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Short-term plasticity at cerebellar granule cell to molecular layer interneuron synapses expands information processing

  1. Kevin Dorgans
  2. Valérie Demais
  3. Yannick Bailly
  4. Bernard Poulain
  5. Philippe Isope
  6. Frédéric Doussau  Is a corresponding author
  1. CNRS UPR 3212, Université de Strasbourg, France
  2. Plateforme Imagerie in vitro, CNRS UPS 3156, France
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Cite this article as: eLife 2019;8:e41586 doi: 10.7554/eLife.41586

Abstract

Information processing by cerebellar molecular layer interneurons (MLIs) plays a crucial role in motor behavior. MLI recruitment is tightly controlled by the profile of short-term plasticity (STP) at granule cell (GC)-MLI synapses. While GCs are the most numerous neurons in the brain, STP diversity at GC-MLI synapses is poorly documented. Here, we studied how single MLIs are recruited by their distinct GC inputs during burst firing. Using slice recordings at individual GC-MLI synapses of mice, we revealed four classes of connections segregated by their STP profile. Each class differentially drives MLI recruitment. We show that GC synaptic diversity is underlain by heterogeneous expression of synapsin II, a key actor of STP and that GC terminals devoid of synapsin II are associated with slow MLI recruitment. Our study reveals that molecular, structural and functional diversity across GC terminals provides a mechanism to expand the coding range of MLIs.

https://doi.org/10.7554/eLife.41586.001

Introduction

Inhibitory interneurons mediating feed-back or feed-forward inhibition (FFI) provide brain microcircuits with an exquisite temporal control over the firing frequency of projecting neurons (Hennequin et al., 2017; Isaacson and Scanziani, 2011; Klausberger and Somogyi, 2008; O'Donnell et al., 1993). In the cerebellar cortex, the FFI microcircuit is activated by granule cells (GCs) that target two types of molecular layer interneurons (MLIs): stellate cells (SCs) and basket cells (BCs). SCs and BCs finally control Purkinje cells (PC), the sole projecting neurons of the cerebellar cortex, through a powerful somatic or dendritic inhibition (Jörntell et al., 2010). In combination with the direct excitatory pathway provided by GC-PC connections, the FFI encodes sensorimotor information through acceleration or deceleration of PC simple spike activity (Armstrong and Edgley, 1988; Jelitai et al., 2016; Ozden et al., 2012).

Sensorimotor information is conveyed to the cerebellar cortex by mossy fibers (MFs) as short high-frequency bursts of action potentials (Chadderton et al., 2004; Chen et al., 2017; Jörntell and Ekerot, 2006; Kennedy et al., 2014; Powell et al., 2015; Rancz et al., 2007). During high-frequency stimulations, cerebellar synapses exhibit several forms of short-term synaptic plasticity (STP) including facilitation and depression of synaptic responses in the millisecond range (Atluri and Regehr, 1996; Bao et al., 2010; Brachtendorf et al., 2015; Dittman et al., 2000; Doussau et al., 2017; Miki et al., 2016; Valera et al., 2012; Zheng and Raman, 2010). STP play multiple roles in information processing (Anwar et al., 2017; Fioravante and Regehr, 2011). At the input stage of the cerebellar cortex, a strategy based on the heterogeneity of STP across MF-GC synapses provides a mechanism for coding multisensory events at the level of single GCs (Chabrol et al., 2015). Also, differences in the profile of STP across synapses involved in the direct excitatory pathway or in the FFI microcircuit control the inhibitory/excitatory balance and shape Purkinje cell discharge (Grangeray-Vilmint et al., 2018). GCs which are the most numerous neurons in the brain, segregate in clonally related subpopulations (Espinosa and Luo, 2008). Despite such number and differences, STP heterogeneity across GC boutons is poorly documented. A seminal study has shown that the behavior of glutamate release during high-frequency activities at GC boutons is determined by the target cell (that is PC, SC or BC, Bao et al., 2010). Following compound stimulations of clusters of GCs or beams of parallel fibers (PFs), it was shown that GC-BC synapses depress during high-frequency stimulation while GC-SC synapses facilitate (Bao et al., 2010). By controlling the spatiotemporal excitability of PC (Bao et al., 2010), and potentially by shaping the inhibitory/excitatory balance (Grangeray-Vilmint et al., 2018), target cell–dependency of STP at the input stage of the FFI pathway must have important functional consequences for cerebellar output. However, target cell–dependency of STP at GC-MLI synapses is challenged by different experimental findings. First, many MLIs cannot be classified solely by their axon profile (e.g. basket versus dendritic synapses) or their position in the molecular layer (Palay and Chan-Palay, 1974; Sultan and Bower, 1998) and it was proposed that MLIs represent a single population of interneurons (Jörntell et al., 2010; Rakic, 1972; Sotelo, 2015; Sultan and Bower, 1998). Second, release properties and STP profiles of GC synaptic inputs to MLIs can be modified by presynaptic long-term plasticity and by local retrograde signaling independently of the target cell (Bender et al., 2009; Soler-Llavina and Sabatini, 2006).

Given the abundance of GCs and the importance of STP at GC-MLI synapses for cerebellar computation, we set out to study the diversity of STP at unitary GC-MLI synapses. Also, we aimed to uncover the molecular determinants of functional heterogeneity. Among presynaptic proteins involved in STP, synapsins (Syn) are good candidates underlying functional heterogeneity across cerebellar synapses. Syn are presynaptic phosphoproteins coded by three distinct genes (Syn I, II and III). Both Syn I and Syn II regulate neurotransmitter release and STP in mature synapses (Cesca et al., 2010; Humeau et al., 2011; Song and Augustine, 2015). The synapse-specific expression of Syn isoforms (Bragina et al., 2010; Patton et al., 2016; Wei et al., 2011) contributes to the diversity of STP profiles (Feliciano et al., 2017; Gitler et al., 2004; Kielland et al., 2006; Song and Augustine, 2016) and determines the inhibitory-excitatory balance in cortical and hippocampal networks (Fassio et al., 2011; Ketzef and Gitler, 2014). Here, we show that MLIs, regardless of their identity, receive four functionally distinct types of synapses from GCs. Each class of connection differentially drives the timing of MLI recruitment characterized by the first-spike latency. Differences in STP across GC-MLI connections are underlain by heterogeneous expression of Syn II at GC-MLI synapses. Functional studies using wild-type (WT) and Syn II knockout (KO) mice demonstrate that Syn II determines the profile of STP and the first-spike latency in MLI. Our observation that single MLIs receive GC inputs with distinct molecular and functional properties suggests that the temporal coding of GC activity by MLIs and thereby the inhibitory control over the cerebellar output via PCs depends on GC subtypes recruited by a given sensorimotor input.

Results

Functional heterogeneity at unitary GC-MLI synapses during high-frequency stimulations

In order to study how information from single GC inputs is encoded by MLIs, we measured STP at unitary GC-MLI synapses in acute parasagittal slices by minimal electrical stimulation of PFs (10 pulses at 100 Hz), in the direct vicinity of the dendritic tree of a recorded MLI (Malagon et al., 2016; Miki et al., 2016). MLIs localized in the vermis (lobules IV-VI) were recorded in whole-cell voltage-clamp configuration and loaded with Atto-594 (n = 49) to visualize their dendritic tree and their morphology (Figure 1—figure supplement 1A). Using two-photon microscopy, the stimulation pipette was visually positioned above an isolated dendrite. The stimulating currents were carefully adjusted to stimulate only a single synaptic contac (see Materials and methods and Figure 1—figure supplement 1C–E). In 28 MLIs, we recorded synaptic responses from at least 2 GCs. STP profiles were highly heterogeneous across unitary GC-MLI contacts (n = 96) including unitary connections contacting the same MLI (Figure 1). To classify the STP profiles, we used principal component analysis (PCA) on the averaged and normalized synaptic charges in trains of EPSCs followed by a k-means clustering analysis (Figure 2—figure supplements 1 and 2). We identified four clusters that characterize STP at GC-MLI synapses (Figure 2A–B). The profiles differ by: (i) the quantity of neurotransmitter released at the first stimuli (Figure 2C, left), (ii) paired-pulse plasticity (Figure 2C, right), (iii) the STP profiles during the first four EPSCs, and (iv) the ability to sustain glutamate release after the fourth stimuli. Because high-frequency stimulation can change the excitability of PFs, it should be ensured that no additional PFs are recruited during the train. Given precautions made to select the lowest stimulation intensity (see Materials and methods), such recruitment occurs randomly during the train. Recruitment of additional PFs may strongly affect the profile of STP from one train to another during successive stimulations and skew the classification of inputs. We checked whether the profile of STP was conserved during 10 successive 100 Hz trains. The systematic narrow clustering of 10 recordings belonging to the same series of stimulations in the cloud of point of PCA transformation (Figure 2—figure supplement 2A,B) clearly indicated that the profiles of STP were conserved from one train to another during successive stimulations of the same synaptic contact. Recruitment of additional PFs might also depend on the intensity of stimulation. However, the lack of correlation between the charge of the first EPSCs, PPR, PC1 or PC2 and the stimulation current intensity indicated that STP profiles were not affected by this parameter (Figure 2—figure supplement 2C). Choosing intensities just above the threshold could also induce failure of fiber recruitment rather than presynaptic mechanisms. However, the classification of inputs using PCA transformation followed by k-mean clustering analysis was weakly affected when EPSCs at the first stimulus were excluded from the dataset (Figure 2—figure supplement 3). Hence, putative experimental errors due to unexpected changes in PF excitability did not influence the overall classification of inputs.

Figure 1 with 1 supplement see all
Heterogeneous profile of STP at unitary GC-MLI synapses.

(A) Typical experiment showing the profile of STP during 100 trains at three unitary inputs recruited by local stimulation of PF at two different locations (Z1 to Z3). Figure shows post-hoc reconstruction of a recorded MLI. The left and right dashed lines represent the location of the Purkinje cell layer (PCL) and the pia, respectively. (B) Superimposed traces correspond to EPSCs recorded during trains of 10 stimuli at 100 Hz after minimal stimulation at Z1, Z2 and Z3 locations. Averaged traces from 10 successive stimulations are represented in purple (Z1), green (Z2) and blue (Z3). (C) Corresponding EPSC charges versus stimulus number at Z1, Z2 and Z3 locations.

https://doi.org/10.7554/eLife.41586.002
Figure 2 with 3 supplements see all
Identification of 4 classes of MC-MLI synapses using PCA followed by k-means clustering analysis of EPSC properties during high-frequency stimulation.

(A) PCA transformation of GC-MLI STP profiles. Scatter plot of the first two principal components (PC1, PC2) obtained by analyzing EPSC properties during 100 Hz trains at numerous unitary GC-MLI synapses (n = 96). The first two components explain 69.4% of the total variance of STP. Synapses with negative PC1 values sustain glutamate release during the 10 EPSCs of the burst while PC1 with positive values synapses are depressing synapses. Positive PC2 synapses are depressing synapses while negative PC2 synapses are facilitating during EPSC #2 and EPSC #3. (B) Representative traces of the four classes of inputs (C1 to C4) determined by k-means clustering analysis during ten minimal stimulations of unitary inputs at 100 Hz. The corresponding values of averaged EPSC amplitudes plotted versus the stimulus number and normalized again the vector space model (see method) were displayed on the right panels. (C) Box plots of the charge of EPSC recorded at the first stimulus (left panel) and the paired pulse ratio (PPR) (right panel) according to the four categories of input. EPSC charges: C1 = 198.78 fC±23.46 fC, C2 = 124.96 fC±18.78 fC, C3 = 126.52 fC±22.10 fC, C4 = 74.41 fC±9.48 fC. PPR: C1 = 1.39 ± 0.07, C2 = 1.97 ± 0.19, C3 = 1.98 ± 0.19, C4 = 1.82 ± 0.14. Red bars refer to means and white bars to medians. Multiple comparisons were performed using one-way ANOVAs with Tukey post hoc tests. Only statistically significant differences between categories were shown above box plots (D) Mean values of normalized EPSC amplitudes during 100 Hz train according to the four categories of inputs. The circular diagram represents the relative proportion of each category of input from 96 unitary GC-MLI synapses.

https://doi.org/10.7554/eLife.41586.004

Among the four groups identified, only C1 connections exhibited depression of glutamate release after the second pulse, while C2 and C3 connections exhibited facilitation (Figure 2D). C2 and C3 connections differed in their responses after the fourth stimulus: while C3 connections sustained release during the entire train, synaptic transmission at C2 connections depressed after the fourth stimuli. On the other hand, C4 connections were characterized by small but stable EPSCs (Figure 2C) suggesting that they correspond to boutons releasing one quanta per stimulation (Bender et al., 2009; Nahir and Jahr, 2013). MLI subtypes were identified in half of the recorded cells based on the presence or not of a basket in the Purkinje cell layer (n = 13 for BCs, n = 12 for SCs and n = 24 for non-identified MLI). Both MLI subtypes were contacted by functionally distinct GC inputs. Except for C3 connections (facilitating profiles) absent in BCs, all classes of GC inputs were found on SCs and BCs (Figure 3). The lack of target-cell dependence of STP at GC-MLI synapses was also confirmed using compound stimulations of GCs in the granule cell layer (Figure 3—figure supplement 1). The strict segregation of MLIs in two subclasses was challenged by several authors and studies (Jörntell et al., 2010; Rakic, 1972; Sotelo, 2015; Sultan and Bower, 1998). Nevertheless, morphological features of MLIs were found to be related to the position of each MLI’s soma in the deepness of the molecular layer (Sultan and Bower, 1998). Our analyses failed to establish correlations between STP profiles (evaluated by PC1 or PC2) and the position of MLIs’ soma (Figure 3—figure supplement 2A) or synaptic inputs (Figure 3—figure supplement 2B) in the molecular layer. Finally, since dendritic integration of excitatory responses in MLI may be influenced by the distance of the synapses from the soma (Tran-Van-Minh et al., 2016) we also checked whether the distances of excitatory inputs from MLI’s soma were correlated with STP parameters. Again, our analyses failed to establish a correlation between those parameters (Figure 3—figure supplement 2C). Our results indicate that the different classes of inputs recruited by minimal stimulations were randomly distributed within the molecular layer.

Figure 3 with 2 supplements see all
The profile of STP is not determined by the target cell.

(A) Post-hoc reconstruction of 2 recorded MLI using a two-photon microscope. SCs were identified by the absence of neuronal process reaching the PCL (left MLI) and by the absence of cut processes (transection of neuronal processes could be clearly identified by swelling at the tip end portion of processes). At the opposite, BCs were identified by the presence of processes entering in the PCL (right MLI). (B) Circular diagrams of the relative proportion of each category of input (determined by k-means clustering analysis during ten minimal stimulation of GC unitary inputs at 100 Hz) contacting BCs and SCs.

https://doi.org/10.7554/eLife.41586.008

Syn II is heterogeneously expressed across GC-MLI presynaptic terminals

Next, we aimed to uncover the role of Syn in functional heterogeneity. We first studied the presence of Syn I and Syn II in GCs boutons by immunohistochemistry using VGluT1 as specific marker of GC presynaptic terminals (Hioki et al., 2003; Zander et al., 2010). Triple staining of cerebellar sections from P20 ~ P22 CD1 mice (N = 4) revealed systematic overlap of VGluT1 with Syn I, but not with Syn II (Figure 4A,B). In presynaptic terminals, VGluT1 and Syn are supposed to be partially colocalized because VGluT1 is localized on synaptic vesicles while Syn are both cytosolic and associated with synaptic vesicles (Cesca et al., 2010). Quantitative analysis revealed a higher correlation between the fluorescence intensity of Syn I and VGluT1 than between the fluorescence intensity of VGlut1 and Syn II (RSyn I =0.584 +/- 0.057, RSyn II = 0.435 +/- 0.075, paired t-test: p<0.001; n = 52). Our results suggest that Syn I is present in all GC terminals while Syn II is restricted to a subpopulation of GC boutons.

Heterogeneous expression of Syn II at GC-MLI synapses.

(A) Representative merged images of VGluT1/Syn I immunostaining (left image, green and red puncta, respectively) or VGluT1/Syn II immunostaining (right image, green and blue puncta, respectively). The two merged imaged were captured in the molecular layer from the same parasagittal cerebellar section. (B) Profile plot (dashed line in A) showing the colocalization of VGluT1 with Syn I in the majority of VGluT1 puncta while there was only a partial colocalization of VGluT1 with Syn II in VGluT1 puncta. (C) Typical immunogold electron micrographs illustrating the ubiquitous expression of Syn I in GC boutons contacting MLIs (left micrograph) and the heterogeneous expression of Syn II in these boutons (right micrograph). GC boutons contacting MLIs were colorized. Insets corresponding to magnifications of areas delimited by white squares show details of immunogold staining. (D) Histogram of the percentage of GC-MLI synapse positive for Syn I (red bar) and Syn II (blue bar).

https://doi.org/10.7554/eLife.41586.011

Since most of GC synapses stained by VGluT1 actually correspond to GC-PC synapses, we could not exclude that Syn II is homogeneously expressed in GC-MLI synapses. We then performed pre-embedding immunogold labeling (Figure 4C) of Syn I and Syn II in parasagittal cerebellar sections. Asymmetrical GC-MLI synapses in the upper part of the molecular layer were identified by the presence of mitochondria within the postsynaptic compartment (Palay and Chan-Palay, 1974). Immunogold labeling confirmed the ubiquitous presence of Syn I in all GC-MLI and presence of Syn II in only 56% of GC terminals contacting MLI (Figure 4D).

Heterogeneous expression of Syn II generates diversities in the profile of STP at unitary GC-MLI synapses

The heterogeneous expression of Syn II in GC terminals may contribute to the diversity of STP profiles across unitary GC-MLI synapses. To test this possibility, we reinvestigated STP diversity at unitary GC-MLI synapses in Syn II KO mice (Figure 5A,B). Absence of Syn II modified the responses of unitary GC-MLI synapses to 100 Hz stimulations. The mean EPSC charges of the first responses was strongly reduced in Syn II KO mice (Figure 5B,C). The percentage of failures at the first stimuli were increased in Syn II KO mice indicating that absence of Syn II decreased the probability of release (pr) of fully-releasable synaptic vesicles (that is, vesicles released by a single action potential, Doussau et al., 2017) (Figure 5D). The paired-pulse ratio was significantly increased in Syn II KO mice (mean/median PPR for WT = 1.1/1.1 ± 0.03, n = 96 and mean/median PPR for Syn II KO mice = 2.6/1.7 ± 0.08, n = 53, p<0.001, MWRST). We then analyzed STP profiles at unitary connections in Syn II KO as in Figure 2 and plotted individual profiles against the first two dimensions of a PCA based on the PCA fit of WT data (Materials and methods) and examined the spread of Syn II KO individual GC-MLI STP observations (Figure 5E). The four profiles of STP found at unitary GC-MLI synapses in WT mice were also found at unitary GC-MLI synapses in Syn II KO mice. However, the distribution of the four classes was strongly skewed toward C3 and C4 profiles (85.8% of the connections, n = 33) in Syn II KO mice (Figure 5E,F) while C1 and C2 connections almost disappeared (C1 connection 3.6%, C2 connections 10.7%) (Figure 5E). These results suggest Syn II lead to STP profiles corresponding to C1 and C2, whereas GC boutons displaying C3 and C4 profiles are devoid of Syn II.

Genetic deletion of Syn II induces a partial loss of functional variability at GC-MLI synapses.

(A) Representative traces of EPSCs from 10 successive trains at 100 Hz recorded at unitary GC-MLI synapse from Syn II KO mice (black traces). Averaged trace is in red. Unitary synapses were stimulated using minimal electrical stimulation. (B) left, Mean values of EPSC1, charges elicited by train of stimulation at 100 Hz recorded in WT and Syn II KO mice (grey and red points, respectively). Right, Corresponding traces recorded during these 100 Hz train in WT mice (black trace, mean trace from 102 recordings) and in Syn II KO mice (red trace, averaging from 33 recordings). The mean EPSC charges of the first responses was strongly reduced in Syn II KO mice (mean EPSC1 charge for WT: 128.11 fC ± 10.51 fC, n = 96, mean EPSC1 charge for Syn II KO mice: 60.03 fC ± 5.55 fC, n = 53, p<0.001, MWRST). (C) Box plots showing the values of EPSC charges at the first, second and third stimulus of 100 Hz train (left, middle and right graph respectively) in WT and Syn II KO mice. (D) Box plots showing the number of failures at the first stimulus in WT and Syn II KO mice. The percentage of failures at the first stimuli was increased in Syn II KO mice (mean failure rate EPSC1 in WT: 15.6% ± 1.6, Syn II KO mice: 32.3% ± 3.8). (E) Scatter plot of PCA1 and PCA2 obtained by analyzing EPSC properties during 100 Hz train in WT mice (gray point, same dataset as in Figure 2A) and Syn II KO mice (red points). (F) Pie chart of k-means clustering analysis clusters obtained in WT (same dataset than in Figure 2C) and Syn II KO mice. Note the near complete disappearance of C1 and C2 connections in Syn II KO mice. The profiles of EPSC charges during 100 Hz train for C3 and C4 connections were identical between WT and Syn II KO mice (bottom graphs), indicating that the genetic deletion of Syn II did not impair the functioning of these two classes of GC-MLI synapses.

https://doi.org/10.7554/eLife.41586.012

We next studied the subcellular localization of synaptic vesicles at GC-MLI synapses from WT and Syn II KO mice using transmission electron microscopy (Figure 6A). Morphometric analysis revealed that the absence of Syn II reduced significantly the number of docked synaptic vesicles and the length of the active zone (Figure 6B) without affecting the positive correlation between the length of the active zone and the number of docked synaptic (Figure 6C).

Genetic deletion of Syn II reduces the heterogeneity of ultrastructural profiles of presynaptic terminals at GC-MLI synapses.

(A) Representative micrographs of GC-MLI synapses captured in the upper part of the molecular layer of cerebellar parasagittal sections from WT and Syn II KO mice. (B) Upper panels, Cumulative distribution (left panel) and mean values of the number of docked SVs at GC-MLI synapses from WT and Syn II KO mice (black line/bar and red line/bar respectively). Lower panels, similar representations for the active zone length. Absence of Syn II reduced significantly the number of docked synaptic vesicles and the length of the active zone (mean number of docked synaptic vesicles: WT 6.83 ± 0.35, n = 108; Syn II KO 5.36 ± 0.28, n = 81, mean length of active zone: WT 521.3 nm ± 15.2, n = 108; Syn II KO 432.2 nm ± 13.2, n = 81). (C) Scatter plot of the number of docked synaptic vesicles (SVs) versus the active zone (AZ) length from dataset obtained in B. Genetic deletion of Syn II led to a specific loss of GC bouton endowed with both a long active zone (>800 nm) and high number of docked synaptic vesicles (>15 synaptic vesicles).

https://doi.org/10.7554/eLife.41586.013

Altogether, our results suggest that the presence of Syn II positively regulate the number of docked synaptic vesicle and the pr of fully-releasable vesicles, thus enhancing the release glutamate at the onset of burst firing.

Diversity of STP profile at GC-MLI connections extends the coding range of MLI

The STP profile shapes the spike output pattern of MLIs following compound stimulation of GCs or PFs (Bao et al., 2010; Carter and Regehr, 2000). This suggests that each class of GC-MLI synapse should influence the MLI spike output pattern specifically. To address this hypothesis, we set out to correlate STP of specific GC units with the spike output pattern of the targeted MLI. We recorded the spike output pattern of MLIs in loose-patch configuration following photostimulation of unitary GC inputs by caged glutamate (Materials and methods and Figure 7—figure supplement 1). Photostimulation of individual GCs increased the MLI firing rate confirming that sufficient glutamate was released by unitary GC boutons during high-frequency stimulation to produce spikes in MLIs (Barbour, 1993; Carter and Regehr, 2002) (Figure 7A). Photostimulations produced burst in GCs with reproducible parameters (Figure 7—figure supplement 1) and were followed by an increase in MLI firing rate (mean baseline frequency: 12.75 ± 5 Hz; peak of acceleration: 33.7 ± 17 Hz, n = 32). Subsequently, EPSCs were recorded upon photostimulation of the same unitary GC-MLI synapse using whole-cell configuration (Figure 7A). Photostimulations yielded heterogeneous profile of STP. PCA followed by k-means clustering analysis revealed three distinct STP profiles (C1’, C2’ and C3’ connections different from C1 to C4 connections since the parameters of bursts elicited in GCs using minimal electrical stimulations and photostimulations are different) that differed by their time course and amplitude (Figure 7B–C). C1’ connections with positive PC1 values were characterized by large responses that peaked at the onset of GC bursts and then rapidly depressed (phasic profile) (averaged EPSC peak charge: −232.8 fC ± 55.2 fC reached at 33.2 ms ± 11.3, n = 14). C2’ connections with low PC1 values were also characterized by a phasic profile, but EPSCs have smaller amplitudes (averaged EPSC peak charge: −121.3 fC ± 21.6 fC reached at 59.7 ms ± 10.3, n = 30) than C1’ synapses. C3’ connections were characterized by smaller responses that peaked with longer delays than the ones of C1’ or C2’ synapses (EPSC peak charge: −133.4 fC ± 21.9 fC reached at 84.4 ms ± 5.4, n = 18).

Figure 7 with 2 supplements see all
The diverse profiles of STP of GC boutons differentially shape the spike output pattern in the target MLI.

(A) Left panel, schematic representing the design of photostimulation in the granule cell layer. Open circles represent the sites where RuBi-glutamate was uncaged and the blue circles represent the locations where photostimulation elicited responses in the recorded MLI. In this example, 2 GCs localized at distal positions in the granule cell layer contact the recorded MLI. Right panels, representative experiment showing the spike output pattern recorded in loose-patch configuration and EPSCs recording in whole cell configuration in the same MLI following photostimulation of two different locations in the granule cell layer. The white arrowheads and dashed lines represent the onset of photostimulation. Note that the onset of firing is time-locked to the first peak of EPSC charge for photostimulations in location #1 (upper panels) while the onset of firing was more variable for photostimulation in location #2 (lower panels). (B) PCA transformation of the evoked charge time course for 63 unitary contacts (see Materials and methods). The EPSC bursts could be differentiated depending on their tonic or phasic component into three different clusters using k-Means clustering analysis. (C) Representative traces of EPSCs and of the corresponding firing profiles recorded in singles MLI following stimulation of C1’, C2’ and C3’ connections. (D) The delays separating the onset of photostimulation and the time the recorded MLIs were firing at their maximum frequency (referred as delay to frequency peak, recorded in loose-patch configuration) and the delays separating the onset of photostimulation and the time EPSCs are reaching their maximum value (referred as delay to EPSC peak, recorded in voltage-clamp configuration) were measured for 12 GC-MLI synapses for which we were able to correlate the spike output pattern with STP profile (C1’, C2’ or C3’). Upper panel, the bar plot shows that stimulation of C1’ connections led to faster accelerations of MLI firing rate than stimulation of C2’ and C3’ connections. Lower panel, the scatter plot shows a clear correlation between the delay to EPSC peak and the delay to frequency peak recorded. Each experimental point was associated with its STP profile by using the same color code for categories than in the upper graph.

https://doi.org/10.7554/eLife.41586.014

In 12 unitary connections out of 62, we could correlate the spike output pattern recorded in loose-patch configuration with the STP profile recorded in whole-cell configuration (Figure 7D). We measured the time separating the onset of photostimulation and the time the recorded MLI is firing is at its maximum frequency (labeled as Delay to frequency peak in Figure 7D). Photostimulation of C1’ connections accelerated MLI firing rate with a very short delay (<50 ms) compared to C2’ and C3’ connections (delay > 60 ms) (Figure 7D). Our analysis also showed a clear relationship between PC1 and the peak frequency (Pearson coefficient, R = 0.7, p=0,008, n = 13). This indicates that a specific STP profiles determines the delay to the first spike in MLI in response to GC photostimulation. These results suggest that the behavior of glutamate release at GC-MLI synapses during high-frequency stimulations is a major determinant of the coding of sensorimotor inputs through the FFI pathway.

In some experiments, after photostimulation of 2–3 GCs contacting the same MLI, we could perform post-hoc reconstruction of the recorded cell. Similarly to what was observed with minimal stimulation experiments (Figure 3), we found that identified SCs and BCs both received inputs of different classes upon photostimulation of individual GCs (Figure 7, Figure 7—figure supplement 2). These results confirmed the lack of target-cell-dependent STP at the level of individual connection at GC-MLI synapses.

We next tested how Syn II deficiency affects the correlation between STP profiles and firing pattern at GC-MLI synapses, using Syn II KO mice (n = 27). Accordingly, photostimulation of single GCs with RuBi-glutamate were performed in Syn II KO mice as described above for WT mice (Figure 7 and Figure 7—figure supplement 1); evoked spikes and synaptic responses were recorded in MLIs using loose-patch and whole cell recordings. PCA followed by k-means clustering analysis showed that genetic deletion of Syn II almost abolished the presence of C1’ connections and nearly doubled the percentage of C3’ connections (Figure 8A–D). Moreover, the delay to the first spike significantly increased in Syn II KO mice (Figure 8E,F), probably due to a lower initial pr at GC-MLI synapses devoid of Syn II (Figure 5A,B). Our results reveal that Syn II is a major determinant of burst coding at the GC-MLI synapses. Synapse-specific expression of Syn II diversifies the profile of excitatory drives on MLIs and expands the coding range in the FFI pathway.

Synapse-specific expression of Syn II diversifies the profile of excitatory drives on MLIs and expands the coding range of MLIs.

(A) Left panels, representative traces of superimposed EPSCs recorded in WT and Syn II KO mice after photostimulations in the GCL (same experimental design than in Figure 7A). Right panel, averaged values of EPSC charges versus the time following photorelease of RuBi-glutamate recorded in WT and Syn II KO mice (black and red traces respectively). Note the strong reduction in the peak of charge in Syn II KO mice. (B) PCA transformation of EPSC properties obtained in WT (green, purple and blue points, same dataset than in Figure 7B) and Syn II KO mice (red points). (C) Line plots display the normalized release time course from GC-MLI synapses belonging to clusters C1’, C2’, C3’ (WT mice, same color code as in B) and GC-MLI synapses from Syn II KO mice (red line). (D) The pie chart shows a partial reduction of STP heterogeneity in Syn II KO condition with a strong reduction of phasic profiles (C1’). (E) Typical raster plots and peristimulus time histogram obtained in WT and Syn II KO mice following photostimulation of unitary GC-MLI synapses. The onsets of photostimulation are represented with white arrowheads and dashed lines. (F) Means values of the firing frequency (upper graph) and the firing probability (lower graph) of MLIs following photostimulation of unitary GC-MLI synapses in WT and Syn II KO mice (black and red lines, respectively).

https://doi.org/10.7554/eLife.41586.017

Discussion

By combining molecular, ultrastructural and functional studies, we show that the firing pattern of MLIs is driven by distinct GC inputs that show distinct profile of STP. This functional heterogeneity caused, at least in part, by synapses-specific expression of Syn II expands the coding range of MLIs.

MLI subtype does not determine the profile of STP at unitary GC-MLI synapse

The rules governing diversity in presynaptic release properties have been extensively studied in neocortical or hippocampal circuits. In most of cases, synaptic efficacy among boutons issued from a single axon varies with the identity of the postsynaptic cell (Blackman et al., 2013; Markram et al., 1998). Target-cell-dependent heterogeneity relies on differences in the probability of release (Koester and Johnston, 2005), responsiveness to neuromodulators (Buchanan et al., 2012; Delaney and Jahr, 2002; Pelkey et al., 2006; Scanziani et al., 1998) or the ability to co-release GABA and glutamate (Galván and Gutiérrez, 2017). Target-cell-dependent STP has also been described at cerebellar GC-MLI synapses by using stimulation of beam of PFs or clusters of GC somata; upon high-frequency stimulation, compound synaptic responses exhibit a facilitating profile at GC-SC synapses whereas these responses depress at GC-BC synapses (Bao et al., 2010). At the opposite, our results show rather heterogeneous unitary inputs to BCs (contacted by three different classes of inputs) or SCs (contacted by four different classes of inputs) (Figure 3). Although the lack of the main facilitating class of input (C3) on BCs may explain why compound GC-BC synaptic responses depress during high-frequency activities, our results argue against target-cell dependency of STP at GC synapses. It is likely that in compound responses, input classes associated with a strong synaptic strength mask the presence and influence of weak inputs exhibiting other profiles. Anyhow, our work suggests that the excitatory drive to MLIs and the tuning of the FFI pathway are more complex than expected.

Organization of synaptic diversity at unitary GC-MLI synapses

Heterogeneous expression and functions of Syn II in different cell types forming a same neural network have been reported previously (Bragina et al., 2010; Feliciano et al., 2017; Gitler et al., 2004; Kielland et al., 2006; Patton et al., 2016; Wei et al., 2011) but our findings bring the first evidence that expression of Syn II at a given connection can be heterogeneous. Syn II expression may be genetically determined at early developmental stages, leading to Syn II(+) and Syn II(-) subclones of GCs. It is interesting to note that clonally related GCs (that is, GCs issued from the same GC progenitors) stack their axons in a specific sub-layer in the molecular layer (Espinosa and Luo, 2008) suggesting that the presence or absence of Syn II may be organized in a beam-dependent way. Since beams of neighboring PFs are activated during sensory stimulations (Wilms and Häusser, 2015), recruitment of Syn II(+) or Syn II(-) connections may be related to the activation of a given sensorimotor task. Alternatively, Syn II targeting at individual PF boutons may be controlled by a complex interplay of mechanisms regulating the traffic of Syn in axons (Gitler et al., 2004) or organizing the assembly of the presynaptic active zone (Owald and Sigrist, 2009).

Presynaptic diversity also arises from other parameters that probably expand the range of synaptic behaviors across GC boutons. Calcium imaging performed on single PFs revealed that Ca2+ dynamics and regulation of Ca2+ influx by neuromodulators in synaptic varicosities from a same PF are highly heterogeneous (Bouvier et al., 2016; Brenowitz and Regehr, 2007; Zhang and Linden, 2009; Zhang and Linden, 2012). Also, local retrograde release of endocannabinoid by MLI dendrites can affect the functioning of subsets of GC boutons upon sustained activity of PF (Beierlein and Regehr, 2006; Soler-Llavina and Sabatini, 2006). Hence, functional heterogeneities among GC terminals also originate from the history of firing of each GC. To summarize, the synaptic behavior of individual GC bouton may be tuned by an intermingled combination of factors including expression of Syn II, presynaptic receptor composition, presynaptic Ca2+ dynamic, number of active release sites, retrograde signaling and history of firing.

Control of glutamate release by Syn II in GC boutons

At GC synapses, releasable synaptic vesicles are segregated in two pools, one with fully releasable vesicles and a second one with reluctant vesicles, which are differentially poised for exocytosis (Doussau et al., 2017). The fully-releasable pool supports glutamate release during single action potentials while the reluctant pool is recruited only by stimuli elicited at high frequencies (Doussau et al., 2017). In Syn II KO mice, synaptic transmission is characterized by a defect in glutamate release by single action potentials and by a rapid recovery of synaptic transmission by 100 Hz stimuli. This suggests that a lack of Syn II impair pr of fully-releasable vesicles without affecting the recruitment of reluctant vesicles. Potentially, Syn II may act with several partners to control the recruitment of fully-releasable vesicles. In GC terminals, Munc13-3 has been involved in superpriming steps that tightly couple synaptic vesicles with P/Q-type Ca2+ calcium channels (positional superpriming) or maturate the fusion machinery (molecular superpriming) (Ishiyama et al., 2014; Kusch et al., 2018; Schmidt et al., 2013). Munc13-3 may indirectly act with Rab3-interacting molecules (RIMs) which are well known organizers of calcium channel and synaptic vesicles in the active zone (Südhof, 2013). Since Syn II interacts with both Rab3 (Giovedì et al., 2004) and P/Q type calcium channels (Medrihan et al., 2013), it cannot be excluded that Munc13-3, Syn II, Rab3 and RIM act in concert to reduce the physical distance between fully-releasable vesicles and Ca2+ channels. Alternatively, Syn II-Rab3-RIM complex may directly regulate the influx of Ca2+ through strong inhibition of voltage-dependent inactivation of P/Q type Ca2+-channels (Hirano et al., 2017; Kintscher et al., 2013).

Physiological consequences

At the input stage of the cerebellar cortex, single GCs receive a combination of MF inputs coding for different modalities (Arenz et al., 2008; Chadderton et al., 2014). The diversity of STP profiles across MFs from different origins provide temporal signatures for each combination of MFs converging on a single GC thus enhancing pattern decorrelation of sensory inputs (Chabrol et al., 2015). Here, we show that temporal coding in GCs is later extended in the FFI pathway by an input-specific control of first-spike latency in MLIs. The combination of heterogeneous presynaptic behaviors at the successive stages of cerebellar computation leading to consecutive temporal signatures, should refine the salient feature of a given combination of MF inputs and ultimately should enhances the representation of sensory information by PCs.

Considering the importance of delay coding for internal models of motor adjustments (Kennedy et al., 2014; Kistler et al., 2000; Mauk and Buonomano, 2004; Wolpert et al., 1998) synapse-specific temporal coding may have essential consequences for learning and predictive functions in the cerebellum. Indeed, long-term potentiation (LTP) of GC-PC connections require the coincidence of strong PF and MLI activities onto the same PC (Binda et al., 2016). As exemplified by the rules governing the induction of associative GC-PC long-term depression triggered by coincident PF and climbing fiber activation (Suvrathan et al., 2016), induction of associative GC-MLI LTP may depend on the time window separating excitatory and inhibitory inputs onto PCs. Heterogeneous profiles of STP at MF-GC synapses and GC-MLI synapses necessarily induce wide range of delays between the direct excitatory pathway and FFI at the level of PC synapses. Hence, the fine tuning of STP at the level of single cells may have fundamental importance for the induction of long-term plasticity and ultimately for motor learning.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or referenceIdentifiersAdditional information
Genetic reagent
(M. musculus)
Syn II KO mice
(CD1 backround)
PMID 7777057See Material
and methods
AntibodyRabbit anti-Syn ISynaptic SystemsCat# 106 104IHC (1:500)
AntibodyMonoclonal anti-Syn II
Clone 27E3
Synaptic SystemsCat# 106 211IHC (1:500)
AntibodyMonoclonal anti-Syn II
Clone 19.4
MilliporeCat# MABN 1573IHC (1:500)
AntibodyGuinea pig anti-VGluT1Synaptic SystemsCat# 135 304IHC (1:600)
AntibodyGoat anti-Rabbit-
Alexa 647
Molecular ProbesCat# A-21070
RRID:AB_2535731
IHC (1:1000)
AntibodyGoat anti-Mice
Alexa-488
Molecular ProbesCat# A-21141,
RRID:AB_141626
IHC (1:1000)
AntibodyGoat anti-Guinea Pig
Alexa-555
Molecular ProbesCat# A-21435,
RRID:AB_2535856
IHC (1:1000)
Chemical
compound, drug
Picrotoxin - GABAA-R
blocker
AbcamCat# Ab120315100 µM in ACSF
Chemical
compound, drug
CGP 52432 GABAB-R
blocker
AbcamCat# Ab12033010 µM in ACSF
Chemical
compound, drug
D-AP5 - NMDA-R
blocker
AbcamCat# Ab120003100 µM in ACSF
Chemical
compound, drug
AM251 CB1-R
blocker
AbcamCat# Ab1200881 µM in ACSF
Chemical
compound, drug
JNJ 16259685 –
mGluR1 blocker
TocrisCat# 23332 µM in ACSF
Chemical
compound, drug
Atto-594Sigma-AldrichCat# 0863750 µM in internal solution
Software,
algorithm
Code used for analyzing
MLI firing following
photostimulation of
single GC
This paper
(Dorgans, 2019a)
Python code deposited on GitHub: https://github.com/Dorgans/eLife2018-STP-GC-MLI/blob/master/2017051_GC_photostimulation__MLI_FIRING_ANALYSIS.py
Software,
algorithm
Code used for analyzing
EPSC charge following
photostimulation of
single GC
This paper
(Dorgans, 2019b)
Python code deposited
on GitHub: https://github.com/Dorgans/eLife2018-STP-GC-MLI/blob/master/20170711_GC_photostimulation_GC-MLI_CHARGE_ANALYSIS.py
Software,
algorithm
Code used for PCA
transformation and
k-mean clustering
analysis of EPSC charges
following
photostimulation
This paper
(Dorgans, 2019c)
Python code deposited on GitHub: https://github.com/Dorgans/eLife2018-STP-GC-MLI/blob/master/20170718_GC_photostim_MLI_SeqPatch_PCA%2Cclustering.py
Software,
algorithm
Code used for
PCA transformation
and k-mean clustering
analysis of EPSC
charges during
high frequency
stimulations
This paper
(Dorgans, 2019d)
Python code deposited
on GitHub: https://github.com/Dorgans/eLife2018-STP-GC-MLI/blob/master/20170718_GC_photostim_MLI_SeqPatch_PCA%2Cclustering.py

Mice

This study was carried out in strict accordance with the national and international laws for laboratory animal welfare and experimentation and was approved in advance by the Ethics Committee of Strasbourg (CREMEAS; CEEA35; agreement number/reference protocol: APAFIS#4354–20 16030212155187 v3). Mice were bred and housed in a 12 hr light/dark cycle with free access to food and water. Wild type (WT) or Synapsin II knock-out (Syn II KO) mice have CD1 genetic background. Syn II KO mice were first derived from synapsin triple knock-out mice (C57BL/6J genetic background, originating from the Italian Institute of Technology, Genova, Italy) (Gitler et al., 2004) bred with CD1 WT mice. Syn II KO hybrid mice were serially bred (10 backcrosses) with CD1 WT mice to obtain Syn II KO mice with CD1 genetic background.

Slice preparation

Acute cerebellar slices were prepared from CD1 mice or Syn II KO mice (Rosahl et al., 1995), aged 20 to 35 days. Mice were anesthetized by isoflurane inhalation and decapitated. The cerebellum was extracted in ice-cold (~1°C) artificial cerebrospinal fluid (ACSF) bubbled with carbogen (95% O2, 5% CO2) containing (in mM): 120 NaCl, 3 KCl, 26 NaHCO3, 1.25 NaH2PO4, 2.5 CaCl2, 2 MgCl2, 10 D-glucose and 0.05 mM minocyclin. Cerebella were sliced (Microm HM650V, Germany) in an ice-cold low-sodium and zero-calcium slicing buffer containing (in mM): 93, 2.5 KCl, 0.5 CaCl2, 10 MgSO4, 1.2 NaH2PO4, 30 NaHCO3, 20 HEPES, 3 Na-Pyruvate, 2 Thiourea, 5 Na-ascorbate, 25 D-glucose and 1 Kynurenic acid. Sagittal or horizontal slices 300 µm thick were immediately transferred for recovery in a bubbled ACSF for 30 min at 34°C and maintained at room temperature (~25°C) in bubbled ACSF before use.

Electrophysiology

After at least 1 hour of recovery at room temperature (~25°C), slices were transferred in a recording chamber continuously perfused with 32 ~ 34°C bubbled ACSF. In order to block all forms of long-term synaptic plasticity and trans-synaptic signaling, blockers of GABAA-receptors (100 µM picrotoxin), GABAB-receptors 10 µm (3-[[(3,4-Dichlorophenyl)- methyl]amino]propyl(diethoxymethyl)phosphinic acid), NMDA-receptor (100 µM D-AP5; D-(-)−2-Amino-5-phosphonopentanoic acid), endocannabinoïd CB1 receptors (1 µM AM251 1-(2,4-Dichlorophenyl)−5-(4-iodophenyl)−4-methyl-N-(piperidin-1-yl)−1H-pyrazole-3-carboxamide) and mGluR1 receptor (2 µM JNJ16259685 (3,4-Dihydro-2H-pyrano[2,3-b]quinolin-7-yl)-(cis-4-methoxycyclohexyl)-methanone) were added in ACSF.

MLI were patch-clamped in lobules IV to VI in the cerebellar vermis using a two-photon microscope setup (Multiphoton Imaging System, Scientifica UK) with 10 MΩ resistance glass electrodes containing a cesium-based intra-cellular medium (140 mM CsCH3SO3, 10 mM Phosphocreatine, 10 mM HEPES, 10 mM BAPTA, 4 mM Na-ATP and 0.3 mM Na-GTP) supplemented with 50 µM Atto-594 fluorescent dye (Sigma-Aldrich, Germany). In all experiments, cells were voltage-clamped at −70 mV in whole-cell configuration (Multiclamp 700B, Molecular Devices). Data were acquired using the WinWCP freeware (John Dempster, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, UK). Electrical stimulations were realized with a ~ 10 MΩ resistance monopolar electrode also filled with ATTO-594 for a precise adjustment of the distance between the stimulation pipette and isolated dendritic processes of MLIs. Electric pulses were adjusted at any GC-MLI contact and evoked with a stimulator (IsoStim01-D, NPI Germany). Variations of input resistance were not corrected but monitored. The recordings were not taken into account if serie resistance has changed by more than 20%.

Minimal stimulation was used to monitor STP at unitary GC-MLI synapses in sagittal slices (Figure 1—figure supplement 1A). To do so, we followed previously established procedures (Malagon et al., 2016). For each synapse, the intensity of electrical stimulation was maintained in an intensity window that avoided both stimulation failures and multiple-synapse stimulation (Figure 1—figure supplement 1). Several parameters were systematically checked to choose the optimal value of the stimulating current ensuring the recruitment of a single synapse. First, the stimulation must induce a high success rate (>0.4) and stable EPSC integral at the first stimuli in the train. The lowest current value reaching these criteria was chosen for minimal stimulation. Stimulating currents were rejected if they generated (i) systematic failures at the first stimulus, (ii) an increase in failure rate at the second stimulus, (iii) no decrease in the failure rate at the second stimulus for synapses with a high failure rate (~0.3 to 0.6) at the first stimulus, (iv) a strong (>5 fold) increase in EPSC amplitude at the first stimulus in one or more responses for the same stimulating current, and/or (v) aberrant values of the paired-pulse ratios (>4) during 10 successive stimuli. In almost 80% of experiments, the current values of minimal stimulations were inferior to 50 µA (Figure 1—figure supplement 1C). Our results showed that current spread in cerebellar slices follows a biexponential decay function with two current-distance constant (τ1 = 12.051 µm for ± 4.06 µm that accounts for 93.3% of current drop and τ2 = 8.8*10−3 µm ± 4.2*10−3 µm that accounts for the last 6.7% of the drop). Given that intersynaptic distance on MLI dendrites is estimated to 10 µm (Soler-Llavina and Sabatini, 2006), nearest synaptic contacts of the recorded synapses received a current 52.9% lower that this latter (Figure 1—figure supplement 1D). To summarize, it is likely that current spread properties ensure the recruitment of single synapses when using stimulation intensities just above the threshold.

We performed glutamate-uncaging assays onto horizontal slices by using MOSAiC patterned illumination system (Andor Technologies). MLI were recorded in ACSF containing 100 µM RuBiGlutamate (Valera et al., 2016). In order to find connected pairs of GC-MLI, we first used full-field arrays composed of very small photostimulation areas (15 ~ 25 µm diameter) and patches of GC were sequentially illuminated with blue light (460 nm). We took advantage on horizontal slice configuration to stimulate GCs localized at distant locations from the recorded MLI. Considering the weak probability of connection between GC and MLI, synaptic activities evoked by photostimulation of small cluster of GCs localized far away of the recorded MLI are likely to originate from unitary GC-MLI synapses.

Post-hoc 3D reconstructions

After the experiments, two-photon Z-stacks (1 µm resolution) were done to reconstruct the recorded MLIs in sagittal configuration using the simple neurite tracer plugin (Longair et al., 2011) from ImageJ freeware (National Institute of Health, USA). Basket cells were identified by the basket-like features observed in the Purkinje cell layer (Palay and Chan-Palay, 1974).

Electron microscopy

CD1 mice aged 20 days (WT and Syn II KO mice) were deeply anesthetized by intra-peritoneal injection of Ketamine (2 ml/kg) and Xylazine (0.5 ml/kg) and intracardiac perfusion was performed with 2.5% glutaraldehyde in phosphate buffer (0.1 M, pH 7.4). For immunogold labeling, the fixative solution was replaced by 0.1% glutaraldehyde and 4% paraformaldehyde in phosphate buffer. Transversal cerebellar vibratome sections (75 µm thick) were cut and processed either for ultrastructural analysis or for pre-embedding immunogold labeling. After three washes in phosphate buffer, sections were post-fixed in phosphate buffer with 1% OsO4 for 1 hr. Slices were dehydrated in a graded alcohol series (ethanol 25%, 50%, 70%, 95% 100%; 10 min per bath) except for ethanol 100% (3 × 10 min) followed by an incubation in propylene oxide for 3 × 10 min. Then slices were embedded in Araldite M (wash in propylene oxide at 1:1 for 1 hr followed by Araldite M for 2 × 2 hr at room temperature; polymerization at 60°C for 3 days). Ultrathin sections were finally contrasted with uranyl acetate.

Pre-embedding immunogold labelling

Sections were permeabilised with 0.2% saponin in phosphate buffer saline (PBS) for 1 hour, rinsed in PBS and blocked in a blocking solution: 2% bovine serum albumin in PBS (PBS-BSA). The sections were incubated overnight with anti-Syn I (1/250) or anti-Syn II (1/100) antibodies (polyclonal, SynapticSystems) in 0.1% BSA in PBS. After washing in PBS-BSA, the sections were incubated in Ultra small nanogold F(ab′) fragments of goat anti-rabbit or goat anti-mouse immunoglobulin G (IgG) (H and L chains; Aurion) diluted 1/100 in PBS-BSA. After several rinses in PBS-BSA and in phosphate buffer (PB), sections were postfixed in glutaraldehyde 2% in PB before washing in PB and distilled water. Gold particles were then silver enhanced using the R-Gent SE-EM kit (Aurion) before being washed in distilled water and PB. Finally, the sections were post-fixed in 0.5% OsO4 in PB for 10 min before classical processing for Araldite embedding (Sigma, St. Louis, MO) and ultramicrotomy. The ultrathin sections were counterstained with uranyl acetate and observed with a Hitachi 7500 transmission electron microscope (Hitachi High Technologies Corporation, Tokyo, Japan) equipped with an AMT Hamamatsu digital camera (Hamamatsu Photonics, Hamamatsu City, Japan). In control sections processed without anti-Syn I or anti-Syn II primary antibodies or gold-labeled secondary antibodies, no gold particles were observed.

Analysis of electron micrographs

PF-MLI and PF-PC synapses are glutamatergic synapses that can be recognized by the presence of an obvious asymmetry with a large postsynaptic density (Korogod et al., 2015). GCs contact MLIs on their dendritic shaft and PCs on their dendritic spines. Since PC dendritic spines are devoid of mitochondrion (Palay and Chan-Palay, 1976), GC-MLI synapses stand out from the large majority of asymmetrical synapses in the cerebellar cortex by the presence on mitochondrion within the postsynaptic compartment. Also, we only took in account synapses located to the upper part of the molecular layer to avoid GC-Golgi cell synapses. Morphometric analyses were performed using ImageJ freeware (National Institute of Health). We binned the number of vesicles (with 50 nm distance bins) starting from the active zone cytomatrix as a reference point (0 nm). Synaptic vesicles within 50 nm of the active zone were considered as docked vesicles (Schikorski and Stevens, 2001).

Immunohistochemistry

CD1 WT mice aged 20 to 25 days were deeply anaesthetized by intra-peritoneal injection of Ketamine (2 ml/kg) and Xylazine (0.5 ml/kg) and perfused with PBS containing 4% paraformaldehyde (PFA). After a 3 hr post-fixation, cerebella were sliced in sagittal configuration (50 µm thickness). Slices were washed in PBS (3 × 10 min). Membranes were permeabilized by 0,1% TritonX100 and non-specific antigens were blocked by 10% bovine serum albumin (BSA) and 1% goat serum albumin (GSA) during 6 hr. Synapses were stained using the same solution supplemented with anti-VGluT1 guinea pig polyclonal antibodies diluted at 1/600 (Synaptic Systems, Germany), polyclonal rabbit anti-Syn Ia/SynIb (Synaptic Systems, Germany) diluted at 1/500 and monoclonal anti-Syn IIa/Syn IIb antibodies. We used two different monoclonal anti-Syn IIa/Syn IIb antibodies both diluted at 1/500: clone 27E3 (Synaptic Systems) targeted again an epitope localized on domains C and clone 19.4 (Millipore) targeted again an epitope localized on domain A-B. Secondary antibodies (Abcam) were applied during 3 hr in a solution containing 10% BSA. Slices were mounted and visualized under confocal microscope (Leica SP5, II).

Data analysis

Analysis were performed with home-made python routines (WinPython 3.3.5, Python Software Fundation) based on custom scripts. All statistical analyses were performed using SciPy plugin (https://scipy.org/) (Dorgans, 2019a; Dorgans, 2019bDorgans, 2019c; Dorgans, 2019d; see key resource table; copies archived at https://github.com/elifesciences-publications/eLife2018-STP-GC-MLI). Error bars represent ± SEMs of data distribution. Student’s t-test was used in the case of a normal distribution of data, Mann-Whitney Rank Sum Test (MWRST) was used in other cases. One way ANOVA with post hoc Tukey tests were used for multiple comparisons. The levels of significance are indicated as ns (not significant) when p>0.05, * when p<0.05, ** when p<0.01 and *** when p<0.001.

Principal component analysis

PCA is a linear transformation algorithm that examines the main sources of variability inside a dataset composed of multiple observations in order to classify the dataset. PCA analyses covariance between the n variables of a dataset and transforms an original dataset in eigenvalues around a small number of dimensions representing the principal components. The first two Principal Components (PC1 and PC2) which explain the highest source of variance from the original dataset are represented in a scatter plot. We used PCA in order to classify STP in our datasets and extract the most relevant inter-individual differences. PCA were computed using the python-based sklearn plugin. Input variables were normalized and centered using Vector Space Model (VSM) that linearly scales the observations between 0 and 1 (Salton et al., 1975). While STP data from WT GC-MLI terminals was used for PCA computation, Syn II KO observations did not take part in the eigenvalue calculation. In order to compare STP heterogeneity between the two populations of synapses, Syn II KO observations were processed as additional values and overlaid to WT cloud of points.

Data processing

For STP analyses using minimal stimulation protocols, data was collected by estimating EPSC charges at any stimulus number from 7 (or more) consecutive trains at 100 Hz elicited every minutes. Failures were arbitrarily detected as signals below a threshold of 3 x σnoise, where σnoise is the standard deviation of the amplitude of the noise calculated on a 300 ms fixed temporal window preceding the stimulation. PCA transformations (Figures 2, 5 and 7) were performed on the median charge value of each EPSC from the 100 Hz train pulse for each synapse (n = 96). The charge of EPSCs evoked at unitary GC-MLI synapses by photostimulation was measured in a minimal number of 7 successive recordings. To calculate the average charge, values were binned (bin width = 5 ms) from the stimulation onset to 100 ms post-stimulus for each sweep (n = 1080) and PCA transformation was applied using the charge value for unitary dataset (Figures 6 and 7 n = 89). The delay of MLI peak frequency was estimated from the stimulation onset.

In rare cases, photostimulating GCs could evoke neurotransmitter release at more than one GC-MLI synaptic contact. To optimize GC-MLI STP dataset, post-hoc monitoring of EPSCs evoked by GC activation was systematically performed using SpAcAn (Spontaneous Activity Analysis), a collection of IGOR pro-functions (WaveMetrics) (https://www.wavemetrics.com/project/SpAcAn). When EPSCs displayed important kinetic variability, the recordings (both loose-patch and whole-cell recordings) were systematically discarded.

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Decision letter

  1. Indira M Raman
    Reviewing Editor; Northwestern University, United States
  2. Gary L Westbrook
    Senior Editor; Vollum Institute, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for sending your article entitled "Short-term plasticity at cerebellar granule cell to molecular layer interneuron synapses expands information processing" for peer review at eLife. Your article is being evaluated by three peer reviewers, and the evaluation is being overseen by a Reviewing Editor and Gary Westbrook as the Senior Editor.

A key question arose in the discussion with the editors and reviewers regarding methodology, which we would like to ask you about before we form a decision. Specifically, it was not clear how you determined that action potential failures did not occur during minimal stimulation. The reviewers noted that the 70 µA intensity seemed to give lower proportions of failures than the 80 µA intensity (in Figure 1—figure supplement 1) and pointed out that if stimulation failures indeed occur, the plasticity profiles could be erroneous. We would like to know whether you think that this concern is easily addressed, and if so, how. That point, as articulated by reviewer 1, as well as the other points raised in the reviews, are included below. It is not necessary for you to complete a revision yet, but please consider the reviews and respond within the next two weeks letting us know how you might address the points, including new experiments and timetable for the completion of the additional work as necessary. We will share your responses with the reviewers and then issue a binding recommendation.

Reviewer #1:

This work by Dorgans et al. examines short-term plasticity (STP) at single parallel fiber-molecular layer interneuron synapses. They find that STP is highly variable across individual synapses and that responses fall into four types groups using principle component analysis. They then show that expression of synapsin II is also variable across synapses and suggest this may account for at least part of the variability in STP. In syn II KO mice, release probability from PF-MLI synapses is reduced generally, and STP responses fall into only two rather than 4 groups, indicating reduced variability of STP responses. They go on to show that differences in STP at PF-MLI synapses results in correlated differences in the firing response of MLIs to synaptic stimulation. The authors argue that diversity of STP at these synapses increases the coding range of MLIs. The data are generally of high quality and the results are of broad interest. However, the manuscript lacks detail on experimental design and analysis in many instances. In several cases it is difficult to assess the conclusions without further detail.

Major comments:

1) The authors use minimal electrical stimulation to measure STP from single synapses. However, the profile of STP can be highly affected by the efficiency of stimulation. Specifically, failure to reliably elicit an action potential on the first stimulus can result in apparent synaptic failures and alter the apparent STP. Very little detail is given on the stimulus protocol in the Materials and methods, though it does state they avoided stimulation failures, how was this avoided? The Materials and methods cite Figure 1—figure supplement 1 where some detail is provided. From this figure, it looks like 70 and 80 µA stimulation activated a single synapses, but there are far more failures at 70 µA (is this true?). If the same synapse is reliably activated in each case the synaptic failure rate should be the same, suggesting the increased failure rate at 70 µA is due to failure to reliably evoke an AP in the axon. The legend indicates 70 µA was chosen as the minimal stimulation in this case, making me concerned that many recordings may not be reliably stimulating action potentials in the axon on the first stimulus. Was the lowest stimulus intensity to evoke responses always used? And did this intensity have more failures than slightly higher intensities as is shown in Figure 1—figure supplement 1? Malagon et al., 2016 (cited in the Materials and methods) appear to have used the plateau of failure rate to determine the minimal stimulus intensity, but (at least from Figure 1—figure supplement 1) that does not appear to be the case here.

2) Looking at the traces, it appears the synapses vary greatly in the degree of asynchronous release, was this measured or considered as a variable? I would guess that differences in asynchronous release could have a profound impact on STP and the pattern of firing in the post-synaptic MLI. Did the amount of asynchronous release correlate with any of the response groups?

3) More detail should be provided on the principal component analysis. It's not clear to me what a positive or negative PC1 or PC2 value means.

4) Regarding Figure 4. Using immunostaining it is not possible to distinguish PF-MLI synapses from PF-PC synapses, and in fact the majority of synapses are likely PF-PC synapses, this should be acknowledged in the text.

Is it possible to conclusively distinguish PF-MLI synapses form PF-PC synapses in EM? If so, how did you determine this? These details are missing. It is important to know that the synapses being analyzed are in fact PF-MLI synapses.

Have you (or others) tested the reliability of the syn II antibody? Is it possible you see less syn II labeling because the antibody is not as good as the syn I antibody?

5) Figures and text frequently report EPSC charge, how was this calculated?

6) In Figure 1C, does this show the quantification of responses from B? If so, why do responses increase on the last stimulus when this does not appear to be the case in the traces in (B). The green trace in particular, EPSC10 is the same amplitude as EPSC1, but this is hard to believe looking at the green trace in (B). This again brings up the question of how EPSC charge was calculated.

7) Figure 7 C, D. Under loose-patch, data are graphed as "firing frequency acceleration,% ", this is somewhat confusing, is there a reason for not simply graphing the firing rate? Graphs in (D) are "Delay to frequency peak" and "Delay to firing peak", how are these different? Some descriptions of these would help.

8) Second paragraph of subsection “Diversity of STP profile at GC-MLI connections extends the coding range of MLI”, is this shown in Figure 7D? Again, what does PC1 represent here? I don't see that either graph in 7D graphs "peak frequency".

Reviewer #2:

Dorgans et al. investigate heterogeneity in synaptic properties of cerebellar granule cells (GC) onto molecular layer interneurons (MLI). The GC-MLI synapse shapes cerebellar cortical computations by recruiting stellate and basket interneurons, thus engaging feedforward inhibition onto Purkinje neurons. Through local stimulation of individual release sites, the authors find that the GC-MLI connection is heterogeneous in terms of its short-term plastic properties. They speculate that this heterogeneity reflects variable initial release probabilities. Unexpectedly, they find that the heterogeneity cannot be explained by target-dependence (stellate cells vs. basket cells), which is known to exist in this circuit, but that it is influenced by presynaptic expression of the synaptic vesicle-associated protein synapsin II: in synapsin II knockout animals, the heterogeneity in short-term synaptic plasticity profiles is reduced. Finally, they show that the diversity in synaptic profiles has functional implications for the differential recruitment of MLI, and could therefore be a mechanism to expand coding of information in the cerebellar cortex.

The study is interesting. A few issues should be addressed:

Experiments are done with very small electrodes (10 MΩ), from cells with very thin dendrites, thus raising concerns about voltage control. There is no mention of series resistance compensation. I wonder to what extent the different synaptic profiles are influenced/shaped by series resistance errors. Is there a relationship between distance of synaptic inputs from cell body and type of synaptic profile?

Synapsins are known to regulate the size of vesicle pools (e.g. Gitler et al., 2008), in addition to release probability. Is paired-pulse plasticity affected in synapsin knockouts? Is the size of the RRP affected? is there an effect on vesicle clustering in EM?

The lack of target cell specificity in the heterogeneity of GC connections on MLI directly contradicts previous findings, which is acknowledged by the authors. One explanation they offer is that in previous studies, strong connections in compound responses (elicited by stimulation of GC clusters or by parallel fiber stimulation) could mask weak responses. The authors can and should directly address this important issue using their GC-specific photostimulation assay. Are there target-dependent differences in photostimulation-evoked synaptic profiles? (they might already have the data to answer this question).

Subsection “Syn II is heterogeneously expressed across GC-MLI presynaptic terminals”: the authors correlate vglut1 signal with synapsin I signal (presumably in the molecular layer, although that is not mentioned) and report R= 0.584. They conclude that "synapsin I is present in ALL GC terminals". I think this conclusion is not justified given the low R-value and should be rephrased.

Reviewer #3:

In their study Dorgans et al. use PCA to classify four classes of Cerebellar Granule Cell – Molecular Interneuron Cell synapses based on the short-term dynamics of the MIC responses to repeated stimulation of the GC. They show that if they knockout synapsin II from the GC axon terminals that the dynamics and time course of the synaptic responses are altered and slowed. Their demonstration of the functional significance of this 'heterogeneity' in synaptic responsiveness involved characterisation of the spiking output of the MICs and indeed they show that cells that receive strong initial synaptic drive spike early while those that receive a slower more gradual ramp in synaptic drive spike later. Overall the experiments are carried out to a very high standard and I have no doubts that the results are robust and their interpretation correct.

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

Thank you for submitting your article "Short-term plasticity at cerebellar granule cell to molecular layer interneuron synapses expands information processing" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Gary Westbrook as the Senior Editor. The reviewers have opted to remain anonymous.

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

Summary:

This study examines short-term plasticity (STP) at single parallel fiber-molecular layer interneuron synapses and demonstrates that STP varies across individual synapses, with responses falling into four categories as defined by principal components analysis. Additionally, expression of synapsin II also differs across synapses and may account for at least part of the variability in STP, as demonstrated by tests of synapsin-II knock-out mice. Additionally, differences in STP parallel fiber to interneuron synapses correlates with differences in the firing responses of interneurons to synaptic stimulation.

Essential revisions:

Both reviewers agreed that the revisions have substantially improved the manuscript. One concern remains regarding the matter of stimulation intensity, namely whether each stimulus reliably evoked action potentials in parallel fibers, and it was suggested that it would be appropriate to show in a supplement a plot of failure (or success) rate versus stimulus intensity. In the consultation, it was acknowledged that even if failures were evident, it would not compromise the results and interpretations in a way that could not be addressed by a straightforward discussion and/or acknowledgment of possible sources error. Our consensus was that such a graph is needed simply for complete disclosure of experimental conditions. In the reviewer's words: "What I would really like to ask them to do, is (1) plot failure rate vs stimulus intensity for every cell and exclude cells in which the failure rate has not reached a plateau at the stimulus intensity used, (2) provide a better example in supplement figure 1B including the failure rate plot and (3) add a statement that the stimulus intensity used was the minimum intensity that produced a failure rate of at least 90% (or whatever the real value is) of the plateau failure rate."

The reviewer's original expression of this point are included below.

Major comments:

The authors have substantially responded to the reviewers' concerns and improved the manuscript, including additional experiments, changes to figures, additional supplementary figures, and major changes in the text. I am satisfied with the use of an additional syn II antibody and other responses to reviewer comments. I am reasonably confident that responses result from stimulation of a single synapse and not multiple synapses. The authors use an impressive array of analysis, experiments, and strict response criteria to establish that EPSCs arise from a single synapse.

However, I am less certain that an AP was reliably evoked in the parallel fiber with each stimulus. As I said in the previous review, in Figure 1—figure supplement 1B there are far more failures at 70 µA stimulus that 80 µA stimulus. This strongly suggests AP were not reliably evoked at this stimulus intensity, and a higher stimulus intensity (80 µA) should be used. The authors need to show a plot of failure (or success) rate versus stimulus intensity (as is shown in figure 2D of Malagon et al., 2016) and indicate the stimulus intensity chosen. Ideally, this plot should show that the failure rate reaches a plateau at the stimulus intensity used for subsequent experiments (though I suspect this is not the case for the data in the figure). The authors do mitigate this concern to a degree by showing that when the first EPSC is excluded the principal component analysis still produces more or less the same groups of responses (Figure 2—figure supplement 3). However, I still think the plot of failure rate vs stimulus needs to be shown in Figure 1—figure supplement 1.

https://doi.org/10.7554/eLife.41586.021

Author response

[Editors' note: the authors’ plan for revisions was approved and the authors made a formal revised submission.]

Reviewer #1:

This work by Dorgans et al. examines short-term plasticity (STP) at single parallel fiber-molecular layer interneuron synapses. They find that STP is highly variable across individual synapses and that responses fall into four types groups using principle component analysis. They then show that expression of synapsin II is also variable across synapses and suggest this may account for at least part of the variability in STP. In syn II KO mice, release probability from PF-MLI synapses is reduced generally, and STP responses fall into only two rather than 4 groups, indicating reduced variability of STP responses. They go on to show that differences in STP at PF-MLI synapses results in correlated differences in the firing response of MLIs to synaptic stimulation. The authors argue that diversity of STP at these synapses increases the coding range of MLIs. The data are generally of high quality and the results are of broad interest. However, the manuscript lacks detail on experimental design and analysis in many instances. In several cases it is difficult to assess the conclusions without further detail.

Major comments:

1) The authors use minimal electrical stimulation to measure STP from single synapses. However, the profile of STP can be highly affected by the efficiency of stimulation. Specifically, failure to reliably elicit an action potential on the first stimulus can result in apparent synaptic failures and alter the apparent STP. Very little detail is given on the stimulus protocol in the Materials and methods, though it does state they avoided stimulation failures, how was this avoided? The Materials and methods cite Figure 1—figure supplement 1 where some detail is provided. From this figure, it looks like 70 and 80 µA stimulation activated a single synapses, but there are far more failures at 70 µA (is this true?). If the same synapse is reliably activated in each case the synaptic failure rate should be the same, suggesting the increased failure rate at 70 µA is due to failure to reliably evoke an AP in the axon. The legend indicates 70 µA was chosen as the minimal stimulation in this case, making me concerned that many recordings may not be reliably stimulating action potentials in the axon on the first stimulus. Was the lowest stimulus intensity to evoke responses always used? And did this intensity have more failures than slightly higher intensities as is shown in Figure 1—figure supplement 1? Malagon et al., 2016 (cited in the Materials and methods) appear to have used the plateau of failure rate to determine the minimal stimulus intensity, but (at least from Figure 1—figure supplement 1) that does not appear to be the case here.

Failure rate

A stable failure rate is indeed an essential criterion for ensuring that minimal stimulation actually recruits a single synapse. Several works performed at unitary GC-BC or GC-SC synapses report a failure rate ranged from 0 to 0.6 at the first stimulus (Ishiyama et al., 2014; Malagon et al., 2016; Miki et al., 2016). As noted by Malagon and coworkers, “stable, high success rate for the first stimulus (Figure 2D, dots), together with a stable EPSC integral (Figure 2D, circles), indicated reliable AP firing for each stimulus in the presynaptic PF” (Malagon et al., 2016). We used these criteria to adjust and choose the intensity of stimulation. We systematically probed the failure rate and the amplitude of EPSCs at the first stimuli and at the second stimuli (to probe the paired-pulse ratio) at several values of current intensity during 10 successive paired-pulse stimulations (real time measurement of EPSC charge was not possible). The lowest value reaching these criteria was chosen for minimal stimulation. In general this value ranged between 30 to 40 µA. Stimulations meeting the following criteria were not considered valid for minimal stimulation:

– stimulations generating systematic failures at the first stimuli (considered as infra-threshold stimuli).

– stimulations generating strong variations of the failure rate at the first and/or the second stimuli during 10 successive stimuli

– stimulations generating strong variations of EPSC amplitudes at the first and second stimuli during 10 successive stimuli (real-time EPSC amplitudes could be measured and visualized by superimposing the 10 traces)

– stimulations generating variable values of the paired-pulse ratio during 10 successive stimuli (again, real time estimation of the paired pulse ration could be performed during the experiment) -stimulation generating aberrant values of the paired pulse ratio

Despite such precautions, we cannot exclude that some of the failures are due to lack of excitability rather than synaptic processes. By using our protocol to select an appropriate intensity of minimal stimulation, most of the failures due to a lack of excitability are likely to occur at the first response. To estimate possible errors in the classification of inputs due to experimental errors at the first stimulus, we checked whether the classification of unitary GC-MLI synapses was influenced by the first response. Accordingly, we compared the classification of inputs by PCA and k-mean clustering analysis during 100 Hz trains by taking into account the first response in the train or not. Results are now presented in a new supplementary figure (Figure 2—figure supplement 3). As shown on this supplementary figure, the classification of inputs was weakly affected when the first responses were not included in the analysis. This suggests that the weight of the first response in the classification of input is weak and those experimental errors due to lack of excitability, if any, weakly affect this classification.

The cutoff distance of stimulation

To stimulate only a single synapse, the field covered by the supra-threshold stimulation current should be restricted to distance inferior to the intersynaptic distances measured in MLI dendrites. It is possible to estimate the distance covered by the supra-threshold electrical stimulation simply by moving the tip of the stimulation pipette away from the recording electrode. The “cutoff distance” of stimuli was measured using currents of increasing intensities elicited at increasing distances from the recorded electrodes. Results have now been added in the Figure 1—figure supplement 1. In our experimental conditions, the current dropped sharply when propagating into the slice. This drop could be fitted by a biexponential decay (Figure 1—figure supplement 1). In MLI, intersynaptic distance in dendrites was estimated to ~10 µm (Soler-Llavina and Sabatini, 2006). As an example, at 10 µm from the stimulating electrode, a current of 25 µA (value used in the majority of minimal stimulation experiments) drops to 16.8 µA. Since we always choose the minimal value of current intensity, it is likely that a 16 µA drop of the current intensity cannot elicit response in neighboring synapses.

Despite all the precautions taken, it could be argued that some stimulation could have systematically recruited 2 or more PFs in a stable manner without the experimenter being able to detect it. If so, this phenomenon is supposed to be associated with strong synaptic weights at the first stimuli in the train and is likely to occur for high current values. Conversely, weak current values can generate unexpected failures of PF excitability. This phenomenon is supposed to be associated with large value of PPR since failure of excitability decrease at the second stimuli. Hence, high and low current values may be associated with specific STP profile. We checked whether the charges of EPSCs at the first stimuli, PPR, PC1 and PC2 where correlated with the current intensity. Our result failed to show such correlations. These results are now displayed in Figure 2—figure supplement 2. The lack of correlation between these parameters and the intensity of current indicated that in most of the experiments, STP profile was shaped by synaptic mechanisms rather than change in PF excitability.

Stability of STP profile during 10 successive100 Hz trains

Because high-frequency stimulation can change the excitability of PFs, it should be ensured that no additional PF is recruited during the train. Given the precaution made to select the lowest stimulation intensity, such recruitment is supposed to occur randomly during the train (that is, not systematically and/or at different stimulus number from one train to another). Consequently, recruitment of additional PFs may strongly affect the profile of STP from one train to another. We checked whether the profile of STP was conserved during 10 successive 100 Hz trains at a given position of the stimulation electrode. The results are displayed in Figure 2—figure supplement 2. It should be noted that PCA transformation was performed blindly on every single trace and not on the mean values of ten successive trains. The systematic narrow clustering of 10 recordings belonging to the same series of stimulations in the cloud of point of PCA transformation (Figure 2—figure supplement 2A) clearly indicated that the profile of STP was very stable from one train to another at a given position of the stimulation electrode.

2) Looking at the traces, it appears the synapses vary greatly in the degree of asynchronous release, was this measured or considered as a variable? I would guess that differences in asynchronous release could have a profound impact on STP and the pattern of firing in the post-synaptic MLI. Did the amount of asynchronous release correlate with any of the response groups?

Asynchronous release is indeed variable from synapse to synapse and generally increases during train of high-frequency stimulations at GC-MLI synapses. Measurements of peak amplitude of EPSC only take in account quanta that are released synchronously and therefore underestimate release if a substantial number of quanta are released asynchronously. As mentioned in several studies (for a review, see Neher, 2015), asynchronous and synchronous events are both taken into account by measuring EPSC charge (current integral) instead of EPSC peak amplitude at each stimulus number. In this study, release events and STP were estimated by measuring EPSC charges. Hence, we are quite confident that STP profile could be correctly compared between all types of synapses exhibiting asynchronous release or not.

3) More detail should be provided on the principal component analysis. It's not clear to me what a positive or negative PC1 or PC2 value means.

In our analysis, input variables were normalized and centered. This linearly scaled the values between -1 and 1 (Salton et al., 1975). PCA computes a linear dimensionality reduction on the original dataset using singular value decomposition ARPACK algorithm and computes eigenvectors from n-dimensional dataset (here, n=10; the charge values for eEPSC1 to eEPSC10) composed of multiple observations (x=116 synapses). The eigenvectors are used to transform original dataset into n’-eigenvalues through covariance reduction and variables are sorted from 1 to n’ (here, PC1 to PC5) where PC1 eigenvalues represent maximal variance into the dataset (and PC2 value, the second maximal variance, PC3 the third, etc.). Each Principal Component is explained by a relative proportion of variance from the n-dimensions of original dataset. Thus, calculating the Principal Components of a n-dimensional dataset is the perfect way to illustrate dissimilarities between x-observations by describing non-linearities between individual observations and neglecting co-varying parameters through a global dataset computation.

4) Regarding Figure 4. Using immunostaining it is not possible to distinguish PF-MLI synapses from PF-PC synapses, and in fact the majority of synapses are likely PF-PC synapses, this should be acknowledged in the text.

Is it possible to conclusively distinguish PF-MLI synapses form PF-PC synapses in EM? If so, how did you determine this? These details are missing. It is important to know that the synapses being analyzed are in fact PF-MLI synapses.

We agree that in the molecular layer, the vast majority of VGluT1 positive synapses are PF-PC synapses; this is now acknowledged in the present version of the manuscript with the following sentence “Since the vast majority of GC synapses stained by VGluT1 actually correspond to GC-PC synapses, we could not exclude that Syn II is homogeneously expressed in GC-MLI synapses.” The heterogeneous expression of Syn II in GC-MLI synapses was confirmed by EM experiments Concerning the method to recognize PF-MLIs synapses, we also added the following precisions (Material and Methods section):

“PF-MLI and PF-PC synapses are glutamatergic synapses that can be recognized by the presence of an obvious asymmetry with a large postsynaptic density (Korogod et al., 2015). GCs contact MLIs on their dendritic shaft and PCs on their dendritic spines. Since PC dendritic spines are devoid of mitochondrion (Palay and Chan-Palay, 1976), GC-MLI synapses stand out from the large majority of asymmetrical synapses in the cerebellar cortex by the presence on mitochondrion within the postsynaptic compartment. Also, we only took into account synapses located to the upper part of the molecular layer to avoid GC-Golgi cell synapses.”.

Have you (or others) tested the reliability of the syn II antibody? Is it possible you see less syn II labeling because the antibody is not as good as the syn I antibody?

This is an important point. Indeed, we cannot excluded that anti-Syn II antibody (monoclonal antibody from Synaptic System) is less good that anti-Syn I antibodies.

To confirm the heterogeneous expression of Syn II in GC boutons, we performed a new set of immunohistochemical experiments with a Syn II antibody produced by another company (Millipore instead of Synaptic System). It should be noted that the use of Millipore anti-Syn II antibodies required to perform a heat-induced epitope retrieval protocol (slices were incubated 3 time in a boiling citrate buffer at pH 6 during 5 minutes) because Syn II signals were too weak with a regular protocol of staining. This retrieval protocol increased substantially the background noise for both VGluT1 and Syn II staining. Nevertheless, profile plots performed in the molecular layer of cerebellar sagittal sections using Millipore anti-Syn II confirmed the presence of i) VGluT1(+)/Syn II(+) puncta (presence of Syn II in GC boutons), ii) VGluT1(+)/Syn II(i) puncta (absence of Syn II in GC bouton) and iii) VGluT1(-)/Syn II(+) puncta (presence of Syn II in inhibitory synapses) (Author response image 1). The fact that anti-Syn II antibodies recognizing different epitopes of Syn II domains stained only subset of GC boutons strongly suggests that Syn II is actually expressed heterogeneously in GC presynaptic terminals.

Author response image 1
Heterogeneous expression of Syn II at GC-MLI synapses.

(A) Domain structure of mammalian Syn IIa and Syn IIb. The red and blue bar represent the domains used as immunogen to produce Millipore and Synaptic Systems (SS) Anti-Syn II antibodies. (B) Representative merged images of Syn II (Alexa-488, green) /VGluT1 (Alexa-55, red) immunostaining using Millipore anti-Syn II antibodies. Yellow puncta denote a presence of Syn II in GC boutons while red puncta denote an absence of Syn II in these synapses. Green puncta correspond to Syn II+ inhibitory synapses devoid of VGluT1. Images were captured in the molecular layer from a cerebellar section. The profile plot (blue line) confirms the presence of GC boutons devoid of SynII (red peaks not associated with a green peak). Calibration bar (left image): 7.5 μm. Anti-Syn II: Millipore #MABN1573, clone 19.4 Purified mouse monoclonal IgG2aκ antibody Immunogen: Purified recombinant rat Syn II Epitope: Domains A and B used at 1/500. Anti-Syn II: Synaptic Systems #106 211, clone 27E3 Purified mouse monoclonal IgG antibody Immunogen: Synthetic peptide corresponding to AA 440 to 458 from rat Syn II Epitope: AA 440 to 458 from rat Syn II used at 1/500

https://doi.org/10.7554/eLife.41586.020

5) Figures and text frequently report EPSC charge, how was this calculated?

EPSC charges were calculated as the signal (current) integral in time-locked windows. The window corresponds to the interstimulus interval and excluded stimulus interval. The offset was previously subtracted as well as the mean charge of the noise (measured of a time-locked window preceding the stimulation).

6) In Figure 1C, does this show the quantification of responses from B? If so, why do responses increase on the last stimulus when this does not appear to be the case in the traces in (B). The green trace in particular, EPSC10 is the same amplitude as EPSC1, but this is hard to believe looking at the green trace in (B). This again brings up the question of how EPSC charge was calculated.

Figure 1C does show the quantification for the traces displayed in 1B. There was obviously a mismatch between these traces and the data. We re-measured amplitudes of EPSCs for the traces in B and found different values. The graph in C has been corrected. We thank the reviewer for this careful inspection of the figure.

7) Figure 7 C, D. Under loose-patch, data are graphed as "firing frequency acceleration,% ", this is somewhat confusing, is there a reason for not simply graphing the firing rate? Graphs in (D) are "Delay to frequency peak" and "Delay to firing peak", how are these different? Some descriptions of these would help.

We recognize that explanations were not clear for Figures 7C-D. In slices, MLI are firing spontaneously at low frequencies. Upon single GC photostimulation, the basal rate of MLI firing is accelerated with a variable delay after the onset of photostimulation. The “delay to frequency peak” corresponds to the time separating the onset of photostimulation and the time the recorded MLI is firing is at its maximum frequency. The y-axis is the same for the 2 panels in Figure 7D. Explanations were implemented in the legend as well as the label of y-axis.

8) Second paragraph of subsection “Diversity of STP profile at GC-MLI connections extends the coding range of MLI”, is this shown in Figure 7D? Again, what does PC1 represent here? I don't see that either graph in 7D graphs "peak frequency".

This was a mistake. The sentence “Our analysis also showed a clear relationship between PC1 and the peak frequency (Pearson coefficient, R= 0.7, p = 0,008, n = 13)” is not related to Figure 1D. The correlation between PC1 and the delay to frequency peak was not shown on this figure. In photostimulation experiment, PC1 values drawn from measurement of EPSC charge in whole cell configuration correspond to the best parameter to express variations in STP profiles.

Reviewer #2:

Dorgans et al. investigate heterogeneity in synaptic properties of cerebellar granule cells (GC) onto molecular layer interneurons (MLI). The GC-MLI synapse shapes cerebellar cortical computations by recruiting stellate and basket interneurons, thus engaging feedforward inhibition onto Purkinje neurons. Through local stimulation of individual release sites, the authors find that the GC-MLI connection is heterogeneous in terms of its short-term plastic properties. They speculate that this heterogeneity reflects variable initial release probabilities. Unexpectedly, they find that the heterogeneity cannot be explained by target-dependence (stellate cells vs. basket cells), which is known to exist in this circuit, but that it is influenced by presynaptic expression of the synaptic vesicle-associated protein synapsin II: in synapsin II knockout animals, the heterogeneity in short-term synaptic plasticity profiles is reduced. Finally, they show that the diversity in synaptic profiles has functional implications for the differential recruitment of MLI, and could therefore be a mechanism to expand coding of information in the cerebellar cortex.

The study is interesting. A few issues should be addressed:

Experiments are done with very small electrodes (10 MΩ), from cells with very thin dendrites, thus raising concerns about voltage control. There is no mention of series resistance compensation. I wonder to what extent the different synaptic profiles are influenced/shaped by series resistance errors. Is there a relationship between distance of synaptic inputs from cell body and type of synaptic profile?

Variations of input resistance were not corrected but monitored. The recordings were not taken into account if series resistance has changed by more than 20%.

Since we systematically measured the position of the stimulation pipette from MLI soma, we were able to establish the relationship between the distance of synaptic inputs from the cell body and STP profile. The corresponding graph was added in Figure 3—figure supplement 1 (panel C). The lack of correlation between the profile of STP (estimated with PCA transformation) and the distance from the soma suggested that STP is not determined by the position of input in the dendritic tree of recorded MLIs. A recent study has shown that the properties of excitatory inputs integration by MLI dendrite are equal for inputs localized at distance superior to 20 µm from the MLI’s soma (Tran-Van-Minh et al., 2016). In our experiments, all inputs but one were localized at distance superior to 20 µm from MLI’s soma.

Synapsins are known to regulate the size of vesicle pools (e.g. Gitler et al., 2008), in addition to release probability. Is paired-pulse plasticity affected in synapsin knockouts? Is the size of the RRP affected? Is there an effect on vesicle clustering in EM?

The strong increase in the failure rate observed at the first stimuli in Syn II KO synapses suggests that the probability of release (pr) is impaired. At many synapses, change in the paired-pulse ratio is indeed a good parameter to probe change in pr. However, the release machinery of GC terminal stands out by its capacity to recruited new sites (Miki et al., 2016) or reluctant synaptic vesicles (Doussau et al., 2017) in a millisecond time scale during paired pulse stimulation at high frequencies meaning that at these synapses, an increase in n (number of sites) underlies part of the large facilitation that occurs during paired pulse facilitation (Miki et al., 2016; Valera et al., 2012). The paired-pulse ratio was not significantly increased in Syn II KO mice (mean/median PPR for WT =

1.8/1.6 ± 0.08, n= 101 and mean/median PPR for Syn II KO mice = 2.6/1.7 Syn II ± 0.08 KO, p = 0.19, note that median are similar). These data have been added in the manuscript. This suggests that in Syn II KO mice, the recruitment of the reluctant pool at the second stimuli is normal and mask effect of low pr of the fully releasable pool on PPR (fully releasable vesicles = vesicles released by a single AP, see Doussau et al., 2017).

In Syn II KO mice, the number of docked synaptic vesicles (SVs) is reduced in comparison to WT synapses (Figure 6). The pool of docked SVs comprised unprimed and primed SVs (RRP). The RRP itself is segregated in heterogeneous population of primed SVs (Doussau et al., 2017; Neher and Brose, 2018). Electron micrographs from brain slices cannot permit to distinguish between the different pools of docked SVs and therefore give only partial information about the ultrastructure-function relationship. Nevertheless the reduction of the number of docked SVs in Syn II KO synapse may underlie, at least in part, the increase in the failure rate.

The lack of target cell specificity in the heterogeneity of GC connections on MLI directly contradicts previous findings, which is acknowledged by the authors. One explanation they offer is that in previous studies, strong connections in compound responses (elicited by stimulation of GC clusters or by parallel fiber stimulation) could mask weak responses. The authors can and should directly address this important issue using their GC-specific photostimulation assay. Are there target-dependent differences in photostimulation-evoked synaptic profiles? (they might already have the data to answer this question).

This is an important issue. We addressed this question in the new version of the manuscript. We were able to identified 2 basket cells and 1 stellate cell (post hoc reconstruction) for which we recorded synaptic responses following photostimulation at 3 different locations (that is, 3 different GCs). These results were now presented in a new supplementary figure (Figure 7—figure supplement 2). The result showed clearly that either BCs or SCs are contacted by excitatory connections belonging to different classes. Photostimulation experiments thus confirmed the behavioral heterogeneity of excitatory synaptic inputs contacting same MLIs. We also performed a new set of experiments using compound stimulations of clusters of GCs or beams of PFs associated with post hoc identification of MLI subtypes (Figure 3—figure supplement 1). Our experiments failed to show a target dependence of STP at GC-MLI synapses.

Subsection “Syn II is heterogeneously expressed across GC-MLI presynaptic terminals”: the authors correlate vglut1 signal with synapsin I signal (presumably in the molecular layer, although that is not mentioned) and report R= 0.584. They conclude that "synapsin I is present in ALL GC terminals". I think this conclusion is not justified given the low R-value and should be rephrased.

In presynaptic terminal, VGluT1 is supposed to be exclusively associated to synaptic vesicles while synapsin are associated to synaptic vesicles or cytosolic depending of their phosphorylation status. Hence, even if both proteins are present in the same terminal they are not supposed to be perfectly localized. This explains the value of the Pearson coefficient for Syn I/ VGluT1 colocalization. We agreed that correlation values with the Pearson coefficient are confusing and we rephrased this part.

References

Neher, E. (2015). Perspective Merits and Limitations of Vesicle Pool Models in View of Heterogeneous Populations of Synaptic Vesicles. Neuron 87, 1131–1142.

Neher, E., and Brose, N. (2018). Dynamically Primed Synaptic Vesicle States: Key to Understand Synaptic Short-Term Plasticity. Neuron 100, 1283–1291.

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

Major comments:

The authors have substantially responded to the reviewers' concerns and improved the manuscript, including additional experiments, changes to figures, additional supplementary figures, and major changes in the text. I am satisfied with the use of an additional syn II antibody and other responses to reviewer comments. I am reasonably confident that responses result from stimulation of a single synapse and not multiple synapses. The authors use an impressive array of analysis, experiments, and strict response criteria to establish that EPSCs arise from a single synapse.

However, I am less certain that an AP was reliably evoked in the parallel fiber with each stimulus. As I said in the previous review, in Figure 1—figure supplement 1B there are far more failures at 70 µA stimulus that 80 µA stimulus. This strongly suggests AP were not reliably evoked at this stimulus intensity, and a higher stimulus intensity (80 µA) should be used. The authors need to show a plot of failure (or success) rate versus stimulus intensity (as is shown in Figure 2D of Malagon et al., 2016) and indicate the stimulus intensity chosen. Ideally, this plot should show that the failure rate reaches a plateau at the stimulus intensity used for subsequent experiments (though I suspect this is not the case for the data in the figure). The authors do mitigate this concern to a degree by showing that when the first EPSC is excluded the principal component analysis still produces more or less the same groups of responses (Figure 2—figure supplement 3). However, I still think the plot of failure rate vs stimulus needs to be shown in Figure 1—figure supplement 1.

Minimal stimulation

We carefully reexamined all parameters concerning minimal stimulations (failure rate, stimulus intensity, paired-pulse ratio) for all synapses that were included in our analysis in the last version of the manuscript. In this novel analysis, we increased the stringency of the criteria applied. In this new analysis, we then excluded 19 synapses (new n=96). We excluded synapses for which the failure rate dropped abruptly at current intensities below the intensity chosen for minimal stimulation as they may potentially be a signature of a recruitment of several synaptic contacts. These experiments were initially included because the increase in current intensity was not associated with an increase in the mean EPSC amplitude at the first stimulus. We also excluded synapses for which the failure rate was inferior to 0.4.

Note that the classification obtains by k-mean clustering analysis depends on sample size. Accordingly, k-mean clustering analysis changed the identity of a few synapses. However, no change in the main conclusion was observed: the same 4 classes of synapses were equally distributed in WT mice and unequally distributed in Syn II KO mice (near complete disappearance of cluster C1 in KO mice).

Figures 2, 5 and Figure 2—figure supplement 3 have been modified following modification of the dataset (pie charts, PCA analysis, means values of EPSC …)

Example of Figure 1—figure supplement 1

We agree that the example chosen in the previous version of the manuscript to illustrate minimal stimulation was not the most appropriate one. As suggested by the reviewers, we now show the plot of the success rate versus stimulus intensity, not only for this example (panel C) but also for all synapses that were included in the dataset for PCA and k-mean clustering analysis (panel B).

https://doi.org/10.7554/eLife.41586.022

Article and author information

Author details

  1. Kevin Dorgans

    Institut des Neurosciences Cellulaires et Intégratives, CNRS UPR 3212, Université de Strasbourg, Strasbourg, France
    Present address
    Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1724-6384
  2. Valérie Demais

    Plateforme Imagerie in vitro, CNRS UPS 3156, Strasbourg, France
    Contribution
    Resources, Investigation, Methodology
    Competing interests
    No competing interests declared
  3. Yannick Bailly

    1. Institut des Neurosciences Cellulaires et Intégratives, CNRS UPR 3212, Université de Strasbourg, Strasbourg, France
    2. Plateforme Imagerie in vitro, CNRS UPS 3156, Strasbourg, France
    Contribution
    Methodology, Writing—review and editing, Conceptualization and supervision of electron microscopy experiments
    Competing interests
    No competing interests declared
  4. Bernard Poulain

    Institut des Neurosciences Cellulaires et Intégratives, CNRS UPR 3212, Université de Strasbourg, Strasbourg, France
    Contribution
    Conceptualization, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2601-5310
  5. Philippe Isope

    Institut des Neurosciences Cellulaires et Intégratives, CNRS UPR 3212, Université de Strasbourg, Strasbourg, France
    Contribution
    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0630-5935
  6. Frédéric Doussau

    Institut des Neurosciences Cellulaires et Intégratives, CNRS UPR 3212, Université de Strasbourg, Strasbourg, France
    Contribution
    Conceptualization, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    doussau@inci-cnrs.unistra.fr
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3769-1402

Funding

Agence Nationale de la Recherche (ANR-2015CeMod)

  • Philippe Isope

Fondation pour la Recherche Médicale (DEQ20140329514)

  • Philippe Isope

Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche

  • Kevin Dorgans

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

Acknowledgements

This work was supported by the Centre National pour la Recherche Scientifique, the Université de Strasbourg, the Agence Nationale pour la Recherche Grant (ANR-2015CeMod) and by the Fondation pour la Recherche Médicale to PI (# DEQ20140329514). KD was funded by a fellowship from the Ministère de la Recherche. We thank Dr. Sophie Reibel-Foisset and the staff of the animal facility (Chronobiotron, UMS 3415 CNRS and Strasbourg University) for technical assistance. We thank Pr. Fabio Benfenati (Italian Institute of Technology, University of Genova, Genova, Italy) for the gift of synapsin triple knock-out mice. We thank Dr. Frank Pfrieger for critical reading of the manuscript.

Ethics

Animal experimentation: This study was carried out in strict accordance with the national and international laws for laboratory animal welfare and experimentation and was approved in advance by the Ethics Committee of Strasbourg (CREMEAS; CEEA35; agreement number/reference protocol: APAFIS#4354-20 16030212155187 v3).

Senior Editor

  1. Gary L Westbrook, Vollum Institute, United States

Reviewing Editor

  1. Indira M Raman, Northwestern University, United States

Publication history

  1. Received: September 11, 2018
  2. Accepted: May 11, 2019
  3. Accepted Manuscript published: May 13, 2019 (version 1)
  4. Version of Record published: May 23, 2019 (version 2)

Copyright

© 2019, Dorgans et al.

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

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