State-Dependent Function of Neocortical Chandelier Cells

Anatomical homogeneity of sampled chandelier cells

We first explored the morphological and physiological diversity of our studied population of neocortical chandelier cells. For this purpose, we reconstructed the morphology of 23 ChCs, and 67 morphological variables (Table 1) describing the soma, dendrites, axon, and location of the cell were extracted from the reconstruction. To determine whether these ChCs belonged to different populations, we first used unsupervised cluster analysis to test for the existence of statistically different subtypes of neurons, based on morphological properties of the recorded ChCs. One of two dimensionality reduction techniques, either PCA or FSS using a correlation filter, was applied before cluster analysis. Three statistical tests were used to determine a significant level (p < 0.05) of clustering (see Materials and Methods). For the morphology data set, the correlation filter removed 27 variables, and cluster analysis with the remaining 40 variables had no significant divisions (data not shown). In cluster analysis with PCA, the bootstrap and two-tailed tests also failed to find any significant divisions. Nevertheless, the best-cut test found significance at the three- and two-cluster levels, indicating the potential existence of different subtypes of ChCs (Fig. 1A, purple, dark orange, light blue clusters, division marked above 6 Euclidean distance squared).

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Figure 1.

Cluster analysis of chandelier cells. A, Ward's method of hierarchical unsupervised cluster analysis based on 67 morphological variables applied to 23 ChCs following PCA. The cutoff linkage for three clusters is marked with a gray line, and each cluster is given a different color. Open circles indicate the centroid of the cluster. B, Scatterplot in the principal component space of the three clusters shown in A. C, Silhouette analysis of the three clusters shown in A. D, Comparison of dimensionality reduction techniques. Map between the physiological clusters found using FSS correlation filter (top) and PCA (bottom). The numbers on the lines indicate how many cells belong to both of the clusters. E, Ward's method of hierarchical unsupervised cluster analysis based on 20 electrophysiological variables applied to 219 ChCs following correlation filter. F, Silhouette analysis of two clusters shown in E. G, As in E, but with PCA as the dimensionality reduction technique. H, Scatterplot in the principal component space of the two clusters shown in G. I, Silhouette analysis of the two clusters shown in G.

We therefore decided to further investigate the group structure of the three- and two-cluster levels found by cluster analysis with PCA, analyzing the three-cluster level for any further evidence of group structure. The data set was plotted in principal component space to search for separation of clusters. The first three principal components were used for analysis based on the scree test and on their carrying >60% of the variance (see Materials and Methods). In all three planes (PC1–PC2, PC1–PC3, and PC2–PC3) and in three-dimensional space, the clusters were not distinct and boundaries of the cluster areas overlapped (Fig. 1B). We assessed the quality of clustering with silhouette analysis (see Materials and Methods). The silhouette plot for the morphological data set showed a very weak group structure, with Clusters 2 and 3 containing cells with moderate positive and negative values, and all Cluster 1 cells having a negative value (Fig. 1C). The silhouette width of the data set was approximately zero, indicating that the clusters were not well separated and not significantly different from the average result of 500 trials of cluster and silhouette analysis on randomized data sets (see Table 3). Thus, the silhouette analysis agreed with the hypothesis that the three clusters are an arbitrary division of the data set.

As the three-cluster division produced no valid groups, we proceeded to analyze the two-cluster division found after PCA (Fig. 1A, purple and orange vs blue cells; division at 8 Euclidian distance squared) with the same analysis method. The two-cluster division also did not have a valid group structure, with poor separation in principal component space, no distinguishing features, and a silhouette value not significantly different from random (data not shown). Based on this analysis, we concluded that there was no substructure to the morphological database, as both the two- and three-cluster PCA divisions did not withstand a more rigorous statistical examination. Therefore, the chandelier cells we recorded from appear to be morphologically homogeneous, indicative of a single, indivisible population.

Physiological homogeneity of sampled chandelier cells

We then performed a completely independent analysis of the physiological characteristics of the database of 219 recorded ChCs. We extracted 20 variables describing passive membrane properties and firing properties (Table 2) from whole-cell current-clamp recordings. The same three statistical tests were applied to determine a significant level (p < 0.05) of clustering. The FSS correlation filter did not remove any variables, and cluster analysis with all 20 variables was found to have no significant clusters by the bootstrap and two-tailed test. However, the best-cut test found significance at the two-cluster level (Fig. 1E, red and light orange clusters). For cluster analysis with PCA, the bootstrap and best-cut tests failed to find significance, while the two-tailed test found significance at the seven-, four-, three-, and two-cluster levels (Fig. 1G, green and blue clusters illustrating the two-cluster level). However, on further analysis, the two clusters found by cluster analysis with FSS correlation filter and with PCA did not correspond well to each other, with neurons from the two FSS clusters equally distributed between both PCA clusters (Fig. 1D).

As cluster analysis results using two different dimensionality reduction techniques both had significant but differing results at the two-cluster level, we chose to further analyze those divisions to test whether either could be a description of subtypes. We first analyzed the group structure of the two clusters found by cluster analysis with correlation filter (Fig. 1E). Poor separation of the clusters was evident in the silhouette plot, in which the majority of cells in the larger cluster have a negative silhouette value (Fig. 1F). The silhouette width was significantly different (p < 0.01) from random, with a more negative value than the randomized trials indicating a lack of group structure (Table 3). Thus, the physiological division of ChCs using correlation filter does not appear to be a valid division.

Next, we tested the two clusters found by cluster analysis with PCA (Fig. 1G, blue and green) for group structure using the same methods as above. We reasoned that if the two clusters found by cluster analysis with PCA demonstrated evidence of group structure, we could then analyze the finer distinctions at the three-, four-, and seven-cluster levels. Indeed, the two-cluster level appeared to withstand our initial statistical analysis: in principal component space, the two clusters roughly divided the area; however, the clusters had overlapping boundaries, so these two territories were not clearly distinct (Fig. 1H). To identify potential variables that differentiated between the two groups, we performed statistical tests, finding that rheobase was the only variable with significant difference between clusters (p < 0.05; Cluster 1, 224.26 ± 72.71 pA; Cluster 2, 170.63 ± 66.37 pA, Mann–Whitney test). To determine whether rheobase could be used as a basis to define physiological subtypes of ChCs, we analyzed the ranges of rheobase values across the two PCA clusters, finding almost identical ranges for Clusters 1 and 2 (cluster 1, 75–470 pA; Cluster 2, 60–400 pA). Therefore, cluster membership could not be determined based on rheobase, as the majority of rheobase values are included in the range of both clusters. Finally, we applied silhouette analysis, which also did not support the two clusters being distinct ChC groups. While the first cluster had predominantly positive silhouette values, the second cluster had predominately negative silhouette values, and the silhouette width was not significantly different from random (Fig. 1I; Table 3).

Overall, the electrophysiological database did not reveal clear differences among potential subtypes of ChCs. While some cluster structure was present in the two-cluster divisions of both correlation filter and PCA, these clusters were inconsistent among themselves, were not based on clearly defined differences in any electrophysiological variables, and had weak silhouette widths. Finally, even though neither clustering by morphology nor clustering by physiology variables produced meaningful subtypes of ChC, it is possible that a multiparameter analysis is needed to define ChC subtypes. Accordingly, we performed cluster analysis based on the combined morphology and electrophysiology variables, using the 23 neurons for which we had both reconstructions and patch-clamp recordings. None of the three statistical tests found a significant level of clustering in either results using FSS or PCA (data not shown). Therefore, the simplest interpretation of our morphological and physiological analysis is that the population of neocortical ChC cells that we sampled is homogeneous.

Gap junctional coupling among ChCs

Electrical coupling between GABAergic interneurons is a common network property and has been suggested to aid in the concerted activity of interneuron populations of the same subtype (Beierlein et al., 2000). Separate support for our classification of the recorded ChCs as a homogenous population came from experiments in which we recorded from two ChCs in close proximity to each other. On 11 occasions, we found weak electrical coupling between these cells (coupling coefficient, 0.05 ± 0.01; n = 11 pairs, tested bidirectionally; Fig. 2A–C). We estimate that coupling occurred in >80% of simultaneously recorded ChC pairs. Aside from the frequent heterologous coupling seen for neurogliaform cells (Simon et al., 2005; Zsiros and Maccaferri, 2005), the finding of electrical coupling among ChCs is consistent with a large volume of work indicating cell-type specific electrical coupling within interneuron subtypes (Hestrin and Galarreta, 2005). At the same time, on one occasion we also encountered gap-junctional coupling between a ChC and BC (identified electrophysiologically and morphologically; Fig. 2D,E), so, although rare, it is in principle possible to find occasional coupling between ChCs and BCs.

Dendritic polarity of ChCs

In our anatomical study, we noticed that the dendrites of the sampled ChCs were characteristically asymmetric, predominantly targeting Layer 1 (Fig. 3A). Extensive Layer 1 dendrites and minimal arborization within Layer 2/3 were not, however, consistent features, with some ChCs showing little to no preference for extending dendritic branches to Layer 1. Nevertheless, comparison of ChC dendritic trees with those of BCs (Fig. 3C) revealed distinct dendritic morphologies. Whereas basket cells appeared isotropic in their dendritic orientation, ChCs showed clear polarization and a preference for vertically projecting dendrites (Fig. 3B,D). We quantified the difference in Layer 1 dendrites, finding a considerable difference in the total length of L1 dendrites (ChC, 1365 ± 198 μm, n = 8; BC, 542 ± 199 μm, n = 6; p < 0.01), which could not be attributed to differences in overall dendritic length (ChC, 1799 ± 189 μm; BC, 1832 ± 312 μm; p = 0.92; Fig. 3F). Thus, the proportion of the dendritic arbor confined to Layer 1 differed significantly between ChCs and BCs (ChC, 76 ± 6%, n = 8; BC, 27 ± 8%, n = 6; p < 0.01 vs ChC; Mann–Whitney two-tailed test; Fig. 3G). This could not be explained simply by a difference in distance of ChC and BC somata from the Layer 1 border (ChC, −10 ± 5 mm; BC, −19 ± 6 mm; p = 0.49; Mann–Whitney two-tailed test; Fig. 3E). The chandelier cells from which we record in this study, located at the top of L2/3, therefore comprise an electrically coupled network of neurons with a preference for extending dendrites to Layer 1.

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Figure 3.

Dendritic morphology of upper Layer 2/3 chandelier and basket cells. A, Neurolucida reconstructions of ChCs used for analysis of dendritic Layer 1 preference. B, Polar plot of dendritic length and location. Neuronal somata are located at center of plots, and the average dendritic length across cells is plotted in 18° bins. The pia is at the top, and distance is indicated by dashed circles. C, D, Same as for A and B for basket cells. E, Location of ChC and BC somata relative to the L1–L2/3 border. F, Total dendritic length for ChCs and BCs. G, Proportion of ChC and BC dendrites located in Layer 1. Remaining dendrites were in Layer 2/3. Error bars indicate SEM.

Chandelier cells can promote or inhibit pyramidal neuron spiking

After this basic morphological and electrophysiological characterization, we focused on analyzing the functional effect of ChCs on the spiking of pyramidal cells. To do this, we made paired recordings and assessed their impact under different experimental conditions. The first set of experiments investigated ChC impact during static membrane potential conditions, most closely approximating the so-called DOWN state observed in anesthetized or sleeping animals (Steriade et al., 1993). The second set investigated the effect of ChC activity in response to electrical stimulation of an afferent pathway, and the third set analyzed ChC impact during awake-like membrane potential fluctuations. Although we performed some experiments using the gramicidin perforated patch technique, the majority of experiments throughout this study were performed in whole cell, using an intracellular pipette solution that contained an elevated chloride concentration. After accounting for the different ionic concentrations in the intracellular solutions (see Materials and Methods), the reversal potential of the ChC–pyramidal neuron synapse obtained in whole cell was not statistically different from E_GABA measured with gramicidin (whole-cell E_GABA, −54.0 ± 0.7, n = 23; gramicidin E_GABA, −51.4 ± 2.2, n = 17; p = 0.20; Fig. 4A).

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Figure 4.

Chandelier cells can both excite and inhibit postsynaptic pyramidal neurons. A, GABA_A reversal potential recorded with gramicidin perforated patch, or in whole cell using an elevated internal chloride concentration. Inset, Traces from one pair recorded in whole cell, voltage clamped at −80 to −40 mV. Calibration: 10 pA, 10 mV. B, Schematic of the recording configuration. C, Example traces from a pyramidal neuron during control (black) and stimulation (gray) trials. Bottom, Expanded traces to illustrate depolarizing ChC input. D, Trial-by-trial plots of pyramidal neuron spiking for individual experiments. E–G, Summary graphs for pyramidal neurons at rest (E), depolarized but below E_GABA (F), and depolarized above E_GABA (G). Error bars indicate SEM.

For the first set of experiments, pyramidal neurons were stimulated from their resting membrane potential (−81.8 ± 1.4 mV) with a brief current pulse of an amplitude set to semireliably evoke APs. This amplitude varied between cells and was determined before each experiment. On alternate trials, two APs separated by 20 ms were evoked in the presynaptic ChC before pyramidal cell stimulation (Fig. 4B,C). This evoked depolarizing GABAergic PSPs (GPSPs), which typically peaked on the second of the two PSPs (peak, 1.0 ± 0.2 mV). Using this protocol, ChCs reliably increased spike probability in the postsynaptic pyramidal neuron (control, 0.46 ± 0.05; ChC, 0.66 ± 0.06, n = 13; p < 0.01; Fig. 4D,E), although the magnitude of effect varied considerably between cells. We next performed the same experiment, but after having depolarized the pyramidal neuron from rest to a level that nevertheless remained below E_GABA (V_m = −66.2 ± 1.2 mV) and that was close to reported V_m values in awake mice (Gentet et al., 2010) (different internal solutions, see Materials and Methods, Comparing V_m values for different internal solutions). The ChC synapse therefore remained depolarizing (peak GPSP2, 0.5 ± 0.1 mV) and again reliably increased pyramidal neuron spike probability (control, 0.42 ± 0.06; ChC, 0.55 ± 0.07, n = 8; p < 0.01; Fig. 4F). Depolarizing the pyramidal neuron to E_GABA or beyond reversed the ChC effect, with spike probability consistently decreasing from 0.56 ± 0.06 to 0.44 ± 0.08 (p < 0.001; n = 6; Fig. 4G). Under these slice conditions, during which the pyramidal neuron membrane potential is essentially static while receiving ChC input, chandelier cells of cortical Layer 2/3 can therefore both promote or inhibit pyramidal neuron output, depending on the pyramidal neuron membrane potential.

The above experiments were performed with the GPSP preceding the current injection by 30 ms (Δt = +30 ms). We also performed the same experiments for Δt = +5 or Δt = +15 ms, to assess the impact of the underlying GABA_A conductance change on the ability to promote spiking. For both time points, the depolarization provided by the ChC GPSP overcame the inhibitory shunt provided by channel opening, leading to increases in spike probability [normalized probability of an action potential, p(AP) +5 ms, 1.8 ± 0.5, p = 0.06 vs control; +15 ms, 1.6 ± 0.1, p < 0.001; +30 ms, 1.4 ± 0.2, p < 0.05). We expect the conductance effect to be stronger at shorter time intervals, although we did not check. However, it should be noted that the ChC synapse is quite electrotonically isolated from the source of glutamatergic excitation on dendritic spines, and that by using somatic current injection rather than dendritic inputs, we are likely overestimating the true shunt provided by the ChC synapse on excitatory synaptic inputs.

Electrical stimulation of Layer 1 enforces ChC-mediated feedforward inhibition

As described above, the ChCs from which we recorded frequently had numerous dendrites extending into Layer 1, and it would therefore seem that the majority of afferent excitation will arise through this pathway. Although in principle many other types of interneurons, such as basket cells, could be activated by Layer 1 inputs, the peculiar asymmetric dendritic morphology of ChCs could make them particularly tuned to Layer 1 activation. To study this, and because the primary target of Layer 2 ChCs is Layer 2/3 pyramidal neurons, whose apical dendrites also extend into Layer 1, we used electrical stimulation of this uppermost cortical layer to assess the impact of ChC activation on Layer 2/3 pyramidal neurons (Fig. 5A).

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Figure 5.

Electrical stimulation of Layer 1 recruits ChC-mediated inhibition. A, Schematic of the recording configuration. Pyramidal neurons were recorded with gramicidin. B, Left, Recruitment of ChC (blue) and pyramidal neuron to increasing stimulation strengths. Action potential amplitude is indicated by a voltage of 60 mV. Right, Summary data showing the minimum stimulation intensity required to evoke spikes in 100% of trials for ChCs and pyramidal neurons. C, Responses of recorded neurons at two stimulation intensities, ChC threshold (left) and pyramidal neuron threshold (right). D, Cell-by-cell plot of the relationship between E_GABA and the ChC GPSP at the stimulation intensity required for ChC activation. E, ChC GPSP polarity depends on the strength of afferent stimulation. F, Assaying ChC effect on pyramidal neuron spiking by applying large hyperpolarizing pulses to the ChC on alternate trials. G, At the stimulation strength required to activate pyramidal neurons, ChCs are strictly inhibitory. Error bars indicate SEM.

A glass stimulating electrode containing ACSF was placed approximately midway between the pia and Layer 2, 50–150 μm laterally displaced from the location of the ChC and pyramidal neuron cell bodies. For ChCs to influence pyramidal neurons, ChCs must be recruited both more easily than and before their postsynaptic partners. We found this to be the case for all ChC–pyramidal neuron synapse pairs recorded from (Fig. 5B). Chandelier cells required 72 ± 3 μA and pyramidal neurons 135 ± 7 μA to be activated (n = 10; p < 0.001).

At the stimulation strength required to activate ChCs, we measured pyramidal neuron V_m at the onset of the IPSP, taken here to be 1 ms after the peak of the presynaptic spike. Pyramidal neurons were consistently below E_GABA for that particular synapse at the onset of the GPSP (V_Pyr = −68.1 ± 0.1 mV; E_GABA = −57.2 ± 1.2 mV; n = 10; p < 0.001; Fig. 5C,D). The peak voltage reached by the EPSP in response to Layer 1 stimulation was −60.4 ± 2.1 mV, not significantly different from E_GABA. Thus, at IPSP onset, the ChC synapse is depolarizing, but rapidly switches to a predominantly shunting effect during Layer 1 stimulation. Although the ChC GPSP was depolarizing and thus potentially excitatory, we were unable to directly assay the impact on pyramidal neuron spiking without further increasing stimulation intensity. We increased stimulation strength to enable pyramidal neuron spiking, which decreased the latency of the ChC spike and allowed it to influence the pyramidal neuron (Fig. 5C). At this stimulation intensity, the more rapid rise of the pyramidal neuron V_m converted the ChC synapse to shunting at GPSP onset and hyperpolarizing immediately before spike generation (Fig. 5C,E). To measure the effect of ChC activation on pyramidal neurons during this protocol, we applied large hyperpolarizing pulses to the ChC on alternate trials to prevent ChC spikes (Fig. 5F). The consequent absence of ChC spikes was associated with an increase in pyramidal neuron spiking, revealing that under these conditions, chandelier cells decrease pyramidal neuron spike probability (control, 0.57 ± 0.1; ChC, 0.39 ± 0.1, n = 6; p < 0.01; Fig. 5G). This effect confirmed that the ChC was indeed firing before the pyramidal neuron and that the readout of the pyramidal neuron spike was not drastically affected by the relatively high R_series of the perforated patch configuration. Electrical stimulation of cortical Layer 1 therefore recruits a ChC-mediated feedforward inhibition of Layer 2/3 pyramidal neurons.

A state-dependent function of ChCs

We also investigated the functional impact of this homogeneous ChC population on the firing of Layer 2/3 pyramidal neurons. We show that the ability of cortical ChCs to excite their postsynaptic targets is critically dependent on E_GABA and the membrane potential trajectory of the postsynaptic neuron. When V_m is essentially static, as in DOWN states, depolarizing GPSPs can have an excitatory influence, facilitating spike generation. Since spiking during the DOWN state is rare, our results may be more applicable to DOWN–UP state transitions, when a hyperpolarized, static V_m rapidly depolarizes due to a barrage of synaptic activity. The excitatory influence we observe occurs for latencies (or Δt) between +30 and +5 ms and is seen in a quiescent slice preparation, but also at more depolarized levels closer to the average V_m seen in vivo. The main requirement for excitation appears to be that the majority of ChC-mediated current flux occurs before the neuronal V_m crossing E_GABA. Our current injection experiments (Fig. 4), in which ChCs were activated before the pyramidal neuron being depolarized, allowed ChCs to excite their targets. In contrast, our electrical stimulation experiments (Fig. 5), in which ChCs were activated during the rising phase of the pyramidal neuron EPSP, consistently showed ChC-mediated inhibition. Under conditions of constantly fluctuating V_m, as is characteristic of neurons in awake animals, the only robust effect observed was spike inhibition, with no consistent direction of effect for time intervals of 15 ms or more. Our inability to detect an effect of ChCs—either excitatory or inhibitory—at all but the shortest Δt intervals may partly reflect the fact that we manipulate only a single ChC. Based on counts of boutons apposed to the axon initial segment (AIS), a single pyramidal neuron has been estimated to receive input from three to six ChCs (Buhl et al., 1994; Tamas and Szabadics, 2004). Temporally coordinated activation of ChCs, perhaps facilitated by the electrical coupling we describe, would therefore be expected to produce significantly larger GPSPs. In addition to producing stronger inhibition, simultaneous activation of ChCs may also enable a more overt excitatory effect on postsynaptic spiking, based on the relationship we found between prespike GPSP amplitude and spike probability.

Our results show that chandelier cells, like other interneurons, can inhibit spiking and can do so quite strongly if the pyramidal cell is active. But simple inhibition, in the absence of excitation, is unlikely to be the only function of chandelier cells. An alternative view is to consider the ChC synapse not in terms of its direct effect on spike output, but rather in terms of its impact on neuronal excitability. We took care in our experiments to make the average V_m during noise stimulation approximate that observed in vivo (our experiments, approximately −63 mV; in vivo, approximately −55 mV; different internal solutions; see Materials and Methods, Comparing V_m values for different internal solutions). The E_GABA of the ChC synapse then lies slightly depolarized relative to average V_m. This is analogous to a recent study examining the subthreshold mechanisms underlying responses of L2/3 pyramidal neurons to whisker touch (Crochet et al., 2011), in which the reversal potential of the synaptic response to whisker contact—reflecting combined excitatory and inhibitory currents—lies above the average V_m but typically below spike threshold. Variations in the reversal potential of the synaptic response correlated strongly with the excitability of the neuron. The ChC synapse may perform a similar function, helping to set the excitability of the neuron by means of its reversal potential.

Consistent with this possibility, our gramicidin recordings in fact revealed considerable variability in E_GABA, and various studies have indicated that GABA_A reversal potential is not static (Wagner et al., 1997; Woodin et al., 2003; Fiumelli et al., 2005; Choi et al., 2008). Two studies in particular relate shifts in E_GABA to activity-dependent modulation of NKCC1, the cation-chloride cotransporter present at the AIS (Khirug et al., 2008). In the first, repeated action potential activity was shown to cause a persistent increase in intracellular chloride accumulation via alterations in the Na⁺–K⁺–ATPase transporter and NKCC1 (Brumback and Staley, 2008). Since NKCC1 is expressed at the AIS, changes in E_GABA at the ChC synapse may occur naturally as a function of the spike output of the postsynaptic neuron, with high levels of activity leading to a depolarizing shift in E_GABA and increasing neuronal excitability. In the second study, near-coincident firing of presynaptic and postsynaptic cells caused a hyperpolarizing shift in E_GABA via NKCC1 alterations (Balena and Woodin, 2008), effectively increasing the strength of inhibition and decreasing excitability. Thus, mechanisms exist to bidirectionally shift E_GABA via changes in NKCC1 activity, and may explain the large variability we observe in our gramicidin recordings.