1. Neuroscience
Download icon

Developmental emergence of two-stage nonlinear synaptic integration in cerebellar interneurons

  1. Celia Biane
  2. Florian Rückerl
  3. Therese Abrahamsson
  4. Cécile Saint-Cloment
  5. Jean Mariani
  6. Ryuichi Shigemoto
  7. David A DiGregorio
  8. Rachel M Sherrard
  9. Laurence Cathala  Is a corresponding author
  1. Sorbonne Université et CNRS UMR 8256, Adaptation Biologique et Vieillissement, France
  2. Institut Pasteur, Université de Paris, CNRS UMR 3571, Unit of Synapse and Circuit Dynamics, France
  3. Institute of Science and Technology Austria, Austria
  4. Paris Brain Institute, CNRS UMR 7225 - Inserm U1127 – Sorbonne Université Groupe Hospitalier Pitié Salpêtrière, France
Research Article
  • Cited 0
  • Views 299
  • Annotations
Cite this article as: eLife 2021;10:e65954 doi: 10.7554/eLife.65954

Abstract

Synaptic transmission, connectivity, and dendritic morphology mature in parallel during brain development and are often disrupted in neurodevelopmental disorders. Yet how these changes influence the neuronal computations necessary for normal brain function are not well understood. To identify cellular mechanisms underlying the maturation of synaptic integration in interneurons, we combined patch-clamp recordings of excitatory inputs in mouse cerebellar stellate cells (SCs), three-dimensional reconstruction of SC morphology with excitatory synapse location, and biophysical modeling. We found that postnatal maturation of postsynaptic strength was homogeneously reduced along the somatodendritic axis, but dendritic integration was always sublinear. However, dendritic branching increased without changes in synapse density, leading to a substantial gain in distal inputs. Thus, changes in synapse distribution, rather than dendrite cable properties, are the dominant mechanism underlying the maturation of neuronal computation. These mechanisms favor the emergence of a spatially compartmentalized two-stage integration model promoting location-dependent integration within dendritic subunits.

Introduction

Dendritic integration of spatiotemporal synaptic activity is fundamental to neuronal computation, which shapes the transformation of input activity into output spiking (Silver, 2010). In particular, the cable properties of dendritic trees can generate isolated electrical compartments that enable nonlinear integration of local synaptic responses. These compartments increase the computational power of single neurons (Cazé et al., 2013; Poirazi and Mel, 2001) and are a prominent feature of human neurons (Beaulieu-Laroche et al., 2018; Gidon et al., 2020). Dendritic morphology and ion channel expression are developmentally regulated, but how they contribute to the maturation of neuronal computations throughout postnatal circuit formation and refinement is less well known. The observation of alterations in dendritic morphology, synaptic connectivity, density, and function in several neurodevelopmental disorders (Marín, 2016; Penzes et al., 2011) indicates that both appropriate neuronal wiring and the maturation of a neuron’s integrative properties are necessary to develop fully functional neuronal networks (Pelkey et al., 2015).

The type and number of computations that a neuron can perform depend on the diversity of the mathematical operations used to combine synaptic inputs within dendritic trees. These can be sublinear, linear, or supralinear (Branco and Häusser, 2011; Cazé et al., 2013; Poirazi and Mel, 2001; Tran-Van-Minh et al., 2015; Vervaeke et al., 2012). Nonlinear dendritic operations depend on (1) dendritic architecture and its associated active and passive membrane properties (Abrahamsson et al., 2012; Hu et al., 2010; Katz et al., 2009; Larkum et al., 2009; Magee, 2000; Magee, 1999; Nevian et al., 2007; Rall, 1967); (2) spatial localization, density, and properties of synapses across the dendritic arbor (Grillo et al., 2018; Larkum et al., 2009; Losonczy et al., 2008; Losonczy and Magee, 2006; Menon et al., 2013; Schiller et al., 2000; Williams and Stuart, 2002); and (3) the spatiotemporal synaptic activity pattern (Bloss et al., 2018; Grillo et al., 2018; McBride et al., 2008; Scholl et al., 2017; Xu et al., 2012). All these factors change during neuronal circuit maturation through cell-autonomous or activity-dependent processes (Sigler et al., 2017; Katz and Shatz, 1996). Indeed the maturation of neuronal excitability and morphology (Cathala et al., 2003; Cline, 2016; McCormick and Prince, 1987; Zhang, 2004) is associated with restriction of neuronal connectivity to subcellular compartments (Ango et al., 2004), activity-dependent synaptic rearrangement (Chen and Regehr, 2000; Cline, 2016; Kwon and Sabatini, 2011; Li et al., 2011), and the maturation of excitatory (Cathala et al., 2003; Hestrin, 1992; Koike-Tani et al., 2005; Lawrence and Trussell, 2000; Taschenberger and von Gersdorff, 2000) and inhibitory synaptic inputs (Ben-Ari, 2002; Sanes, 1993; Tia et al., 1996). Despite this knowledge, how developmental changes in cellular parameters dictate dendritic operations and their associated neuronal computations, remains largely unexplored.

Interneurons are fundamental to normal circuit function throughout development. They contribute to the developmental regulation of critical periods (Hensch et al., 1998; Gu et al., 2016), are important for establishing direction selectivity in the retina (Wei et al., 2011), and their dysfunction is associated with neurodevelopment disorders (Akerman and Cline, 2007; Le Magueresse and Monyer, 2013; Marín, 2016). Cerebellar stellate cells (SCs) are parvalbumin-positive (PV+) GABAergic interneurons that share anatomical and functional features with PV+ interneurons found in the neocortex and hippocampus (Hu et al., 2014) such as high fidelity of synaptic transmission (Chen and Regehr, 2000), expression of Ca2+-permeable α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA receptors) (AMPARs) (Soler-Llavina and Sabatini, 2006), low levels of synaptic N-méthyl-D-aspartate receptors (NMDARs) ( Clark and Cull-Candy, 2002), and weakly excitable dendrites (Abrahamsson et al., 2012), which together provide precise temporal control of principal neuron activity (Mittmann et al., 2005; Pouille and Scanziani, 2001). SCs receive excitatory inputs from granule cells and, in turn, modulate the excitability and firing precision of the cerebellar output neurons, Purkinje cells (Arlt and Häusser, 2020; Häusser and Clark, 1997; Mittmann et al., 2005). The thin SC dendrites (~0.4 μm diameter) filter synaptic potentials as they propagate to the soma and confer sublinear summation of synaptic input (Abrahamsson et al., 2012; Tran-Van-Minh et al., 2015). Nevertheless, the mechanisms underlying the maturation of these dendritic operations and neuronal computation of interneurons have not been explored.

Here, we study in detail the maturation of the synaptic and integrative properties of SCs in the cerebellar cortex. We combined patch-clamp recordings with fluorescence-guided electrical stimulation, fluorescence and electron microscopy three-dimensional (3D) reconstructions, and numerical simulations, to examine synapse strength and spatial distribution. Unlike unitary inputs in other neuron types, we found that adult SCs had smaller and slower miniature excitatory postsynaptic currents (mEPSCs) than those observed in immature SCs. This could be explained by enhanced electrotonic filtering since immature SCs are thought to be electrotonically compact (Carter and Regehr, 2002; Llano and Gerschenfeld, 1993). However, we found that their dendrites are as thin as in adult SCs and capable of robust electrotonic filtering and sublinear summation of synaptic inputs. Using a novel fluorescence synaptic tagging approach, we found a significantly larger contribution of distal dendritic synapses in adult SCs, due to a substantial increase in dendritic branching combined with constant synapse density. Multicompartment biophysical modeling confirmed that developmental changes in synapse distribution could reproduce the developmental reduction and slowing of recorded mEPSCs and the increased sublinear integration observed in adult SCs. Our findings provide evidence that SCs implement different neuronal computations throughout development: a predominant global summation model in immature SCs shifts to sublinear dendritic integration in adult SCs, favoring the developmental emergence of the two-layer integration model. This work provides a mechanistic description of the maturation of neuronal computation resulting from both functional and anatomical changes in synaptic transmission and integration. Our findings and approach also provide a framework for interpreting the functional implications of dendritic morphology and connectivity alterations on information processing within neural circuits during disease.

Results

AMPAR-mediated mEPSCs become smaller and slower during development

The strength and time-course of synaptic transmission are fundamental to information processing within neural networks since they influence the efficacy and temporal precision of the transformation of synaptic inputs into neuronal outputs. Excitatory synaptic inputs trigger postsynaptic conductance changes due to the opening of neurotransmitter-gated receptors, which are activated following transmitter release. These conductance changes are integrated within dendrites into local excitatory postsynaptic potentials (EPSPs) that then propagate to the cell body and contribute to somatic voltage. The strength and time-course of synaptic conductances are known to change during development (Cathala et al., 2003; Chen and Regehr, 2000; Koike-Tani et al., 2005) and can affect dendritic integration, which in turn may alter neuronal computation (Tran-Van-Minh et al., 2015).

To identify factors that shape the postnatal development of SC integrative properties, we first compared excitatory postsynaptic currents (EPSCs) recorded in acute brain slices from immature SCs soon after they reach their final position in the outer layer of the cerebellar cortex (postnatal days P13–19) and from adult SCs (postnatal days P35–57). Because of their low release probability, excitatory synapses formed by granule cell axons (parallel fibers, PFs) release on average only one synaptic vesicle per synaptic contact, despite the presence of multiple release sites per synaptic contact (Foster et al., 2005). We, therefore, examined these physiologically relevant ‘quantal synaptic events’ using somatic recordings of spontaneously occurring AMPAR-mediated miniature EPSCs (mEPSCs) in the presence of TTX to block spontaneous presynaptic activity. mEPSCs arise from the release of a single neurotransmitter vesicle and occur randomly at all synapses converging onto a single neuron. We did not examine NMDAR currents since they are located extrasynaptically and do not contribute to postsynaptic current under low-intensity and low-frequency stimulation (Carter and Regehr, 2000; Clark and Cull-Candy, 2002; Tran-Van-Minh et al., 2016). Therefore, AMPAR-mediated mEPSCs can provide an unbiased assessment of the effective distribution of postsynaptic strengths throughout the entire somatodendritic compartment.

We found that AMPAR-mediated mEPSCs occurred with a similar frequency at both ages (1.37 ± 0.27 vs. 1.14 ± 0.13 Hz, p > 0.05), but mEPSCs were significantly smaller and slower in the adult (Figure 1A-C). In immature SCs, the average mEPSC amplitude was 48 ± 7 pA, with 10–90% rise and decay times (τdecay, see Methods) of 0.16 ± 0.01 and 0.68 ± 0.06 ms, respectively. In contrast, mEPSCs from adult SCs were smaller 24 ± 2 pA (p < 0.05), with slower rising (0.24 ± 0.02 ms; p < 0.05) and decaying kinetics (τdecay = 1.31 ± 0.14 ms; p < 0.05). The mEPSC amplitudes are consistent with those described in previous studies describing large miniature events (Llano and Gerschenfeld, 1993) capable of influencing immature SC firing (Carter and Regehr, 2002). Nevertheless, we observed that mEPSCs mature past the third postnatal week, becoming smaller and slower.

Developmental maturation of the AMPA receptor (AMPAR)-mediated miniature excitatory postsynaptic current (mEPSC) in stellate cell (SC).

(A) Left panels show superimposed single mEPSCs (gray) and the corresponding average (bold) recorded at −70 mV obtained from a representative immature SCs (P17, blue trace) and adult SCs (P44, green trace). Right panels show plots of the 10–90% rise time versus peak amplitude, with the superimposed amplitude distributions. A significant correlation was observed in the adult SC (p < 0.05, Spearman rank correlation, r = −0.59). (B) Superimposed mEPSC average aligned on event onset. Inset: traces normalized to their peak. (C) Box and whisker plots showing the median (line) peak amplitude, 10–90% rise time and decay (τdecay) at both ages (P 17.4 ± 0.27 days, n = 13 and P 45 ± 1.75 days, n = 14, respectively), the 25th and 75th percentile (box), range (whiskers), and mean (+). Superimposed filled circles represent individual cells (asterisks denote p < 0.05; p = 2.18e−5, p = 0.0028, and p = 3.0e−4, respectively). (D) Representative examples of single mEPSC events (gray) and the corresponding average (bold) recorded from an immature SC for control (CTL) and in the presence of gamma-D-Glutamylglycine (γDGG) (1 mM). Inset shows the corresponding mEPSC peak amplitude distributions. (E) The effect of γDGG on mEPSCs at the two ages (n = 7 with p = 0.002 and n = 9 with p = 0.005, respectively): left panel its effect on individual mean peak amplitude for each SC (blue for immature and green for adult SC); and right panel, summary plot showing the % reduction of mEPSC peak amplitude (p = 0.83). (F) Plot summarizing the effect of γDGG on mEPSC rise time (left panel; for immature SC p = 0.52 and for adult SC p = 0.09) and decay (τdecay, right panel, for immature SC p = 0.44 and for adult SC p = 0.53) at the two ages for individual cells (open symbols) and on population averages (± standard error of the mean (SEM)). See Figure 1—source data 1.

Figure 1—source data 1

Developmental maturation of the AMPAR-mediated mEPSC in SC.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig1-data1-v2.xlsx

Previous studies have described developmental alterations in the glutamate content of synaptic vesicles (Yamashita et al., 2003) and synaptic structure (Cathala et al., 2005), both of which can modulate the neurotransmitter concentration in the synaptic cleft. To test whether the reduced amplitude and slower time-course could be due to alteration in effective amplitude and time-course of glutamate concentration ([Glut]) seen by synaptic AMPARs, we recorded mEPSCs in the presence of a rapidly dissociating, low-affinity competitive AMPAR antagonist, γDGG (Diamond and Jahr, 1997; Liu et al., 1999). Application of a submaximal concentration of γDGG (1 mM) reduced mEPSC peak amplitude (Figure 1D, paired p < 0.05) similarly at both ages (44.42% ± 4.36%, n = 7 in the immature vs. 42.38% ± 3.69%, n = 9 in the adult, p > 0.05; Figure 1E), with no apparent effect on mEPSC kinetics (Figure 1F, paired p > 0.05). This result suggests that the decreased amplitude and slowing of mEPSCs is unlikely due to a change in the synaptic [Glut]. We, therefore, explored whether postsynaptic mechanisms such as electrotonic cable filtering, as we described for adult SCs (Abrahamsson et al., 2012) and/or a smaller synaptic conductance (i.e., the number of activated synaptic AMPARs) could explain the changes in mEPSC during maturation.

Dendritic morphology supports electrotonic filtering in both immature and adult SCs

Dendrites of adult SCs exhibit electrotonic cable filtering, which reduces the amplitude of synaptic responses and slows their time-course as they propagate to the soma (Abrahamsson et al., 2012), thus modifying mEPSCs recorded at the soma. We considered whether the developmental difference in mEPSC amplitude and kinetics was due to the development of electrotonic filtering. To test this hypothesis, we first estimated the dendrite diameter of immature SCs. We previously demonstrated that the small diameters (<0.5 µm) of adult SCs were responsible for slowing and reducing the amplitude of the fast AMPAR-mediated synaptic responses despite short dendritic lengths (<100 µm) (Abrahamsson et al., 2012). The dendritic diameter was estimated from the full-width at half-maximum (FWHM) of the fluorescence profile perpendicular to the dendrite from confocal images of live SCs aged P13–P17 filled with Alexa 488 (Figure 2A). Diameters ranged from 0.26 to 0.93 µm with a mean of 0.47 ± 0.01 µm (n = 93 dendritic segments of 18 neurons; Figure 2B), which is close to the average adult value of 0.41 ± 0.02 µm (range 0.24–0.9 μm, n = 78 dendrites; data from Abrahamsson et al., 2012; p < 0.05).

Numerical simulations of stellate cell (SC) dendrites indicate significant cable filtering and large local depolarizations in immature SC.

(A) Maximal intensity projection of one-photon confocal images of an immature SC labeled with Alexa 488. Examples of intensity profiles (yellow line) of three dendritic locations from proximal to distal = a, (b and c) superimposed on the image. Dendrite diameter is approximated by the full-width half-maximum (FWHM) of the Gaussian fit of the line profile (broken red line). (B) Top: histogram showing the distribution of immature SC dendrite diameters from 93 dendrites (in blue) and the adult SC distribution (from Abrahamsson et al., 2012; in green), with a Gaussian fit indicating a mode centered at 0.43 ± 0.008 μm. Bottom left: summary box and whisker plot showing dendritic diameters for individual SCs. Superimposed filled circles represent individual dendritic branch measurements. Bottom right: summary box and whisker plot showing dendritic diameters as a function of dendritic branch order in immature SC. Filled circles represent individual dendritic diameters (with p = 0.19 between orders 1 and 2, p = 0.014 between orders 2 and 3, and p = 0.046 between orders 3 and 4). The dotted line indicates the mean dendritic diameter for immature SC. (C) Numerical simulations of somatic quantal excitatory postsynaptic currents (qEPSCs) in a passive immature SC under voltage-clamp (Cm = 0.9 pF/cm2, Rm = 20,000 Ω.cm2, and Ri = 150 ± 50 Ω.cm) with a dendritic diameter set to 0.47 µm. Left: top traces show simulated qEPSCs (sim qEPSC at a Vm = −70 mV) in response to a quantal synaptic conductance (gsyn) injected at the soma (magenta) and at a distance of 45 μm on a dendrite (gray trace). gsyn was set to reproduce the experimental qEPSCs following somatic synapses activation (see Figure 3). Bottom traces (green), the corresponding local voltage transients at the site of synaptic conductance injection. Boundaries of shaded region indicate simulations with a Ri of 100–200 Ωcm. Right: summary plot shows the distance dependence of simulated qEPSC amplitude, rise time, and half-width. Boundaries of the shaded region indicate simulations with a Ri of 100–200 Ω.cm. The dotted line indicates the 50% amplitude reduction. (D) Two-photon laser scanning microscopy (2PLSM) image of a P16 SC (maximal intensity projection) patch loaded with 30μM Alexa 594 and the corresponding 3D reconstruction in NeuronStudio (red: soma, brown: dendrite, blue: axon). (E) Superimposed numerical simulation of qEPSCs in the reconstructed P16 SC (with Cm = 0.9 pF/cm2, Rm = 20,000 Ω.cm2, and Ri = 150 Ω.cm) in response to a quantal conductance (gsyn) at the soma (red dot, magenta trace) or at a distance of 60μm on six different dendritic branches (blue dots, gray traces). gsyn was set to reproduce immature qEPSCs evoked by somatic synapses. (F) Simulated qEPSCs from synapse locations at the soma (red dot, magenta trace) or along a single dendrite (blue dot, gray traces). The summary plot shows the simulated qEPSC amplitudes as a function of synaptic location along the somatodendritic compartment. Boundaries of the shaded region indicate simulations with a Ri of 100–200 Ω.cm. The dotted line indicates the 50% amplitude reduction. (G) Same as in (D) but for a P42 SC. (H) Same as in (F) but with the reconstructed P42 SC and gsyn to reproduce experimental adult somatic qEPSC. See Figure 2—source data 1.

Figure 2—source data 1

Numerical simulations of SC dendrites indicate significant cable filtering and large local depolarizations in immature SC.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig2-data1-v2.xlsx

To understand the potential functional influence of such small diameters, we calculated the dendritic space constant (see Methods), i.e., the distance along a cable over which a steady-state membrane voltage decreases by 1/e. Using the estimated 0.47 μm dendritic diameter, a membrane resistance (Rm) of 20,000 Ω.cm2 matching that measured immature SCs membrane time constant τm of 19 ± 2.2 ms, n = 16, which was similar to a τm of 17 ± 2.7 ms for adult SCs (Abrahamsson et al., 2012) and an internal resistivity Ri ranging from 100 to 200 Ω.cm, we calculated the steady-state dendritic space constant (λ) to be between 343 and 485 µm, which is 3- to 5-fold longer than the actual dendritic length. The long space constants confirm that for steady-state membrane voltages, immature SCs are indeed electrically compact, as previously suggested (Carter and Regehr, 2002). However, the frequency-dependent space constant (λf; assuming that rapid mEPSCs are well approximated by a 1 kHz sine wave) was calculated to be between 46 and 60 µm (for Ri of 100–200 Ω.cm, respectively). These values are similar to the dendritic lengths of immature SCs (Figure 6) and suggest that, like in adult SCs (Abrahamsson et al., 2012), somatic recording of EPSC originating in dendrites may be smaller and slower due to electrotonic cable filtering.

We confirmed these frequency-dependent estimations using multicompartmental biophysical models to simulate the somatic response to quantal synaptic release throughout the somatodendritic compartment. We first used an idealized SC model (Abrahamsson et al., 2012), and then fully reconstructed immature SC dendritic trees. For the idealized immature SC morphology (Figure 2C), we used an 8 µm soma diameter (8.07 ± 0.23 µm, estimated from confocal images of 31 immature SC somata) and an unbranched dendrite with a uniform 0.47 µm diameter (see mean value from Figure 2B), an Rm of 20,000 Ω.cm2 and an Ri of 100–200 Ω.cm. The simulated synaptic conductance gsyn amplitude and time-course were adjusted to reproduce recorded quantal EPSCs (qEPSC) generated by release of a single vesicle following the activation of somatic synapses (see experimental approach below and Figure 3). Simulated qEPSCs were large and fast for synapses located at the soma (magenta trace, Figure 2C), but qEPSCs evoked from synapses located on the dendrites (gray trace; at 45 µm from the soma, Ri of 150 Ω.cm) were 48 % smaller and showed a 195 % slower rise time and a 180 % slower half-width. This dendritic filtering was also associated with a large increase in the local synaptic depolarization (green trace, Figure 2C) that would substantially reduce the local driving force during synaptic transmission onto dendrites, potentially causing a sublinear read-out of the underlying conductance, as observed in adult SCs (Abrahamsson et al., 2012; Tran-Van-Minh et al., 2016). Examination of the amplitude, rise time, and half-width as a function of synapse distance shows that beyond ~40 µm there is little additional cable filtering (Figure 2C, right).

Quantal excitatory postsynaptic current (EPSC) properties and postsynaptic density (PSD) areas for synapses contacting the soma and dendrites of immature stellate cell (SC).

(A) Diagram of a parasagittal cerebellar slice showing parallel fibers (PFs) projecting perpendicular (blue dots) to the dendritic plane of SCs (in red), allowing precise positioning of the stimulus electrode with respect to the soma (top panel) or an isolated SC dendrite (lower panel). (B) Two-photon laser scanning microscopy (2PLSM) images (maximal intensity projection) of two immature SC loaded with the patch-pipette with 30 μM Alexa 594 with the location of stimulating pipette indicated with a green triangle. (C) Single trials (gray) showing detected EPSCs (successes only) in response to extracellular stimulation at the soma (top) and on a dendrite (bottom) under low release probability conditions (external [Ca2+]/[Mg2+] was 0.5/5 mM; failure rate >90%). The corresponding averaged traces are in bold and represent the estimate of a quantal EPSC (qEPSC). (D) Box and whisker plots showing the median (line) peak amplitude, 10–90% rise time and full-width half-maximum (half-width) following somatic (magenta) or dendritic (gray) synapses activation (n = 25 and n = 18, respectively), the 25th and 75th percentile (box), range (whiskers), and mean (+). Superimposed filled circle represents individual cells (asterisks denote p < 0.05; p = 0.001, p = 4.31e−8, and p = 1.49e−6, respectively). (E) 2PLSM image of an Alexa 594-loaded P14 SC (maximal intensity projection) before fixation and (below) its 3D rendering after an electron microscopy (EM) reconstruction. Light dots indicate postsynaptic density (PSD) locations. Scale bar, 10 μm (F) Electron micrographs of an immunogold labeled SC soma with proximal and distal dendritic segments. The outer bound of excitatory synapses are indicated by arrows. Scale bar, 200 nm. (G) Plot of synapse area versus distance from soma. Orange and purple circles indicate data obtained from two immature SCs (P14, n = 172 synapses and P17, n = 220 synapses – total n = 392). The green line is a linear fit through all the points (R2 = 0.001, p = 0.85). See Figure 3—source data 1.

Figure 3—source data 1

qEPSC properties and PSD areas for synapses contacting the soma and dendrites of immature SC.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig3-data1-v2.xlsx

We predicted similar cable filtering with morphologically accurate passive biophysical models derived from 3D reconstructed SCs (Figure 2D and E). SCs were patch loaded with the fluorescence indicator Alexa 594, imaged with two-photon laser scanning microscopy (2PLSM; Figure 2D), and reconstructed with NeuronStudio (Rodriguez et al., 2008). The 3D reconstruction was then imported into the NEURON simulation environment, with the membrane properties indicated above. Activating a synaptic contact at 60 µm from the soma on any dendrite of the reconstructed immature P16 SC produced a simulated qEPSC that was consistently smaller and slower (gray traces) than the one produced following the activation of a somatic synapse (magenta trace; Figure 2E). Similarly, the activation of synaptic inputs along a dendrite at increasing distance from the soma produced soma-recorded qEPSCs that become smaller and slower with distance (Figure 2F), similar to those in the idealized passive model (Figure 2C) with a dendritic diameter matching that obtained from confocal images (Figure 2B). We also simulated qEPSCs from a reconstructed adult SC (Figure 2G and H), which showed a similar distance-dependent decrease in amplitude as for the immature SC (Figure 2F). These simulations suggest that, like their adult counterparts, the passive morphometric characteristics of immature SCs should also produce significant cable filtering of both the amplitude and time-course of EPSCs.

Synaptic events are electrotonically filtered in immature SCs

To confirm modeling predictions, we next explored whether dendrite-evoked quantal events in immature SCs show evidence of cable filtering. Taking advantage of the orthogonal projection of PFs, we used parasagittal cerebellar slices to stimulate specific PF beams that are synaptically connected to well-defined regions of an Alexa 594-loaded SC by placing an extracellular electrode either above the soma or close to the distal part of an isolated dendrite branch (Figure 3A and B). We recorded evoked qEPSCs using whole-cell voltage clamp of the SC soma. This approach allows precise control of the location of the activated synapses, in contrast with mEPSCs that can arise from unknown synapse locations anywhere along the somatodendritic axes. Dendritic filtering could then be examined by measuring the amplitude and time-course (response width at half-peak, half-width) of these synaptic events, typically used to estimate cable filtering (Rall, 1967). To isolate qEPSCs, PFs were stimulated in low release probability conditions (EPSC success rate of <10 %; 0.5 mM extracellular [Ca2+] and 5 mM [Mg2+]). In these conditions, the average EPSC generated from all successful trials is a good approximation of the quantal current amplitude and time-course (Silver, 2003). When stimulating somatic synapses, qEPSCs recorded at the soma had a mean peak amplitude of 62 ± 3 pA, a 10–90% rise time of 0.14 ± 0.004 ms, and a half-width of 0.60 ± 0.02 ms (n = 25; Figure 3C and D). In contrast, the stimulation of distal synapses, in the outer third of the dendritic field, produced somatically recorded qEPSCs that were significantly smaller (mean amplitude: 46 ± 3 pA) and slower (10–90% rise time of 0.24 ± 0.012 ms and a half-width of 0.88 ± 0.04 ms; n = 18; all p < 0.05, Figure 3C and D). Simulated qEPSCs generated from a dendritic location 45 µm from the soma (Figure 2C) exhibit amplitude and kinetics values with the range of experimental values. Taken together, these results are consistent with cable filtering of EPSCs as they propagate along dendrites in immature SCs.

The decreased amplitude of qEPSCs evoked in the dendrite could also be due to lower AMPAR content of dendritic synapses. As AMPAR density in SC synapses is constant (Masugi-Tokita et al., 2007), we used postsynaptic density (PSD) size as a proxy for the number of AMPARs. We measured PSD area of somatic and dendritic synapses from 3D electron microscopy reconstruction of immature SCs. We reconstructed the soma and the dendritic tree of two SCs (P14 and P17) loaded with Alexa 594 and biocytin (Figure 3E). Immunogold labeling of biocytin made it possible to identify PSDs along soma and dendrites originating from the labeled SC (Figure 3F). PSD area was constant along somatodendritic axes (Figure 3G), ruling out synaptic scaling as a mechanism for reducing dendrite-evoked qEPSCs. Thus, the difference in qEPSC amplitude and time-course observed between somatic and dendritic synapses in immature SCs is likely due to cable filtering.

Developmental changes in synaptic conductance amplitude, but not time-course

Because cable filtering of synaptic responses is present in both immature and adult SCs, we next explored whether differences in mEPSC amplitude between the two ages were due to the maturation of quantal synaptic conductance. To avoid the effects of cable filtering, we measured qEPSCs only at somatic synapses. In immature SCs, somatic qEPSCs were 41 % larger than those in adult SCs (62.3 ± 3.3 pA, n = 25 vs. 44 ± 2.4 pA, n = 12, Abrahamsson et al., 2012), p < 0.05, Figure 4A and B, but with no difference in the half-width (0.60 ± 0.02 ms, n = 25 vs. 0.59 ± 0.06 ms, n = 12, p > 0.05, Figure 4B) suggesting a reduction in postsynaptic strength but no change in kinetics.

Developmental changes in somatic quantal excitatory postsynaptic current (qEPSC) properties and somatic postsynaptic density (PSD) size.

(A) The left panel shows a diagram of a parasagittal cerebellar slice showing parallel fibers (PFs) projecting perpendicular (blue dots) to the dendritic plane of a stellate cell (SC) (in red), showing the stimulus electrode (top) position above the soma. The right panel shows the superimposition of representative qEPSC averages aligned on event onset, from immature SC (blue trace) and adult SC (green trace, from Abrahamsson et al., 2012). (B) Summary box and whisker plot showing the qEPSC amplitude, 10–90% rise time and half-width for immature (blue, n = 25) and adult (green, n = 12) SC (asterisks denote p < 0.05; p = 6.2e−4, p = 0.0035, and p = 0.074, respectively). (C) Serial electron micrographs of an asymmetrical synapse made by axon terminals on an immature SC soma. Right panel: summary box and whisker plot showing the synapse area obtained from immature (n = 83 synapses from three cells) and adult SC (n = 97 synapses from two cells) somata. Superimposed filled circle represent individual synapses (asterisks denote p < 0.05; p = 1.03e−5). The outer boundaries of an excitatory synapse are indicated by arrows. Scale bar is 100 nm. See Figure 4—source data 1.

Figure 4—source data 1

Developmental changes in somatic qEPSC properties and somatic PSD size.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig4-data1-v2.xlsx

This reduction in synaptic conductance could be due to a reduction in the number of synaptic AMPARs activated and/or a developmental change in AMPAR subunits. SC synaptic AMPARs are composed of GluA2 and GluA3 subunits associated with TARP γ2 and γ7 (Bats et al., 2012; Liu and Cull-Candy, 2000; Soto et al., 2007; Yamazaki et al., 2015). During development, GluR2 subunits are inserted to the synaptic AMPAR in an activity-dependent manner (Liu and Cull-Candy, 2002), affecting receptors calcium permeability (Liu and Cull-Candy, 2000). However, those developmental changes have little impact on AMPAR conductance (Soto et al., 2007), nor do they appear to affect EPSC kinetics (Liu and Cull-Candy, 2002); the latter being consistent with our findings. Therefore, the developmental reduction in postsynaptic strength most likely results from fewer AMPARs activated by the release of glutamate from the fusion of a single vesicle.

To confirm the reduction in synaptic AMPARs during maturation, we compared somatic PSD areas between immature and adult SCs. In immature SCs, mean somatic PSD size was 0.039 ± 0.002 μm2 (n = 83 synapses; Figure 4C), which is 39 % larger than that of adult SCs (0.028 ± 0.0015 μm2, n = 97, p < 0.05; data obtained by the same method from Abrahamsson et al., 2012). The developmental reduction in PSD size is similar to the amplitude reduction of recorded somatic qEPSC and thus will contribute to the developmental reduction in mEPSC. This is supported experimentally by a slower 10–90% rise time in immature SCs compared to adult (0.14 ± 0.02 ms, n = 25 vs. 0.12 ± 0.004, n = 12, p < 0.05; Figure 4B, middle panel), consistent with larger immature synapses (Cathala et al., 2005). However, fewer synaptic AMPARs cannot explain the observed change in mEPSC time-course.

Dendritic distribution of excitatory synaptic inputs changes during maturation

In addition to cable filtering of synaptic currents, a neuron’s somatic response is also determined by the pattern of actived synapses within its dendritic tree (Branco and Häusser, 2011; Tran-Van-Minh et al., 2015). Moreover, the distribution of synaptic contacts within the dendritic tree may not be uniform, as demonstrated for starburst amacrine interneurons (Vlasits et al., 2016) and CA1 pyramidal neurons (Katz et al., 2009; Magee and Cook, 2000). We hypothesized that changes in synapse distribution could underlie the slowing of mean mEPSCs time-course observed adult SCs. In support of this hypothesis, immature SC mEPSC rise and decay kinetics are similar to those of somatic qEPSCs (compare Figure 1 vs. Figure 4), whereas adult SC mEPSC kinetics are closer to those of dendritic qEPSCs (Figure 1 vs. Figure 3). Specifically, in immature SCs the mean mEPSC decay (τdecay = 0.68 ± 0.06 ms) is similar to the qEPSC decay from somatic synapses (half-width = 0.60 ± 0.02 ms; p > 0.05), but significantly different from those of dendritic synapses (half-width = 0.88 ± 0.04 ms; p < 0.05). This suggests that synaptic responses from distal synapses do not participate significantly to the mean mEPSC at this developmental stage. In contrast, the adult mEPSC decay (τdecay = 1.31 ± 0.14 ms) is close to the dendritic qEPSC decay (half-width = 1.14 ± 1.92, p > 0.05), but significantly different from that of somatic synapses (half-width = 0.59 ± 0.06, p < 0.05), estimated previously (Abrahamsson et al., 2012). These results are consistent with mEPSCs in adult SCs arising more often from more distal synapses.

To test this hypothesis, we examined the distribution of excitatory synaptic inputs along the somatodendritic compartment in immature and adult SCs. Since SC dendrites lack spines, we used transgenic mice conditionally expressing Venus-tagged PSD95 to label putative excitatory synapses. We then mapped Venus-tagged PSD95 puncta associated with the somata and dendritic trees of Alexa 594-filled immature and adult SCs (Figure 5A–D), defining puncta located within 200 nm of the dendritic surface (Figure 5E) as excitatory synapses targeting the dendrite (Figure 5F; see Methods). Venus-tagged PSD95 puncta within the soma and dendritic tree of nine immature and eight adult 3D-reconstructed SCs (Figure 5G), showed ~80 % more puncta on adult SCs (582 ± 48 puncta vs. 324 ± 21 in immature SCs, p < 0.05; of which 27 ± 3.2 and 21.17 ± 2.5 are at the soma of immature and adult SCs, respectively). Synapse distribution was assessed by counting the number of PSD95 puncta within 10 µm segments at increasing distance from the soma. In immature SCs, ~ 80 % are within 35 µm of the soma, in contrast to only ~40 % this close to the soma in adult SCs (Figure 6A and B). However, the ratio of detected puncta (Figure 6B) to dendritic segments (Figure 6C) shows that puncta density remained constant across the dendritic tree at both ages (Figure 6D). Thus, the larger number of puncta located further from the soma in adult SCs is not due to increased puncta density with distance, but larger dendritic lengths (Figure 6E and F) and many more distal dendritic branches (Figure 6G, Sholl analysis) due to a larger number of branch points (Figure 6H), but not a "greater number of primary dendrites (Figure 6I). The similarity between the shapes of synapse (Figure 6B) and dendritic segment (Figure 6C) distributions was captured by a similarity in their skewness (0.38 vs. 0.32 for both distributions in immature and −0.10 and −0.08 for adult distributions). These data demonstrate that increased dendritic complexity during SC maturation is responsible for a prominent shift toward distal synapses in adult SCs. Therefore, if mEPSCs were generated from a homogeneous probability of release across all synapses, the bias toward distal synapses in adult SC will generate quantal responses that experience stronger cable filtering.

PSD95 puncta distribution on immature and adult stellate cell (SC).

(A) Maximum intensity projection of merged images showing a P14 (top) and P42 (bottom) SC labeled with Alexa 594 (magenta) and Venus-tagged PSD95 puncta (green). (B) Example of single optical sections from (A). (C) Inset indicated in (B) showing details of a dendritic branch with Venus-tagged PSD95 puncta. (D) Detected PSD95 puncta on the same focal plane overlayed on the fluorescent image. (E) Diagram describing the criteria for assignment of PSD95 puncta to a dendrite branch. Puncta were considered as associated with the dendrite if their centers were located within a search radius (Rsearch) defined as the local dendritic radius rdendrite + α, where α = 0.2 μm. (F) Examples of detected PSD95 puncta connected to dendritic structure in an immature (top) and adult (bottom) SC. (G) Skeleton representation of the dendritic tree of an immature (left) and adult (right) SC with detected PSD95 puncta in green. The edge and the center of the soma are indicated by orange squares and magenta crosses, respectively.

PSD95 puncta distribution and morphological analysis of immature and adult stellate cell (SC).

(A) Cumulative plot showing Venus-tagged PSD95 puncta distributions for nine immature (blue) and eight adult (green) SCs. The bold trace is the population average (shaded region indicates SEM), showing a significant difference between immature and adult distributions (Kolmogorov–Smirnov test p < 0.0001). (B) Superimposed histograms of the mean PSD95 puncta count on the soma and dendrites of immature (blue) and adult (green) SC with a 10 μm increment (Pearson’s median skewness: 0.38 vs. −0.1). (C) Superimposed histograms of the average dendritic segment count (segment length of 100 nm) for immature (blue) and adult (green) SCs (Pearson’s median skewness: 0.32 vs. −0.08). (D) Dendritic mean PSD95 puncta density as a function of distance estimated from B and C (for all p > 0.05 except at 5 μm; MW test). (E) Superimposed histograms of the dendritic segment length (measured as the distance from the tip of dendrites to the soma) for immature (n = 314 dendrites, blue) and adult (n = 854 dendrites, green) SCs. (F) Summary box and whisker plot showing the maximal dendritic length per neuron for immature and adult SCs. Filled circles represent individual cells (asterisks denote p < 0.05; p = 6.0e−4). (G) Sholl analysis showing increased arbor complexity with development. (H) Summary box and whisker plot showing the number of branch points per primary dendrites for immature and adult SCs. Branches were defined as dendritic branches from primary dendrites that were longer than 10 μm. Superimposed filled circle represents individual cells (p = 0.0011). (I) Summary box and whisker plot showing the number of primary dendrites arising from the soma of immature and adult SCs, with filled circles representing individual cells (p = 0.6833). See Figure 6—source data 1.

Figure 6—source data 1

PSD95 puncta distribution and morphological analysis of immature and adult SC.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig6-data1-v2.xlsx

Change in synapse distribution underlies developmental slowing of mEPSC

We next examined whether differences in synapse distribution could account quantitatively for the observed changes in mEPSC amplitude and time-course. We performed numerical simulations using reconstructed immature and adult SCs (Figure 2D and G) and a quantal synaptic conductance (gsyn) that reproduced measured immature and adult qEPSCs induced at somatic synapses (Figure 4). We simulated qEPSCs evoked by synaptic activation at the soma and at 10 µm intervals along the somatodendritic axes (Figure 7A). Assuming that mEPSCs are generated randomly with an equal probability at all synapses, we generated a simulated mean mEPSC by summing the qEPSCs generated at each distance (qEPSCd) each weighted by its relative frequency according to the synapse distribution (Figure 7B, right panel). For each dendritic segment, the obtained weighted qEPSCd describes its relative contribution to the mean mEPSC waveform (Figure 7B, left panel). One can see that qEPSCds arising from distal locations in the adult were relatively larger in amplitude as compared to immature SCs and therefore would contributed more to the simulated mean mEPSC waveform. As an example, the contribution of the weighted qEPSCd at 45 µm to mEPSC was small in immature SC while it was more prominent in adult SC (compare the green traces in Figure 7B). As a result, the mean mEPSC was smaller in adult SCs than in immature SCs (26.0 ± 0.6 pA, n = 9 dendrites vs. 52.2 ± 0.4 pA, n = 7 for Ri 150 Ω.cm) and its time-course had slower rise (10–90% rise time = 0.24 ± 0.05 vs. 0.17 ± 0.01 ms) and decay (half-width = 1.11 ± 0.04 vs. 0.77 ± 0.01 ms; for all p < 0.05). Thus, distally generated qEPSCds dominate the mean mEPSC more strongly in adult than in immature SCs, making the mean mEPSC smaller and slower in adult SCs. Moreover, the simulated mean mEPSCs lay within one standard deviation of measured experimental mEPSC values (Figure 7C and D). Thus, by implementing the experimentally observed synapse distributions in our simulations, we could reproduce the experimental mean mEPSCs. These results demonstrate that developmental increases in dendritic branching complexity, provided that synapse density is homogeneous, can account for the changes in mEPSC kinetics (Figure 1).

The developmental change in synaptic distribution recapitulates the developmental change in miniature excitatory postsynaptic current (mEPSC) properties.

(A) Numerical simulations of somatic and dendritic quantal excitatory postsynaptic currents (qEPSCs) for synapses placed on the soma (red dot) and at 10 μm intervals (blue dots) along seven of the longest dendrites of a reconstructed immature (P16; top) and nine of the longest dendrites of a reconstructed adult (P42; bottom) stellate cell (SC) (with Cm = 0.9 pF/cm2, Rm = 20,000 Ω.cm2, and Ri = 150 ± 50 Ωcm). The right panel shows an example of qEPSC for somatic (magenta) and dendritic (gray traces) synapses along a single dendrite labeled with an asterisk. The synaptic quantal conductances gsyn were set to reproduce the experimental qEPSCs when stimulating somatic synapses at both ages. (B) Shows simulated qEPSCs elicited at different distances from the soma that are weighted by the relative frequency of the synapse distances (qEPSCds) extracted from the histogram to the right (derived from Figure 6B). For clarity, only a subset of normalized qEPSCds are displayed with a different color for somatic and each 10 μm dendritic segments and correspond to solid bars in the histogram. (C) Superimposed mEPSC waveforms obtained from the weighted average of qEPSC for each dendrite (gray) and the corresponding averaged simulated mEPSC (bold) for the immature (P17, blue trace) and adult (P44, green trace) SC reconstructions. Inset: traces normalized to their peak. (D) Summary box and whisker plots showing the median (line) peak amplitude, 10–90% rise time and decay, the 25th and 75th percentile (box), range (whiskers), and mean (+) of the sim mEPSC for the immature (blue) and adult (green) SCs. Individual dendritic mEPSCs are illustrated with filled circles (asterisks denote p < 0.05; with p = 0.0002, p < 0.0001, and p = 0.0002, respectively). The dotted line shows the experimental mEPSC average values ±1 SD (shaded region). See Figure 7—source data 1.

Figure 7—source data 1

The developmental change in synaptic distribution recapitulates the developmental change in mEPSC properties.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig7-data1-v2.xlsx

Influence of synapse distribution on dendritic integration of multiple synaptic inputs

We previously showed in adult SCs that the activation of dendritic synapses conveys sublinear integration as compared to the soma (Abrahamsson et al., 2012) due to the large local input resistance of thin dendrites resulting in synaptic depolarizations that reduced synaptic current driving force (Bloomfield et al., 1987; Rall, 1967). This also produced a distance-dependent decrease in short-term plasticity (STP). We examined whether dendritic integration in immature SCs is also sublinear by comparing STP following dendritic and somatic synapse activation. PFs targeting SC somata and dendrites were stimulated using a pair of extracellular voltage pulses with an interval of 20 ms. The paired-pulse ratio (PPR; the ratio of the amplitudes of the second vs. the first EPSC) was 2.1 ± 0.1 for somatic synapses (n = 10) and decreased to 1.8 ± 0.1 for distal dendritic synapses (n = 15; p < 0.05; Figure 8A), consistent with sublinear integration. These results were reproduced by numerical simulations of evoked EPSCs (Figure 8B) or EPSPs (data not shown) in the idealized passive SC model (with a synaptic gsyn matching the recorded EPSC evoked at the soma and a 2.25 conductance ratio). These findings show that immature SC dendrites also display an STP gradient, suggesting that they are capable of sublinear integration.

Location dependence of short-term plasticity and sublinear behavior in immature stellate cell (SC).

(A) Single-trial excitatory postsynaptic current responses (eEPSCs, gray) and their corresponding average (bold) evoked by a pair of electrical stimuli (at 50 Hz).The stimulation electrode was placed above the soma (magenta) or on the distal portion of an isolated dendrite (gray). Right: summary box and whisker plot of paired-pulse ratio of eEPSC amplitudes (paired-pulse ratio [PPR] = eEPSC2/eEPSC1) for somatic synapses (n = 10) and dendritic synapses (n = 15). The PPR ratio was assessed from recordings where the first EPSC had an amplitude inferior to 400 pA and had a failure rate below 30 % (for somatic synapses, Amp = 239.4 ± 32 pA with a half-width = 0.88 ± 0.07 ms, n = 10). Superimposed filled circles represent individual cells (asterisks denote p < 0.05; p = 1.82e−2). (B) Numerical simulations of paired-pulse facilitation of eEPSCs using the idealized SC model (Figure 2C). Simulated eEPSCs for somatic (magenta) and dendritic (gray) synapses are superimposed, showing a lower dendrite PPR. To simulate eEPSC1, the gsyn from Figure 2C was scaled to match the mean recorded eEPSC amplitude. The simulation of eEPSC2 was further scaled by a factor of 2.25. Right: summary plot showing the simulated PPR at the soma (magenta cross) and the lack of influence of a variable number of dendritic branches (gray crosses). Shaded region indicates simulations with a Ri of 100–200 Ωcm. (C) Simulated evoked EPSP (sim eEPSP) under current-clamp conditions for synapses at the soma (magenta) and at 20 or 60 μm on dendrites (gray) of a reconstructed immature SC. The gsyn peak amplitude was set to simulate 5 or 20 quanta. (D) Subthreshold input–output relationship obtained by plotting the average peak sim eEPSP amplitude for an increasing number of quanta versus the algebraic sum of the sim eEPSPs for a reconstructed immature (left; from Figure 7A) and adult (right; from Figure 7A) SC. The dotted black line has a slope of 1. The circles indicate sim eEPSP resulting from a gsyn peak amplitude of 1, 5, 10, and 20 quanta. (E) Summary plot showing dendritic sublinearity (1 − (dendritic EPSP amp/soma EPSP amp) with EPSP converted to number of quanta) as a function of the number of quanta for reconstructed immature (blue) and adult (green) SCs. See Figure 8—source data 1.

Figure 8—source data 1

Location dependence of short-term plasticity and sublinear behavior in immature SC.

https://cdn.elifesciences.org/articles/65954/elife-65954-fig8-data1-v2.xlsx

To address this possibility, we used biophysical modeling of subthreshold synaptic input–output relationships (I/O) that has been shown to accurately reproduce experimental sublinear I/Os recorded after neurotransmitter photouncaging (Abrahamsson et al., 2012). Evoked EPSPs were simulated (sim eEPSP) in response to increasing synaptic conductance (gsyn), equivalent to one to 20 quanta in order to encompass sparse and clustered activation of PFs (Wilms and Häusser, 2015) at the soma and at 10 µm intervals along the reconstructed dendrites of the immature SC (Figure 8C). I/O plots showed that sim eEPSPs in the soma and dendrites were less than the linear sum of eEPSPs (dashed line). This sublinear summation was apparent for dendritic eEPSPs generated from synaptic conductances equivalent to one quantum for dendritic synapses and became more pronounced for distal synapses (Figure 8D). The sublinearity increased with increasing number of simultaneously activated quanta. These simulations show that immature dendrites demonstrate sublinear summation, supporting experimental difference between somatic and dendritic STP (Figure 8A).

To estimate the impact of development changes in synapse distribution on the maturation of SC computations, we compared simulated subthreshold I/Os between immature and adult SCs with their respective age-dependant gsyn (Figure 8D). For immature SCs we examined subthreshold I/Os when activating synapses at 15 µm along reconstructed dendrites (Figure 8D, green), a distance with the highest relative number of synaptic contacts (Figure 7B), and compared to I/Os generated from synapse activation at 45 µm in adult SCs (distance with the largest relative number of synapses). Sublinearity was quantified by normalizing sim eEPSPs to an sim eEPSP evoked by injecting a gsyn of 0.1 quanta, a conductance to which the voltage is linearly related. Because large gsyn can generate sublinear integration at the soma (Figure 8D), we estimated the dendrite-specific sublinearity for each gsyn by taking the ratio between the relative sim eEPSPs amplitude (normalized to quanta) at a given distance and the normalized sim eEPSP amplitude for somatic synapses. The final estimate of dendritic sublinearity was then defined as one minus this ratio (Figure 8E). While both immature and adult SCs exhibited sublinear integration, dendritic sublinearity was larger in adult SCs for all postsynaptic strengths, supporting an increased difference between the two layers of integration (i.e., soma vs. dendrite). The smaller difference in sublinearity between soma and dendrite in the immature SC resulted from both fewer distal synapses and the larger gsyn. Thus, the developmental increase in dendritic field complexity and decreased postsynaptic strength together contribute to the establishment of a two-stage integration model and provide a cellular substrate for a developmental increase in computational power of SCs.

Discussion

Dendritic integration of synaptic inputs is a critical component of neuronal information processing, but little is known about how it matures during neuronal network formation and maturation. We took advantage of the late development of the cerebellar cortex to characterize developmental changes in synaptic and dendritic properties. By combining patch-clamp recording of cerebellar SC interneurons, 3D reconstructions of their aspiny dendrites, along with the identification of excitatory synapse locations, and numerical simulations, we showed for the first time how the maturation of synapse distribution within interneurons combines with changes in postsynaptic strength and increased dendritic branching to shape the development of neuronal computation. This maturation process favors the emergence of a compartmentalized two-stage integrator model, which extends the repertoire of transformations of synaptic inputs into neuronal output in adult SCs. These results highlight the importance of characterizing not only dendritic morphology, but also synapse placement and synaptic strength, in order to correctly infer a neuron’s computational rules.

Comparison of synaptic properties between immature and adult SCs

Whole-cell voltage-clamp recordings in immature and adult SCs showed a developmental decrease in mEPSC amplitude and slowed kinetics (Figure 1). These results contrast with findings in other neurons that show faster mEPSCs during maturation due to changes of AMPAR subunits, vesicular content of neurotransmitter, and/or synapse structure (Cathala et al., 2005; Chen and Regehr, 2000; Koike-Tani et al., 2005; Yamashita et al., 2003). Knowing that dendritic inputs could be electrotonically filtered, we took advantage of the ability to selectively stimulate somatic synapses to isolate somatic qEPSCs for comparison between the two ages. Evoked qEPSCs showed a developmental reduction in amplitude (~40%), with no change in kinetics (Figure 4). This likely results from the smaller adult PSD size (Figure 4), and hence a lower AMPAR number (Masugi-Tokita et al., 2007), rather than a developmental reduction in the peak glutamate concentration at the postsynaptic AMPARs (Figure 1) or change in AMPAR subunits composition (see Results).

A defining feature of immature SCs is the high propensity of quantal EPSPs to generate spikes (Carter and Regehr, 2002). However, the observed developmental decrease in synaptic conductance (Figure 4) and increased filtering of mEPSCs (Figures 1 and 3) will tend to reduce the influence of single synaptic inputs on somatic voltage in adult SCs, increasing their dynamic range of subthreshold integration. Therefore, individual synaptic inputs are less likely to influence adult SC neuronal output. Moreover, since PSD area is constant along the somatodendritic axis (Figure 3), the observed developmental reduction in synaptic conductance can be extrapolated to the whole dendritic tree. Thus, there is no evidence of synaptic conductance scaling mechanisms that offset dendritic filtering, as described for pyramidal neurons (Katz et al., 2009; Magee and Cook, 2000; Menon et al., 2013; Nicholson et al., 2006). As a result, SCs show a strong dependence of somatic voltage responses on synapse location within the dendritic arbor.

Implications of developmental alterations of dendritic morphology

We showed that soon after SC integration into the cerebellar molecular layer microcircuit, their dendrites are nearly the same diameter as adult SCs (Figure 2), suggesting a similar capacity for cable filtering of synaptic responses. Since previous studies suggested that SCs were electronically compact (Carter and Regehr, 2002; Llano and Gerschenfeld, 1993), the observation that mEPSCs from adult SCs were slower led us to consider changes in cable filtering as the underlying mechanism for the developmental change in mEPSC kinetics. However, combining experiments and simulation we showed that immature SCs have similar dendritic integration properties as adult SCs (Abrahamsson et al., 2012): (1) dendrite-evoked qEPSCs are smaller and slower than those evoked at the soma (Figure 3), consistent with cable filtering (Figure 2); (2) STP differed between dendrite and somatic stimulation and did not reveal supralinear recruitment of voltage-gated channels (Nevian et al., 2007; Figure 8); and (3) simulated subthreshold I/Os were sublinear (Figure 8). These results are supported mechanistically by several experimental findings such as (1) the measured membrane time constants that are identical at the two developmental stages, (2) the dendritic diameters being similarly small (Figure 2), and (3) the paired-pulse facilitation difference between dendrite and soma which is reproducible with a passive biophysical model like in adult SCs (Figure 8). Taken together, the data are consistent with SCs displaying little developmental changes in voltage-gated channel expression (Molineux et al., 2005) and low densities of sodium (Myoga et al., 2009) and calcium channels (Tran-Van-Minh et al., 2016).

The integration of fast synaptic events such as AMPAR-mediated EPSC is critically influenced by the intracellular resistivity, the local membrane capacitance, and dendritic diameter (Equation 2). Since internal resistivity is a challenge to estimate without dual electrode recordings, we examine a wide range of internal resistivities and found that they did not alter qualitatively our conclusions. The narrow dendritic diameters at both ages (Figure 2B) suggest that the local input resistance is only slightly different. We cannot rule out that developmental changes in gap junction expression could contribute to the maturation of SC dendritic integration, since they are thought to contribute to the axial resistivity and capacitance of neurons (Szoboszlay et al., 2016). All the recordings were made with gap junctions intact, including for membrane time constant measurements. However, their expression in SCs is likely to be lower than their basket cell counterparts (Hoehne et al., 2020; Rieubland et al., 2014). Overall, the basic electrotonic machinery to filter synaptic responses is already present as soon as SC precursors reach their final location in the outer third of the molecular layer.

However, the developmental differences between mEPSCs, which presumably reflect synaptic responses from the entire dendritic tree, suggested that another factor must be contributing to the difference in apparent electrotonic filtering. Previous studies have shown that synapses are not uniformly distributed along dendrites, allowing pyramidal neuron dendrites to operate as independent computational units (Katz et al., 2009; Menon et al., 2013; Polsky et al., 2004), and retinal starburst amacrine interneurons to compute motion direction (Vlasits et al., 2016). We considered the possibility that the distribution of synapse locations within the dendritic tree was altered during SC maturation. We found that synapses were uniformly distributed along the somatodendritic axis with a similar density at the two ages. However, adult SCs had more synapses located at further electrotonic distances (~2/3 vs. 1/3 of synapses were more than 30 μm from the soma; Figures 6 and 7) due to increased dendritic branching. Thus, the distal-weighted synaptic distribution in adult SCs favors inputs that experience stronger cable filtering. This was confirmed by simulating a mean mEPSC at the two ages, that fully reproduce the mEPSC recorded experimentally (Figure 1), by weighting simulated qEPSCs according to the relative number of synapses at specific distances along the dendrites (Figure 7). Indeed, the large fraction of distal synapses in adult SCs was sufficient to account for the observed developmental difference in mEPSC amplitude and time-course. Moreover, since the space constant does not change significantly with development and the dendritic tree complexity increases, the number of computational segments is expected to increase with age.

Developmental changes in computational rules

Our findings highlight the critical importance of understanding both the structural and functional mechanisms underlying developmental refinement of synaptic integration that drives a neuron’s computational properties and, the emergence of mature microcircuit function. While a defining feature of immature SCs is the high propensity of quantal EPSPs to generate spikes (Carter and Regehr, 2002), the observed developmental decrease in synaptic conductance (Figure 4) and increased filtering of mEPSCs (Figures 2 and 3) will tend to reduce the influence of single synaptic inputs on somatic voltage in adult SCs, increasing their dynamic range of subthreshold integration. Although dendrites in immature and adult SCs exhibit similar electrotonic filtering, the distal bias in synapse location promotes sublinear subthreshold dendritic integration in adult SCs. Unlike pyramidal neurons, where synapse strength and density are scaled to normalize the contribution of individual inputs to neuronal output (Katz et al., 2009; Magee and Cook, 2000; Menon et al., 2013), the spatially uniform distribution of synapse strength and density in SCs do not compensate the electrotonic filtering effects of the dendrites or the increased number of distal synapses due to branching.

These properties of quantal synaptic responses, together with the larger difference in sublinearity between soma and dendrites (Figure 8D), will favor the emergence of a spatially compartmentalized two-stage integration model in adult SCs, thereby promoting location-dependent integration within dendritic subunits (Polsky et al., 2004) and enhanced neuronal computations (Cazé et al., 2013). In immature SCs, the repertoire of computations is more similar to a simple single-stage integration model where large and fast synaptic potentials will promote reliable and precise EPSP–spike coupling (Cathala et al., 2003; Fricker and Miles, 2001; Hu et al., 2010), which may be critical for driving the functional maturation of the local microcircuit (Akgül and McBain, 2020). In contrast, synaptic integration and summation in adult SCs can obey different rules depending on synapse location within the dendritic tree enabling to discriminate a larger number of spatial patterns of synaptic activation (Tran-Van-Minh et al., 2015) and therefore favor spatially sparse synaptic representations (Abrahamsson et al., 2012; Cazé et al., 2013) that might be essential for the development of enhanced pattern separation by Purkinje cells (Cayco-Gajic et al., 2017). Since a recent theoretical study showed that sublinear integration is also a property of hippocampal fast-spiking interneurons (Tzilivaki et al., 2019) that influence memory storage, it will also be important to determine if these interneurons exhibit a similar maturation of their neuronal computation.

Implications for neurodevelopmental and neurological disorders

The increasing complexity of dendritic arbors, accompanying changes in synaptic connectivity and function during development is not limited to the cerebellum. These maturational processes are altered in neurodevelopmental disorders, such as mental retardation (Kaufmann and Moser, 2000), autism spectrum disorders (Antoine et al., 2019; Peng et al., 2016), or Rett syndrome (Blackman et al., 2012; Ip et al., 2018), as well as in neurodegenerative disease. Indeed, these developmental processes are particularly relevant for interneurons since they play a pivotal role in the establishment of the correct excitation/inhibition balance for normal circuit function. During development, inhibitory interneurons are essential for defining critical periods (Gu et al., 2016; Hensch et al., 1998) or direction selectivity in the retina (Vlasits et al., 2016; Wei et al., 2011), so that interneuron dysfunction is associated with neurodevelopment disorders (Akerman and Cline, 2007; Le Magueresse and Monyer, 2013; Marín, 2016). Our work demonstrates how developmental changes in neuronal morphology, and synapse distribution and strength, combine to determine the impact of synaptic inputs on neuronal output. Our findings provide a functional template of how dendritic integration matures throughout development to enrich interneurons with more complex neuronal computations, promoting location-dependent integration within dendritic subunits.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus – male and female)CB6F1Mouse Genome InformaticsRRID:MGI:5649749
Strain, strain background (Mus musculus – male and female)PSD-95-CreNABLED miceHaining Zhong, Vollum InstituteDOI: https://doi.org/10.1523/JNEUROSCI.3888-14.2014
Strain, strain background (Mus musculus – male and female)B6.129-Nos1tm1(cre)Mgmj/JMouse Genome Informaticshttps://www.jax.org/strain/017526
Chemical compound, drugGabazine (SR-95531)AbcamCat#:ab120042
Chemical compound, drugQX-314AbcamCat#:ab120118
Chemical compound, drug7-Chlorokynurenic acidAbcamCat#:ab120255
Chemical compound, drugAlexa Fluor 488ThermoFisher ScientificCat#:A10436
Chemical compound, drugAlexa Fluor 594ThermoFisher ScientificCat#:A10438
Chemical compound, drugD-AP5AbcamCat#:ab120003
Chemical compound, drugGamma DGGAbcamCat#:ab120307
Chemical compound, drugTTXAbcamCat#:Ab120055
Chemical compound, drugStrychnineSigmaCat#:S0532
Software, algorithmReconstructJC FialaDOI: 10.1111 /j.13652818.2005.01466 .x
Software, algorithmIgor ProWavemetricsRRID:SCR_000325
Software, algorithmNeuromaticRothman and Silver, 2018; DOI:10.3389RRID:SCR_004186
Software, algorithmGraphPad Prism 6GraphPad SoftwareRRID:SCR_002798
Software, algorithmImageJNational Institutes of HealthRRID:SCR_003070
Software, algorithmImarisBitplane Imaris SoftwareRRID:SCR_007370
Software, algorithmZeiss AtlasZeisshttps://www.zeiss.com/microscopy/int/products/microscope-software/atlas.html

Slice preparation and electrophysiology

Request a detailed protocol

Acute cerebellar parasagittal slices (250 or 200 µm thick, respectively) were prepared from immature (postnatal day P13–19) and adult (P35–57) mice (F1 of BalbC and C57B6 or C57BL/6J) as described previously (Abrahamsson et al., 2012). Animal housing and all procedures were approved by the Comité National d’Ethique pour les Sciences de la Vie et de la Santé (Comité Charles Darwin, authorisation no. 1492-02) and the Ethics Committee #89 of Institut Pasteur (CETEA; protocol approval #DHA180006). Briefly, mice were killed by decapitation, the brains rapidly removed and placed in an ice-cold solution containing (in mM): 2.5 KCl, 0.5 CaCl2, 4 MgCl2, 1.25 NaH2PO4, 24 NaHCO3, 25 glucose, and 230 sucrose, bubbled with 95% O2 and 5% CO2. Slices were cut from the dissected cerebellar vermis using a vibratome (Leica VT 1000S or VT1200S), incubated at 32°C for 30 min in the following solution (in mM): 85 NaCl, 2.5 KCl, 0.5 CaCl2, 4 MgCl2, 1.25 NaH2PO4, 24 NaHCO3, 25 glucose, and 75 sucrose and subsequently maintained at room temperature for up to 8hr in the recording solution containing (in mM): 125 NaCl, 2.5 KCl, 2 CaCl2, 1 MgCl2, 1.25 NaH2PO4, 25 NaHCO3, and 25 glucose. Unless otherwise noted, this solution included during patch recordings 10μM SR-95531, 10μM D-AP5, 20μM 7-chlorokynurenic acid, and 0.3μM strychnine, to block GABAA, NMDA, and glycine receptors, respectively.

Whole-cell patch-clamp recordings were made from SCs located in the outer one-third at molecular layer at temperatures ranging from 33 to 36°C using an Axopatch-200A or a Multiclamp 700 amplifier (Axon Instruments, Foster City, CA, USA) with fire-polished thick-walled glass patch-electrodes (tip resistances of 6–8 MΩ) that were backfilled with a solution containing (in mM): 117 K-MeSO4, 40 HEPES, 6 NaOH, 5 EGTA, 1.78 CaCl2, 4 MgCl2, 1 QX-314-Cl, 0.3 NaGTP, 4 NaATP, and, when applied 0.03 Alexa 594, adjusted to ~300 mΩ and pH 7.3. Series resistance and capacitance measures were determined directly from the amplifier settings.

All EPSCs were recorded at −70 mV holding membrane potential (not corrected for LJP ~ +6 mV), were filtered at 10 kHz, and digitized at 100 kHz using an analog-to-digital converter (model NI USB 6259, National Instruments, Austin, TX, USA) and acquired with Nclamp (Rothman and Silver, 2018) within the Igor Pro 6.2 environment, WaveMetrics. Series resistance was on average of 16 mΩ, uncompensated, and monitored throughout the experiment. Recordings were discarded if series resistance exceeded 20 mΩ. To evoke EPSC, PFs were stimulated with a glass patch electrode filled with external recording solution that was placed close to a fluorescently labeled dendrite or close to the soma. 50 μs pulses between 5 and 55 V (Digitimer Ltd, Letchworth Garden City, UK) were delivered as described previously (Abrahamsson et al., 2012). Somatic and dendritic quantal EPSCs were obtained from experiments where [Ca2+] was lowered to 0.5 mM while [Mg2+] was increased to 5 mM to obtain an evoked EPSC success rate <10% known to produce a qEPSC with a <10% amplitude error (Silver, 2003). Trials with a synaptic event could be clearly selected by eye. The stimulation artifact was removed by subtracting from single success traces the average obtained for the traces with failed synaptic transmission.

Current-clamp recordings were performed using a Multiclamp 700 amplifier. Patch electrodes were coated with dental wax and series resistance was compensated by balancing the bridge and compensating pipette capacitance. Current was injected to maintain a holding membrane potential near −70 mV. Data were filtered at 10 kHz, and digitized at 100 kHz.

D-AP5, 7-chlorokynurenic acid, γDGG, QX-314 chloride, SR 95531, and tetrodotoxin were purchased from Ascent Scientific (http://www.ascentscientific.com). Alexa 594 and Alexa 488 were purchased from Invitrogen (https://www.thermofisher.com/invitrogen). All other drugs and chemicals were purchased from Sigma-Aldrich (https://www.sigmaaldrich.com).

Multicompartmental biophysical modeling

Request a detailed protocol

Passive cable simulations of EPSC and EPSP propagation within idealized and reconstructed SC models were performed using Neuron 7.1, 7.2, and 7.5 (Hines and Carnevale, 1997). The idealized SC model had a soma diameter of 9 μm and three 90 μm long dendrites of 0.47 μm diameter, with either one, three, or five branches. An immature (P16) and adult SC (P42) were patch loaded with 30 µM Alexa 594 in the pipette and imaged using 2PLSM. Both cells were reconstructed in 3D using NeuronStudio in a semiautomatic mode which uses a robust subpixel estimation algorithm (calculation of Rayburst diameter [Rodriguez et al., 2008]). We manually curated the diameters to verify that it matched the fluorescence image to faithfully account for all variations in diameter throughout the dendritic tree. The measured diameter across the entire dendritic tree of the reconstructed immature and adult SCs was 0.42 and 0.36 µm, respectively. The 16 % smaller diameter in adult was similar to the 13 % obtained from confocal image analysis from many SCs (see Figure 2B).

The 3D reconstruction was then imported into NEURON. Passive properties were assumed uniform across the cell. Specific membrane capacitance (Cm) was set to 0.9 µF/cm2. Rm was set at 20,000 Ω.cm2 to produce in the SC models a membrane time constant matching the one measured experimentally: 19 ± 2.2 ms for immature SCs (n = 16) and 17 ± 2.7 ms for adult SC (n = 10). Ri was set to 150.Ω cm to match the filtering of EPSC decay in the dendrites of mature SC (Abrahamsson et al., 2012) and allowed to vary from 100 to 200 Ω cm to sample a large range of physiological Ri since its physiological value is not known. The peak and kinetics of the AMPAR-mediated synaptic conductance waveforms (gsyn) were set to simulate qEPSCs that matched the amplitude and kinetics of experimental somatic quantal EPSCs and evoked EPSCs. Immature quantal gsyn had an peak amplitude of 0.00175 μS, a 10–90% RT of 0.0748 ms and a half-width of 0.36 ms (NEURON synaptic conductance parameter Tau0 = 0.073 ms, Tau1 = 0.26 ms, and Gmax = 0.004 μS) while mature quantal gsyn had an peak amplitude of 0.00133 μS, a 10–90% RT of 0.072 ms and a half-width of 0.341 ms (NEURON synaptic conductance parameters Tau0 = 0.072 ms, Tau1 = 0.24 ms, and Gmax = 0.0032 μS). For all simulations, the reversal potential was set to 0 mV and the holing membrane potential was set at −70 mV. Experimental somatic PPR for EPSCs was reproduced with a gsyn2/gsyn1 of 2.25.

Electron microscopy and 3D reconstructions

Request a detailed protocol

Electron microscopy and 3D reconstructions of two SCs from acute slices (postnatal days 14 and 17) were performed as described previously (Abrahamsson et al., 2012). Slices containing SCs whole-cell patched with a K-MeSO3-based internal solution containing biocytin (0.3%) and Alexa 594 (30 μM) were transferred to a fixative containing paraformaldehyde (2.5%), glutaraldehyde (1.25%), and picric acid (0.2%) in phosphate buffer (PB, 0.1 M, pH = 7.3), and fixed overnight at room temperature. After washing in PB, slices were transferred to sucrose solutions (15 % for 30 min, then stored in 30%) for cryoprotection and frozen in liquid nitrogen, then subsequently thawed. The freeze–thaw cycle was repeated twice, then followed by incubation with a 1.4 nm gold-conjugated streptavidin (Nanoprobe, 1:100 in Tris-buffered saline [TBS] and 0.03 % Triton X100). After washing in TBS and dH2O, slices were treated with HQ silver enhancement kit (Nanoprobe) for 5 min, fixed in 1 % OsO4 in PB for 30 min, and block stained with 1 % uranyl acetate for 40 min. After dehydration through a series of ethanol solutions (50%, 70%, 80%, 90%, 95%, 99%, and 100%) and propylene oxide twice for 10 min, slices were embedded into Durcupan (Fluka) and flat embedded. The labeled SCs were trimmed and 300–400 serial ultrathin sections were cut at 70 nm using ATUMtome (RMC Boeckeler). Serial sections containing immunogold labeled profiles were imaged with a scanning electron microscope (Merlin Compact, Zeiss) and Zeiss Atlas package at ×22,000 for whole-cell reconstruction and at ×55,000 for synapses. In order to compare synapse area measurement comparable to those done on adult soma (Abrahamsson et al., 2012), we performed serial sections and then standard transmission electron microscopy (TEM) on three unlabeled SCs (which avoids potential distortions due to the patching). These sections were cut at 70 nm using an ultramicrotome (Leica EM UC7), observed with a transmission electron microscope (Tecnai 12, FEI), and photographed at ×21,000. Asymmetrical synapses made by axon terminals onto SC somata and dendrites were analyzed only if they were fully present within the serial sections. The PSD length of the asymmetrical synaptic membrane specialization was measured on each ultrathin section, and the PSD area was calculated by multiplying the summed synaptic length from each synapse with the thickness (70 nm) of the ultrathin sections. The 3D reconstruction of the two SC soma and parts of their dendritic trees was performed using the software Reconstruct (JC Fiala). The distances from each synapse to the soma were measured along the dendrites in the reconstructed volume. In order to compare PSD area to those in adult SC, we only considered those measurements using the same TEM method (Abrahamsson et al., 2012).

Transmitted light and fluorescence imaging

Request a detailed protocol

SC somata in the outer one-third of the molecular layer were identified and whole-cell patched using infrared Dodt contrast (Luigs and Neumann). LED illumination or in some cases two-photon excitation, coupled with the Dodt contrast, were used to visualize Alexa 594-filled SCs and position extracellular stimulating electrodes along isolated dendrites of SCs fluorescence. Two-photon excitation was performed with a pulsed Ti:Sapphire laser (MaiTai DeepSee, Spectra Physics) tuned to 810 nm and images were acquired with an Ultima two-photon laser scanning microscope system (Bruker) mounted on an Olympus BX61WI microscope equipped with a ×60 (1.1 NA) water-immersion objective. LED excitation (470 nm) was performed with a CAIRN LED module (optoLED) and wide-field fluorescence images were acquired with a CCD camera (QIclick, QImaging) mounted on an Olympus BX51 microscope equipped with a ×60 (1 NA) water-immersion objective.

One-photon confocal laser scanning fluorescence microscopy was performed with an Ultima scan head mounted on a Nikon EFN microscope. SCs were filled with 40 μM Alexa 488. Maximal intensity projections of confocal images were performed using a ×100 1.1 NA Nikon dipping objective in 0.2 μm increments as described previously (Abrahamsson et al., 2012). Dendritic diameters were estimated from the FWHM of pixel intensity line profiles on 1 µm segments of dendrites, made perpendicular to dendritic length using the same procedure as for adult SCs (Abrahamsson et al., 2012). In brief, we selected dendritic segments for measurement throughout the dendritic tree to homogeneously sample the diversity of diameters. We collected 430 line profiles from 18 immature SCs, then averaged the profiles from the same dendritic segments (n = 93). The imaging resolution within the molecular layer was estimated from the width of intensity line profiles of SC axons. The FWHM was 0.30 ± 0.01 µm (n = 57 measurements over 16 axons) and a mean of 0.27 ± 0.01 µm (n = 16) when taking into account the thinnest section for each axon. Only 2 % of all dendritic measurements are <270 nm, suggesting that the dendritic diameter estimation is hardly affected by the resolution of our microscope.

2P imaging: The two-photon scanning microscope (2PLSM, Ultima IV, Bruker) was equipped with a Ti:Sapphire Laser (Chameleon II, Coherent Inc) at 940 nm (SC body, Alexa 594) and 810 nm (Venus-tagged PSD95 puncta) using a ×60 water-immersion objective (LUMFL N, 1.10 NA, Olympus). The point spread function of the microscope was estimated from the FWHM value of the x-, y-, and z-intensity line profiles from 3D images of 200 nm yellow-green fluorescent latex beads (FluoSpheres, F8811, Thermo Fisher Scientific): PSF810x/y = 325 ± 27 (SD) nm, PSF810z = 1178 ± 121 (SD) nm, and PSF940x/y = 390 ± 23 (SD), and PSF940z = 1412 ± 141 (SD) nm.

3D reconstructions and puncta detection from 2P images

Request a detailed protocol

We examined the distribution of excitatory synaptic inputs along the somatodendritic compartment in SCs from a transgenic mouse line that conditionally expresses Venus-tagged PSD95 under the control of the nitric oxide synthase one promoter (PSD95-Enabled [Fortin et al., 2014] × Nos1 Cre [Kim et al., 2014]). We patch-loaded single SCs with the fluorescence indicator Alexa 594, then performed live two-color 2PLSM to identify Venus-tagged PSD95 puncta associated with the labeled somata and dendritic trees. Z-Stacks were acquired for each wavelength with a z-step of 300 nm, a pixel size of 154 nm, and an image size of 512 × 512 pixels. To correct for a shift in the focal point for the different wavelengths and a potential drift in x/y-directions, the individual stacks were registered to each other using as a reference the dendritic structure imaged using Alexa 594 emission, which is primarily excited at 810 nm, but weakly excited at 940 nm which allows to record both the puncta and the cell body simultaneously. The registration was performed using the IMARIS stitcher tool. Fluorescence emission was spectrally separated from laser excitation using a custom multipass dichroic (zt 405/473–488/nir-trans; Chroma) and a short pass IR blocking filter (ET680sp-2p8; Chroma). Venus and Alexa 594 fluorescence emission were spectrally separated and detected using detection filter cubes consisting of a long-pass dichroic (575dcxr; Chroma) and two bandpass filters (HQ525/70 m-2p and HQ607/45 m-2p, respectively; Chroma). A multialkali (R3896, Hamamatsu, Japan) photomultiplier tubes was used to detect Alexa 594 fluorescence and gallium arsenide phosphide tube (H7422PA-40 SEL, Hamamatsu) for the Venus channel. Proximal and substage detection were used to increase signal to noise.

Dendrite tracing: The image analysis software IMARIS 9.5 (Bitplane) was used for dendritic tracing and fluorescence puncta detection. Fluorescence images were filtered using a 3 × 3 px median filter to remove noise. Image stacks were then further combined using the IMARIS stitcher tool to create one contiguous file that permits tracing of the entire dendritic tree. Dendrites, but not axons, were traced in IMARIS using the filament tool in a semiautomatic mode using the AutoPath method and the options AutoCenter and AutoDiameter activated. The soma center was chosen as the starting point with centripetally tracing along dendrites. Dendritic lengths were estimated as the dendritic path distance from the center of the soma and Sholl analysis was performed with IMARIS from all reconstructed SC.

PSD-95 Venus puncta detection: Fluorescence puncta were detected using the IMARIS spot creation tool, using background fluorescence subtraction to compensate for different levels of fluorescent intensity along the z-axis of the stack. The initial minimal spot search size was set to 300 × 300 × 1100 nm, slightly smaller than the PSF at 810 nm. No further thresholds or criteria were applied inside IMARIS. Parameters describing the fluorescent puncta, including the intensity at the center, their spatial coordinates, and their diameters, were exported as excel files via the Statistics tab and further analyzed using custom python scripts. In order to separate the PSD95 puncta from false detection of noise, two threshold criteria were applied. (1) All spots with a diameter smaller than and equal to the PSF (300 × 300 × 1100 nm) were rejected. (2) Only spots with a peak intensity larger than the mean of the background intensity plus three times its standard deviation of the background noise, were considered for subsequent analysis. In combination, the thresholds ensure that the spots originated from fluorescent puncta and not false positives generated from noise fluctuations. The background intensity and its corresponding standard deviation were measured for each file in Fiji, by selecting regions without puncta and using the Measure tool to calculate the mean and standard deviation. In order to ensure consistent sampling of the background, mean and noise estimates were made from at least ten different regions at different z positions in each stack, corresponding to an area between 17–27 μm2 (790–1210 pixels) and 10–40 μm2 (430–1850 pixels), for the immature and adult SCs, respectively. This approach is limited by the resolution of 2P fluorescence imaging to differentiate individual synapses within clusters and thus may result in an underestimate of absolute synapse density, but allowed for an unbiased estimate of synapse distributions at the two developmental stages.

Puncta located on somata were selected from the total pool of detected puncta (described above) using the following criteria: (1) spots were associated with the soma if the peak intensity at the position of the spot center in the Alexa 594 channel was larger than half the maximum of the whole stack, (2) detected spot diameters were greater than the size PSF (300 × 300 × 1100 nm), and (3) spot intensities were larger than the mean plus 3*SD of background intensity of the PSD95-Venus channel, measured from within the soma. Puncta from somata that showed saturation in the Alexa 594 channel were not included in the analysis.

Analysis of spot and dendrite distances: The structure of the dendritic tree, as well as the position of the puncta, were further analyzed using custom python scripts (Rückerl F.2021. DevNonlinSynIntCIN. Github. https://github.com/Unit-of-Synapse-and-Circuit-Dynamics/DevNonlinSynIntCIN (Rückerl, 2021) copy archived at swh:1:rev:04b0c50f05a67a008e52c515b0fb21bad1e2f5e5). The dendritic tree was reconstructed with the center of the soma as its root using the python NetworkX package. Fluorescence puncta were considered to arise from the labeled dendrite if they were located within a maximal distance from the center of the dendrite. This distance was taken as the dendritic radius, estimated from IMARIS, plus 200 nm (~HWHM of the PSF810). The estimation of local radius was made from IMARIS binary masks using a threshold of the local contrast (DiameterContrastThreshold) set at three times the standard deviation above the background fluorescence noise. The diameter is then calculated using the Shortest Distance from Distance Map algorithm, which estimates the diameter as the distance from the center of the dendrite to the closest part of the surface determined by the above threshold. The average dendritic radius, using this approach, was found to be 0.66 ± 0.28 (SD) µm for adult, and 0.72 ± 0.27 (SD) µm for immature mice. As this value was larger than that estimated from single-photon confocal imaging, it was only used as a part of the criteria for assigning a fluorescence puncta to a reconstructed dendrite. For each dendritic branch, the number of PSD95 puncta and their distance to the soma surface were calculated. As the data points of the dendrite structure obtained from IMARIS are not homogeneously spaced, the dendritic structure was resampled in 100 nm intervals with the distance for each segment recalculated with respect to the starting point of each corresponding branch from the soma surface (estimated using the binary mask as for the dendrites). Histograms for the distribution of PSD95 puncta and the number of dendritic segments at a given distance from the soma were then generated in 10 µm bins and used to estimate the puncta density along the dendritic tree. Cumulative plots were sampled at 1 µm intervals.

Electrophysiology analysis

Request a detailed protocol

Data analysis was performed using the Neuromatic analysis package (Rothman and Silver, 2018) written within the Igor Pro environment (WaveMetrics, Lake Oswego, OR, USA). mEPSCs were detected with a threshold detection method and mEPSC population average is calculated from the mean EPSC response calculated for each SC. All EPSCs were baseline subtracted using a 1 ms window before the stimulation artifact. Peak amplitudes were measured as the difference between the baseline level immediately preceding the stimulation artifact, and the mean amplitude over a 100 μs window centered on the peak of the response. EPSC rise time was estimated as the time the EPSC rose from 10 to 90% of the peak amplitude (in ms). Decay kinetics were assessed either as the width of the EPSC at the amplitude one-half of the peak (half-width in ms) or as the weighted time constant of decay (τdecay) calculated from the integral of the current from the peak, according to:

τdecay=tpeaktI(t)dtIpeak

where tpeak is the time of the EPSC peak, t is the time at which the current had returned to the pre-event baseline, and Ipeak the peak amplitude of the EPSC.

All data are expressed as average ± SEM otherwise noted. Statistical tests were performed using a nonparametric Wilcoxon–Mann–Whitney two-sample rank test routine for unpaired or a Wilcoxon signed-rank test routine for paired comparisons, unless otherwise stated. Linear correlations were determined using a spearman test. Unless otherwise noted, unpaired tests were used and considered significant at p < 0.05 (OriginPro, Northampton, MA, USA).

Equation 1. Length constant for an infinite cable.

λDC=dRm4Ri,

where d is the dendritic diameter and Rm and Ri are the specific resistance of the membrane and internal resistivity, respectively.

Equation 2. Frequency-dependent length constant for an infinite cable.

λAC=λDC21+1+(2πfτm)2,

where f is the frequency representing an AMPAR current and τm is the membrane time constant.

When f is greater than 100 Hz, this can be simplied when f is greater than 100 Hz:

λACd4πfRiCm

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1,2,3,4,6,7 and 8.

References

    1. Silver RA
    (2010) Neuronal arithmetic
    Nature Reviews. Neuroscience 11:474–489.
    https://doi.org/10.1038/nrn2864

Decision letter

  1. Linda Overstreet-Wadiche
    Reviewing Editor; University of Alabama at Birmingham, United States
  2. John R Huguenard
    Senior Editor; Stanford University School of Medicine, 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:

Several properties that affect neuronal function change over the course of cell maturation, including morphology, membrane properties and synaptic connectivity. Here the authors delineate the contribution of these parameters to synaptic integration in developing cerebellar stellate cells. Using a range of cutting edge approaches, they report that changes in synaptic distribution dominate the observed maturation of dendritic integration, providing new insight into the mechanisms contributing to neuronal maturation.

Decision letter after peer review:

Thank you for submitting your article "Developmental emergence of two-stage nonlinear synaptic integration in cerebellar interneurons" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by John Huguenard as the Senior Editor. The reviewers have opted to remain anonymous.

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

Essential revisions:

1. The authors should provide additional verification that the biophysical parameters of the model built for adult SCs in Abrahamsson et al., (2012) are appropriate for cells from younger animals and address the potential contribution of developmental changes in AMPAR/synaptic properties.

2. Broaden the scope of the conclusions by exploring additional implications of the observed developmental changes, for example, by testing whether inclusion of NMDARs alters the relative strength of distal inputs, how gap junction coupling between SCs would affect the results, and testing different dendritic impedance scenarios.

3). Provide all the technical details that would allow reproduction of the experiments. More methodological information for both the electrophysiology and simulations need to be included, including series resistance errors.

Reviewer #1 (Recommendations for the authors):

1) Line 150-158. Foster et al., 2005 shouldn't be used to support exclusive uni-vesicular release at PF-MLI synapses, since it addresses multi-vesicular release and AMPAR saturation at PF-PC synapses. In general, a central assumption that mEPSCs provide an unbiased assessment of the effective synaptic strength is not convincing because there is abundant evidence that PF-MLI synaptic contacts are heterogeneous in terms of the number of vesicles released per action potential (Malagon et al., 2016; Pulido and Marty, 2019; Vaden et al., 2019). Nahir and Jahr (2013) provide support that a majority of evoked release is mediated by a single vesicle at PF-MLI synapses in adolescent rats, but considering the goals of this manuscript to assess the contribution of 'synaptic strength" over development, the authors should address whether this variable contributes to the differences between mEPSC and qEPSCs as well as qEPSCs over development.

2) Figure 2. It looks like the amplitude of the simulated EPSC drops off faster as a function of distance in the mature vs. immature SC (these data should be presented on the same scale). Is this due to increased branching in the mature cell that in turn mediates the difference in Figure 8D? Please discuss more explicitly.

3) Figure 7. It would strengthen the conclusion to see this model validated experimentally with electrical stimulation, such as by plotting the qEPSC amplitudes and kinetics as a function of distance of the stim electrode from the soma. It seems like these data might already exist, but aren't presented directly. Including experimental data like what is shown in Figure 1 and 2 of Abrahammson et al., but in the adolescent SCs would validate that the previously established model still applies in cells from younger animals, and help rule out other contributions from AMPARs or dendritic membrane properties, or synaptic glutamate concentration (see pt 1).

4) Figure 8 B,C,D. It is not surprising that the same biophysical model with smaller dendritic branches recapitulates the behavior of the adult SC with minor quantitative differences (it is hard to imagine a different outcome). But one wonders whether this simulation can be verified experimentally or perhaps more importantly, whether conclusions would be generalizable if NMDAR activation (which seems likely to change over maturation) is considered. The exclusive focus on AMPAR-only integration seems of limited relevance for understanding the computational changes occurring over SC development.

Reviewer #2 (Recommendations for the authors):

P. 7 l. 197 the different sections of the dendrites per cell should have been quantified separately to determine whether there are variations among dendrites in the same cell and among different cells. The question of how locations for diameter measurements (see also the next point) were chosen is relevant in particular when adult SCs presenting higher dendritic complexity reflected in more branches are being compared to immature SCs. It would be worth considering to break down the diameters per branch order, instead of collating all data in a global mean (see below P15, l. 429-430). All this could affect the properties of dendrites as well as the distribution of synapses along the dendrites (see Figure 2B p33).

P. 31 Figure 2A,B how did the authors ensure fair sampling of the dendritic tree for the measurement of dendritic diameters?

P. 31 Figure 2B Compare to Abrahamsson et al., 2012 Figure 4B: it would be helpful to see the two distributions (immature and mature SC) together.

P. 9 l. 280 "PSD area was constant…." But in Figure 3G there is no quantification of this. It is just an observation. Provide quantitative data about PSD are at the soma vs dendrites and distance from the soma, as well as cumulative plots and bin distributions instead of Figure 3G (as done for LM data in Figure 6).

P. 10 l. 298-299 n = 83 from 3 cells (see Figure 4C legend) vs n = 97 from 2 cells from Abrahamsson et al., 2012. N = 83 from 3 cells from immature when n=97 from 2 cells in mature: is the number of excitatory synapses onto the soma the same in immature vs mature SC? A density measurement would be more appropriate to compare variations, if any.

P. 9 l. 277: Why did the authors not compare (and pool if statistically possible) the data from the 3 unloaded SCs (Figure 4C) and the 2 loaded cells (used in Figure 3F)?

P. 10 l. 306-311 "In addition to cable filtering of synaptic currents, a neuron's somatic response to dendritic input is also affected by synapse distribution within its dendritic tree,….." and P. 10 l. 320-321 "To test this hypothesis, we examined the distribution of excitatory synaptic inputs along the somato-dendritic compartment in immature and adult SCs." Light microscopy was utilized to quantify these parameters (Figure 6). These parameters should also be quantifiable from the EM data the authors already have. From these data it should be possible to quantify and plot "synapse number vs distance from the soma". These data should then be compared to the data presented at P. 11 l. 326-336, as well as to the previous EM data about PSD size once these have also been quantified (P. 9 l. 280). EM data should accompany the LM data even more so when the authors themselves point out in Methods (l. 719-722) "This approach is limited by the resolution of 2P fluorescence imaging to differentiate individual synapses within clusters and thus may result in an underestimate of absolute synapse density, but allowed for an unbiased estimate of synapse distributions at the two developmental stages."

P. 11 l. 323-324 "We then mapped Venus-tagged PSD95 puncta associated with the somata and dendritic trees of Alexa 594-filled immature and adult SCs (Figure 5A-D)" and l. 326-328 "Venus-tagged PSD95 puncta within the soma and dendritic tree of 9 immature and 8 adult 3D-reconstructed SCs (Figure 5G), showed ~ 80 % more puncta on adult SCs (582 {plus minus} 48 puncta vs. 324 {plus minus} 21 in immature SC, p<0.05)."

No data about the PSD95 puncta on somata only are presented (i.e. separated from the puncta on dendrites data). These should be added and compared to the data obtained by EM.

In the data presented in Figure 6B, in mature SCs the mode of the synapse distribution is in the 45 µm bin from the soma, whereas in the immature SCs it is in the 15 µm bin. But the distributions are different in shape: in mature SCs, the distribution is quite gaussian whereas in immature SC it is skewed. It might be interesting to know the skewness of the two distributions. This is particularly evident in Figure 6A cumulative plot where the mature is very shifted to the right compared to the immature. As pointed out, interestingly the synapse distribution at the two ages (Figure 6B) is very similar to the shape of the Sholl analysis in Figure 6F. Overall how do these LM data compare to EM data quantified as suggested above?

P. 11 l. 351 "EPSCs arising from distal locations in the adult were larger and contributed relatively more to the simulated mean mEPSC waveform (Figure 7B).": This is confusing. The colour scheme (including especially the white bars) in Figure 7B is also highly confusing. Please choose a better way of presenting this key result of the paper in the text and Figure 7 – distally generated mEPSCs dominate the mean mEPSC more strongly in adult than in immature SCs, making the mean mEPSC smaller and slower in adult SCs.

P. 14 l. 467 "Voltage-clamp recordings of mEPSCs showed that their time course and amplitude are both halved during development (Figure 1).": confusing. The amplitude is halved but the rise and decay time constants are doubled.

P. 19 The authors state that "For the area measurement of synapses on soma, serial sections through three unlabeled neighbor SCs were also used to avoid potential turbulence due to the patching." (l. 627-629) Are the data presented for the PSD size of synapses onto the soma in Figure 3G from these 2 patched cells or are they from the 3 unloaded cells in Figure 4C? These should be specified and only used if the data from the patched cells in Figure 3 are statistically comparable to those from the unloaded cells in Figure 4. The data for Figure 3F and 4C have also been obtained with two different EM sectioning and imaging methods. The rationale should be explained behind passing from a 300-400 section long ribbons on tape (ATUM), that allows proper 3D reconstructions of synapses, to a "classic" serial section on grid/slots with much lower number of sections per ribbon as well as the higher risk of losing sections containing part of synapses.

Reviewer #3 (Recommendations for the authors):

– Modeling throughout the manuscript is done without taking into consideration the effect of series resistance. In the methods section the authors note that no series resistance compensation was used for the recordings, which considering the 6-8 MΩ tip resistance is presumably substantial. This may have a dramatic effect on the recorded current kinetics.

– Dendritic width are calculated from fluorescent images, even though the dendritic diameter is around the limit of diffraction limited techniques. The authors may have access to relevant electron microscopic data, which could be used for comparison to check for accuracy if possible.

– Dendritic impedance needs to be investigated electrophysiologically for young SCs, or the model needs to be repeated and illustrated for several different dendritic input impedance profiles.

https://doi.org/10.7554/eLife.65954.sa1

Author response

Essential revisions:

1. The authors should provide additional verification that the biophysical parameters of the model built for adult SCs in Abrahamsson et al., (2012) are appropriate for cells from younger animals and address the potential contribution of developmental changes in AMPAR/synaptic properties.

We thank the reviewer for pointing out that we had not clearly discussed our experimental confidence in our immature SC model parameters. The principal factors required for generating insightful predictions about dendritic integration are (a) the passive membrane properties, (b) the dendritic morphology and (c) an estimate of voltage gated channels expression.

Immature SC passive membrane properties. We estimated the passive membrane properties (membrane time constant) of immature SCs using the same method as for adult SCs (Abrahamsson et al., 2012) and found similar values. The procedure is described in the Methods. To perform model simulations considering these immature SC values we adjusted the specific membrane resistance (Rm) to match the experimentally determined membrane time constant for all biophysical models types (idealized and 3D reconstructed SC models). Developmental differences in morphology were explicitly incorporated for each model.

Immature SC dendritic tree morphology. We quantified developmental changes in dendritic morphology in two ways: (1) by directly measuring the dendritic diameter (430 measurements over 93 dendritic segments) – which critically influences dendritic filtering, and (2) by performing full dendritic tree reconstruction of two-photon images. In this manner, we were able to assess the developmental changes in dendritic tree length and branching, and synapse density. Because neither the small changes in membrane time constant or dendritic diameter could account for the developmental slowing of mEPSCs, we proposed that the larger relative number of synapses at longer electrotonic distances could account for a slower averaged mEPSC. This was confirmed by comparing model simulations from careful manual reconstructions of immature and adult morphology and synapses distribution SCs.

– We modified the Results and Discussion sections (see below for details) to reiterate the measured biophysical and morphological parameters estimated for the two developmental stages and used in numerical simulations. The principal motivation for model simulations was for illustration of proof of principle. First, we showed that immature SCs exhibit cable filtering of their synaptic responses. Secondly, we used biophysical models based on full 3D reconstructions to show that increased electrotonic filtering of mEPSCs is a result of a larger fraction of synapses at longer distance from the soma. Our conclusions are sufficiently justified by model simulations that explicitly implement the difference in synaptic conductance and morphological properties (line 533):

“However, combining experiments and simulation we showed that immature SCs have similar dendritic integration properties as adult SCs (Abrahamsson et al., 2012): (a) dendrite-evoked qEPSCs are smaller and slower than those evoked at the soma (Figure 3), consistent with cable filtering (Figure 2); (b) STP differed between dendrite and somatic stimulation and did not reveal supralinear recruitment of voltage-gated channels (Nevian et al., 2007) (Figure 8); and (c) simulated subthreshold I/Os were sublinear (Figure 8). These results are supported mechanistically by several experimental findings such as (a) the measured membrane time constant that are identical at the two developmental stages, (b) the dendritic diameters being similarly small (Figure 2), and (c) the paired-pulse facilitation difference between dendrite and somatic reproducible with a passive biophysical model like in adult SCs (Figure 8). Taken together, the data are consistent with SCs displaying little developmental changes in voltage-gated channel expression (Molineux et al., 2005) and low densities of sodium (Myoga et al., 2009) and calcium channels (Tran-Van-Minh et al., 2016).

The integration of fast synaptic events such as AMPAR-mediated EPSC is critically influenced by the intracellular resistivity, the local membrane capacitance, and dendritic diameter (Equation 2). Since internal resistivity is a challenge to estimate without dual electrode recordings, we examine a wide range of internal resistivities and found that they did not alter qualitatively our conclusions. The narrow dendritic diameters at both ages (Figure 2B) suggest that the local input resistance is only slightly different. We cannot rule out that developmental changes in gap junction expression could contribute to the maturation of SC dendritic integration, since they are thought to contribute to the axial resistivity and capacitance of neurons (Szoboszlay et al., 2016). All the recordings were made with gap junctions intact, including for membrane constant measurements. However, their expression in SCs is likely to be lower than their basket cell counterparts (Hoehne et al., 2020; Rieubland et al., 2014). Overall, the basic electrotonic machinery to filter synaptic responses is already present as soon as SC precursors reach their final location in the outer third of the molecular layer.”

Lack of voltage-gated channels in immature SCs: Dendritic nonlinearities can arise from the expression of voltage-gated channels that can amplify or counteract EPSPs. We did not explicitly examine the contribution of voltage-gated channels in immature SCs in this study, as we did for adult SCs (Abrahamsson et al., 2012). Still, for the following reasons, we did not implement voltage-gated channels in our simulations:

1) Studies on young animals (P14) and older (P24) suggest similar voltage-gated channel expression (Molineux et al. 2005), indicating that the results from Abrahamsson et al., 2012 apply to immature SC.

2) Sodium channels are expressed at low densities in SC dendrites, if at all (Myoga and Regehr, 2009).

3) Paired-pulse ratios of evoked multi-quantal EPSCs in immature SCs show no evidence of voltage-gated channel recruitment (see Nevian et al., 2007). They are also well-predicted by our passive biophysical models, which we have now added to Figure 8 (panel B).

4) Most of the study focuses on quantal EPSCs, which are less likely to depolarize dendrites to recruit voltage-gated channels.

Regarding the potential contributions of developmental changes in AMPAR to EPSC. At some synapses, properties of synaptic AMPAR have been shown to change during development, underlying a developmental speeding of mEPSCs. EPSC in immature SC have been shown to be mediated by CP-AMPAR predominantly containing GluR3 associated with TARP γ2 (Stargazin) and TARP γ7 (Soto et al., 2007; Bats et al., 2012). During development, GluR2 subunits are inserted into synaptic AMPARs in an activity-dependent manner (Liu et al., 2000), affecting calcium permeability of the receptors (Liu et al., 2002: Soto et al. 2007) without impacting EPSC kinetics (Liu et al., 2002) or channel conductance (Soto et al. 2007). Consistent with these findings, we did not observe any changes qEPSC kinetics for somatic synapses between immature and adult SCs. We, therefore, concluded that the developmental slowing of mEPSCs is unlikely due to alterations in AMPAR subunits or associated proteins.

We have modified the Results.

“This reduction in synaptic conductance could be due to a reduction in the number of synaptic AMPARs activated and/or a developmental change in AMPAR subunits. SC synaptic AMPARs are composed of GluA2 and GluA3 subunits associated with TARP γ2 and γ7 (Bats et al., 2012; Liu and Cull-Candy, 2000; Soto et al., 2007; Yamazaki et al., 2015). During development, GluR2 subunits are inserted to the synaptic AMPAR in an activity-dependent manner (Liu and Cull-Candy, 2002), affecting receptors calcium permeability (Liu and Cull-Candy, 2000). However, those developmental changes have little impact on AMPAR conductance (Soto et al., 2007), nor do they appear to affect EPSC kinetics (Liu and Cull-Candy, 2002); the latter is consistent with our findings. Therefore the developmental reduction in postsynaptic strength most likely results from fewer AMPARs activated by the release of glutamate from the fusion of a single vesicle.”

We have also modified the Discussion.

“Comparison of synaptic properties between immature and adult SCs

Whole-cell voltage-clamp recordings in immature and adult SCs showed a developmental decrease in mEPSC amplitude and slowed kinetics (Figure 1). These results contrast with findings in other neurons that show faster mEPSCs during maturation due to changes of AMPAR subunits, vesicular content of neurotransmitter, and/or synapse structure (Cathala et al., 2005; Chen and Regehr, 2000; Koike-Tani et al., 2005; Yamashita et al., 2003). Knowing that dendritic inputs could be electrotonically filtered, we took advantage of the ability to selectively stimulate somatic synapses to isolate somatic qEPSCs for comparison between the two ages. Evoked EPSCs showed a developmental reduction in amplitude (~ 40%), with no change in kinetics (Figure ). This likely results from the smaller adult PSD size (Figure 4), and hence a lower AMPAR number (Masugi-Tokita et al., 2007), rather than a developmental reduction in the peak glutamate concentration at the postsynaptic AMPARs (Figure 1) or change in AMPAR subunits composition (see Results section).”

2. Broaden the scope of the conclusions by exploring additional implications of the observed developmental changes, for example, by testing whether inclusion of NMDARs alters the relative strength of distal inputs, how gap junction coupling between SCs would affect the results, and testing different dendritic impedance scenarios.

NMDARs:

In this manuscript, we focused on the integration of single-vesicle release events, which comprise those synaptic events during low-frequency stimulation (see below for a detailed justification). NMDARs are expressed in SC, but due to extrasynaptic localization, are not significantly activated by single vesicle release of neurotransmitter. This observation has been well-documented by several labs in immature (Glitsch et al., 1999; Carter et al., 2000; Clark et al., 2002) and adult SC (Abrahamsson et al., 2012, Tran-Van-Minh et al., 2016), which have shown that EPSC and EPSP integration is insensitive to NMDAR antagonist. As a result, NMDARs would not alter the relative strength of distal inputs under low-frequency stimulation and thus will not contribute to synaptic integration of quantal EPSPs in either somata or dendrites.

It is now addressed in the Result:

“We did not examine NMDAR currents since they are located extrasynaptically and do not contribute to postsynaptic current under low intensity and low-frequency stimulation (Carter and Regehr, 2000; B. A. Clark and Cull-Candy, 2002; Tran-Van-Minh et al., 2016). Therefore, AMPAR-mediated mEPSCs can provide an unbiased assessment of the effective distribution of postsynaptic strengths throughout the entire somato-dendritic compartment.”

Gap junctions:

All recordings were performed without perturbing gap junction and therefore implicitly account their contribution to the passive properties of SCs. Gap junctions are expected to contribute to the axial resistivity and capacitance of neurons (Szoboszlay et al., 2016). Unlike in Golgi cells (Szoboszlay et al., 2016), the precise location and conductance of gap junctions in SCs is not known and thus difficult to implement accurately in biophysical models. Moreover, Gap junctions in SCs are likely to be much less prominent than their basket cell and Golgi cell counterparts both in both immature (Rieubland 2015) and mature SCs (Hoehne et al., 2020). We, therefore, think that accurate and explicit modeling of gap junctions in SCs is beyond the scope of this study. Nevertheless, because we matched the membrane time constant of the model to that experimentally measured with any gap junctions intact, we do not expect any impact on our conclusions.

We now discuss the implication of gap junctions in the Discussion:

“We cannot rule out that developmental changes in gap junction expression could contribute to the maturation of SC dendritic integration, since they are thought to contribute to the axial resistivity and capacitance of neurons (Szoboszlay et al., 2016). All the recordings were made with gap junctions intact, including for membrane constant measurements. However, their expression in SCs is likely to be lower than their basket cell counterparts (Hoehne et al., 2020; Rieubland et al., 2014).”

Testing dendritic impedance scenarios:

The most important parameter required for accurate estimation of the local impedance is the dendritic diameter. For fast AMPAR mediated synaptic conductances, cable filtering primarily depends on the internal resistivity, the dendritic diameter, and the membrane capacitance (Abrahamsson et al., 2012 and equation 2). Lack of influence of Rm was verified in adult SC models (see the panel A of Figure S4 in Abrahamson et al. 2012). For this reason, we did not think it was necessary to explore this issue here. The specific membrane capacitance is assumed to be 0.9 uF/um2, a well-accepted value for rodent neurons. In simulations, we varied Ri over a range of published estimates, which we show as errors on most simulation plots. Because variations in Ri do not alter our conclusions, the critical parameter to be estimated is the dendritic tree morphology, particularly the dendritic diameter, which critically influences input impedance. We, therefore, conscientiously measured dendritic diameter in young SCs using confocal microscopy, the same method used for adult dendrites (Abrahamsson et al., 2012).

We have added the following sentence to the Discussion section:

“The integration of fast synaptic events such as AMPAR-mediated EPSC is critically influenced by the intracellular resistivity, the local membrane capacitance, and dendritic diameter (Equation 2). Since internal resistivity is a challenge to estimate without dual electrode recordings, we examine a wide range of internal resistivities and found that they did not alter qualitatively our conclusions. The narrow dendritic diameters at both ages (Figure 2B) suggest that the local input resistance is only slightly different.”

Equation 2 from manuscript:λACd4πfRiCm

3). Provide all the technical details that would allow reproduction of the experiments. More methodological information for both the electrophysiology and simulations need to be included, including series resistance errors.

Methodological information requested by reviewers for both the electrophysiology and simulations have been added to the method section.

Reviewer #1 (Recommendations for the authors):

1) Line 150-158. Foster et al., 2005 shouldn't be used to support exclusive uni-vesicular release at PF-MLI synapses, since it addresses multi-vesicular release and AMPAR saturation at PF-PC synapses.

Indeed, our sentence was misleading. We reworded it to explain that even though GC-SC synapses exhibit multivesicular release, the low release probability of GC synapses implies that, on average, only one vesicle is released per synaptic connection per action potential (AP). We, therefore, postulate that the study of dendritic integration of quantal EPSCs is physiologically relevant. We corrected the sentence in the manuscript:

Line 171:

“Because of their low release probability, excitatory synapses formed by granule cell axons (parallel fibers, PFs) release on average only one synaptic vesicle per synaptic contact, despite the presence of multiple release sites per synaptic contact (Foster et al., 2005).”

In general, a central assumption that mEPSCs provide an unbiased assessment of the effective synaptic strength is not convincing because there is abundant evidence that PF-MLI synaptic contacts are heterogeneous in terms of the number of vesicles released per action potential (Malagon et al., 2016; Pulido and Marty, 2019; Vaden et al., 2019).

We thank the reviewer for pointing out these studies. The studies from Dr. Marty’s group used 3 mM extracellular calcium, which would un-physiologically increase the fraction of docked vesicles released at single contacts and the vesicle fusion probability, thereby increasing quantal content for each AP. The physiological extracellular calcium concentration is thought to be between 1.2 and 1.5 mM. Finally, the Pulido study was performed on GABAergic synapses formed between molecular interneurons. The Vaden et al., 2019 study of climbing fiber to Purkinje cells synapses, neither of which is comparable to our study.

In summary, we prefer to comment on the physiological relevant quantal content described in Foster et al., 2005, which uses 1.2 mM extracellular calcium.

Nahir and Jahr (2013) provide support that a majority of evoked release is mediated by a single vesicle at PF-MLI synapses in adolescent rats, but considering the goals of this manuscript to assess the contribution of 'synaptic strength" over development, the authors should address whether this variable contributes to the differences between mEPSC and qEPSCs as well as qEPSCs over development.

We do not understand the reference to Nahir and Jahr (2013) with respect to single vesicle release. Nahir and Jahr demonstrated both multivesicular and univesicular release. However, importantly, when the number of vesicles released per AP per synapse increases, partly due to presynaptic facilitation of release and temporal summation of residual glutamate, spillover onto extrasynaptic receptors is observed. They and others (Clark et al., 2002) have shown that this does not occur in response to single vesicle fusion. Since in this study, mEPSCs and qEPSCs are responses to the release of single vesicles, we focused on the influence of dendritic morphology contributed to the filtering of single vesicle release events without considering NMDAR activation. Consideration of the nonlinear recruitment of NMDARs during high-frequency burst stimulation is beyond the scope of this study. Nevertheless, the slow time course of NMDA receptor-mediated synaptic currents would produce synaptic responses that are less sensitive to cable filtering in SCs.

We have modified the Results section accordingly:

“We did not examine NMDAR currents since they are located extrasynaptically and do not contribute to postsynaptic current under low intensity and low-frequency stimulation (Carter and Regehr, 2000; B. A. Clark and Cull-Candy, 2002; Tran-Van-Minh et al., 2016)”.

In our study, we define mEPSCs as spontaneous synaptic currents that arise from synapses throughout the dendritic tree in the absence of presynaptic APs (recordings performed in the presence of TTX), whereas qEPSCs are elicited from specific locations along the somatodendritic axis following extracellular stimulation of a presynaptic AP, but under very low release probability conditions such that only single vesicle release events are evoked. Thus, both synaptic current types can be used to assess postsynaptic strength. The contribution of release probability and the number of release sites are not pertinent to our study of mEPSCs and qEPSCs.

We have also clarified throughout the manuscript that our study primarily examines

developmental alterations in postsynaptic strength.

2) Figure 2. It looks like the amplitude of the simulated EPSC drops off faster as a function of distance in the mature vs. immature SC (these data should be presented on the same scale). Is this due to increased branching in the mature cell that in turn mediates the difference in Figure 8D? Please discuss more explicitly.

We replotted the curves on the same scale and there is no notable difference (see Figure 2).

3) Figure 7. It would strengthen the conclusion to see this model validated experimentally with electrical stimulation, such as by plotting the qEPSC amplitudes and kinetics as a function of distance of the stim electrode from the soma. It seems like these data might already exist, but aren't presented directly.

A systemic study of the distance-dependence of quantal EPSCs was not performed. Such an experiment requires two-photon imaging for precise estimates of stimulus electrode to soma distance and is not available in the PI’s laboratory. Nevertheless, dendritic stimulation was performed by placing the stimulation electrode on the outer third of the dendritic field on isolated dendrites. However, in a subset of recording combined with 2P imaging, we obtained dendritic qEPSC with a peak amplitude of 42.5 +/- 7 pA , a 0.24 +/- 0.02 ms RT and 0.83 +/- 0.07 ms halfwidth at a distance of 51.57 μm (range 43 to 63 um, n = 5), similar to the data set described in the manuscript. Since those data represent only represent a subset of results, they were not included in the manuscript. We, however, clarified in the manuscript the location of the dendritic simulation:

“In contrast, stimulation of distal dendritic synapses, in the outer third of the dendritic field, produced somatically recorded qEPSCs that were significantly smaller (mean amplitude: 46 ± 3 pA) and slower (10-90 % rise time of 0.24 ± 0.012 ms and a half-width of 0.88 ± 0.04 ms; n = 18; all P<0.05, Figure 3C, 3D). Simulated qEPSCs generated from a dendirtic location 45 μm from the soma (Figure 2C) exhibit amplitude and kinetics values with the range of experimental values. Taken together, these results are consistent with cable filtering of EPSCs as they propagate along dendrites in immature SCs.”

Specific validation of the model by direct comparison of simulations and recorded EPSCs is a standard challenge in studying dendritic integration. The variability of passive properties combined with morphological difference across dendrites and cells and variability in synaptic strength make it difficult to predict specific experimental results from simulations using mean parameters obtained from a different population of neurons. While these challenges are not unsurmountable, they are well beyond the scope of this manuscript. However, simulations by immature SC models reproduce the reduction in qEPSCs amplitude and slowing in their kinetics that falls within the range (+/- SD) of the experimental qEPSCs evoked at distal dendritic and somatic synapses. In addition, the simulations in Figure 7 using the reconstructed morphology of a single immature SC and a single adult SC reproduce experimental mEPSC data. Nevertheless, to better describe how the simulation match experiments, we modified Figure 2C by adding plots illustrating the distance-dependence of EPSC amplitude, rise time, and half-width and now directly compare measured qEPSCs to those predictions:

“Simulated qEPSCs were large and fast for synapses located at the soma (magenta trace, Figure 2C), but qEPSCs evoked from synapses located on the dendrites (grey trace; at 45 μm from the soma, Ri of 150 Ohm·cm) were 48% smaller and showed a 195% slower rise time and a 180% slower half-width. This dendritic filtering was also associated with a large increase in the local synaptic depolarization (gren trace, Figure 2C) that would substantially reduce the local driving force during synaptic transmission onto dendrites, potentially causing a sublinear read-out of the underlying conductance, as observed in adult SCs (Abrahamsson et al., 2012; Tran-Van-Minh et al., 2016). Examination of the amplitude, rise time, and half-width as a function of synapse distance shows that beyond ~40 μm there is little additional cable filtering (Figure 2C, right).”

“Similarly, activation of synaptic inputs along a dendrite at increasing distance from the soma produced soma-recorded qEPSCs that become smaller and slower with distance (Figure 2F), similar to those in the idealized passive model (Figure 2C) with a dendritic diameter matching that obtained from confocal images (Figure 2B). We also simulated qEPSCs from a reconstructed adult SC (Figure 2G, 2H), which showed a similar distance-dependent decrease in amplitude as for the immature SC (Figure 2F). These simulations suggest that, like their adult counterparts, the passive morphometric characteristics of immature SCs should also produce significant cable filtering of both the amplitude and time course of EPSCs.”

“In contrast, stimulation of distal dendritic synapses, in the outer third of the dendritic field, produced somatically recorded qEPSCs that were significantly smaller (mean amplitude: 46 ± 3 pA) and slower (10-90 % rise time of 0.24 ± 0.012 ms and a half-width of 0.88 ± 0.04 ms; n = 18; all P<0.05, Figure 3C, 3D). Simulated qEPSCs generated from a dendirtic location 45 μm from the soma (Figure 2C) exhibit amplitude and kinetics values with the range of experimental values. Taken together, these results are consistent with cable filtering of EPSCs as they propagate along dendrites in immature SCs.”

Including experimental data like what is shown in Figure 1 and 2 of Abrahammson et al., but in the adolescent SCs would validate that the previously established model still applies in cells from younger animals, and help rule out other contributions from AMPARs or dendritic membrane properties, or synaptic glutamate concentration (see pt 1).

Regarding a possible developmental change in synaptic glutamate concentration, we ruled this out directly by performing low-affinity antagonist experiments. Postsynaptic AMPAR blockade using γDGG produced an identical reduction in EPSC peak amplitude in immature and adult SCs (Figur 1E), with no effect on kinetics (Figure 1F). In addition, we found that the ~30% in qEPSC amplitude observed at the soma (Figure 4B) is associated with a similar reduction in PSD size (figure 4C) indicative of a smaller number of AMPAR activated upon glutamate release rather than a change in the glutamate concentration waveform. This point is discussed in the Results.

We also ruled out a contribution of different AMPAR subunits because qEPSCs recorded at the soma have identical kinetics at both ages (Figure 4B). It, therefore, seems unlikely that development changes in AMPAR subunits contribute to the observed kinetic changes, consistent with the observation that the insertion of GluR2 subunits to the synaptic AMPAR does not affect EPSC kinetics (Liu et al., 2002). This point is now discussed in the Results.

We ruled out developmental changes in passive membrane properties by examining membrane time constant, which we found almost identical for the two developmental stages. See the response to the Editors for a more detailed reply.

4) Figure 8 B,C,D. It is not surprising that the same biophysical model with smaller dendritic branches recapitulates the behavior of the adult SC with minor quantitative differences (it is hard to imagine a different outcome). But one wonders whether this simulation can be verified experimentally

Indeed, it is possible to perform two-photon uncaging to examine the altered sublinearity between immature and adult SCs, but this technique is not available currently in the PI’s laboratory. We agree with the reviewer that experimental validation is important. We, therefore, demonstrated that dendritic the EPSC paire-pulse ratio is lower than at the soma due to sublinear summation (Figure 8A), as shown in adult SCs (Abrahamsson et al., 2012). Moreover, we now show in a new panel (Figure 8B) that the dendritic PPR from immature SCs can be reproduced with our passive model of immature SCs. Description of the new panel starts on line 441.

or perhaps more importantly, whether conclusions would be generalizable if NMDAR activation (which seems likely to change over maturation) is considered. The exclusive focus on AMPAR-only integration seems of limited relevance for understanding the computational changes occurring over SC development.

We think that our study of exclusively AMPAR mediated synaptic responses is relevant since NMDAR are extrasynaptic and require long, high-frequency trains of presynaptic stimuli for their activation. This observation has been well documented by several labs in immature (Glitsch et al., 1999; Carter et al., 2000; Clark et al., 2002) and adult SCs (Abrahasson et al., 2012; Tran-Van-Minh et al., 2016). These studies also showed that EPSCs evoked by single and paired extracellular stimulation are insensitive to NMDAR antagonists. As a result, NMDAR would not alter the relative strength of distal inputs and could not contribute to synaptic integration on both somatic and dendritic EPSP. In summary, NMDAR activation is not relevant for our study of developmental determinants of changes in the strength of postsynaptic quantal responses or EPSCs following single or pairs of presynaptic stimuli. Understanding the contribution of NMDARs to synaptic strength during train stimuli is titillating but beyond the scope of this study.

Nevertheless, we have added text to the Discussion justify our emphasis on AMPAR-mediated EPSCs:

“We did not examine NMDAR currents since they are located extrasynaptically and do not contribute to postsynaptic current under low intensity and low-frequency stimulation (Carter and Regehr, 2000; B. A. Clark and Cull-Candy, 2002; Tran-Van-Minh et al., 2016)”

Reviewer #2 (Recommendations for the authors):P. 7 l. 197 the different sections of the dendrites per cell should have been quantified separately to determine whether there are variations among dendrites in the same cell and among different cells. The question of how locations for diameter measurements (see also the next point) were chosen is relevant in particular when adult SCs presenting higher dendritic complexity reflected in more branches are being compared to immature SCs. It would be worth considering to break down the diameters per branch order, instead of collating all data in a global mean (see below P15, l. 429-430). All this could affect the properties of dendrites as well as the distribution of synapses along the dendrites (see Figure 2B p33).

We thank the reviewer for this comment and have analyzed the dendritic widths according to location along the dendrite. We have added 2 new panels to Figure 2B: (a) one describing the variability in diameter observed between dendritic segments that reflects the variability between dendrites in the same cell and between different SC, and (b) a second describing dendritic width as a function branch order showing a small reduction of dendritic segments as a function of distance from the soma (0.35 to 0.6 μm). This variation of diameter would not alter cable filtering properties significantly since dendritic filtering is comparable for diameters ranging from 0.3 to 0.6 μm, (Figure 4G and 4H, Abrahamsson et al., 2012).

P. 31 Figure 2A,B how did the authors ensure fair sampling of the dendritic tree for the measurement of dendritic diameters?

We arbitrarily selected pixel intensity line profiles along the dendritic tree (n=430) and average the profiles from the same dendritic branch segment (n=93) from 18 immature SCs. We now describe this procedure more clearly in the Methods:

“Dendritic diameters were estimated from the full-width at half maximum (FWHM) of pixel intensity line profiles on 1 μm segments of dendrites, made perpendicular to dendritic length using the same procedure as for adult SCs (Abrahamsson et al., 2012). In brief, we selected dendritic segments for measurement throughout the dendritic tree to homogeneously sample the diversity of diameters. We collected 430 line profiles from 18 immature SCs, then average the profiles from the same dendritic segments (n=93). The imaging resolution within the molecular layer was estimated from the width of intensity line profiles of SC axons. The FWHM was 0.30 +/- 0.01 μm (n = 57 measurements over 16 axons) and a mean of 0.27 +/- 0.01 μm (n = 16) when taking into account the thinnest section for each axon. Only 2% of all dendritic measurements are less than 270 nm, suggesting that the dendritic diameter estimation is hardly affected by the resolution of our microscope.”

P. 31 Figure 2B Compare to Abrahamsson et al., 2012 Figure 4B: it would be helpful to see the two distributions (immature and mature SC) together.

We have added the distribution from Abrahamsson et al., to Figure 2B.

P. 9 l. 280 "PSD area was constant…." But in Figure 3G there is no quantification of this. It is just an observation. Provide quantitative data about PSD are at the soma vs dendrites and distance from the soma, as well as cumulative plots and bin distributions instead of Figure 3G (as done for LM data in Figure 6).

The reviewer makes a good point; the linear correlation estimate was inadvertently omitted from Figure 3G. We elected not to bin the data in order to correctly weigh the analysis according to the number of sample measures at each distance. The linear fit was added to Figure 3G and the following text added to the figure legend: “The green line is a linear fit through all the points (R2 = 0.001, p = 0.85).”

P. 10 l. 298-299 n = 83 from 3 cells (see Figure 4C legend) vs n = 97 from 2 cells from Abrahamsson et al., 2012. N = 83 from 3 cells from immature when n = 97 from 2 cells in mature: is the number of excitatory synapses onto the soma the same in immature vs mature SC? A density measurement would be more appropriate to compare variations, if any.

Because not all the synapses on the soma were reconstructed in the EM samples, the number of somatic synapses is best estimated using the light microscopy approach. The N=83 from 3 cells is just the number of sampled synapses and not all synapses on soma. Therefore, we cannot compare it with n = 97 from 2 cells from Abrahamsson et al.

P. 9 l. 277: Why did the authors not compare (and pool if statistically possible) the data from the 3 unloaded SCs (Figure 4C) and the 2 loaded cells (used in Figure 3F)?

Because of different methods used for the two groups of cells (classical serial sections observed with TEM for the 3 unloaded and ATUMtome sections observed with SEM for the 2 loaded cells), it is not appropriate to pool the data sets. Between the three unloaded SCs, there is no significant difference in PSD size. For the two loaded SCs, there is also no significant difference in PSD size (p = 0.19). However, the data of PSD sizes in the 3 unloaded and 2 loaded SCs are significantly different (0.039 ± 0.002 μm2, n = 83 vs. 0.020 ± 0.001 μm2, n = 137, p<0.0001), perhaps due to the different methods.

We have added this information to the methods:

“In order to compare synapse area measurement comparable to those done on adult soma (Abrahamsson et al., 2012), we performed serial sections and then standard transmission electron microscopy (TEM) on three unlabeled SCs (which avoids potential distortions due to the patching).”

P. 10 l. 306-311 "In addition to cable filtering of synaptic currents, a neuron's somatic response to dendritic input is also affected by synapse distribution within its dendritic tree,….." and P. 10 l. 320-321 "To test this hypothesis, we examined the distribution of excitatory synaptic inputs along the somato-dendritic compartment in immature and adult SCs." Light microscopy was utilized to quantify these parameters (Figure 6). These parameters should also be quantifiable from the EM data the authors already have. From these data it should be possible to quantify and plot "synapse number vs distance from the soma". These data should then be compared to the data presented at P. 11 l. 326-336, as well as to the previous EM data about PSD size once these have also been quantified (P. 9 l. 280). EM data should accompany the LM data even more so when the authors themselves point out in Methods (l. 719-722) "This approach is limited by the resolution of 2P fluorescence imaging to differentiate individual synapses within clusters and thus may result in an underestimate of absolute synapse density, but allowed for an unbiased estimate of synapse distributions at the two developmental stages."

Unfortunately, we could not use the EM data appropriately for “synapse number vs distance from the soma” because not all synapses along the dendritic tree were sampled. We used 300-400 serial sections obtained by ATUMtome as described in the Methods section but the number of sections was not sufficient for a complete reconstruction of soma and dendrites of single cells. We used the LM method to overcome this limitation for a systematic and unbiased comparison of synapse (puncta) densities between immature and adult SCs.

P. 11 l. 323-324 "We then mapped Venus-tagged PSD95 puncta associated with the somata and dendritic trees of Alexa 594-filled immature and adult SCs (Figure 5A-D)" and l. 326-328 "Venus-tagged PSD95 puncta within the soma and dendritic tree of 9 immature and 8 adult 3D-reconstructed SCs (Figure 5G), showed ~ 80 % more puncta on adult SCs (582 {plus minus} 48 puncta vs. 324 {plus minus} 21 in immature SC, p<0.05)."

No data about the PSD95 puncta on somata only are presented (i.e. separated from the puncta on dendrites data). These should be added and compared to the data obtained by EM.

The number of somatic synapses detected using LM is now presented in panel B of Figure 6 and listed in the data source file. We found 27 +/- 3.2 synapses in immature SC versus 21.17 +/- 2.5 synapses in Adult SC. These values have been added to the text (Line 375):

“Venus-tagged PSD95 puncta within the soma and dendritic tree of 9 immature and 8 adult 3D-reconstructed SCs (Figure 5G), showed ~ 80 % more puncta on adult SCs (582 ± 48 puncta vs. 324 ± 21 in immature SC, p<0.05; of which 27 ± 3.2 and 21.17 ± 2.5 are at the soma of immature and adult SC, respectively).”

Because not all the synapses on the soma were sampled within the serial sections, we considered LM our only reliable somatic synapse number estimate.

In the data presented in Figure 6B, in mature SCs the mode of the synapse distribution is in the 45 µm bin from the soma, whereas in the immature SCs it is in the 15 µm bin. But the distributions are different in shape: in mature SCs, the distribution is quite gaussian whereas in immature SC it is skewed. It might be interesting to know the skewness of the two distributions. This is particularly evident in Figure 6A cumulative plot where the mature is very shifted to the right compared to the immature. As pointed out, interestingly the synapse distribution at the two ages (Figure 6B) is very similar to the shape of the Sholl analysis in Figure 6F. Overall how do these LM data compare to EM data quantified as suggested above?

We assessed the skewness of the distribution of PDS95 puncta and found that there is a small difference between adult (basically no skew) and the immature (slight positive skew). The values are added to the Figure legend. We also compared the skewness of the PSD95 distributions to the skewness of the dendritic segment distributions for the two ages and we found comparable skewness supporting further our finding that puncta density remained constant across the dendritic tree at both ages.

Pearson’s median skewness (3 (mean − median)/standard deviation):

Immature Adult

PSD95 dendrites PSD95 dendrites

0,38 0,32 -0,10 -0,08

We thank the reviewer for his suggestion and have added a sentence to point out the similarity of skew in the dendritic segment and PSD-95 labeled puncta distributions (see Results):

“The similarity between the shapes of synapse (Figure 6B) and dentric segment (Figure 6C) distributions was captured by a similarity in their skewness (0.38 vs. 0.32 for both distributions in immature and -0.10 and -0.08 for adult distributions)”

As stated above we could not appropriately use the EM data for “synapse number vs distance from the soma” because not all synapses were sampled by the serial sections.

P. 11 l. 351 "EPSCs arising from distal locations in the adult were larger and contributed relatively more to the simulated mean mEPSC waveform (Figure 7B).": This is confusing. The colour scheme (including especially the white bars) in Figure 7B is also highly confusing. Please choose a better way of presenting this key result of the paper in the text and Figure 7 – distally generated mEPSCs dominate the mean mEPSC more strongly in adult than in immature SCs, making the mean mEPSC smaller and slower in adult SCs.

The text has been modified in the result section:

“Assuming that mEPSCs are generated randomly with an equal probability at all synapses, we generated a simulated mean mEPSC by summing the qEPSCs generated at each distance (qEPSCd)each weighted by its relative frequency according to the synapse distribution (Figure 7B, right panel). For each dendritic segment, the obtained weighted qEPSCd describes its relative contribution to the mean mEPSC waveform (Figure 7B, left panel). One can see that qEPSCds arising from distal locations in the adult were relatively larger in amplitude as compared to immature SCs and therefore would contributed more to the simulated mean mEPSC waveform. As an example, the contribution of the weighted qEPSCd at 45 μm to mEPSC was small in immature SC while it was more prominent in adult SC (compare the green traces in Figure 7B). As a result, the mean mEPSC was smaller in adult SCs than in immature SCs (26.0 ± 0.6 pA, n = 9 dendrites vs. 52.2 ± 0.4 pA, n = 7 for Ri 150) and its time-course had slower rise (10-90 % rise time = 0.24 ± 0.05 vs. 0.17 ± 0.01 ms) and decay (half-width = 1.11 ± 0.04 vs. 0.77 ± 0.01 ms; for all P<0.05). Thus, distally generated qEPSCds dominate the mean mEPSC more strongly in adult than in immature SCs, making the mean mEPSC smaller and slower in adult SCs.”

The color scheme of the Figure 7B has been explained in the Figure legend:

“For clarity only a subset of normalized qEPSCd are displayed with a different color for somatic and each 10 μm dendritic segments. Hollow bars in the histogram correspond to distance not illustrated with a normalized qEPSCd on the left panel”

P. 14 l. 467 "Voltage-clamp recordings of mEPSCs showed that their time course and amplitude are both halved during development (Figure 1).": confusing. The amplitude is halved but the rise and decay time constants are doubled.

This sentence has been modified:

“Whole-cell voltage-clamp recordings in immature and adult SCs showed a developmental descrease in mEPSC amplitude and slowing of their kinetics (Figure 1).”

P. 19 The authors state that "For the area measurement of synapses on soma, serial sections through three unlabeled neighbor SCs were also used to avoid potential turbulence due to the patching." (l. 627-629) Are the data presented for the PSD size of synapses onto the soma in Figure 3G from these 2 patched cells or are they from the 3 unloaded cells in Figure 4C? These should be specified and only used if the data from the patched cells in Figure 3 are statistically comparable to those from the unloaded cells in Figure 4. The data for Figure 3F and 4C have also been obtained with two different EM sectioning and imaging methods. The rationale should be explained behind passing from a 300-400 section long ribbons on tape (ATUM), that allows proper 3D reconstructions of synapses, to a "classic" serial section on grid/slots with much lower number of sections per ribbon as well as the higher risk of losing sections containing part of synapses.

We are sorry about the confusion. The data of the PSD size in Figure 3G are from the 2 loaded cells, using“classical”serial sectioning and TEM imaging methods, as was performed for Abrahamsson et al., 2012. This was essential for the comparison of PSD size between immature and adult SCs. Unfortunately, the data obtained with ATUMtome and SEM were statistically different from those of the unloaded cells, as mentioned above, and could not be pooled together.

Reviewer #3 (Recommendations for the authors):

– Modeling throughout the manuscript is done without taking into consideration the effect of series resistance. In the methods section the authors note that no series resistance compensation was used for the recordings, which considering the 6-8 MΩ tip resistance is presumably substantial. This may have a dramatic effect on the recorded current kinetics.

Simulations with an idealized SC model were performed using an Rs of 20 Mohm, whereas modeling with the reconstructed SC were performed using a series resistance of 16 Mohm. The mean experimental Rs was 16 Mohm.

– Dendritic width are calculated from fluorescent images, even though the dendritic diameter is around the limit of diffraction limited techniques. The authors may have access to relevant electron microscopic data, which could be used for comparison to check for accuracy if possible.

Dendritic width was measured using the higher resolution technique of single photon confocal microscopy. Since SC axon diameters are less than 200 nm, we used them to test the resolution of our microscope within the same cerebellar slice and region. The FWHM of line intensity profiles was 0.295 +/- 0.009 μm (n = 57 measurements over 16 axons) with a mean of 0.27 +/- 0.007 μm (n = 16) when taking into account the thinnest section for each axon. Those measures are smaller 98% of the FWHMs from SC dendrites and shows that our dendritic diameter estimation is not affected by the resolution of our microscope. We therefore assumed that the larger FWHMs of line intensity profiles measured from dendrites is an accurate measure of their width.

We clarified this procedure in the Methods:

“Dendritic diameters were estimated from the full-width at half maximum (FWHM) of pixel intensity line profiles on 1 μm segments of dendrites, made perpendicular to dendritic length using the same procedure as for adult SCs (Abrahamsson et al., 2012). In brief, we selected dendritic segments for measurement throughout the dendritic tree to homogeneously sample the diversity of diameters. We collected 430 line profiles from 18 immature SCs, then average the profiles from the same dendritic segments (n=93). The imaging resolution within the molecular layer was estimated from the width of intensity line profiles of SC axons. The FWHM was 0.30 +/- 0.01 μm (n = 57 measurements over 16 axons) and a mean of 0.27 +/- 0.01 μm (n = 16) when taking into account the thinnest section for each axon. Only 2% of all dendritic measurements are less than 270 nm, suggesting that the dendritic diameter estimation is hardly affected by the resolution of our microscope.”

EM diameter estimates are likely to be in error due to fixation artifacts that generally result in tissue shrinkage. The use of confocal microscopy also allowed us to compare immature and adult SC using the same method and to minimize experimental biases between the two measurements.

– Dendritic impedance needs to be investigated electrophysiologically for young SCs, or the model needs to be repeated and illustrated for several different dendritic input impedance profiles.

As indicated above we estimated several important electrophysiological and morphological parameters in SCs, including membrane time constant, synaptic conductance amplitude and time course, dendrite diameter (and now the diameters per branch order and inter- and intra-neuronal diversity – see added panels to Figure 2B and our response to reviewer 2), length and branching, which when incorporated into biophysical models reproduced the slowing of mEPSCs dendrite evoked quantal EPSCs, and paired-pulse facilitation within dendrites. All of the simulations were performed with multiple internal resistivities, as this could not be measured. Variations across values in the literature of this parameter did not alter our conclusions. As mentioned in responses to Reviewer 1, we do not think it is possible to perform additional experiments to constrain parameters due to the natural variability of dendritic morphology and AMPAR content at each synapse. Moreover, our goal was to use model simulations to support our conclusion that dendritic branching has a surprising effect on dendritic computations due to the larger number of distal synapses without changes in cable properties.

Thus, the dendritic input resistance is best examined using simulations that are grounded by experimentally validated parameters. The dendritic diameter is one of the most important and one we experimentally determined.

https://doi.org/10.7554/eLife.65954.sa2

Article and author information

Author details

  1. Celia Biane

    Sorbonne Université et CNRS UMR 8256, Adaptation Biologique et Vieillissement, Paris, France
    Contribution
    Formal analysis, Investigation, CB performed electrophysiology experiments and analysis
    Competing interests
    No competing interests declared
  2. Florian Rückerl

    Institut Pasteur, Université de Paris, CNRS UMR 3571, Unit of Synapse and Circuit Dynamics, Paris, France
    Contribution
    Data curation, Formal analysis, Visualization, Writing – review and editing, FR and DAD designed PSD95-venus experiments. FR performed the analysis of PSD95 puncta
    Competing interests
    No competing interests declared
  3. Therese Abrahamsson

    Institut Pasteur, Université de Paris, CNRS UMR 3571, Unit of Synapse and Circuit Dynamics, Paris, France
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Cécile Saint-Cloment

    Institut Pasteur, Université de Paris, CNRS UMR 3571, Unit of Synapse and Circuit Dynamics, Paris, France
    Contribution
    Resources, CS-C was in change of the Nos1-PSD95 mouse management
    Competing interests
    No competing interests declared
  5. Jean Mariani

    Sorbonne Université et CNRS UMR 8256, Adaptation Biologique et Vieillissement, Paris, France
    Contribution
    Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Ryuichi Shigemoto

    Institute of Science and Technology Austria, Klosterneuburg, Austria
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8761-9444
  7. David A DiGregorio

    Institut Pasteur, Université de Paris, CNRS UMR 3571, Unit of Synapse and Circuit Dynamics, Paris, France
    Contribution
    Data curation, Supervision, Funding acquisition, Validation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6417-4566
  8. Rachel M Sherrard

    Sorbonne Université et CNRS UMR 8256, Adaptation Biologique et Vieillissement, Paris, France
    Contribution
    Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Laurence Cathala

    1. Sorbonne Université et CNRS UMR 8256, Adaptation Biologique et Vieillissement, Paris, France
    2. Paris Brain Institute, CNRS UMR 7225 - Inserm U1127 – Sorbonne Université Groupe Hospitalier Pitié Salpêtrière, Paris, France
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Software, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing, Visualization, LC designed all experiments except PSD95-venus experiments, which were designed by FR and DAD. LC performed electrophysiology experiments and analysis, confocal and 2P imaging, confocal images analysis, reconstruction in Neuronstudio and numerical simulations. LC wrote the manuscript and all authors edited it
    For correspondence
    laurence.cathala@sorbonne-universite.fr
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6253-1390

Funding

Agence Nationale de la Recherche (ANR-13-BSV4-00166)

  • Laurence Cathala
  • David DiGregorio

Agence Nationale de la Recherche (ANR-18-CE16-0018-03)

  • David A DiGregorio

Agence Nationale de la Recherche (ANR-19-CE16-0019-02)

  • David A DiGregorio

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

Acknowledgements

This study was supported by the Centre National de la Recherche Scientifique and the Agence Nationale de la Recherche (ANR-13-BSV4-00166, to LC and DAD). TA was supported by fellowships from the Fondation pour la Recherche Medicale and the Swedish Research Council. We thank Dmitry Ershov from the Image Analysis Hub of the Institut Pasteur, Elodie Le Monnier, Elena Hollergschwandtner, Vanessa Zheden, and Corinne Nantet for technical support and Haining Zhong for providing the Venus-tagged PSD95 mouse line. We would like to thank Alberto Bacci, Ann Lohof, and Nelson Rebola for comments on the manuscript.

Ethics

Animal housing and all procedures were approved by the Comité National d’Ethique pour les Sciences de la Vie et de la Santé (Comité Charles Darwin, authorisation N° 1492-02) and the Ethics Committee #89 of Institut Pasteur (CETEA; protocol approval #DHA180006).

Senior Editor

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

Reviewing Editor

  1. Linda Overstreet-Wadiche, University of Alabama at Birmingham, United States

Publication history

  1. Received: December 20, 2020
  2. Preprint posted: January 9, 2021 (view preprint)
  3. Accepted: September 28, 2021
  4. Version of Record published: November 3, 2021 (version 1)
  5. Version of Record updated: November 12, 2021 (version 2)

Copyright

© 2021, Biane et al.

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

Metrics

  • 299
    Page views
  • 59
    Downloads
  • 0
    Citations

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

Download links

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

Downloads (link to download the article as PDF)

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

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

Further reading

    1. Neuroscience
    Rawan AlSubaie et al.
    Research Article Updated

    Projections from the basal amygdala (BA) to the ventral hippocampus (vH) are proposed to provide information about the rewarding or threatening nature of learned associations to support appropriate goal-directed and anxiety-like behaviour. Such behaviour occurs via the differential activity of multiple, parallel populations of pyramidal neurons in vH that project to distinct downstream targets, but the nature of BA input and how it connects with these populations is unclear. Using channelrhodopsin-2-assisted circuit mapping in mice, we show that BA input to vH consists of both excitatory and inhibitory projections. Excitatory input specifically targets BA- and nucleus accumbens-projecting vH neurons and avoids prefrontal cortex-projecting vH neurons, while inhibitory input preferentially targets BA-projecting neurons. Through this specific connectivity, BA inhibitory projections gate place-value associations by controlling the activity of nucleus accumbens-projecting vH neurons. Our results define a parallel excitatory and inhibitory projection from BA to vH that can support goal-directed behaviour.

    1. Cell Biology
    2. Neuroscience
    Angela Kim et al.
    Research Article Updated

    Insulin-induced hypoglycemia is a major treatment barrier in type-1 diabetes (T1D). Accordingly, it is important that we understand the mechanisms regulating the circulating levels of glucagon. Varying glucose over the range of concentrations that occur physiologically between the fed and fuel-deprived states (8 to 4 mM) has no significant effect on glucagon secretion in the perfused mouse pancreas or in isolated mouse islets (in vitro), and yet associates with dramatic increases in plasma glucagon. The identity of the systemic factor(s) that elevates circulating glucagon remains unknown. Here, we show that arginine-vasopressin (AVP), secreted from the posterior pituitary, stimulates glucagon secretion. Alpha-cells express high levels of the vasopressin 1b receptor (V1bR) gene (Avpr1b). Activation of AVP neurons in vivo increased circulating copeptin (the C-terminal segment of the AVP precursor peptide) and increased blood glucose; effects blocked by pharmacological antagonism of either the glucagon receptor or V1bR. AVP also mediates the stimulatory effects of hypoglycemia produced by exogenous insulin and 2-deoxy-D-glucose on glucagon secretion. We show that the A1/C1 neurons of the medulla oblongata drive AVP neuron activation in response to insulin-induced hypoglycemia. AVP injection increased cytoplasmic Ca2+ in alpha-cells (implanted into the anterior chamber of the eye) and glucagon release. Hypoglycemia also increases circulating levels of AVP/copeptin in humans and this hormone stimulates glucagon secretion from human islets. In patients with T1D, hypoglycemia failed to increase both copeptin and glucagon. These findings suggest that AVP is a physiological systemic regulator of glucagon secretion and that this mechanism becomes impaired in T1D.