Heterogeneity of postsynaptic receptor occupancy fluctuations among glycinergic inhibitory synapses in the zebrafish hindbrain

Abstract

The amplitude of glycinergic miniature inhibitory postsynaptic currents (mIPSCs) varies considerably in neurons recorded in the isolated hindbrain of 50-h-old zebrafish larvae. At this age, glycinergic synapses are functionally mature. In order to measure the occupancy level of postsynaptic glycine receptors (GlyRs) and to determine the pre- and/or postsynaptic origin of its variability, we analysed mIPSCs within bursts evoked by alpha-latrotoxin (0.1-1 nM). Two types of burst were observed according to their mIPSC frequencies: 'slow' bursts with clearly spaced mIPSCs and 'fast' bursts characterised by superimposed events. Non-stationary noise analysis of mIPSCs in some 'slow' bursts recorded in the presence or in the absence of Ca2+ denoted that mIPSC amplitude variance did not depend on the quantity of neurotransmitters released (presynaptic origin), but rather on intrinsic stochastic behaviour of the same group of GlyRs (postsynaptic origin). In these bursts, the open probability measured at the peak of the mIPSCs was close to 0.5 while the maximum open probability is close to 0.9 for the synaptic isoform of GlyRs (heteromeric alpha1/beta GlyRs). In 'fast' bursts with superimposed events, a correlation was found between the amplitude of mIPSCs and the basal current level measured at their onset, which could suggest that the same group of GlyRs is activated during such bursts. Altogether, our results indicate that glycine synapses can display different release modes in the presence of alpha-latrotoxin. They also indicate that, in our model, postsynaptic GlyRs cannot be saturated by the release of a single vesicle.

Figure 3. Determination of the maximum open probability of postsynaptic glycine receptors (GlyRs) during ‘slow’ bursts

Aa, example of mIPSCs occurring with a low frequency within a burst. Ab, plot of the baseline-to-peak mIPSC amplitudes as a function of the recording time. The dashed line represents the baseline current. B, plot of the instantaneous frequency of mIPSCs versus time within the burst shown in A. The mIPSC occurrence frequency periodically increased and occasionally reached 20–25 Hz in this example. C, histogram of the inter-event time intervals for the burst shown in A. The distribution of the inter-event time intervals was fitted by a single exponential curve with a time constant of 338 ms. D, the amplitude distribution of the detected events was fitted by a single Gaussian curve. Events attributed to background synaptic activity stand out of the Gaussian distribution. Non-stationary fluctuation analysis was performed on events standing within the Gaussian distribution (between arrows). The insert shows the distribution of the basal current noise giving a s.d. noise of 2.9 pA. E, plot of the mean variance versus the mean amplitude after subtracting the basal variance (8.4 pA²). Insert, example of 15 consecutive events selected for noise analysis (filter cut-off frequency, 2 kHz; V_H, −50 mV). The parabolic fit gives an estimation of the number of the available postsynaptic GlyRs (N) and the elementary current generated by the activation of one receptor (i). In this case N = 46 and i = 2.36 pA. The elementary conductance of the GlyR channel (γ) was calculated for a Cl⁻ equilibrium potential of 0 and a V_H of −50 mV. The open probability of GlyR at the peak of the response (P_o) was obtained by dividing the mean peak current by the estimated maximum mean amplitude when the variance reaches 0. In this case γ = 47.2 pS and P_o = 0.56.

Figure 4. Non-stationary noise analysis of ‘slow’ bursts with large mIPSC amplitude variance recorded in the absence of external Ca²⁺

A, example of a ‘slow’ burst recorded in the absence of external Ca²⁺. B, 25 consecutive mIPSCs occurring within the burst shown in A (filter cut-off frequency, 1 kHz; V_H, −50 mV). C, the amplitude distribution of the detected events had a CV of 0.34 and a mean of 103.6 pA. The coefficient of skewness (C_s) was 0.16 (bin width, 10 pA; n = 67). The insert shows the distribution of the basal current noise giving a s.d. noise of 1 pA. D, correlation plot between 20–80 % rise times and mIPSC amplitudes. No significant correlation was found (Spearman rank order test P > 0.1). E, correlation plot between mIPSC half-widths and amplitudes. No significant correlation was found between these parameters (Spearman rank order test P > 0.1). Non-stationary analysis was performed on all events. F, plot of the mean variance versus the mean amplitude. Note that this plot deviates from the predicted parabolic function as shown in Fig. 3, indicating that the mIPSC amplitude variance cannot be explained only by intrinsic channel activity fluctuation. G, plot of the mean variance versus the mean amplitude of responses normalised with respect to their mean amplitude. The data points were fitted by a parabola allowing to extract the elementary current generated by a single GlyR channel giving an elementary conductance (γ) of 48.6 pS.

Figure 6. Correlation between mIPSC amplitude and current baseline within ‘fast’ bursts

A, ‘fast’ burst recorded in the presence of 1.3 mm external Ca²⁺. Ba, portion of the ‘fast’ burst shown in A with many superimposed events. Bb, enlarged portion of this ‘fast’ burst (box in Ba) where the parameters measured for the occupancy analysis are shown: a₁ is the amplitude of the baseline measured from the zero baseline current determined before and after the burst, and a₂ is the peak amplitude of the mIPSCs measured from the baseline current at the onset of the response. C, mIPSC (upper graph) and baseline current (lower graph) amplitude fluctuations with recording time. Note that mIPSC amplitude has a tendency to decrease when the baseline current increases. D, plot of the peak response amplitude (a₂) versus the baseline current amplitude (a₁). These values were then corrected to account for the decay occurring in the first mIPSC while the second rose to peak (see Methods). The data points in grey are individual measurements showing the fluctuation of mIPSC amplitudes for a given baseline value, while points in black are the mean amplitudes of mIPSCs obtained for a 5 pA change in the baseline amplitude. The amplitudes of mIPSCs and their fluctuation decreased when the baseline increased, indicating that superimposed mIPSCs are likely to result from the activation of a given group of postsynaptic glycine receptors. Single linear regression of the plot yielded a significant correlation between the mean peak amplitudes of mIPSCs and the baseline current amplitudes (Spearman rank order test P < 0.001). The slope of the fit was −0.445. E, no significant correlation was found between the mean percentage of receptor occupancy (slope in D) and the mean amplitude of mIPSCs for baseline current = 0 pA (Spearman rank order test P > 0.1).

Figure 1. Glycinergic mIPSCs recorded in control conditions and after α-latrotoxin application

Aa, example of mIPSC activity recorded in control conditions in the presence of 0.5 μmm TTX, 2 μmd-aminophosphonovalerate (APV) 20 μm 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) and 2 μm GABAzine. Note the large variation in the mIPSC amplitudes. Ab, amplitude histogram of mIPSCs recorded for a period of 300 s The distribution is highly skewed with a coefficient of skewness (C_s) of 1.66 and a coefficient of variation (CV) of 0.94. The insert shows the basal noise amplitude distribution with a s.d. noise of 2 pA. Ba, burst of mIPSCs after the application of 1 nmα-latrotoxin (α-LTX). Bb, the amplitude distribution of mIPSCs within the burst Ba could be fitted by a Gaussian curve showing their homogeneity compared to control mIPSCs. The insert shows the basal noise amplitude distribution after α-LTX application. Records in Aa and Bb are from the same reticular neurons (holding potential (V_H), −50 mV).

Figure 2. Mean amplitude of mIPSCs can strongly vary among α-LTX-induced bursts of mIPSCs

Aa, example of a burst with large amplitude mIPSCs (V_H, −50 mV). In this burst, mIPSC frequency decreased with time while at the beginning of the burst many synaptic events were superimposed. Ab, another burst with mIPSCs of much smaller amplitudes was recorded on the same cell as in Aa (V_H, −50 mV). B, distribution of the mean amplitude values of mIPSCs obtained for 36 different bursts. This amplitude distribution is highly skewed (C_s, 1.93) with a CV of 1.08, as observed in control conditions (see Fig. 1Aa and Ab).

Figure 5. Frequency pattern of mIPSCs within α-LTX-induced ‘fast’ bursts

A, example of a burst with many superimposed events (‘fast’ burst) recorded in the absence of external Ca²⁺ (V_H, −50 mV). B, plot of the instantaneous frequency of mIPSCs versus time within the burst shown in A. The mIPSC occurrence frequency periodically increased and reached 920 Hz in this example. C, histogram of the inter-event time intervals for the burst shown in A. The distribution of the inter-event time intervals was fitted by the sum of two exponential curves with time constants of 14.3 ms (59.2 %) and 42.4 ms. D, amplitude histogram of the detected events. This distribution has a CV of 0.36 and a mean of 74.7 pA (between arrows).