing all control and gabazine recordings corresponding to these experiments in a supplemental figure.

The result section only briefly describes how we calculated the linearity; we measured the difference of calculated and experimentally recorded IPSP peak amplitudes from converging inputs. This information was added to the methods section as well to increase clarity. Following the Reviewer’s suggestion, we added two author response images that show the average IPSPs in different experiments during control condition and in the presence of gabazine (see Author response image 4 and 5).

Author response image 4
Summation of convergent GABAA and GABAB receptor mediated IPSPs elicited by NGFCs.

IPSPs recorded from the postsynaptic PC under control conditions from individually (light and dark blue) and synchronously (light green) activated NGFCs. Dark green line indicates the arithmetic sum of individual IPSP responses. Each figure shows the mean of IPSPs through the experiments. Black lines indicate the linearity of the summation in different experiments. Linearity was calculated as a difference between the average of the arithmetic sum and experimentally recorded converging IPSPs from individual NGFCs. Grey dashed line shows the complete linearity.

Author response image 5
Summation of convergent GABAB receptor mediated IPSPs elicited by NGFCs.

IPSPs recorded from the postsynaptic PC under the presence of gabazine from individually (light and dark blue) and synchronously (light green) activated NGFCs. Each figure shows the mean of IPSPs through the experiments. Dark green line indicates the arithmetic sum of individual IPSP responses. Black lines indicate the linearity of the summation in different experiments. Grey dashed line shows the complete linearity. Linearity was calculated as a difference between the average of the arithmetic sum and experimentally recorded converging IPSPs from individual NGFCs.

8) Why are sublinear GABA-B responses observed when driven by three or more action potentials (Figure 5C)? It is not clear why the authors do not address this observation considering that it is inconsistent with the study's overall message.

As addressed in the general section above, our main conclusion, i.e. predominantly linear summation of GABAB responses triggered by NGFCs is based not only on summation experiments shown on Figure 5, it is conjointly supported by the overall anatomical arrangement of the compound axonal structure of the population of NGFCs in the upper layers of the cortex: interaction of NGFC boutons is dominated by one or two cells with linear summation, three or more NGFCs have a relatively minor chance for potentially sublinear interaction. Thus, the in vivo relevance of the observation shown on Figure 5C is questionable and so we stand by our conclusion. We explicitly state in the discussion that “Our experimental approach indicates linear interactions between GABAB receptor mediated responses in case the number of converging presynaptic cells corresponds to the number of NGFCs cooperating during in vivo network operations.” However, we believe it is informative to show the readers that interaction of NGFCs at the extremes of the range shows saturation. In addition, the observed differences between the experimental and modeling data in the summation inhibitory signals during 3 and 4 APs may be due to the depletion of the readily releasable pool of vesicles as we already briefly covered in the discussion. Moreover, our simulations could not cover the extensive and complex intracellular signaling pathways, which would likely have an effect on the GIRK channel activation.

9) Line 248: The authors imply that under physiological conditions, PCs will receive, at most, two co-active NGFC inputs. That is, linear summation is the rule under physiological conditions. Is this reasonable?

Yes, this is one of the major conclusions of the manuscript as discussed in our response to point 8 above.

10) Line 257: Can the authors please expand on the hypothesis they are testing with their MCell model? It is not immediately obvious to this reviewer why locally constrained GABA-B receptor-GIRK channel interactions would lead to linear summation of synaptic responses.

We agree with the reviewer that the sentence can be better formulated. We changed the text to “Theoretical studies suggest that the distance between the receptor and effector limits the recruitment of effector molecules to the vicinity of receptors (Brinkerhoff et al., 2008; Kulik et al., 2006), thus we asked if summation properties were influenced by the relative location of GABAB receptors and GIRK channels when several presynaptic inputs converge.”

11) Line 273: The text here does not seem to best capture what is going on with the GABA concentration as a function of distance (Supp. Figure 4). It seems reasonable that the concentration of GABA within the synapse (i.e. point 0.0) is close to 1mM, and that it rapidly drops off with distance. It seems that the authors want to convey that [GABA] is 60uM at 0.5um and 1uM at 2.0um. Currently, the text seems to imply that NGFC-associated GABA transients can vary considerably from 1uM to 60uM, when in reality it seems that they want to say that the amount of released GABA is fairly consistent from release event to release event, but that it drops off exponentially with distance.

We thank the reviewer for the suggestion and clarification. Text in the Results section was modified based on the Reviewer’s suggestion: “In our model, the amount of released GABA was consistent from release event to release event and dropped off exponentially with distance from the synapse ([GABA]: 0.0 μm, 1 mM; 0.5 μm, 60 μM; 2.0 μm, 1 μM; Supplementary Figure 6). “

12) Line 283-312: The description of GABA-B receptor/GIRK channel activation is quantitative, a strength of the passage. However, a general summary of the observations would be helpful. Moreover, relating the simulation results back to the original motivation for the MCell model would be very helpful (i.e. the authors asked whether "linear summation was potentially a result of the locally constrained GABAB receptor – GIRK channel interaction when several presynaptic inputs converge"). It seems as if performing "experiments" on the model wherein local constraints are manipulated would begin to address this question. Why not use the model to provide some data – albeit theoretical – that begins to address their question?

We re-formulated the problem to be addressed in this Results section as described in our response to point 10 above. We admit that our model has several limitations in the Discussion and, consequently, we restricted it's application to a limited set of quantitative comparisons paired to our experimental dataset or directly related to pioneering studies on GABAB efficacy on spines vs shafts. We believe that a proper answer to the reviewer’s suggestion would be worth a separate and dedicated study with an extended set of parameters and an elaborated model.

13) Line 291-299: It seems that total GIRK channel activation follows a nearly linear summation from 1-4 action potentials. Is this modeling result inconsistent with the experimental result shown in Figure 5C?

We agree and added a sentence to the end of the relevant paragraph: “The increased linearity of total GIRK channel activation relative to experimental results could be a result of several unknown properties of the cascade linking GABAB receptors to GIRK channels and/or due to vesicle depletion in terminals of NGFCs during multiple rapid release of GABA not incorporated into our model.”

Reviewer #3 (Recommendations for the authors):

1. The authors should explicitly state whether there is a significant statistical difference in the linearity of the GABAA and GABAB responses (this seems likely). Most importantly, using the same approach to experimentally address underlying mechanisms of linear GABAB summation would enhance the significance of the work. As acknowledged in the discussion, there are several alternatives that have not been ruled out and the mechanism supported by the simulation seems quite speculative.

We statistically compared the linearity of GABAA and GABAB responses and the signals were statistically different at the level of p = 0.021, two sided MW U test. We added to the text:

“Taken together, our triple recordings in gabazine versus control conditions suggest significantly different (p = 0.021, two sided MW U test) linear interactions between slow, GABAB IPSPs as opposed to sublinearly summating fast, GABAA IPSPs elicited by the same presynaptic interneuron population.“

2. The authors make a point to apply a method of quantal analysis that allows estimation of synaptic parameters with smaller datasets than conventional quantal analysis, yet the BQA method still requires more observations than is provided in the current dataset (60 observations in each of 2 data set). This is a minor concern mitigated by the fact that quantal analysis is often undertaken without the prerequisite large data sets.

In the Bhumbra and Beato 2013 paper, the authors evaluated the accuracy of the BQA method with different sizes of datasets. Although the authors recommend using 60 observations in each of 2 data sets, the performance of BQA analysis, as they show in the paper, is still reasonable while only using only a limited 30 observations in each of 2 data sets.

More importantly, the approach of Figure 1 would more convincing if the authors validated it with another QA method, or by showing that the proposed quantal size is reasonable. One option could be recording unitary IPSCs under low Pr conditions where failures dominate and thus successes are likely quantal (optimization of the recordings would likely be required). Alternatively, the authors could address whether BQA applied to volume transmission yields the expected changes in parameters following experimental manipulations of quantal size (gabazine) or release probability.

To follow the reviewer’s suggestion, we made an effort to validate that the quantal feature is present in neurogliaform output by measuring single vesicle release responses. We had two experiments among the BQA experiments in which using low extracellular Ca2+ the release probability was reduced to a level at which postsynaptic uniqantal events appeared following neurogliaform cell activation. We performed two additional experiments and measured the amplitudes of the uniquantal events (Figure 1 -figure supplement 1.). We found that quantal amplitude among these experiments was 4.46±0.83 pA (n=4) which is statistically not different (P=0.8, Mann-Whitney Test) from quantal amplitude measured with BQA.

4. It is also essential to assess GABA release during repetitive stimulation (Figure 5) in order to constrain the subsequent simulation in Figure 6 which assumes the same amount of GABA is released with each action potential (this is highly unlikely due to use-dependent depression of NGFCs). The contribution of HCN channels to summation needs additional analysis.

We briefly covered the disparity in the original discussion:

“Despite having similar summation arithmetics of two consecutive APs to the triple-recording configuration, it remains undefined as to what extent multiple presynaptic APs resemble synchronous activation of individual release sites. Presynaptic GABAB receptor-mediated decrease in ca2+ is unlikely (Karayannis et al., 2010), however, depletion of the readily releasable pool of vesicles leading to synaptic depression cannot be ruled out.”

We added the following sentence to assess the differences in the simulation:

“- this may contribute to the apparent inconsistency between the simulation and the experimental data concerning summation during more than three repetitive APs, since our model does not incorporate short-term synaptic depression.”

Additional analysis concerning HCN1 channels was also requested by Reviewer#1 (major point 3), please see our answer above.

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

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

All the three Reviewers found that the revised manuscript is significantly improved and successfully addresses most of their criticism. Overall, all the Reviewers are convinced that this study provides an important contribution to the understanding of cortical neurogliaform cell signaling and integration of postsynaptic GABAB receptor-mediated responses.

However, the approach of applying quantal analysis to volume transmission still needs some clarification and improvement. There are still some concerns.

First, the new data quantifying the amplitude of quantal events can be problematic, as the failures and successes are difficult to distinguish given the noise level.

We re-evaluated our uniquantal event detection dataset by measuring multiple parameters of each event. We measured the slope of fitted line on the initial phase of events/failures, amplitude (averaged maximum) and the area of events. Using K-means cluster analysis on the three parameters, we managed to separate the events into 3 groups having failures, uniquantal and multiquantal responses as separate groups. Having separated uniquantal events this way, we found that averaged quantal amplitude was 4.59±0.73 pA (n=4) which is statistically not different (P=0.68, Mann-Whitney Test) from quantal amplitude measured with BQA.

Second, the current analysis relies heavily on assuming that anatomically-detected axonal boutons are in fact functional release sites, and recent work highlights the inaccuracy of this method at least for mouse cortical synapses (Holler et al., Nature 2021).

Using EM tomography and serial sectioning combined with paired recordings, we concluded that rat and especially human neocortical layer 2/3 pyramidal cells use multivesicular release in their output (Molnar et al. eLife 2016). This was corroborated by Holler et al. (Nature 2021) recently and, importantly, both studies confirm that presynaptic boutons and vesicles in the readily releasable pool are essential for quantal transmission. Accordingly, we are not aware of evidence contradicting our assumption that the sites of presynaptic release are anatomically detected axonal boutons. We actually applied procedures similar to Holler et al. 2021 (paired recordings and serial EM on the recorded synapses) when studying the output of neurogliaform cells (Oláh et al. Nature 2009) and concluded that indeed, light microscopic predictions for ultrastructural synaptic contacts are inaccurate. This was crucial in putting forward the idea of single cell driven volume transmission (Oláh et al. Nature 2009).

We clarify our limitations for single or multivesicular release in the modified discussion: “However, our light microscopic methods applied in this study are insufficient for definitive claims concerning single or multivesicular release from neurogliaform boutons, especially in the light of studies highlighting the inaccuracy of comparing the number of functional release sites with the anatomical synaptic contacts detected by light microscopy (Oláh et al. Nature 2009, Molnár et al. 2016, Holler et al., 2021)”.

The Authors should better describe the limitation of their model and to make less clear-cut conclusions throughout the manuscript. For example, they should remove the quantification of the distance of volume transmission from the releasing sites from the abstract. It would be also useful to include some predictions, such as the range of volume transmission if a reasonable fraction of anatomically-defined boutons were not functional.

We agree with the editors and clearly stated in the Discussion that “The distance of effective operation through NGFC driven volume transmission, however, is not clear.” We changed the abstract and in addition, we further emphasize in the revised Discussion that “Given that our distance estimates are not based on ultrastructural evidence and we cannot exclude that fraction of anatomically-defined boutons were not functional, the accuracy of our prediction for the range of volume transmission is limited.” We do not wish to speculate about the maximal range of volume transmission without supporting dataset.

https://doi.org/10.7554/eLife.65634.sa2

Article and author information

Author details

  1. Attila Ozsvár

    MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5803-1174
  2. Gergely Komlósi

    MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Gáspár Oláh

    MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
    Contribution
    Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Judith Baka

    MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
    Contribution
    Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Gábor Molnár

    MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
    Contribution
    Formal analysis, Supervision, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Gábor Tamás

    MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences,, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    gtamas@bio.u-szeged.hu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7905-6001

Funding

Eötvös Loránd Research Network (ELKH-SZTE Research Group for Cortical Microcircuits)

  • Gábor Tamás

Hungarian Science Foundation (GINOP 2.3.2-15-2016-00018)

  • Gábor Tamás

Hungarian Science Foundation (Élvonal KKP 133807)

  • Gábor Tamás

NRDI Office (OTKA K128863)

  • Gábor Molnár
  • Gábor Tamás

European Commission (ÚNKP-20-5 - SZTE-681)

  • Gábor Tamás

Hungarian Academy of Sciences (János Bolyai Research Scholarship)

  • Gábor Molnár

Innovation and Technology Fund (New National Excellence Program)

  • Gábor Molnár

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

Acknowledgements

This work was supported by the ERC INTERIMPACT project, by the Eötvös Loránd Research Network, the Hungarian National Office for Research and Technology GINOP 2.3.2-15-2016-00018, Élvonal KKP 133807, the National Brain Research Program, Hungary, the National Research, Development and Innovation Office (OTKA K128863) (GT, GM); ÚNKP-20–5 – SZTE-681 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund (GM); and János Bolyai Research Scholarship of the Hungarian Academy of Sciences (GM). We would like to thank Dr Angus Silver for the useful comment on an earlier version of the manuscript, Dr Chandrajit Bajaj for the permission of using the 3D reconstruction of a dendritic structure and Éva Tóth and Nelli Ábrahám Tóth for their exceptional technical assistance.

Ethics

Animal experimentation: Experiments were conducted to the guidelines of University of Szeged Animal Care and Use Committee (ref. no. XX/897/2018).

Senior Editor

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

Reviewing Editor

  1. Marco Capogna, University of Aarhus, Denmark

Reviewers

  1. Marco Capogna, University of Aarhus, Denmark
  2. Mark P Beenhakker, University of Virginia, United States

Publication history

  1. Received: December 10, 2020
  2. Preprint posted: December 11, 2020 (view preprint)
  3. Accepted: July 14, 2021
  4. Accepted Manuscript published: July 26, 2021 (version 1)
  5. Version of Record published: August 12, 2021 (version 2)

Copyright

© 2021, Ozsvár 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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