Functional specification of CCK+ interneurons by alternative isoforms of Kv4.3 auxiliary subunits

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Version of Record updated: June 8, 2020 (Go to version)
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Accepted: May 20, 2020
Received: May 2, 2020

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Translation of dipeptide repeat proteins in C9ORF72 ALS/FTD through unique and redundant AUG initiation codons

Yoshifumi Sonobe, Soojin Lee ... Paschalis Kratsios

Research Article Sep 29, 2023
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Abstract

CCK-expressing interneurons (CCK+INs) are crucial for controlling hippocampal activity. We found two firing phenotypes of CCK+INs in rat hippocampal CA3 area; either possessing a previously undetected membrane potential-dependent firing or regular firing phenotype, due to different low-voltage-activated potassium currents. These different excitability properties destine the two types for distinct functions, because the former is essentially silenced during realistic 8–15 Hz oscillations. By contrast, the general intrinsic excitability, morphology and gene-profiles of the two types were surprisingly similar. Even the expression of Kv4.3 channels were comparable, despite evidences showing that Kv4.3-mediated currents underlie the distinct firing properties. Instead, the firing phenotypes were correlated with the presence of distinct isoforms of Kv4 auxiliary subunits (KChIP1 vs. KChIP4e and DPP6S). Our results reveal the underlying mechanisms of two previously unknown types of CCK+INs and demonstrate that alternative splicing of few genes, which may be viewed as a minor change in the cells’ whole transcriptome, can determine cell-type identity.

Introduction

The biophysical and morphological properties of complementary GABAergic cell classes are specifically tuned for regulating the activity of the much more populous principal cells in broad temporal (from second to sub-millisecond; Hu et al., 2014; Overstreet-Wadiche and McBain, 2015) and spatial (from axons to distal dendrites Freund and Buzsáki, 1996) domains. Thus, specialized features of GABAergic neurons help the hippocampus to comply with the vast computational demand related to various behaviors (Klausberger and Somogyi, 2008). However, the functional diversity of currently known GABAergic cell types cannot match the vast amount of hippocampal behavioral tasks. Therefore, a more complete understanding of GABAergic cells is one of the major goals of current research. Emerging evidence using single-cell transcriptomics suggests that the number of GABAergic types may be higher than previously recognized (Földy et al., 2016; Fuzik et al., 2016; Harris et al., 2018; Que et al., 2019; Tasic, 2018; Zeisel et al., 2015). While these studies accelerated cell classification efforts by identifying a large number of genes that appear to define further subtypes in neuronal taxonomy, the functional relevance of most transcriptomic marker genes remain poorly understood. To better understand the relationship between gene expression and physiological function, and how these properties define neuronal types, we employed a multidisciplinary approach to study a previously unrecognized level of functional diversity among CCK-expressing hippocampal interneurons (CCK+IN) in rat CA3 area.

Several properties distinguish CCK+INs from other major GABAergic cell classes. Unlike in other GABAergic cells, the axons of CCK+INs are highly enriched with CB1 receptors, which mediate activity-dependent regulation of GABA release (Freund and Katona, 2007). As a result of this delicate control, CCK+INs are ideally suited for dynamic inhibition of a subset of principal cells based on the context of ongoing activity. The in vitro firing of CCK+INs is believed to be homogeneous typically displaying intermediate AP widths (between that of pyramidal cells and classical fast-spiking interneurons) and clear spike frequency accommodation (Cea-del Rio et al., 2011; Glickfeld and Scanziani, 2006; Szabadics and Soltesz, 2009; Szabó et al., 2014). These excitability properties are crucial for the generation of characteristic CCK+INs firing in vivo, which is observed during exploration-associated with theta and gamma oscillations (Klausberger et al., 2005; Lasztóczi et al., 2011). In contrast to their apparent biophysical homogeneity, the morphology and molecular content of CCK+INs are diverse. Based on their axonal morphology basket-, mossy fiber-associated, Schaffer collateral-associated and perforant path-associated types (Cope et al., 2002; Vida and Frotscher, 2000; Vida et al., 1998) can be distinguished. These distinct morphological types selectively control excitation from different afferents (hence their names). At the molecular level, complimentary expression of marker genes characterize subsets of CCK+INs, such as VGluT3 and VIP (Somogyi et al., 2004), and single-cell RNA-sequencing also uncovered several additional molecular varieties (Fuzik et al., 2016). Due to their distinction from other major classes and large interclass variability, CCK+INs are ideal for examining relationships between genes and cellular identity and boundaries between GABAergic cell classes.

In this study, we investigated hippocampal CCK+INs from a broader perspective that could reveal previously undetected excitability parameters suitable for specific physiological functions. A hint that such diverse physiological parameters exist came from previous in vivo recordings that showed that individual CCK+INs are differentially active during various oscillatory states (Klausberger et al., 2005; Lasztóczi et al., 2011), where many of them appeared to prefer either lower or higher frequency ranges. We found two types of hippocampal CCK+INs in rats based on their different excitability, with potentially different contributions to network events, particularly in the range of theta oscillations. Detailed realistic simulations showed that switching only the properties of Kv4.3-mediated currents can sufficiently convert one functional cell type to the other. Combined analyses of the complete mRNA content and protein expression of single cells revealed that the pronounced functional distinction between these two CCK+IN type is defined by differential isoform usage of three auxiliary subunits of the Kv4.3 channels.

Results

A large portion of CCK+INs show state-dependent firing in the CA3 area

To explore potential differences in the excitability of individual CCK+INs in the CA3 region of the rat hippocampus, first we characterized their firing properties in two different conditions. Specifically, we recorded their spiking in response to current steps from two, physiologically plausible membrane potential ranges of post hoc identified CCK+INs. We focused mostly on the CA3 region because here the diversity of CCK+INs is the largest within the hippocampus. When CCK+INs (n = 557 cells) were stimulated from slightly depolarized membrane potentials (MP, range: −60 – −65 mV) relative to rest (−64.7 ± 0.4 mV), action potential (AP) firing always showed spike-frequency accommodation, which is one of the most characteristic features of this cell class (Cea-del Rio et al., 2011; Glickfeld and Scanziani, 2006; Szabadics and Soltesz, 2009; Szabó et al., 2014). However, we noticed that numerous CCK+INs (n = 290 cells) showed MP-dependent firing: their initial spiking was strongly inhibited and its onset was delayed when it was evoked from hyperpolarized MPs (between −75 to −85 mV, Figure 1A–B). On average, these cells started firing after a 252 ± 15 ms silent period from hyperpolarized MP (measured from the start of the current injection). We named these cells as Transient Outward Rectifying cells or TOR cells (a term that was used to describe cells with similar firing patterns in other brain regions: Stern and Armstrong, 1996). The rest of CCK+INs (n = 267 cells) were characterized as regular spiking or RS cells, as they fired regularly irrespective of their MP and they started firing with a short delay (33 ± 2 ms) when stimulated from hyperpolarized MP. At depolarized MP (−55 to −65 mV), the first APs of both TOR and RS cells occurred with similar short delays (48 ± 3 ms and 26 ± 1 ms, respectively, Student t-test, p=0.09, t(160) = −1.706).

Figure 1

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Two distinct firing patterns within CA3 CCK+ cells.

(A) Firing properties of two representative CCK+INs in the CA3 hippocampal region. Firing was elicited with square pulse current injection of identical amplitude, but from depolarized (grey traces), or hyperpolarized MPs (blue traces). Several trials are superimposed to show the stability of the timing of the first action potential. Insets show the immunolabelling of the biocytin filled (BIO) recorded cells for CCK. (B) Average time course of AP occurrence in TOR and RS cells from two MP ranges (n = 120 and 113 representative cells, respectively). (C) Timing of the first AP and probability of APs during the first 150 ms of the square pulse stimulus shows steep MP-dependence in TOR cells, whereas the initial spikes are stable in the RS cells. The amplitude of stimulating current steps was standardized for each cell and only the preceding holding current (3 s) was varied in individual trials. Traces show a representative recording from a TOR cell. The average data derived from 85 TOR and 81 RS cells.

Figure 1—source data 1 Electrophysiological parameters of TOR and RS CCK+INs. Note that only those cells were included in this table from which all parameters could be retrieved. Thus, the number of analyzed cells may differ from specific analysis of the main text.: https://cdn.elifesciences.org/articles/58515/elife-58515-fig1-data1-v1.docx
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Next, we applied a protocol allowing the detailed quantification of the MP-dependence of firing in individual cells (n = 81 RS and 85 TOR cells). Specifically, firing was evoked by a current step that was calibrated for each cell to elicit a similar mean firing frequency (10–20 Hz) from slightly depolarized MP. The holding current preceding the current step was systematically varied to reach a wide range of steady-state MPs (3 s, in the range of −50 and −90 mV, above the theoretical reversal potential of potassium current, Figure 1C). In TOR cells, the number of APs within the first 150 ms and the timing of the first AP showed steep voltage dependence (V_1/2 value of the Boltzmann fits were −67.4 mV and −73 mV, respectively, R² = 0.995 and 0.999, Figure 1C). In contrast, the delay and number of spikes did not show membrane potential-dependence in RS cells. Importantly, both RS and TOR firing was stable in prolonged patch-clamp recordings, which lasted for 45–64 min (average 58 min, n = 4 TOR and 3 RS cells).

In contrast to the difference in initial spiking, other firing and membrane properties of TOR and RS cells were similar, including input resistance, AP threshold, half-width, rate of rise and after-hyperpolarization (Figure 1—source data 1). Furthermore, the frequency and amplitude of spontaneous synaptic events (including both IPSCs and EPSCs; 2.47 ± 0.57 Hz and 3.26 ± 0.86 Hz, Student t-test, p=0.45, t(15) = −0.78; −51.6 ± 3.3 pA and −51.5 ± 2.5 pA, Student t-test, p=0.98, t(15) = −0.02, n = 9 TOR and 8 RS cells) were similar. We found TOR cells in the CA1 region as well. However, here their prevalence was lower compared to CA3 (2 TOR out of 13 CA1 CCK+IN). Finally, both TOR (n = 15 cells) and RS (n = 12 cells) CCK+INs were detected in the CA3 region of adult rats (older than 70 days) suggesting that these biophysical features are not age-dependent. In summary, the firing of TOR cells shows a remarkable sensitivity to a physiologically plausible 20 mV shift in the MP, despite having no other distinctive passive electrical or spiking properties compared to RS cells.

TOR and RS firing types do not correlate with previously known subtypes of CCK+ cells

The CCK+IN class has been previously divided into several subtypes based on various functionally relevant features. Therefore, next we investigated whether the two MP-dependent firing phenotype could be linked to previously identified subtypes of CCK+INs. Three subtypes have been previously identified within CA3 CCK+INs based on the target zones of their axons: (i) basket cells (BCs) that innervate the soma and proximal dendrites of pyramidal cells (Hendry and Jones, 1985), (ii) mossy fiber-associated cells (MFA) with axons branching in stratum lucidum and hilus regions that are also occupied by the axons of dentate gyrus granule cells (Vida and Frotscher, 2000), and (iii) Schaffer-collateral associated cells (SCA), targeting the same regions as Schaffer collaterals (Cope et al., 2002). Altogether, 172 of the recorded CCK+INs were unequivocally identified either as BCs (n = 31), MFAs (n = 96) or SCAs (n = 45; Figure 2A). Both TOR and RS firing types occurred similarly among these morphological subtypes (Figure 2B) indicating that neither of the two firing phenotypes could be assigned to any of the morphology-based CCK+IN subtypes. This assessment was further supported by quantitative analyses of axonal and dendritic features (Figure 2—figure supplements 1 and 2).

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Different excitability does not correlate with previously known diversity of CCK+ cells.

(A) Axonal (black) and dendritic (blue) reconstructions of four CCK+INs in the CA3 region representing two major morphological types, the mossy-fiber associated cells and basket cells. The firing of cell#1 and #2 are shown in detail in Figure 1A. The firing from hyperpolarized MP is shown for each cell. Immuno-labelling for CCK and the nuclear protein Satb1 are shown next to each recorded cell. (B) Prevalence of TOR and RS cells within three major morphological types of CA3 CCK+. Numbers of identified cells are indicated on each bar (BC: CCK+ basket cells, MFA: mossy-fiber associated cells, SCA: Schaffer-collateral associated cells). (C) Single cell RNAseq characterization of recorded TOR (left, n = 8) and RS (right, n = 8) cells for known GABAergic cell markers. Each column corresponds to single identified CCK+INs. The bars on the right shows the logarithm of p values of the statistical comparison of TOR and RS cells for each genes. Significantly different mRNA content between TOR and RS cells was found for Satb1 and Lypd1 (p=0.0095 and p=0.0006, Mann-Whitney Test). (D) Comparison of immunohistochemical and single cell RNAseq data. Bar plots show the fraction of the recorded cells with detectable RNA content (threshold: 0.2) for the selected markers and immunopositivity for the same proteins (for examples of the immunolabelling see Figure 2—figure supplement 3). The number of tested cells are shown on each bar.

In addition to the variable morphological features, CCK+INs heterogeneously express several molecular markers (Somogyi et al., 2004). To study their molecular heterogeneity, we performed single cell RNA sequencing (SC-RNAseq) (Földy et al., 2016) of individually recorded TOR cells (n = 8) and RS cells (n = 8). Only those cells were analyzed in detail, which had detectable mRNA for both CCK and cannabinoid-receptor type 1 (CB1R or Cnr1; Figure 2C). In accordance with previous observations (Földy et al., 2016), the included cells contained the transcripts of at least 3000 different genes (the range of detected genes in the included cells was between 4173 and 9629 with mean 6946 ± 430 genes). Most CCK+IN-relevant marker genes (calbindin/Calb1, vesicular glutamate transporter VGlut3/Slc17a8, vasoactive intestinal protein/VIP, serotonin receptor subtype 3a/5HT3a, Reelin, neuropeptide-Y/Npy) were expressed at comparable levels in both TOR and RS cells (Figure 2C). One of the exception was the activity-dependent transcription regulator, Satb1 (Close et al., 2012), whose mRNA was present in TOR cells (n = 8 out of 8), whereas only 4 of the eight tested RS cells had detectable levels (Mann-Whitney test, p=0.0095, z = 2.59, U = 57). Furthermore, we found that Lypd1, which is enriched in a subpopulation of CCK+INs in the CA1 (Harris et al., 2018) was expressed in all tested TOR cells (n = 8) and in only one of the RS cells (1 out of 8, Mann-Whitney test, p=0.0006, z = 3.45, U = 64). Despite these differences, hierarchical cluster analysis of the total transcriptome did not categorize these cells into two distinct groups that would correspond to their firing phenotype (Figure 2—figure supplement 2), suggesting that these cells have highly similar transcriptomic identities (Fuzik et al., 2016; Harris et al., 2018). Our complete SC-RNAseq dataset is available at NCBI GEO as GSE133951.

To confirm these findings at the level of protein expression, we performed immunohistochemistry on identified CCK+ TOR and RS cells in separate experiments (Figure 2D and Figure 2—figure supplement 3). As expected, CB1R protein was detected in the axon of almost all tested cells (15 out of 18 cells). Furthermore, VGluT3 (23 out of 46 TOR cells, 16 out of 30 RS cells), Reelin (all 11 tested TOR and 7 out 10 RS) and Calb1 (10 out 30 TOR and 7 out of 29 RS) proteins occurred similarly in RS and TOR types. By contrast, while the majority of TOR cells were positive for Satb1 protein (109 out of 132, 82.6%), only few RS cells were Satb1 positive (14 out of 128 tested, 10.9%). Satb1 protein was also prevalent in non-recorded CCK+ cells within the CA3 region of the acute slices (126 Satb1+/CCK+ out of 425 CCK+), suggesting that the detection of this activity-dependent protein was not a consequence of patch-clamp recording. The somata of Satb1+/CCK+ cells were found throughout CA3 strata oriens, pyramidale, lucidum and radiatum and their proportion to all CCK+ cells were similar (34.1%, 21.4%, 33.6%, and 27.2%, respectively). In agreement with the lower occurrence of TOR cells in the CA1, non-recorded CCK+ cells in this region were less likely to be Satb1 positive (21.6% of 231 CCK+ cells). In the hilar region of the dentate gyrus the proportion of Satb1+/CCK+ cells was also low (4.8% of 123 CCK+ cells). We have also detected Satb1 and CCK overlap in tissue samples derived from transcardially perfused animals (20.2%, 97 Satb1+/CCK+ out of 480 CCK+ cells in the CA3 area). To conclude, transcriptomic and immunohistochemical data suggest that Satb1 is predictive of TOR identity in hippocampal CCK+INs and that these TOR and RS firing phenotypes are not related to previously known subtypes within CCK+INs.

Differences in low-voltage-activated potassium currents (I_SA) underlie the heterogeneity of CCK+IN firing

Next, we investigated ionic conductances that are responsible for the two firing types in CCK+INs. We focused on near-threshold potassium currents because they have been shown to effectively regulate firing responses under similar experimental conditions in various brain regions (Lammel et al., 2008; Margrie et al., 2001; Neuhoff et al., 2002; Stern and Armstrong, 1996). First, we characterized each cell as TOR or RS type under normal recording conditions. Next, we recorded near-threshold potassium currents between −100 and −25 mV, in the presence of voltage-gated sodium channel and hyperpolarization-activated cation channel inhibitors (3 µM TTX and 10 µM ZD7288). Post hoc immunolabelling confirmed that all included cells were CCK+. TOR cells had a substantial amount of potassium currents at firing threshold (Figure 3A, 683 ± 109 pA at −40 mV, n = 17). By contrast, potassium currents in RS cells activated at more positive voltages and were much smaller at threshold (148 ± 28 pA, n = 11, p=0.0006, t(26)=3.89, Student’s t-test). The robust low-voltage-activated potassium currents (I_SA), which are activated 30 to 40 mV below AP threshold, can underlie the strong inhibition of AP generation in TOR cells, whereas I_SA in RS cells is much smaller at near-threshold voltage range.

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Differences in the Kv4-mediated I_SA currents underlie the heterogeneity of CCK+IN firing.

(A) Representative traces from two single cells and average voltage dependence of activation of I_SA in TOR (n = 17) and RS (n = 11) cells. The grey dotted line indicates AP threshold (−37.52 ± 0.3 mV) measured before TTX application. Notice the large amount of outward current in TOR cells at subthreshold MPs. (B) Representative traces and voltage dependence of inactivation of I_SA at −30 mV from different holding potentials (n = 14 and 10 for TOR and RS cells, respectively). Blue and grey shaded areas indicate the voltage ranges from which state-dependent firing was tested (see Figure 1). (C) Representative traces and average voltage dependence of I_SA conductance in nucleated patches from TOR and RS cells (n = 7 and 8, respectively). The activation curves are shown both as the average of raw and normalized data to highlight the differences both in the amount and voltage dependence of the conductance. Blue points connected in the right panel represent I_SA currents in the presence of HpTX (1 µM). (D) Representative CCK+INs firing patterns recorded before, during and after HpTX application (in response to identical current steps) and the average AP probabilities in TOR (n = 6) and RS cells (n = 9) from hyperpolarized MP ranges (−82 to −77 mV). (E) Average delay of the first AP and reversible effect of HpTX. Connected symbols represent individual measurements (paired t-test: p=0.01). (F) HpTx-sensitive I_SA in representative TOR and RS cells (left) and the average voltage dependence (n = 7 for both TOR and RS cells).

Next, we investigated the availability of I_SA at different MPs. For these measurements, potassium currents were evoked by a voltage step to −30 mV following various pre-pulse potentials between −100 to −35 mV. The V_1/2 of the average inactivation curve was −64.5 ± 0.2 mV (Boltzmann fit, R² = 0.999, mean of V_1/2 from individual cells: −63.8 ± 0.9 mV, n = 13 cells, Figure 3B) in TOR cells. Importantly, the majority of I_SA was available at slightly hyperpolarized MPs (91.3 ± 1.6% at −80 mV), where the inhibition of firing was clearly observable during the characterization of TOR cells (see Figure 1 data). But at −60 mV, where the inhibition of spiking was not prominent, the majority of outward currents in TOR cells were inactivated; only 35.7 ± 3.4% of the total current remained available. Similarly to the activation, the inactivation of I_SA in RS cells was shifted toward positive voltage ranges (V_1/2: −57.4 ± 0.3 mV, n = 8 cells, mean of individual data: −55.6 ± 2 mV, comparison with TOR cells, see above, p=0.0006, t(19)=4.12, Student’s t-test) and a larger portion of this smaller current was available at −60 mV (52.7 ± 6.3%, Figure 3B). Thus, hyperpolarization of the RS cells cannot add a substantial amount of inhibitory conductance.

In addition, the inactivation time constant of I_SA was faster in RS cells compared to TOR cells (18.8 ± 1.7 ms, n = 7, vs. 71.2 ± 9.1 ms, n = 14, measured at −25 mV, p=0.0007, t(19)=4.025, Student’s t-test). The recovery from inactivation was slower in TOR compared to RS cells, and it showed steep voltage dependence (TOR cells: time constants of the recovery were 58.2 ± 2.9 ms, 46.2 ± 2.9 ms, 29.8 ± 2.5 ms and 4.4 ± 0.1 ms at −65,–75, −85 and −120 mV, respectively; whereas in RS cells: 11.8 ± 4.4 ms, 8.0 ± 2.2 ms, 7.1 ± 1.6 ms and 3.3 ± 0.4 ms, respectively). Due to these properties, I_SA currents in RS cells remained limited around AP threshold either from −80 or −60 mV preceding voltage. In summary, the different properties of I_SA currents, particularly the left-shifted inactivation and activation curves, may underlie the differences in TOR and RS firing phenotypes.

In our whole-cell recordings the majority of the membrane conductances remained intact, which prevented fully controlled voltage clamping at more depolarized MPs and the determining the exact voltage dependence of I_SA. Therefore, we compared the I_SA in TOR and RS cells in nucleated patch recordings. The better voltage control allowed us to record currents at more depolarized voltage ranges and compare the total density of I_SA in the two types of CCK+INs. The density of I_SA in TOR cells was smaller compared to RS cells (Figure 3C; TOR: 1.91 ± 0.46 nS/pF, RS: 3.86 ± 0.8 nS/pF, n = 7 and 8). However, consistent with our findings using whole-cell configuration, I_SA activated at a more hyperpolarized voltage range in TOR cell nucleated patches (V_1/2 TOR: −16.4 ± 4.7 mV, V_1/2 RS: −8.9 ± 1.7 mV, with 7% and 1.4% median, 15 ± 6.5% and 3.6 ± 1.8% average activation at −40 mV, respectively, Mann-Whitney test, p=0.024, z = 2.257, U = 48, n = 7 and 8) and the current had slower inactivation time constant in TOR (38.1 ± 4.7 ms, n = 6) compared to RS (16.8 ± 5 ms, n = 8, p=0.011, t(12) = 2.984) cells. Thus, in spite of the smaller conductance density, I_SA generated larger inhibitory charge in TOR cells than in RS cells during 100 ms long voltage steps to −40 mV (0.47 ± 0.1 and 0.21 ± 0.05 pC/pF, p=0.032, t(13) = 2.394, n = 7 and 8).

Because whole-cell and nucleated patch configurations do not report on the subcellular distribution of I_SA currents, we investigated local I_SA currents by pulling outside-out patches from the soma and dendrites of RS and TOR cells (Figure 3—figure supplement 1). These recordings confirmed that the density of somatic I_SA was significantly larger in RS cells (70.5 ± 15.2 pA/µm², n = 26) compared to that of the TOR (42.2 ± 5.2 pA/µm², n = 17, p=0.047, t(41) = −2.05) cells. The density of dendritic I_SA was similar in TOR and RS cells (19.6 ± 3.3 pA/µm², n = 53 vs. 19.5 ± 3.0 pA/µm², n = 47). Furthermore, we did not observe a significant gradient along the dendrites (up to 300 µm from soma; TOR: R² = 0.011, p=0.21, n = 56; RS: R² = 0.01, p=0.22, n = 53). On average, the kinetics of patch currents matched those of whole-cell currents and confirmed slower inactivation in TOR cells. However, there was a pronounced variability of I_SA between individual patches from TOR, but not RS, cells (Figure 3—figure supplement 1D), even when multiple patches were pulled from the same cell. In many TOR cell patches the current kinetics resembled the patch- and whole-cell currents of the RS cells. While in other TOR cell patches much slower currents were also detected. This variability might be due to the clustered occurrence of voltage-gated potassium channels in GABAergic cells (e.g. see Kollo et al., 2006).

Kv4 channels are responsible for both types of I_SA currents in CCK+INs

Next, we investigated the identity of potassium channel subunits responsible for the differences of I_SA in RS and TOR cells and for the MP-dependent firing in TOR cells. Neither TEA (0.5 and 10 mM, blocking Kv3 channels) nor a low concentration of 4-AP (100 µM, blocking Kv1 and Kv3 channels) eliminated the initial firing gap in TOR cells (Figure 3—figure supplement 2A). Only a high concentration of 4-AP (5 mM) was able to diminish the TOR phenomenon. These, together with the above determined low voltage activation properties, suggest the involvement of Kv4 channels (Lien et al., 2002), of which Kv4.3 has been shown to be present in hippocampal CCK+INs (Bourdeau et al., 2007; Kollo et al., 2006). To specifically test the contribution of Kv4 channels to the TOR phenomenon, we applied Heteropodatoxin-1 (HpTX, 1 µM), which selectively inhibits Kv4.2 and Kv4.3 subunit-containing channels by shifting the voltage dependence of the activation (DeSimone et al., 2011; Sanguinetti et al., 1997). In the presence of HpTX, significantly more APs were evoked in TOR cells during the first 125 ms of the stimulus compared to control conditions in the same cells (Figure 3D, from −80 mV preceding MP, 9.7 ± 2.9% vs 5.1 ± 2.2%, n = 9, p=0.0037, t(5) = −5.12, paired t-test). Furthermore, the delay of the first APs was reversibly shortened from 239 ± 61 ms to 116 ± 38 ms (Figure 3E, p=0.01, t(7) = 3.493, paired t-test, n = 8 TOR cells). However, HpTX did not change the number and temporal distribution of APs in TOR cells from −60 mV (data not shown) and the firing of RS cells also remained unchanged both at −80 and −60 mV (n = 8 cells). Furthermore, HpTX had only minor effects on the half-width of the APs (TOR cells: control: 0.48 ± 0.02 ms, HpTX: 0.52 ± 0.03 ms, n = 8, p=0.15, t(7) = −1.6; RS cells: control: 0.55 ± 0.04 ms, HpTX: 0.59 ± 0.06 ms, n = 8, p=0.11, t(7) = −1.83). Finally, the HpTX effect was completely reversible on the TOR phenomenon, which provides an additional evidence for the stability of the TOR phenomenon in individual cells during long recording periods (25–58 min). In summary, HpTX-sensitive, Kv4-mediated currents are crucial for the TOR phenomenon.

Next, we analyzed the contribution of HpTX-sensitive currents to I_SA in TOR and RS cells. In whole-cell conditions, both types had substantial amounts of HpTX-sensitive currents. In TOR cells, the HpTX-sensitive currents activated at more negative voltage (Figure 3F) and the time constant of inactivation was slower than in RS cells (49.1 ± 12.6 ms vs. 11.8 ± 1.6 ms, at −25 mV). The voltage dependence of inactivation of the HpTX-sensitive current was also left-shifted in TOR relative to RS cells (V_1/2 TOR: −64.0 ± 0.6 mV, n = 7; RS: −56.8 ± 1.3 mV, n = 7). In nucleated patch experiments, consistent with the known gating modification effects of HpTX (DeSimone et al., 2011; Sanguinetti et al., 1997), the activation voltage of I_SA was shifted to more depolarized membrane potentials in both TOR and RS cells (change in V_1/2 TOR: 12.4 ± 3.3 mV, n = 4; RS: 21.5 ± 6.9 mV, n = 5; Figure 3C), which resulted in a similar reduction in the conductance at −30 mV (TOR: 60 ± 10%, n = 4; RS: 61 ± 16%, n = 5). To conclude, our results suggest that the majority of inactivating I_SA potassium currents are mediated by Kv4 channels in both TOR and RS cells, in spite of the different properties of the currents.

Realistic models of TOR and RS firing

In the previous experiments, we examined excitability properties in CCK+INs using steady-state current injections. However, excitation of neurons in vivo is more dynamic and the membrane potential constantly fluctuates according to the ongoing state of the network. The different temporal properties of I_SA in TOR and RS suggest that these cells may differently follow membrane potential fluctuations. Therefore, to predict the frequency ranges of oscillations that are optimal for the distinct CCK+IN types with distinct excitability and the brain states that TOR and RS cells favors, we simulated the realistic dynamic behavior of the two phenotypes.

First, we equipped the morphology of 10 reconstructed CCK+INs (5 TOR and 5 RS cells) with known voltage-dependent conductances and passive properties of hippocampal CCK+INs (Bezaire et al., 2016; Figure 4—source data 1) and reproduced core CCK+IN firing (Figure 4A). Next we used two sets of I_SA conductances based on our outside-out patch recordings and tuned their densities to recreate RS and TOR firing properties in simulations. In the models, I_SA and firing properties were best reproduced if RS cells were equipped with a single type of I_SA potassium conductance (I_SARS). Consistent with our recordings, I_SA currents of TOR cells were reproduced by a mixture of two I_SA: I_SARS and a left-shifted, slowly inactivating current (I_SATOR) in a 3:1 ratio (Figure 4B). After adding I_SARS or I_SATOR+RS to the core CCK+IN properties (Bezaire et al., 2016) in current-clamp simulations, the models reproduced the common firing properties of CCK+INs (including AP width, peak and frequency and AHP shape), but in the presence of I_SATOR+RS all 10 cells showed MP-dependent firing with similar temporal dynamics and voltage dependence as recorded experimentally. When the same cells were equipped with I_SARS only, they turned to RS firing type (Figure 4—figure supplement 1). Thus, swapping I_SARS to I_SATOR+RS alone is sufficient to generate TOR properties, even if the reconstructed cell originally belonged to the RS type and vice versa. Importantly, the simulations confirmed that TOR firing required less I_SA potassium conductance, than RS firing in the same reconstructed cells (57.9% less; TOR: 1.72 ± 0.27 mS/cm², RS: 2.97 ± 0.47 mS/cm²).

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Different I_SA currents in TOR and RS cells tune them for different network states.

(A) I_SATOR+RS and I_SARS were added to 10 reconstructed CCK+INs, which possess every known voltage-dependent conductance (Bezaire et al., 2016). Changing I_SARS to I_SATOR+RS in the same cells transformed the firing from RS to TOR phenotype. These simulated cells were equipped with synaptic conductance to simulate input drives in various network states. (B) Voltage clamp simulations with complete morphology and realistic conductances reproduced whole-cell I_SA currents of TOR and RS cells, including voltage-dependence, the total current measured at the soma (left and middle graphs), and inactivation and the kinetics of inactivation and recovery from inactivation (right graph, where recovery was measured at −120,–85, −75 and −65 mV). (C) Representative traces showing the number and temporal distribution of 8 Hz-modulated synaptic inputs to simulated CCK+INs and its MP and I_SA conductance in two conditions with either I_SARS or I_SATOR+RS. (D) Average effects of exchanging I_SARS to I_SATOR+RS on the output of 10 CCK+INs during various input frequency ranges (x-axis) and baseline output activity (i.e. with I_SARS conductance, y-axis). Yellow color shows small change in firing when I_SATOR+RS replaced I_SARS, whereas red color indicates robust reduction in AP output. Representative traces on the right depict three examples with different input frequencies. Red triangles highlight inhibited spikes.

Figure 4—source data 1 Average conductance densities and membrane properties of ten simulated cells. Each cell (n = 10) was simulated both as TOR and RS type by exchanging I_SA only. With the exception of I_SA, all parameters were identical in the two conditions for individual cells. Some parameters were fixed to decrease computational demand (gray), whereas other parameters were variable between cells to optimally reproduce the average firing properties. Fixed conductance values derived from previous publications.: https://cdn.elifesciences.org/articles/58515/elife-58515-fig4-data1-v1.docx
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TOR cells are selectively silenced by I_SATOR in a narrow range of oscillatory states

Next, we investigated the activity of RS and TOR firing cells during in vivo-like simulations. For this, we modeled excitatory and inhibitory inputs arriving onto the somato-dendritic axis of the 10 reconstructed CCK+INs. The occurrence of excitatory events was clustered and tuned to frequencies ranging from 1 to 100 Hz, to mimic oscillations, with kinetic parameters derived from the above experiments (see Materials and methods). All 10 model cells received the same input patterns and we varied excitatory strength as a parameter (by changing the number of EPSCs, Figure 4C). This configuration recapitulated oscillating membrane potentials (from −72 mV to threshold) and also a wide range of spiking frequencies in CCK+INs representing the frequency ranges that have been observed in vivo (Klausberger et al., 2005; Lasztóczi et al., 2011).

Next, we compared the average spiking of I_SATOR+RS potassium conductance-equipped cells (n = 10 cells) with the spiking of the same cells during the same conditions except that they were equipped with I_SARS only. We found that CCK+INs were efficiently silenced by I_SATOR+RS, as compared to I_SARS, in the 8–15 Hz input frequency range. On average, 37.2 ± 0.4% fewer APs were evoked (see red areas in the middle of the Figure 4D graph and example traces, quantified in the 4–10 Hz output range). By contrast, during lower and higher input frequencies the presence of I_SATOR+RS only slightly reduced firing (spiking was decreased by 9.8 ± 0.3% and 8.8 ± 0.2%, between 1–6 Hz and 25–100 Hz, respectively). To summarize, I_SATOR+RS conductance alone enables CCK+INs to be selectively silenced during 8–15 Hz input regime. This single conductance changed the way TOR and RS cells integrate and respond to physiologically relevant input patterns. This observation adds a novel level of complexity to the diverse functions of GABAergic cells and can contribute to their observed heterogeneous firing during different network states (Klausberger et al., 2005; Klausberger and Somogyi, 2008; Lasztóczi et al., 2011). The input frequency dependence of the inhibition of firing can be explained by the specific temporal properties of I_SATOR+RS. Specifically, in addition to the voltage dependence of activation and inactivation (Figure 3), the time constant of inactivation (Figure 3—figure supplement 1D) and the recovery from inactivation determines the difference in availability of these currents during various oscillatory states. Thus, minor modifications in the properties of I_SA enable distinct functions in individual cells.

Molecular identity of Kv4.3 channels in TOR and RS cells

Next, we aimed to uncover molecular differences in TOR and RS cells that could explain their different I_SA properties. First, we checked the expression of Kv4 channel coding genes in RS and TOR cells using the SC-RNAseq data (Figure 5A–B). Kv4.2 was absent in most of the tested 16 CCK+INs in accordance with previous findings (Bourdeau et al., 2007; Rhodes et al., 2004). The mRNA of Kv4.3 subunits was detected in most CCK+INs (15 out of 16 cells), including both RS and TOR types. This was consistent with the finding that both RS and TOR cells possess HpTX-sensitive currents. Interestingly, in line with the predictions of the simulation, the average expression of Kv4.3 was not lower in RS than in TOR cells (RS: 2.03 ± 0.4 versus TOR: 1.56 ± 0.34, Mann-Whitney, p=0.37, z = −0.89). We found differences in other Kv channels as well, such as Kv3.2, Kv1.3 and Kv1.6 subunits (Figure 5A). However, because these subunits do not generate low-voltage-activated, inactivating currents, and/or they are sensitive to low concentrations of 4-AP and TEA (Figure 3—figure supplement 2), we did not investigate further the currents generated by these subunits (Lien et al., 2002). To confirm these transcriptomic findings, we performed immunohistochemistry to localize the Kv4.3 subunits. Overall, we observed an intense neuropil labelling for Kv4.3 in DG, CA3 strata radiatum and oriens, but not in the CA1 area. A subset of INs were also labelled throughout the hippocampus (Bourdeau et al., 2007; Rhodes et al., 2004). Kv4.3 proteins appeared to be enriched in the somatic and dendritic plasma membranes, but the subcellular distribution was often uneven and clustered (Figure 5D and Figure 5—figure supplement 1A) in line with the known distribution pattern of the Kv4.3 subunit (Kollo et al., 2006). Next we analyzed Kv4.3 immunolabelling in biocytin-filled CCK+INs. In agreement with our pharmacology and SC-RNAseq data, the Kv4.3 signal was present in both TOR (Figure 5B-D, 15 out of 20 tested cells) and RS CCK+INs (19 out of 23 cells). However, the immunosignal was usually stronger in RS than in TOR cells even within the same section, which was consistent with our observations from nucleated patch recordings (Figure 3). The same trend was observable in the CA3 area obtained from perfusion-fixed brain (Figure 5—figure supplement 1A), in which the Kv4.3 signal was detectable at strong or moderate level in most CCK+/Satb1- cells (putative RS cells, 99%, 338 out of 342 tested cells) in contrast to weak or hardly detectable labelling in most CCK+/Satb1+ cells (putative TOR cells, 97%, 78 out of 80 tested cells). In summary, immunolabelling confirms that TOR cells have a lower density of Kv4.3 channels, which is critical in their unique firing.

Figure 5 with 1 supplement see all

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Similar Kv4.3-expression and different auxiliary subunit, KChIP and DPP-content in CCK+INs.

(A) Normalized gene count of primary and auxiliary subunits of voltage-gated potassium channels from single cell RNAseq data of TOR (n = 8) and RS (n = 8) cells. (B) Percentage of recorded cells with detectable levels of Kv4.3 mRNA (left bars, n = 8 and 9 cells) and protein (right bars, n = 20 and 23 tested cells). (**C–D**) Immunofluorescent co-localization of CCK and Kv4.3 in a TOR (C), and a RS cell (D) in CA3 stratum lucidum. (E) Percentage of recorded cells with detectable levels of KChIP1 mRNA (n = 8 and 8 cells) and proteins (n = 20 and 27 cells). (**F–H**) Immunofluorescent co-localization of CCK with CB1 (green) and KChIP1 in a TOR (G), and a RS cell (H) from the same slice shown in low magnification image (F). (I) Percentage of recorded cells with detectable levels of KChIP4 mRNA (left bars, n = 8 and 8 cells) and protein (right bars, n = 29 and 30 tested cells). (J) Major KChIP4 splicing isoforms consist of different exons in the N-terminal region (represented as red boxes). Each row represents a single cell in the color-mapped data and columns correspond to individual exons aligned to the schematic illustration of isoforms above. Red and blue colors code high and low mRNA levels, respectively. The average exon counts from the two types of CCK+INs (n = 8 and 8) are shown at the bottom using the same color code scheme. (K) Percentage of recorded cells with detectable levels of DDP6 and DPP10 mRNA. (L) Assembly of major DPP6 isoforms and exon levels in individual CCK+INs are shown as above (J).

Auxiliary subunits of Kv4 channels in TOR and RS cells

Core Kv4 channel proteins form ternary complexes with dipeptidyl aminopeptidase-like proteins (DPLPs, including DPP6 or DPP10) and K⁺ channel interacting proteins (KChIP1-4 from Kcnip1-4 genes), which fundamentally influence current properties. These auxiliary subunits are expressed in various splicing isoforms (Jerng and Pfaffinger, 2014; Pongs and Schwarz, 2010), and their large diversity allows delicate modification of Kv4-mediated current properties. For example, most KChIP isoforms promote surface expression and accelerate inactivation and recovery from inactivation. However, the so-called transmembrane KChIPs (tmKChIPs) have opposite effects as they can retain Kv4 from the plasma membrane and decelerate inactivation (Holmqvist et al., 2002; Jerng and Pfaffinger, 2008; Jerng and Pfaffinger, 2014; Pruunsild and Timmusk, 2012).

KChIP1, as one of the classical KChIPs, accelerates inactivation and increases surface expression of Kv4.3 channels (Beck et al., 2002; Bourdeau et al., 2011; Jerng and Pfaffinger, 2014; Pongs and Schwarz, 2010). We detected KChIP1 mRNA in most RS cells (6 of 8 tested), but it was absent in 5 out of 8 TOR cells. The average mRNA level was significantly higher in RS than in TOR cells (RS: 2.27 ± 0.5, n = 8; TOR: 0.87 ± 0.42, n = 8; Mann-Whitney, p=0.025, z = −2.25). KChIP1 immunohistochemistry revealed an even clearer distinction between RS and TOR cells (Figure 5E). KChIP1 protein was detected in the majority of RS cells (20 out of 27), whereas only few TOR cells showed positive immunoreaction (5 out of 21). Furthermore, the signal in the positive TOR cells was typically weaker compared to RS cells (Figure 5F–H). We also found that the KChIP1 signal was present not only in the plasma membrane, but also in the cytosol, in agreement with their role in trafficking (Pongs and Schwarz, 2010). This data from recorded cells were confirmed by the analysis of CCK+INs in perfusion fixed brains (Figure 5—figure supplement 1A). We detected strong KChIP1 signal in the majority of CCK+/Satb1- cells (corresponding to RS cells, 122 out of 132 tested cells, 92.4%). In contrast, only 4.9% of CCK+/Satb1+ cells (corresponding to TOR cells, 2 out of 41 tested cells) showed strong KChIP1 immunosignal. With regards to other KChIPs, while we did not detect significant amounts of KChIP2 and KChIP3 mRNAs, the splicing-invariant sequence of KChIP4s was detected in all TOR cells (8 out of 8), but only in 3 out of 8 RS cells. In biocytin-labelled cells, the available antibody detected KChIP4 protein only in a very few CCK+INs regardless of their firing type (4 out of 29 TOR, 5 out of 30 RS cells, Figure 5I and Figure 5—figure supplement 1B–E), which was in apparent contradiction with our RNAseq data. However, we can explain this contradiction based on the following observations. In general, the relatively weak KChIP4 immunosignal in CCK+INs was surrounded by strong neuropil labelling in the stratum radiatum (Figure 5—figure supplement 1B). While KChIP1 is expressed only in INs, KChIP4 is known to be associated with Kv4.2 channels in pyramidal cells as well (Rhodes et al., 2004). At high magnification (Figure 5—figure supplement 1D), we found KChIP4 signal to be associated with tube-like structures, likely representing the plasma membranes of putative pyramidal cell dendrites. However, our KChIP4-specific antibody was raised against a long amino acid segment, which includes the highly variable N-terminal region, which endow various KChIP4-isoforms with different effects on Kv4 channel function (Holmqvist et al., 2002; Jerng and Pfaffinger, 2008; Jerng and Pfaffinger, 2014; Pruunsild and Timmusk, 2012). This clue prompted us to further analyze the mRNA data at the level of individual KChIP4 exons (Figure 5J). KChIP4e belongs to the so-called tmKChIPs (Jerng and Pfaffinger, 2008), which, in contrast to most KChIP types and isoforms, do not promote the plasma membrane expression of Kv4 and have opposite effects on the inactivation kinetics (Jerng and Pfaffinger, 2008; Pruunsild and Timmusk, 2012). We detected the KChIP4e isoform-specific exon in 7 TOR cells (out of 8 tested). KChIP4b, which acts as classical KChIPs, was detected in only one TOR cell (from eight tested). Out of the 3 RS cells that had detectable KChIP4 levels, one expressed KChIP4e specific exons, whereas other two expressed the KChIP4b isoform. Altogether, these data indicate that RS cells express KChIP1, whereas TOR cells primarily express KChIP4e. We suggest that the differential expression of these isoforms underlie some of the distinct properties of I_SA in RS and TOR cells because KChIP1 is known to accelerate inactivation and increase surface expression, whereas tmKChIPs, such as KChIP4e, do not facilitate Kv4 plasma membrane trafficking and confer slow channel inactivation.

However, KChIPs alone cannot account for all differences between I_SA currents in TOR and RS cells as they cooperate with DPLPs to fine-tune Kv4 functions. DPP10 and DPP6 proteins effectively shift the voltage dependence of Kv channel activation and inactivation (Jerng et al., 2007; Jerng and Pfaffinger, 2012; Jerng and Pfaffinger, 2014; Nadal et al., 2006; Nadal et al., 2003; Pongs and Schwarz, 2010). Therefore, next we analyzed the expression of these molecules in the RNAseq data (Figure 5K–L). Conserved sequences of DPP10 were present in both TOR (7 out of 8) and RS (8 out of 8) cells and the DPP10c was the dominant isoform in all cells regardless of their firing type. DPP6 gene was also detected in most cells regardless of their firing type. At isoform level, RS cells primarily expressed DPP6L. By contrast, TOR cells expressed a combination of DPP6S (7 out of 8 cells) and/or DPP6L-specific exons (4 out of 8 cells). To conclude, in addition to KChIPs, DPLP isoform expression displays correlation with TOR and RS firing types of CCK+INs.

Discussion

I_SA current properties tune CA3 CCK+IN function

CCK+INs can dynamically control the activity of selected cell assemblies (Freund and Katona, 2007). However, during network oscillations, the activity of individual CCK+INs is highly variable and individual CCK+INs display different preferences for distinct oscillatory regimes (Klausberger et al., 2005; Klausberger and Somogyi, 2008; Lasztóczi et al., 2011). Our results describe two novel CCK+IN populations, TOR and RS cells in the CA3, and provide a mechanistic explanation for their activity patterns during 8–15 Hz theta oscillations. We found that under basal conditions TOR cells are unlikely to contribute to theta oscillations unless they are primed by preceding depolarizations. However, during lower and faster network oscillations, there may be no difference in TOR and RS cell functions. Because distinct network state-dependent activity is considered as a cell type classification criteria (Klausberger and Somogyi, 2008), TOR and RS cells can be viewed as separate partitions within the CCK+IN class. The TOR phenomenon potentially explains the variable recruitment of individual CCK+INs during subsequent theta cycles enabling state and activity history-dependent control of network functions. As a key feature of CCK+INs, effective neurotransmitter release from these cells often require sustained activity (Földy et al., 2006; Losonczy et al., 2004). Thus, the elimination of multiple AP-consisting bursts in TOR cells during the theta range (Figure 4D) is expected to largely diminish the GABAergic inhibition conveyed by this specific subset of CCK+INs onto CA3 neurons.

Despite the functional distinction between TOR and RS cells, their morphology, basic electrophysiological characteristics and overall transcriptomic profiles are surprisingly similar. Genes and proteins were present in both types similarly that have been shown previously to delineate certain subpopulations within CCK+INs (such as VGluT3 or Calb1). Thus, TOR and RS cells cannot be distinguished based on their general gene expression profiles, which is recently used for identification of individual cell types (Fuzik et al., 2016; Harris et al., 2018). Furthermore, RS and TOR cells are also morphologically diverse, targeting either or both somata and dendrites with their axons. Thus, TOR and RS types cannot be assigned with the previously established morphological types of CCK+INs. Biophysical signatures of AP shapes, intrinsic excitability and synaptic inputs are also similar in RS and TOR cells. Thus, TOR and RS distinction of CCK+INs apparently does not comply with morphological and genomic cell type-definitions. Instead, the functional divergence of TOR and RS types is allowed by the modification of a single ionic conductance. Kv4.3 current alone is sufficient to switch between TOR and RS firing modes. Changing only I_SARS to I_SATOR+RS in the same realistically modelled CCK+INs specifically silenced them during 8–15 Hz input regimes and reproduced the recorded differences in their firing. Pharmacology confirmed the crucial role of Kv4.3 channels in TOR firing. However, RS cells also express Kv4.3 channels, and in fact, their density is higher in RS cells compared to TOR cells. Thus, paradoxically, in spite of Kv4.3-mediated currents are responsible for the unique and distinctive firing properties of TOR cells, Kv4.3 currents are present in both cell types. We found that a potential explanation for this paradox is the differential expression of auxiliary subunits of Kv4.3 channels (see below).

The transcription regulating function of Satb1 depend on activity (Close et al., 2012) and the function of KChIPs is under the control of activity-related intracellular Ca²⁺ signaling. This raises the possibility that RS and TOR firing modes are convertible in CCK+INs depending on neuronal activity. However, we did not detect changes in the firing patterns – including the length of self-inhibition in TOR cells – during long recording sessions indicating that under our experimental conditions CCK+INs stably maintain RS or TOR firing.

Same channel protein, but distinct auxiliary subunits may be responsible for different I_SA currents in TOR and RS cells

Although fine-tuned combination of largely different conductance sets can converge to similar firing patterns (Marder and Goaillard, 2006), a single conductance can completely convert the way a neuron elicits APs. The majority of fundamental spiking properties were similar in TOR and RS cells. However, due to unique properties of Kv4.3-mediated current, TOR cells generate late firing – similarly as in other brain regions (Stern and Armstrong, 1996; Zheng et al., 2019) – and are unresponsive to theta frequency band inputs. While the Kv4.3 channel was present both in RS and TOR cell types, the availability of auxiliary subunits was different. KChIP1 is strongly expressed by RS cells, whereas most TOR cells lacked this cytosolic auxiliary subunit. The known effects of the KChIP1 (Beck et al., 2002; Bourdeau et al., 2011; Jerng and Pfaffinger, 2014; Pongs and Schwarz, 2010) correlate well with the properties of the I_SA in RS cells. As a classical KChIP, KChIP1 increases surface expression of Kv4 channel complex. Indeed, we measured larger density of I_SA current in outside out and nucleated patches from RS than in TOR cells. We also observed that Kv4.3 immunosignal was usually stronger in RS compared to TOR cells. Furthermore, we were able to reproduce TOR and RS firing phenotypes in the realistically simulated conditions only if the total amount of I_SA conductance was larger in RS than in TOR cells. The apparent paradox between larger current density and the smaller measured current amplitude near the AP threshold in RS cells derives from the differences in the voltage dependence of activation in RS and TOR cells. I_SA channels are submaximally activated at physiological subthreshold voltage. However, because of the left-shifted activation curve, a much larger fraction of I_SA channels is activated in TOR cells at lower voltage ranges. Therefore, larger currents can be generated even by a smaller number of channels. Another substantial influence of KChIP1 on Kv4.3 is the acceleration of steady-state inactivation kinetics and the recovery time from inactivation (Beck et al., 2002; Bourdeau et al., 2011; Jerng and Pfaffinger, 2014; Pongs and Schwarz, 2010). These are also correlated well with the differences of I_SA in RS and TOR cells, as both parameters were much faster in the former type of cells.

KChIP proteins and their splice-variants show unusual functional diversity. Different splicing of the same protein can have opposing effects on Kv4 functions, whereas, splice-variants of different proteins can have analogous effects. One outstanding group is the tmKChIP family that consist of KChIP2x, KChIP3x, KChIP4a and KChIP4e. Their common structural feature is an extra N-terminal hydrophobic domain that binds them to the membrane (Holmqvist et al., 2002; Jerng and Pfaffinger, 2008; Jerng and Pfaffinger, 2014; Pongs and Schwarz, 2010). In contrast to classical KChIPs, tmKChIPs typically retain Kv4 channels from the plasma membrane, slow the inactivation kinetics and the recovery from inactivation. KChIP4 was the dominant KChIP in TOR cells and it was not detected in most RS cells. TOR cells expressed a tmKChIP isoform, KChIP4e. The slower inactivation and recovery of I_SA in KChIP4e-expressing TOR cells were critical for their unique functionality during 8–15 Hz network states because they define the availability of the I_SA conductance. KChIP4 was present only in three RS cells and two of them expressed the KChIP4b isoform, which is a classical KChIP (Jerng and Pfaffinger, 2008). In contrast to classical KChIPs, all tmKChIPs including KChIP4e do not facilitate surface expression of Kv4 channels (Holmqvist et al., 2002; Jerng and Pfaffinger, 2008; Pruunsild and Timmusk, 2012). As explained above, several lines of evidence suggest lower Kv4.3 channel density in the KChIP4e-expressing TOR compared to KChIP1-expressing RS cells. The exact effects of KChIP4e on the kinetics of Kv4.3-mediated currents are not known. Albeit little data is available about the modification of Kv4 gating properties by KChIP4e in expression systems, its structural similarities suggest that it acts like other better studied tmKChIPs. The presence of other tmKChIPs results in slow inactivation kinetics, often slower than that of the solitary Kv4 channels (Holmqvist et al., 2002; Jerng et al., 2007; Jerng and Pfaffinger, 2008; Tang et al., 2014). As a consequence of enhanced closed-state inactivation of Kv4.3 channels, KChIP4a causes a leftward shift in the voltage dependence of inactivation (Tang et al., 2013; Tang et al., 2014). Albeit it is a likely possibility due to the similarity of their N-terminal domains, it remains to be answered whether the effects of KChIP4e on Kv4.3 kinetics are similar to the other studied tmKChIPs. Altogether, the presence of KChIP4e in TOR cells and KChIP1 in RS cells is consistent with their different I_SA kinetics and densities that underlie the different functionality of these cells. Future studies are expected not only to confirm that these alternatively spliced variants are solely responsible for the two firing types, but can also address whether these differences in subunits are determined by the destiny of the cells from early of their development or whether these subunits are actively regulated throughout the life span and may underlie activity-dependent modification of the CCK+INs population.

We should also note that the apparent discrepancy in KChIP4 detection between the mRNA and protein levels may also be explained by the presence of different isoforms, as our antibody may have targeted the highly variable N-terminal region.

Both DPLPs were present in TOR and RS cells. DPP10c isoform, which is known to affect voltage dependence of Kv4 channels, but does not accelerate inactivation (Jerng et al., 2007), was ubiquitous in both types of CCK+INs. Albeit all tested CCK+INs had a significant amount of DPP6 mRNA, TOR and RS cells expressed different isoforms. RS cells expressed only DPP6L and in TOR cells the primary isoform was DPP6S (but DPP6L was also present in several cells). DPP6S can contribute to the left-shifted voltage dependence of activation of I_SA in TOR cells. The left-shifted voltage dependence is important for the sufficient prevention of spiking. The dual set of DPP6 proteins fits well with our observations that many properties of TOR cells can be described only if two populations of Kv4.3-mediated currents are present. Altogether, these observations suggest that apparently small modifications in the available components of ion channel complexes underlie the different functions of TOR and RS cells.

The effects of KChIPs are not isolated from the other auxiliary subunits of Kv4. The various stoichiometries of individual Kv4 channels with DPPs and KChIPs allow delicate settings of the channel kinetics. The net effects of KChIPs and DPLPS are not simply the sum of the effects of individual subunits, and the combinatorial possibilities are not yet fully explored with the known 17 variants of KChIPs and 8 variants of DPPs. Some subunits can dominate others or the interaction can result in unexpected effects (Jerng et al., 2005; Nadal et al., 2006; Zhou et al., 2015). For example, when DPP6K is present, KChIP4e causes a leftward shift of the voltage dependence of inactivation and deceleration of the recovery from inactivation compared to other KChIPs (Jerng and Pfaffinger, 2012). In this regard, it is an important observation for our results that DPP6S, unlike some other DPLPs, cannot overcome the tmKChIP4-mediated deceleration of inactivation (Jerng et al., 2007; Jerng and Pfaffinger, 2008; Seikel and Trimmer, 2009). Thus, the expression of DPP6S in TOR cells may preserve the unique modulatory effects of KChIP4e. Because of the composition of these ternary channel complexes by multiple proteins and splicing variants, whose interactions are not yet characterized, the exact contribution of individual components of DPP6S/L/DPP10c-KChIP4e-Kv4.3- and DPP6L/DPP10c-KChIP1-Kv4.3-complexes cannot be predicted yet. In addition to the direct modulation, Kv4 protein complexes can be phosphorylated by various kinases and are involved in complex post-phosphorylation signaling (Hu et al., 2020), which require the presence of auxiliary subunits and modify the mediated currents (Vacher and Trimmer, 2011). Thus, in spite of the large number of potential mechanisms that can modulate Kv4.3 functions, all differences that we observed between RS and TOR cells (i.e., the higher channel density, faster inactivation kinetics and faster recovery from inactivation of I_SA in RS compared to TOR cells) are consistent with the differential expression of KChIP1 and KChIP4e subunits. In combination with the cell type-specific expression and contributions of DPP10c, DPP6L and DPP6S, these results may explain the unique voltage-dependency in the two types of CCK+INs without the involvement of additional differences between RS and TOR cells. Thus, the different firing properties and responsiveness during 8–15 Hz network states of RS and TOR cells can be established by surprisingly minor modifications in a few auxiliary subunits.

Materials and methods

Animal protocols and husbandry practices were approved by the Institute of Experimental Medicine Protection of Research Subjects Committee (MÁB-7/2016 for slice recording and anatomy experiments and MÁB-2/2017 for immunolabelling experiments in perfusion fixed brains) and by the Veterinary Office of Zurich Kanton (single cell RNAseq experiments).

Slice preparation, solutions and chemicals

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Hippocampal slices were prepared from 21 to 33 days old Wistar rats (deeply anaesthetized with isoflurane) in ice-cold artificial cerebrospinal fluid (85 mM NaCl, 75 mM sucrose, 2.5 mM KCl, 25 mM glucose, 1.25 mM NaH₂PO₄, 4 mM MgCl₂, 0.5 mM CaCl₂, and 24 mM NaHCO₃, Leica VT1200 vibratome). The slices were incubated at 32°C for 60 min after sectioning and were then stored at room temperature until they were used. The normal recording solution was composed of 126 mM NaCl, 2.5 mM KCl, 26 mM NaHCO₃, 2 mM CaCl₂, 2 mM MgCl₂, 1.25 mM NaH₂PO₄, and 10 mM glucose. For standard recordings, pipettes were filled with an intracellular solution containing 90 mM K-gluconate, 43.5 mM KCl, 1.8 mM NaCl, 1.7 mM MgCl₂, 0.05 mM EGTA, 10 mM HEPES, 2 mM Mg-ATP, 0.4 mM Na₂-GTP, 10 mM phosphocreatine, and 8 mM biocytin (pH 7.2; 270–290 mOsm). Chemicals for the intra- and extracellular solutions were purchased from Sigma-Aldrich, ion channel blockers were from Tocris or Alomone and fluorophores were from Invitrogen.

Somatic recordings

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For recordings, cells in slices were visualized with an upright microscope (Eclipse FN-1; Nikon) with infrared (900 nm) Nomarksi DIC optics (Nikon 40x NIR Apo N2 NA0.8W objective). Electrophysiological recordings were performed at 34.5–36°C. During current-clamp recordings, firing patterns were elicited by square shaped current pulses with increasing amplitudes (starting from −100 pA up to 700 pA, Δ20 pA, duration: 1 s) or with standard steps (eliciting average firing between 10–20 Hz), which was preceded by 3-second-long different amplitude holding current steps (−450 to 120 pA, with 20 or 30 pA increments) to reach preceding MP range between −90 and −50 mV. We aimed to restrict hyperpolarization above the reversal of potassium ions, which may case shifts in the ionic equilibrium and affect the excitability. However, we did not observe such effects even in those cases when the membrane potential was below the reversal of potassium. AP distributions were calculated from all recorded traces, which contained action potentials and binned by 50 ms from each recorded cell. In these recordings, the pipette capacitance was neutralized (2.5–5 pF remaining capacitance) and bridge balance compensation was set to eliminate apparent voltage offsets upon current steps. AP threshold was determined as the voltage corresponding to 50 mV/ms slope. Voltage values were not corrected for the liquid junction potential (theoretically: −15.4 mV). Traces were low pass filtered at 6–20 kHz and digitized at 40–100 kHz using a Multiclamp 700B amplifier with Digidata 1440 A interface (Molecular Devices).

For voltage-clamp recordings, cells were patched in normal extracellular solution to first record their firing patterns, and then 2.5 µM TTX and 10 µM ZD7288 was added to reduce sodium and Ih currents respectively. Voltage protocols for potassium current measurements consisted of a 300 ms long conditioning pulse at −120 mV, followed by a 300 ms long voltage steps between −120 and −20 mV (for current activation and decay time constant measurements), and a last voltage step to −30 mV for 100 ms (to measure voltage dependence of potassium current inactivation). Series resistances were between 6–20 MΩ (75–80% compensated with 53 µs lag) and were constantly monitored. Data were discarded if the series resistance changed more than 25%.

The recovery from inactivation test protocol consisted four voltage steps: (1) −120 mV for 345 ms, allowing full recovery, (2) −30 mV for 500 ms, resulting complete inactivation, (3) −65, −75,–85 or −120 mV steps with variable duration between 1–233 ms, (4) −30 mV test pulse. The area of current (which is less sensitive to filtering than the peak) was analyzed according to the voltage and duration of the preceding recovery step. To isolate I_SA currents from non-inactivating current control traces (steps 1 and 3 were set to −50 mV with identical duration) were subtracted from each trace. Furthermore, these measurements were performed in the presence of TTX, 1 mM 4-AP and 10 mM TEA to reduce contamination from non-Kv4 channels.

Outside-out patch recordings

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To obtain outside-out patch recordings, first we made somatic whole-cell recordings from the selected cells using IR-DIC optics. This was necessary for classifying their firing as TOR or RS cells, for loading biocytin for subsequent CCK immunolabelling, and for loading 20 µM Alexa-594 fluorescent dye, which allowed the visualization of their dendrites. After at least a 5-minute-long loading period the somatic pipette was retracted. Intact dendrites (30–80 µm from the surface) were visualized and patched using epifluorescent illumination (less than a minute illumination time). The thick-wall pipettes (resistance: 20–55 MΩ) were filled with 5 µM Alexa-594. After break-in, we confirmed that no neighboring structure was loaded with the fluorescent dye and outside-out configuration was achieved by the slow retraction of the pipette. By applying similar voltage steps as described for whole-cell voltage-clamp configurations, we recorded dendritic potassium currents (without pharmacological isolation). After outside-out patch recordings, the pipette was pushed into a Sylgard ball (Sylgard 184, Merck), and capacitive responses to voltage steps were recorded and subtracted from the capacitive responses recorded in outside-out patch configuration. The surface area of the patch membrane was calculated as described earlier (Gentet et al., 2000) with the specific membrane capacitance determined from 18 nucleated patch experiments (cm = 1.015 ± 0.014 µF/cm²). Distances of the recording sites from the soma were measured based on posthoc epifluorescent or confocal images. Somatic outside-out patches were obtained using the same protocol, with similar pipettes. Current traces were low pass filtered at 10 kHz and digitized at 100 kHz. Capacitive membrane responses were digitized at 250 kHz, without filtering. Leak and capacitive current components were subtracted during potassium current recordings using online p/−4 method. Inactivating potassium currents (at 0 mV) were isolated offline by subtracting currents obtained with a −50 mV prepulse from currents measured with a prepulse of −80 mV.

Nucleated patch recordings

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First we made whole-cell recordings from the selected cells to determine their TOR or RS identity. Then, TTX (1 µM), ZD7288 (10 µM) and TEA (10 mM) were added to the normal extracellular solution, to block sodium currents, Ih currents and a fraction of potassium currents, respectively. TEA does not affect Kv4 channels (Lien et al., 2002). Negative pressure was applied through the pipette to bring the nucleus close to the pipette tip, which was then slowly retracted and the plasma membrane sealed around the nucleus. These nucleated patches from somatic membrane were voltage clamped at a holding potential of −80 mV. Voltage commands were identical as in whole cell voltage protocols, except the test voltages ranged between −100 mV and 70 mV allowed by the better voltage control of these isolated membranes. In some of the experiments, when the recording was stable, HpTx (1 µM) was applied from a nearby (<100 µm) small diameter (2–4 µm) pipette to block Kv4 mediated currents. Peak potassium currents were converted to conductance by normalization to the driving force (step voltage minus −90 mV). The capacitance of the excised membrane was determined by dividing the weighted double-exponential fits of the transient responses after the onset of a −40 mV voltage step in a 5 ms time window with the input resistance of the recorded structure. Conductance density was determined by dividing the obtained conductance value with the capacitance of the structure. Series resistances were between 5–15 MΩ (86–98% compensated with 53 µs lag). Leak and capacitive current components were subtracted during potassium current recordings using online p/−4 method. Traces were low pass filtered at 10 kHz and digitized at 100 kHz. Because the removal of the nucleus damaged the cell body, the identity of these cells was determined with CB1 immuno-labelling in their preserved axons.

Note that the three different recording configurations (whole-cell, outside-out and nucleated patch) may be affected by different conditions, such as local shifts on the ionic milieu, space clamp issue or junction potentials. Therefore, the absolute voltage and kinetics values are not necessarily comparable between the different recording conditions. However, currents from TOR and RS were compared only under the same conditions, thus, their differences were relevant and also consistent in the three recording configurations.

Anatomical and immunohistochemical characterization of CCK+ cells

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All recorded cells were filled with biocytin and processed for immunhistochemistry. After the recordings, slices were fixed in 0.1 M phosphate buffer containing 2% paraformaldehyde and 0.1% picric acid at 4°C overnight. After fixation, slices were re-sectioned at 60 μm thickness (Neubrandt et al., 2018). Immunopositivity for CCK was tested with a primary antibody raised against cholecystokinin (1:1000, CCK, Sigma-Aldrich, Cat# C2581, RRID:AB_258806, rabbit polyclonal). The general labelling with this antibody was similar as with a previously used antibody against pro-CCK, including a double neuropil band labelling in the dentate moleculare, perisomatic staining of scattered interneurons throughout the hippocampus and strong axonal bleb labelling at the surface of acute slices (Szabadics and Soltesz, 2009). Biocytin labeling was visualized with either Alexa 350-, Alexa 488-, Alexa 594- or Alexa 647-conjugated streptavidin. For additional neurochemical markers, further immunolabeling was used either against Vesicular Glutamate Transporter 3 (1:2000, VGluT3; Merck, Cat#AB-5421, RRID:AB_2187832; guinea pig polyclonal), Cannabinoid Receptor type 1 (1:1000, CB1R, Cayman Cat#10006590, RRID:AB_10098690, rabbit polyclonal), special AT-rich sequence-binding protein-1 (1:400, Satb1; Santa-cruz Cat#sc-5989, RRID:AB_2184337, goat polyclonal, 1:400), Reelin (1:400, Merck Cat#MAB5364, RRID:AB_2179313, mouse monoclonal) or calbindin (1:1000, Calb1; Swant Cat#300, RRID:AB_10000347, mouse monoclonal). Only those cells were included in the analysis, which were positive for CCK (or for CB1, n = 15 cells).

Known morphological subtypes of CCK+INs in the hippocampal CA3 region were determined based on the layer-preference of their axonal arborization. Schaffer collateral associated cells (SCA)(Cope et al., 2002) innervate dendrites in the stratum radiatum. Albeit the definition of basket cells (BCs) (Hendry and Jones, 1985) and mossy-fiber associated cells (MFA) (Vida and Frotscher, 2000) are clear, with termination zones in the strata pyramidale and lucidum, respectively, their practical identification is more complicated because both cell types have a substantial amount of axons in the adjacent strata, especially near to their soma. Therefore, only cells with extended axonal arbor (at least reaching 200 µm from soma) were identified either as BC or as MFA. In this distal region, BCs had clearly targeted stratum pyramidale, whereas distal axons of MFA ran in the stratum lucidum parallel with the cell layer, and they often invaded the hilar region of the dentate gyrus.

Analysis of potassium channel subunits using immunohistochemistry

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For perfusion fixed brain samples two (P25 and P45) Wistar rats were anesthetized and perfused through the aorta with 4% paraformaldehyde and 15 v/v% picric acid in 0.1M Na-phosphate buffer (PB, pH = 7.3) for 15 min. Immunohistochemistry was performed on 70 μm thick free floating coronal sections from the hippocampus. We used a different fixation protocol for acute hippocampal slices containing biocytin-filled cells. After short recordings (5–15 min), slices were transferred to a fixative containing 2% paraformaldehyde and 15 v/v% picric acid in 0.1 M PB for 2 hr at room temperature. Slices were washed in PB, embedded in agarose and re-sectioned to 70–100 µm thick sections. Sections were blocked in 10% normal goat serum (NGS) in 0.5 M Tris buffered saline (TBS) for one hour and incubated in a mixture of primary antibodies for overnight at RT. The following antibodies were used: rabbit polyclonal anti-CCK antibody (1:500, Sigma-Aldrich Cat# C2581, RRID:AB_258806) mixed either with a mouse monoclonal anti-KChiP1 (1:500, IgG1, UC Davis/NIH NeuroMab Facility Cat# 75–003, RRID:AB_10673162), a mouse monoclonal anti-KChiP4 (1:400, IgG2a, UC Davis/NIH NeuroMab Facility Cat# 75–406, RRID:AB_2493100), or a mouse monoclonal anti-Kv4.3 antibody (1:500, IgG1, UC Davis/NIH NeuroMab Facility Cat# 75–017, RRID:AB_2131966). In some sections, a rabbit polyclonal anti-CB1 receptor (1:2000, Cayman Chemical Cat# 10006590, RRID:AB_10098690) was also used together with the anti-CCK antibody to more reliably characterize filled cells. Potential TOR cells were identified by Satb1 immunolabelling in the perfusion fixed sections. Satb1 and Kv4.3 were visualized by the same secondary antibody but their labelling pattern could be reliably distinguished based on their different subcellular locations. The following secondary antibodies were used to visualize the immunoreaction: Alexa 488-conjugated goat anti-rabbit (1:500, Thermo Fisher Scientific), Cy5 conjugated goat anti-rabbit (1:500, Jackson ImmunoResearch), Alexa 488-conjugated goat anti-mouse IgG1 (1:500, Jackson ImmunoResearch), and Cy3 conjugated goat anti-mouse IgG1 or IgG2a (1:500, Jackson ImmunoResearch) IgG-subclass-specific secondary antibodies. Biocytin was visualized with Cy5 conjugated Streptavidin (1:1000, Jackson ImmunoResearch). High magnification fluorescent images were acquired with an Olympus FV1000 confocal microscope using a 60x objective (NA = 1. 35).

Single-cell mRNA analysis

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Single-cell mRNA was performed using the Clontech’s SMARTer v4 Ultra Low Input RNA Kit. Cells were collected via pipette aspiration into sample collection buffer, were spun briefly, and were snap frozen on dry ice. Samples were stored at −80°C until further processing, which was performed according to the manufacturer’s protocol. Library preparation was performed using Nextera XT DNA Sample Preparation Kit (Illumina) as described in the protocol. Then, cells were pooled and sequenced in an Illumina NextSeq500 instrument using 2 × 75 paired end reads on a NextSeq high-output kit (Illumina). After de-multiplexing the raw reads to single-cell datasets, we used Trimmomatic and flexbar to remove short reads, remove adapter sequences and trim poor reads. The remaining reads were aligned to the GRCm38 genome with STAR aligner. Aligned reads were converted to gene count using RSeQC. All data analyses were performed using python3. The analysis included the removal of poor quality cells (at least 3000 genes were detected in each cell), normalization of gene expression data using scran, and analysis of differential expression of genes across cell types. Sequences of splicing variants of KChIP4, DPP6, and DPP10 were validated in the NCBI Genebank and UCSC databases.

The inclusion criteria to the analysis were the expression both Cck and Cnr1. Furthermore, to exclude cells that were potentially contaminated by other cells we considered the following genes as microglia- (Tyrobp, Ctss, C1qc, Cyba, Ly86), astrocyte- (Gfap, Aldh1l1, Sox9, S100b, Slc1a2), oligodendrocyte- (Olig1, Fgf2, Mtcp1, Olig2, Olig3) and pyramidal cell-specific (Baiap2l2, Slc17a7, Ptk2b, Nrn1, Fhl2, Itpka, Neurod6, Nptx1, Sv2b, Kcnv1) (Luo et al., 2019), and tested these against our single-cell samples. We found that one cell, which met the above criteria, showed significant expression of all five microglia-related genes. This single cell was excluded from the study. Genes related to apoptosis (Bcl2, Casp2, Casp8, Fas; not shown) were absent or present in low copy numbers in both RS and TOR cells indicating that the two firing phenotypes are not due to differential damage during slice preparation. The incomplete biocytin-labelling of most RNAseq analyzed cells prevented us to perform detailed analysis of their morphology. Nevertheless, these sample included at least one SCA, MFA and BC cells.

Computational modelling

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We performed computer simulations using the NEURON simulation environment (version 7.5 and 7.6, downloaded from http://neuron.yale.edu). To create a realistic model of CCK+INs, first we made detailed reconstructions of ten biocytin labelled, electrophysiologically characterized cells (using Neurolucida and the Vaa3D software Peng et al., 2010. The passive electrical parameters of the simulated cells were set as follows: first, axial resistance values were set to 120 Ωcm, then specific membrane capacitance and leak conductances (the substrate of membrane resistance) were fitted based on passive membrane responses to small amplitude (20 pA) current injections (ranges: cm: 1.05–0.82 µF/cm², gl: 5*10⁻⁵ – 5.8*10⁻⁵ S/cm²). The set of active conductances were selected based on a previous publication (Bezaire et al., 2016). Conductance densities were adjusted for each individual cell to reproduce firing characteristics representing our average measurements. Additionally, two variants of A-type potassium conductance models were created. First, the ratio of somatic and dendritic inactivating potassium conductances was set to 2.96:1 somatic to dendritic ratio (see Figure 4—source data 1). Based on the observation, that patches pulled from RS cells produced inactivating potassium currents with similar MP-dependence to those recorded in whole-cell configuration, we modelled a single potassium conductance (I_SARS) constrained on somatic whole-cell recordings (HpTx-1 sensitive currents measured in RS cells, Figure 3F), with the appropriate uncompensated series resistance implemented in the models (2.76 ± 0.28 MΩ, n = 14). For TOR cell models we used a mixture I_SA consisting of I_SARS and I_SATOR, to account for the variability of patch currents from TOR cells. I_SATOR has a left shifted voltage dependence and slower inactivation than I_SARS. I_SATOR+RS reproduced potassium currents from our whole-cell measurements and the typical characteristics of TOR firing. Thus, each of the ten model neuron was simulated both as an RS and TOR phenotype by changing the ratio of gTOR and gRS conductances. Specifically, RS firing type was achieved by adding gRS with 297*10⁻⁵ ± 47.2*10⁻⁵ S/cm² density to the soma and 99.1*10⁻⁵ ± 15.7*10⁻⁵ S/cm² to the dendrites. Whereas, when TOR phenotype was generated with the same active conductance set in the same reconstructed cells, gTOR was added at 44*10⁻⁵ ± 7*10⁻⁵ S/cm² to the soma and 14.7*10⁻⁵ ± 2.33*10⁻⁵ S/cm² to the dendrites and gRS was reduced to 128*10⁻⁵ ± 20.3*10⁻⁵S/cm² at the soma and 42.7*10⁻⁵ ± 6.78*10⁻⁵ S/cm² in the dendrites (Figure 4—figure supplement 1). Thus, the overall density of I_SA was larger when the cell was in the RS firing mode. Control simulations showed that combined gRS and gTOR better represents TOR firing than a larger amount of gTOR alone.

To investigate the behavior of these model cells in in vivo relevant conditions constructed physiologically plausible input conditions with a large number of temporally organized synaptic excitation and inhibition. For these, first we measured the amplitude and kinetics of glutamatergic and GABAergic currents in TOR and RS cells, using intracellular solutions, containing CsCl (133.5 mM CsCl, 1.8 mM NaCl, 1.7 mM MgCl2, 0.05 mM EGTA, 10 mM HEPES, 2 mM Mg-ATP, 0.4 mM Na2-GTP, 10 mM phosphocreatine, and 8 mM biocytin, pH: 7.2; 270–290 mOsm). To investigate isolated excitatory or inhibitory events, 5 µM SR 95531 hydrobromide (6-Imino-3-(4-methoxyphenyl)−1(6H)-pyridazinebutanoic acid hydrobromide) or 10 µM CNQX (6-Cyano-7-nitroquinoxaline-2,3-dione) and 20 µM D-AP5 (D-(-)−2-Amino-5-phosphonopentanoic acid) was added, respectively. At the end of these recordings, the identity of the recorded synaptic events was confirmed by the application of the above mentioned specific antagonists. These recordings showed no significant difference in the amplitude of excitatory events in TOR and RS cells (TOR: −43.4 ± 0.6 pA, RS: −41.6 ± 0.4 pA, Mann-Whitney test; p=0.1398, U = 3.42002*106, Z = 1.47654). The simulated excitatory conductances corresponded to these events as their magnitude followed a normal distribution (mean: 0.22 nS, variance: 0.01 nS). The simulated inhibitory conductance represented both tonic and phasic inhibition (model distribution mean: 2 nS, variance: 0.1 nS) and was based on the similar inhibitory events recorded in CCK+INs (TOR: −89.1 ± 1.4 pA, RS: −78.1 ± 1.7 pA). Synaptic conductances were distributed along the dendrites and somatas of the simulated cells uniformly. Physiologically plausible membrane potential oscillations were driven by these synaptic inputs at different frequency ranges. Specifically to group inputs into a specific frequency, excitatory events were aggregated into normal distributions packages at various frequencies to recreating in vivo relevant MP oscillations in single cells, as follows:

o n s e t_{G l u t} = \frac{1000}{f r e q} * 0.633 + (69.25112 * ({0.67747}^{f r e q})) + \frac{1000}{f r e q}

where onset_Glut is the timing of an individual excitatory event, and freq is the frequency by which excitatory packages occur. This equation is necessary for setting the width of each normal distribution according to the desired frequency, and therefore keeping the sinusoid shape of the MP during the simulated oscillations. Inhibitory synaptic inputs prevented over-excitation and they followed a uniform random temporal distribution. In these simulations, 20 oscillatory cycles or in case of high frequencies at least 5 s simulation times were used. Before each run, excitatory and inhibitory event amplitudes and onsets were randomized in a unique but reproducible manner (pseudo-randomization with seed value). If the RS model (I_SARS) elicited APs, simulations were repeated with TOR model (I_SATOR+RS). The amount of excitation was set to produce firing frequencies below 50 Hz in RS cells. Changes in firing rate caused by the replacement of I_SARS with I_SATOR+RS was calculated by subtraction of the TOR firing rate from RS firing rate and normalized to the latter. Simulations were run on the Neuroscience Gateway (Sivagnanam et al., 2015).

Data analysis and statistics

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Data was analysed using Molecular Devices pClamp, OriginLab Origin, Microsoft Excel software and Python-based scripts. Normality of the data was analyzed with Shapiro-Wilks test. Data are presented as mean ± s.e.m.

Data availability

Sequencing data have been deposited in GEO under accession code GSE133951.

The following data sets were generated

1. Oláh VJ
2. Lukacsovich D
3. Winterer J
4. Lörincz A
5. Nusser Z
6. Földy C
7. Szabadics J
(2019) NCBI Gene Expression Omnibus
ID GSE133951. Functional specification of CCK+ interneurons by alternative isoforms of Kv4.3 auxiliary subunits.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133951

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Decision letter

Katalin Toth

Reviewing Editor; Université Laval, Canada
Gary L Westbrook

Senior Editor; Oregon Health and Science University, United States
Lisa Topolnik

Reviewer; Université Laval, Canada

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors demonstrate the existence of functional subpopulations of CCK+ interneurons based on their firing pattern. They identified the underlying mechanism of this diversity and show the potential physiological relevance of the distinct firing patterns in relation to oscillations. The study uses comprehensive and complementary approaches, the quality of the data is high, and the analysis is thorough and carefully executed. The central question is important, that CCK neurons play an important role in information processing in the hippocampal network.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Functional specification of CCK+ interneurons by alternative isoforms of Kv4.3 auxiliary subunits" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor.

Thank you for submitting the manuscript to eLife. After careful revision by three reviewers, we concluded, that the study is very interesting. However, based on the long list of comments we are afraid that the revision will take a substantial period of time and therefore needed to reject the manuscript. We would, however, be interested in re-reviewing the study once the authors performed a thorough and in-depth revision of the main concerns listed below and re-submitted their work to eLife. Of course, at this time you are free to submit elsewhere. If you don't agree with these reviews and the requested new work, we assume you will take the work elsewhere. If you agree that the manuscript would benefit from the additional work then you can resubmit it to us, with a detailed description of the new data. This is in keeping with our unwillingness to ask authors to do experiments that they don't agree will benefit their manuscript.

Essential revisions:

1) Improve the cell identity following the suggestions of reviewer #2.

2) Perform additional controls for patch seq analysis as suggested by reviewer #2.

3) Improve the discussion on the potential functional role of both interneuron types as questioned by reviewers #1 and 2.

4) Discuss the similarities and differences between native and recombinant channels KChIP4e. If data to recordings from recombinant channels are available, include them in the study. If they are not available, discuss the limitations of your data (see comment of reviewer #3).

5) Improve the discussion on errors related to voltage-clamp recordings of K⁺ channel-mediated currents.

6) Activation and inactivation curves of several ion channels are affected by patch configuration and time-dependent changes, e.g. shifts to more negative values over time (Donnan effects may contribute to these changes). This raises the question whether the curves were similar in different recording configurations and affected by nonstationarities. At the very least, statements should be added to the Materials and methods section.

7) Discus the opposing views that different K⁺ channels or fine-tuning in gating of the same K⁺ channels determines the action potential phenotype as suggested by reviewer #3.

8) Improve the discussion on the correlative nature of evidences between alternatively spliced auxiliary subunits and their effects on neuronal activity patterns.

9) Improve the modeling section by following the suggestions of reviewer #3.

10) Discuss, that acute modifications such as phosphorylation could also influence the gating kinetics of the channels.

11) Increase the number of modeled single cells following reviewer #1.

Reviewer #1:

This study examines the diversity of CCK-expressing interneurons in CA3. Electrophysiological recordings show that CA3-CCK cells can be distinguished in two types based on their firing dynamics into (a) regular spiking (RS) and (b) transient outward rectifying (TOR) interneurons. Interestingly, morphological and intrinsic physiological properties are similar between the two groups of CCK cells. Electrophysiological and pharmacological analysis indicates that Kv4.3 conductances are important in shaping the discharge pattern of TOR cells. However, the study shows that RS cells also express this channel type. Indeed, the density of Kv4.3 is even higher in RS than TOR interneurons. The authors provide the explanation that auxiliary subunits, which seem to differ between the two CCK types, and which can modify the gating properties of Kv4.3 channels, may explain the differential gating characteristics of the channel and the discharge patterns among the two cell types. One of the auxiliary subunits is the KChip protein. KChip1 is strongly expressed in RS cells and only rarely in TOR neurons. In contrast, KChip4 was largely found in TOR but not in RS cells. Further differential expression profiles were observed for DPLPs. RS cells express only DPP6L and TORs largely DPP6S. The main question is, whether minor changes in auxiliary subunit compositions indeed may underlie the significant differences in discharge patterns observed between TOR and RS cells.

1) In view of the fact that a large body of modelling experiments are based on only three TOR and two RS cells, the number of reconstructed cells plus the modelling experiments, need to be increased to 4 TOR plus 4 RS cells.

2) The observed differential expression densities of the Kv4.3 channels in RS vs TOR cells would require systematic out-side out patch recordings along the somato-dendritic domain.

3) The authors tried to block Kv4 channels with HpTx (blocks Kv4.2 and 4.3) which increased the number of APs during the initial 150 ms of a depolarizing current step in TOR cells, however, the effect was only minimal (Figure 5A/B). Since the authors argue that auxiliary subunits are important in defining the gating properties of the Kv4.3 one option would be to knock out one or the other subunit using small interference RNA. It would be great if the proposed effect of the auxiliary subunit could be further proven.

4) Can we exclude the possibility that activity states of the CCK cells may drive the expression of the auxiliary subunits? Thus, could it be that we look on the same 'class' of CCK cells of various morphologies at different 'homeostatic' plasticity states? Some CCK INs may receive a higher drive than others resulting in differential expression profiles of auxiliary subunits that 'set' their discharge patterns.

Reviewer #2:

The manuscript by Olah et al.,. is focusing on firing phenotypes of CCK-expressing interneurons in rat hippocampal CA3 area. Using a combination of whole-cell and outside-out patch-clamp recordings, patch-seq, IHC analysis and computational modelling, the authors report two firing phenotypes in the population of CCK+ cells. In particular, the low-voltage-activated delayed firing due to Kv4.3 channels and distinct Kv4 auxiliary subunits is revealed in ~50% of CCK+ cells. When put in the cell model with realistic synaptic inputs mimicking in-vivo-like conditions, this feature had impact on the cell excitability during 8-15 Hz oscillations. This work is very interesting as it demonstrates how differences in the K⁺ channel auxiliary subunit isoforms may affect the cell functional phenotype; it therefore brings a significant contribution to the field. However, additional analysis and text revision will be required to support the major conclusions. Specifically, additional efforts are required to (1) clarify on the cell types/identity, (2) expand the patch-seq analysis, and (3) discuss the functional significance of the observed phenomenon. Below I summarize these points:

1) Cell types/identity: the cells were identified as CCK+ based on post-hoc IHC immunolabelling. Only half of these cells exhibited the TOR firing, suggesting that there may be two different CCK+ cell sub-types. The authors put additional efforts to examine the morphological properties of recorded cells. 172 neurons were reconstructed and identified as BCs, MFAs and SC-ACs. While the TOR firing was detected in morphologically different CCK+ cell types, it would be important to validate this observation by performing cluster analysis combining electrophysiological and morphological features of recorded cells (see Hosp et al., 2014). The authors should including additional membrane properties: membrane time constant, slow AHP, Ih, rebound depolarization (e.g. CA1 CCK+ BCs and SC-ACs have different Ih and rebound depolarization, Evstratova et al., 2011)) and firing parameters (e.g., discharge frequency, ISIfirst/last, CV of ISI at different Vm levels etc.) in the cluster analysis and combine it with morphological features (e.g., axon distribution in different sublayers, dendritic features) to explore CCK+ CA3 population with TOR vs RS firing. The SC-ACs reconstructions are not illustrated in the manuscript, and, as the authors mention in the Materials and methods section, distinguishing BCs from MFA cells may be challenging due to their partially overlapping axonal termination zones. Quantitative analysis of the axon distribution is required to validate the cell identification.

2) Patch-seq analysis is prone to contamination as can be concluded from most of articles published so far using this technique (e.g., see Tripathy et al., 2018; for in-depth assessment of this issue). The authors should provide data on sample contamination before any conclusion can be made. Expression of excitatory, microglial, astrocytic and oligo markers should be shown next to the gene expression data. Negative control data from the rat CA3 region (see Luo et al., 2019) should be included. As far as I can see, cells included in patch-seq were only identified based on firing pattern and expression of Cck and Cnr1 genes. The authors should confirm the morphology of the included cells, as many interneuron types express Cck and Cnr1 genes (e.g., VIP+ subiculum-projecting cells, Luo et al., 2019). In relation to the previous point (cell identity), the authors should provide more information on gene expression by TOR vs RS cells: common vs specific genes for each firing phenotype. It is unclear how the cluster analysis of gene expression was performed (on genes detected in 3 out of 17 cells?). This analysis needs to be shown. While normalization for gene expression is a good approach, for comparison with previous published data it would be important to show the gene expression in TPM. What is shown in Figure 6A: mean +/- SE? I'm not sure that this is the right way to illustrate this data given a high variability in gene expression between individual samples. Here, the authors claim significant differences in the expression of Kv1.3, Kv3.2 and Kv4.3 genes but it is not indicated on the figure. Was it statistically significant?

3) Functional significance of two firing phenotypes within the same interneuron population (if the latter is confirmed after additional analysis) needs to be discussed more. Having two firing phenotypes within the same cell type would increase the overall variability in their firing, thus decreasing their reliability under some conditions. Under which in-vivo conditions it may happen? Do CCK+ cells exhibit a hyperpolarized Vm of -70 to -90 in vivo? What will be the network outcome given that apparently different cell types such as BCs, SC-ACs and MFAs can show this phenomenon?

Reviewer #3:

The paper by Olah et al., examines the molecular determinants of the action potential phenotype of CCK+ interneurons in the hippocampus. To address this question, the authors use electrophysiology, immunohistochemistry, single-cell RNA seq, and modeling. The main findings are:

- CCK+ interneurons in CA3 fall into two functional classes with distinct action potential phenotypes.

- Different excitability properties lead to differential recruitment of these interneurons during theta network oscillations.

- KV 4.3 contributes to the excitability in the two classes of cells, but does not explain the difference in the action potential phenotype.

- Firing properties are correlated with differential expression of Kv4 auxiliary subunits (KChIP1 vs. KChIP4e and DPP6S).

Based on these results, the authors conclude that alternative splicing of a small number of genes significantly changes the action potential phenotype of a neuron. Overall, I found this a quite nice paper. The question is interesting, the experiments are technically well performed, the results are mostly convincing, and the paper is clearly written. However, several points need to be addressed before the manuscript can be further considered.

Essential revisions:

1) The links between auxiliary subunit expression and channel function remain loose. If I read the statements correctly, (1) the effects of KChIP4e have not been investigated in recombinant expression systems, (2) it is unclear whether DPP6L and DPP6S shift the activation and inactivation curves in the proposed manner in recombinant channels, and (3) it is unclear whether ternary or higher order complexes (subsection “Same channel protein but distinct auxiliary subunits are responsible for the different I_SA currents and for the different functionality of TOR and RS cells”) show the proposed properties when recombinantly expressed. Ideally, the authors should recombinantly express the proposed subunit combinations and compare the functional properties. At the very least, they should better discuss the similarities and differences between native and recombinant channels. A systematic comparison of properties in a supplementary table might help.

2) Several conclusions are based on analysis of K⁺ currents in the whole-cell voltage-clamp configuration. However, voltage-clamp errors may be a problem in these measurements. At the very least, they should better discuss this point. Additionally, pharmacological isolation of Kv4 currents in high TEA concentrations might be useful to improve voltage clamp.

3) Activation and inactivation curves of several ion channels are known to be affected by patch configuration and time-dependent changes, e.g. shifts to more negative values over time (Donnan effects may contribute to these changes). This raises the question whether the curves were similar in different recording configurations and affected by nonstationarities. At the very least, statements should be added to the Methods section.

4) The authors conclude that fine-tuning in gating and conductance density of K⁺ channels is critically important for determining the action potential phenotype. This seems in contrast to the previous demonstration that the same firing pattern can be generated by several combinations of conductances (Marder and Goaillard, 2006). The authors should better discuss these opposing views.

5) The evidence for an involvement of alternatively spliced auxiliary subunits is compelling but remains correlative. The authors should clearly mention the limitations of the correlative approach in both Abstract and Discussion section.

6) The modeling section is unclear. Inclusion of a table with the main free parameters may provide the necessary clarification. Furthermore, the Materials and methods section addressing this part needs to be rewritten. Finally, it is not entirely clear whether two populations of channels are indeed required to describe I_SA channel gating in TOR cells. A single population with intermediate gating properties may do the same job.

7) Surely, mRNA and protein expression are important, but acute modifications (e.g. by phosphorylation) may be also relevant. At the very least, appropriate caveat sentences should be added.

8) Finally, the paper is too long in relation to the novel information it contains. It should be shortened to approximately 60 to 70%. A scheme of the proposed mechanisms at the end of the paper might increase clarity and help in shortening the text.

https://doi.org/10.7554/eLife.58515.sa1

Author response

Essential revisions:

1) Improve the cell identity following the suggestions of reviewer #2.

As requested by the reviewer new data and analysis were included. We now show cluster analysis using multiple different parameters, including electrophysiology, morphology and mRNA content (Figure 2—figure supplement 2). Furthermore, we present more detailed data about the electrophysiological and morphological properties of our CCK+IN data set (Figure 1—source data 1). For more information see our response to reviewer #2, point 1, which is a related question.

2) Perform additional controls for patch seq analysis as suggested by reviewer #2.

We have revised the RNA data and performed new analysis as requested. For details see our response to reviewer #2 (point 2) and the revised Figure 2 and Figure 5 and text subsection “TOR and RS firing types do not correlate with previously known subtypes of CCK+ cells”, Materials and methods section. Briefly, we tested contamination of our CCK+IN samples by other cells, including microglia, oligodendrocytes, astrocytes and excitatory cells. This analysis revealed that one of our cells was likely contaminated by microglia and, therefore, it was removed from the analysis. However, our original conclusions remain unchanged.

We also added new analysis using hierarchical cluster analysis of CCK+INs based on their total gene expression as well as their physiological properties (see new Figure 2—figure supplement 2). For more information see our response to reviewer #2.

Finally, we now include an additional potential marker of a subpopulation of CCK+INs, Lypd1, which was previously described in a subpopulation of CA1 CCK+INs (Harris et al., 2018), but is expressed in the majority of TOR cells per our RNAseq analysis.

3) Improve the discussion on the potential functional role of both interneuron types as questioned by reviewer #1 and 2.

We revised the manuscript regarding the discussion about the potential role of the two firing types of CCK+INs as suggested by the reviewer. For more information, see our response to reviewers #1 (point 4) and reviewer #2 (point 3).

4) Discuss the similarities and differences between native and recombinant channels KChIP4e. If data to recordings from recombinant channels are available, include them in the study. If they are not available, discuss the limitations of your data (see comment of reviewer #3).

We thoroughly revised the manuscript to better explain the functional diversity of KChIPs and to highlight that KChIP4e, whose presence correlates with TOR firing in CCK+INs, is the least known among the auxiliary subunits of Kv4 channels (see revised text in subsection “Auxiliary subunits of Kv4 channels in TOR and RS cells”, Discussion section).

The precise electrophysiological properties of KChIP4e containing channels are not available from isolated expression systems. There are apparent contradictions between the results of previous papers, which may likely stem from the functional diversity of different KChIP splicing variants. In fact, there are often larger differences between the effects of the different variants of the same KChIP proteins than between different KChIP proteins. For example, the effects of KChIP4a are more similar to KChIP4e, KChIP2x or KChIP3x than to KChIP4bL. By contrast, KChIP4bL shows functional similarities with KChIP1a, KChIP1c, KChIP2a, KChIP3a and KChIP4d. The first group forms the so-called tmKChIPs, which retain Kv4.3 from the plasma membrane, which we also observed in TOR cells were KChIP4e is abundant. In expression systems, most tmKChIPs slow inactivation and affect voltage dependence, but the effect of KChIP4e on the kinetics of the Kv4.3-mediated current remains elusive. The evaluation of KChIP-effects is further complicated by potential interactions with DPP proteins, because the effects of DPPs and KChIPs do not appear to be simply additive. We would like to emphasize that, although it is not possible to prove now that KChIP4e is responsible for the unique subthreshold potassium conductance and firing of TOR cells, our results show strong correlation between gene expression and function. We found that KChIP4e is exclusive to TOR cells, whereas KChIP1 is abundant in RS cells. Furthermore, the KChIP sets did not overlap between the two types. We thoroughly revised the discussion regarding KChIP and DPP functions to better reflect the current state of knowledge in this field. We also explicitly state that the precise contribution of KChIP4e is not completely understood. However, the structural similarities of KChIP4e to other tmKChIPs and their similar effects on the surface expression suggest a causal relationship between this subunit and the unique properties of I_SA currents in TOR cells.

5) Improve the discussion on errors related to voltage-clamp recordings of K⁺ channel-mediated currents.

To address this question, we have performed new experiments that are less affected by voltage-clamp errors. Specifically, we compared potassium currents in nucleated patches from the two types of CCK+INs in the presence of TEA, which inhibits most Kv channels, but leaves Kv4 unaffected. We also tested the effects of specific Kv4 channel inhibitor, HpTX on these currents. This experimental configuration allowed recording of well-controlled Kv4-mediated currents even with large voltage steps and the direct comparison of the properties and densities Kv4-mediated conductances in TOR and RS cells.

The new data not only address the questions of better voltage control but strengthens our conclusions. These results provide a more direct evidence for left-shifted voltage dependence and slower inactivation kinetics of the current in TOR cells. Importantly, the results also confirm that RS cells have larger Kv4.3-mediated total conductance than TOR cells, which accords with the expected effects of KChIP1 and KChIP4e. This new data is now shown in Figure 3C. For additional information, see our response to reviewer #3 point 2.

6) Activation and inactivation curves of several ion channels are affected by patch configuration and time-dependent changes, e.g. shifts to more negative values over time (Donnan effects may contribute to these changes). This raises the question whether the curves were similar in different recording configurations and affected by nonstationarities. At the very least, statements should be added to the Materials and methods section.

We agree that long-lasting hyperpolarization may cause changes in the ionic milieu. However, our experiments were devised to minimize this effect. We applied large hyperpolarization only in a few experiments and they lasted not longer than 3 seconds in the current clamp recordings. We highlight this in the manuscript (subsection “A large portion of CCK+INs show state-dependent firing in the CA3 area” and Materials and methods section).

Furthermore, we would like to emphasize that I_SA currents from TOR and RS cells were compared under the same conditions, including whole-cell current- and voltage-clamp, outside-out patches and nucleated patches. Recording conditions were stable in individual cells and the differences were consistent. Thus, it is unlikely that the Donnan-effect influenced the two cell types differently.

Third, now we also include detailed data about the stability of firing patterns of individual cells during long recording sessions (>45 minutes, see subsection “A large portion of CCK+INs show state-dependent firing in the CA3 area” and subsection “Kv4 channels are responsible for both types of I_SA currents in CCK+INs”). For more information regarding this question, see our response to reviewer #3.

7) Discus the opposing views that different K⁺ channels or fine-tuning in gating of the same K⁺ channels determines the action potential phenotype as suggested by reviewer #3.

We have included a new subsection in the Discussion section about the functional convergence of different conductance sets and the effect of a single conductance.

8) Improve the discussion on the correlative nature of evidences between alternatively spliced auxiliary subunits and their effects on neuronal activity patterns.

We have revised the Discussion section as suggested to highlight that some of our findings provide correlative evidences between the availability of different KChIPs and firing properties of TOR and RS CCK+INs.

The correlative nature of the evidences is clearly emphasized in the abstract: “the firing phenotypes were correlated with the presence of distinct isoforms of Kv4 auxiliary subunits” and “alternative splicing of few genes… can determine cell-type identity “.

9) Improve the modeling section by following the suggestions of reviewer #3.

We have included a summary table about the ionic conductances in the models as suggested by reviewer #3 (see Figure 4—figure supplement 1). Furthermore, we added more reconstructed cells to the modelling, which now includes 10 cells. Furthermore, we revised the description of the modelling as suggested. For additional information, see our response to reviewer #3 point 6.

10) Discuss, that acute modifications such as phosphorylation could also influence the gating kinetics of the channels.

As suggested by the reviewers we now discuss that differential phosphorylation of Kv4 channel complexes may also contribute to differences in the kinetic properties of the currents. We also cite relevant literature about sub-type-specific contribution of KChIPs to the different phosphorylation states of Kv4 channels as a potential molecular mechanism for the modulation the channel (Discussion section). See our response to reviewer #3.

11) Increase the number of modeled single cells following reviewer #1.

We have reconstructed an additional five cells and performed the same simulations as we did with the original five cells. The simulations on the new cells confirmed our previous observations. Furthermore, we used this additional analysis to present more detailed comparisons regarding the morphology of TOR and RS cells (Figure 2—figure supplement 1).

List of new experiments:

- Nucleated patch recordings to measure Kv4-mediated currents in the two types of CCK+INs (Figure 3C).

- Five new, completely reconstructed cell morphologies (Figure 2 and Figure 2—figure supplement 1 and Figure 2—figure supplement 2).

- All simulations were repeated with the five newly reconstructed cells (Figure 4 and its supplements).

List of new analysis:

- Additional cluster analyses considering gene expression, morphology and firing in various combinations (Figure 2—figure supplement 2A-G).

- Additional morphological analysis to map the primary axon termination zone of the ten reconstructed cells (Figure 2—figure supplement 2H).

- Dendritic distance-dependence of current density (Figure 3—figure supplement 1C).

- We re-analyzed the SC-RNAseq data and correlations after the removal of one cell from the data set based on the suggestion of reviewer #2.

New figure panels and tables:

- Figure 3C: nucleated patch recordings of Kv4.3-mediated currents from TOR and RS cells.

- Figure 2 – figure supplement 1: Scholl-analyses of 3-dimensionally reconstructed morphologies of CCK+INs.

- Figure 2—figure supplement 2: cluster analysis of CCK+INs based on active genes, electrophysiological and morphological properties.

- Figure 1—source data 1: detailed electrophysiological parameters of CCK+INs.

- Figure 4—figure supplement 1: conductance and membrane parameters of the modelled CCK+INs.

Revised figure panels:

- The original Figure 3 and Figure 5 are merged into a single figure. This new figure now summarizes all experiments regarding Kv4-mediated current and conductance parameters in CCK+INs.

- Figure 2C, D: revised data (see our response to reviewer #2 point 1).

- Figure 4D: updated with additional cells, now n = 10 cells.

- Figure 5 (previously Figure 6): revised data (see our response to reviewer #2 point 1).

Reviewer #1:

[…]

1) In view of the fact that a large body of modelling experiments are based on only three TOR and two RS cells, the number of reconstructed cells plus the modelling experiments, need to be increased to 4 TOR plus 4 RS cells.

We have reconstructed five more cells and included them in all simulations. The results of the five new cells were similar to those obtained in the original five cells. Revised Figure 4 now shows the average outcomes of ten simulations.

2) The observed differential expression densities of the Kv4.3 channels in RS vs TOR cells would require systematic out-side out patch recordings along the somato-dendritic domain.

These data are central to the revised manuscript (see subsection “Differences in low-voltage-activated potassium currents (I_SA) underlie the heterogeneity of CCK+IN firing” and Figure 3—figure supplement 1). The observed current densities were similar along the somato-dendritic axis both in RS and TOR cells. In addition, we performed new experiments using the nucleated patch configuration, which allowed the direct comparison of Kv4-mediated conductances between RS and TOR cells (Figure 3C).

3) The authors tried to block Kv4 channels with HpTx (blocks Kv4.2 and 4.3) which increased the number of APs during the initial 150 ms of a depolarizing current step in TOR cells, however, the effect was only minimal (Figure 5A/B). Since the authors argue that auxiliary subunits are important in defining the gating properties of the Kv4.3 one option would be to knock out one or the other subunit using small interference RNA. It would be great if the proposed effect of the auxiliary subunit could be further proven.

Please note that we used rats for our experiments for which knock-out models are not easily accessible. However, to strengthen our conclusion for the HpTX effects on firing, we performed new experiments with nucleated patches. The results revealed that HpTX-effects are voltage-dependent, which is consistent with previous reports that we cited in the manuscript. In fact, HpTX does not simply block Kv4.3 currents, but shift the activation curve to the right, which limits the channel availability. Therefore, at near threshold voltages HpTX results in 19.3% inhibition of Kv4.3-mediated currents in TOR cells (see new Figure 3C panel). Thus, the incomplete inhibition explains our observations on the HpTX-effect on the firing.

We decided against the employment of an shRNA knock-down strategy because it would not be possible to prove whether the infected cells would have been TOR or RS phenotypes without the Kv4.3-KD and because this channel is present in both cell types.

4) Can we exclude the possibility that activity states of the CCK cells may drive the expression of the auxiliary subunits? Thus, could it be that we look on the same 'class' of CCK cells of various morphologies at different 'homeostatic' plasticity states? Some CCK INs may receive a higher drive than others resulting in differential expression profiles of auxiliary subunits that 'set' their discharge patterns.

We have revised the manuscript to raise the possibility that CCK+INs may switch between TOR and RS firing modes (see the new discussions in subsection “I_SA current properties tune CA3 CCK+IN function” and subsection “Same channel protein, but distinct auxiliary subunits may be responsible for different I_SA currents in TOR and RS cells). To address this point experimentally, we have tried to alter general signaling mechanisms by changing the extracellular or intracellular calcium ion concentration and assess if these were sufficient to change the RS or TOR firing type. However, none of these trials suggested switch between the two firing phenotypes.

We also state in the Results section that based on the rate and amplitude of spontaneous events, the excitatory drive to TOR and RS cells appears to be similar (subsection “A large portion of CCK+INs show state-dependent firing in the CA3 area”).

Reviewer #2:

[…] However, additional analysis and text revision will be required to support the major conclusions. Specifically, additional efforts are required to (1) clarify on the cell types/identity, (2) expand the patch-seq analysis, and (3) discuss the functional significance of the observed phenomenon. Below I summarize these points:

1) Cell types/identity: the cells were identified as CCK+ based on post-hoc IHC immunolabelling. Only half of these cells exhibited the TOR firing, suggesting that there may be two different CCK+ cell sub-types. The authors put additional efforts to examine the morphological properties of recorded cells. 172 neurons were reconstructed and identified as BCs, MFAs and SC-ACs. While the TOR firing was detected in morphologically different CCK+ cell types, it would be important to validate this observation by performing cluster analysis combining electrophysiological and morphological features of recorded cells (see Hosp et al., 2014). The authors should including additional membrane properties: membrane time constant, slow AHP, Ih, rebound depolarization (e.g. CA1 CCK+ BCs and SC-ACs have different Ih and rebound depolarization, Evstratova et al., 2011)) and firing parameters (e.g., discharge frequency, ISIfirst/last, CV of ISI at different Vm levels etc.) in the cluster analysis and combine it with morphological features (e.g., axon distribution in different sublayers, dendritic features) to explore CCK+ CA3 population with TOR vs RS firing. The SC-ACs reconstructions are not illustrated in the manuscript, and, as the authors mention in the Methods section, distinguishing BCs from MFA cells may be challenging due to their partially overlapping axonal termination zones. Quantitative analysis of the axon distribution is required to validate the cell identification.

We have added detailed data about the electrophysiological parameters of all recorded cells in Figure 1—source data 1, and detailed morphological data of ten reconstructed cells (Figure 2—figure supplement 1). Note that we added five new morphologies in the revised manuscript.

The quantitative comparison of the electrophysiological and morphological parameters, together with the actively transcribed genes are shown in Figure 2—figure supplement 2 (see also subsection “TOR and RS firing types do not correlate with previously known subtypes of CCK+ cells”). Cluster analysis was not able to distinguish TOR and RS cells based on their morphological features or gene expression. However, as a positive finding, cluster analysis separated out cells based on known CCK+IN morphological subcategories, such as basket- and two types of dendrite targeting cell types (Figure 2—figure supplement 2H).

The reconstructed cells represent different morphological subtypes, which can be classified based on the target zone preference of their axons as basket, mossy fiber associated and dendrite-targeting perforant path-associated cells. Our initial hypothesis, similar to what this Reviewer suggests, was that the two types of firing correlate with known subgroups of CCK+INs. However, our results prompt us to reject this hypothesis.

2) Patch-seq analysis is prone to contamination as can be concluded from most of articles published so far using this technique (e.g., see Tripathy et al., 2018; for in-depth assessment of this issue). The authors should provide data on sample contamination before any conclusion can be made. Expression of excitatory, microglial, astrocytic and oligo markers should be shown next to the gene expression data. Negative control data from the rat CA3 region (see Luo et al., 2019) should be included. As far as I can see, cells included in patch-seq were only identified based on firing pattern and expression of Cck and Cnr1 genes. The authors should confirm the morphology of the included cells, as many interneuron types express Cck and Cnr1 genes (e.g., VIP+ subiculum-projecting cells, Luo et al., 2019). In relation to the previous point (cell identity), the authors should provide more information on gene expression by TOR vs RS cells: common vs specific genes for each firing phenotype. It is unclear how the cluster analysis of gene expression was performed (on genes detected in 3 out of 17 cells?). This analysis needs to be shown. While normalization for gene expression is a good approach, for comparison with previous published data it would be important to show the gene expression in TPM. What is shown in Figure 6A: mean +/- SE? I'm not sure that this is the right way to illustrate this data given a high variability in gene expression between individual samples. Here, the authors claim significant differences in the expression of Kv1.3, Kv3.2 and Kv4.3 genes but it is not indicated on the figure. Was it statistically significant?

We thank the reviewer for raising this excellent point. While off-cell contamination is a concern, our previous study did not indicate off-cell mRNA as a major source of contamination (Lukacsovich et al., 2019). Furthermore, we found that some of the genes that we previously regarded as contamination may be cell type specifically expressed in select interneuron types (Winterer et al., 2019). We therefore did not include negative or “empty” controls in this study. Because such controls should be properly run alongside the single-cell samples, we also decided not to generate a new control data set on its own. However, we carefully considered the reviewer’s suggestion as follows.

We assembled lists of genes which would be considered microglia- (Tyrobp, Ctss, C1qc, Cyba, Ly86), astrocyte- (Gfap, Aldh1l1, Sox9, S100b, Slc1a2), oligodendrocyte- (Olig1, Fgf2, Mtcp1, Olig2, Olig3) and pyramidal cellspecific (Baiap2l2, Slc17a7, Ptk2b, Nrn1, Fhl2, Itpka, Neurod6, Nptx1, Sv2b, Kcnv1), and tested these against our single-cell samples. We found that one of our cells showed significant expression of all 5 microglia-related genes and based on this we decided to completely exclude this cell from the study. We now specifically mention this in the revised manuscript (Materials and methods section). Testing of the other 16 cells did not reveal gene expression patterns characteristic to the above cell types.

For transcriptomic analysis of CCK+ cells, we used the inclusion criteria of the cells expressing both Cck and Cnr1 (as described in subsection “TOR and RS firing types do not correlate with previously known subtypes of CCK+ cells”). Furthermore, we would like to emphasize that all cells showed typical CCK+INs firing patterns at slightly depolarized membrane potentials (between -60 and -65 mV), such as accommodation of firing, and moderate AP width (see our response above). We agree with the reviewer that expression of these two genes does not unambiguously identify the recorded cells as either SCA, MFA or BC. However, our goal was to include diverse populations of the large CCK+IN group, as we showed in Figure 1, that TOR and RS firing types are present in all morphological classes. Unfortunately, in the RNAseq analyzed cells, labeling was incomplete in most cases (13 out of 16). Nevertheless, at least one basket, mossy-fiber-associated and Schaffer-collateral associated cells were included in these 16 cells which were identified based on partial axonal recovery. We now state this in the manuscript (Materials and methods section). Since we already demonstrated that TOR and RS firing cells are present among all 3 morphological populations, we do not expect that any of our conclusions would change even if we possessed morphological reconstruction from each of the transcriptomically analyzed cells.

We also made further clarifications about the SC-RNAseq data in the manuscript (see subsection “TOR and RS firing types do not correlate with previously known subtypes of CCK+ cells”).

We now show cluster analysis of cells in a new supplementary data set based on their firing pattern and RNA content or morphological characteristics. We would like to emphasize that the cluster analysis was done based on genes that “were detected in at least 3 of the 16 tested cells”. We used 3 as an arbitrarily determined cut-off, which represented at least 35% of the cells in TOR group (n=8 and 8; Figure 2). Using this cut-off, we aimed to not use genes which have very sparse expression in either population, but also not exclude genes, which may be sparsely but specifically expressed in one of the groups.

For comparability with other data sets, we now include TMP values in the online data repository together with the normalized data.

Significant differences in Kv genes are now highlighted in the revised Figure 5 as suggested.

3) Functional significance of two firing phenotypes within the same interneuron population (if the latter is confirmed after additional analysis) needs to be discussed more. Having two firing phenotypes within the same cell type would increase the overall variability in their firing, thus decreasing their reliability under some conditions. Under which in-vivo conditions it may happen? Do CCK+ cells exhibit a hyperpolarized Vm of -70 to -90 in vivo? What will be the network outcome given that apparently different cell types such as BCs, SC-ACs and MFAs can show this phenomenon?

Thank you for these suggestions. We revised the manuscript text accordingly. We discuss in more detail the potential functional implications of the two types of CCK+INs during different brain states, synaptic networks, and reworked the parts regarding the interpretation of findings within the Discussion section.

We now also explicitly state in the Results section that the membrane potential remained in physiological ranges during oscillating inputs (see subsection “TOR cells are selectively silenced by I_SATOR in a narrow range of oscillatory states”).

We also better describe that our simulations are aimed to address the question whether the two distinct firing modes of CCK+INs favor distinct in vivo oscillations and conditions (subsection “Realistic models of TOR and RS firing”, subsection “TOR cells are selectively silenced by I_SATOR in a narrow range of oscillatory states”). With the aggregated inputs, our aim was to generate physiologically relevant membrane potential oscillations that are driven by realistic synaptic events. Synaptic inputs, ionic conductances and cellular morphologies were virtually identical in RS and TOR simulations. Properties of the subthreshold-activated potassium conductances were the only varied parameter, which was also the subject of our study.

Reviewer #3:

[…]

Essential revisions:

1) The links between auxiliary subunit expression and channel function remain loose. If I read the statements correctly, (1) the effects of KChIP4e have not been investigated in recombinant expression systems, (2) it is unclear whether DPP6L and DPP6S shift the activation and inactivation curves in the proposed manner in recombinant channels, and (3) it is unclear whether ternary or higher order complexes (subsection “Same channel protein but distinct auxiliary subunits are responsible for the different I_SA currents and for the different functionality of TOR and RS cells”) show the proposed properties when recombinantly expressed. Ideally, the authors should recombinantly express the proposed subunit combinations and compare the functional properties. At the very least, they should better discuss the similarities and differences between native and recombinant channels. A systematic comparison of properties in a supplementary table might help.

We extensively revised the manuscript to better explain the functional diversity of KChIPs and to highlight that KChIP4e is the least known among the auxiliary subunits of Kv4 channels (see below the list of corrections).

The apparent contradictions and lack of complete understanding are likely to stem from the unusually large functional diversity of different splicing variants of KChIPs. Known differences between variants of the same proteins are often larger than between different proteins. For example, the effects of KChIP4a are more similar to KChIP2x or KChIP3x than KChIP4bL. By contrast, KChIP4bL shows functional similarities to KChIP1a, KChIP1c, KChIP2a, KChIP3a and KChIP4d.

The available publications on KChIP4e in expression systems focused primarily on its roles in the regulation of the surface expression of Kv4s. These results are in full agreement with our observations showing that in TOR cells where KChIP4e isoform is abundant, the amount of Kv4.3 channels in the plasma membrane is lower than in RS cells, which express other KChIP isoforms. Unfortunately, the effects of KChIP4e on the Kv4.3-mediated current kinetics are not explored. Nevertheless, KChIP4e is expected to have similar general effects on the channel kinetics as other tmKChIPs. The known tmKChIPs always slow inactivation of Kv4-madiated currents, they retain Kv4 from the plasma membrane and slow recovery from inactivation. Most of the left shifts the voltage dependence of the activation and steady-state inactivation depending on the presence of DPPs.

Our data confirmed a correlation between the presence of KChIP4e and the lower surface expression of Kv4.3, the slower inactivation and recovery kinetics, and the left shifted voltage dependence and in TOR cells. Furthermore, our new experiments with nucleated patch recordings provide now an additional direct evidence for lower Kv4.3 density in the plasma membrane of TOR cells. We hope that our correlational observation will drive future research to directly compare the combined effects of KChIP4e-DPP6S/L with KChIP1-DPP6L in because now our results provide the functional relevance for these experiments.

We thoroughly revised the manuscript at several points. For example, in the Discussion section we added the following text: “KChIP proteins and their splice-variants show unusual functional diversity. Different splicing of the same protein can have opposing effects on Kv4 functions, whereas, splice-variants of different proteins can have analogous effects. One outstanding group is the tmKChIP family that consist of KChIP2x, KChIP3x, KChIP4a and KChIP4e. Their common structural feature is an extra N-terminal hydrophobic domain that binds them to the membrane. In contrast to classical KChIPs, tmKChIPs typically retain Kv4 channels from the plasma membrane, slow the inactivation kinetics and the recovery from inactivation.”

In subsection “Same channel protein, but distinct auxiliary subunits may be responsible for different I_SA currents in TOR and RS cells” we also explicitly state that “The exact effects of KChIP4e on the kinetics of Kv4.3-mediated currents are not known.”

In subsection “Same channel protein, but distinct auxiliary subunits may be responsible for different I_SA currents in TOR and RS cells” we added: “The effects of KChIPs are not isolated from the other auxiliary subunits of Kv4. The various stoichiometries of individual Kv4 channels with DPPs and KChIPs allow delicate settings of the channel kinetics. The net effects of KChIPs and DPLPS are not simply the sum of the effects of individual subunits, and the combinatorial possibilities are not yet fully explored with the known 17 variants of KChIPs and 8 variants of DPPs.” We also modified the text accordingly in subsection “Same channel protein, but distinct auxiliary subunits may be responsible for different I_SA currents in TOR and RS cells”. In addition to the direct subsection “Auxiliary subunits of Kv4 channels in TOR and RS cells”.

2) Several conclusions are based on analysis of K⁺ currents in the whole-cell voltage-clamp configuration. However, voltage-clamp errors may be a problem in these measurements. At the very least, they should better discuss this point. Additionally, pharmacological isolation of Kv4 currents in high TEA concentrations might be useful to improve voltage clamp.

Thank you for raising this point. We performed new experiment to address the potential issues of whole-cell voltage-clamp recordings.

We also worried about the limited voltage clamping of the enormous I_SA currents. As a consequence of precaution, in the original whole-cell experiment we restricted recordings to lower voltage ranges, where the total currents are smaller.

Now, we performed new experiments using nucleated patch configuration from identified TOR and RS cells, which allows better voltage control. As suggested these experiments were made in the presence of high concentration of TEA, which does not affect Kv4-mediated currents, to further improve the voltage clamp. Furthermore, in some of these recordings we applied HpTX to further prove that I_SA is mediated by Kv4 channels in both types of CCK+INs. An additional advantage of this recording configuration is that it allowed recordings of Kv4-mediated currents in large voltage ranges, which is necessary to direct comparison of the amount of I_SA conductance in the two types of cells. The results confirmed the suggestions of the original experiments. Namely, RS cells have larger Kv4-mediated conductance than TOR cells, but the activation of I_SA is left-shifted in TOR cells. This confirms that, in spite of the smaller total conductance, I_SA in TOR cells can effectively inhibit firing. Furthermore, our data confirms previous observations about the unusual voltage dependence of HpTX blockade of Kv4, which also explains the partial inhibition of firing. The new data is shown on the revised Figure 3C and the details are described in subsection “Differences in low-voltage-activated potassium currents (I_SA) underlie the heterogeneity of CCK+IN firing” and subsection “Kv4 channels are responsible for both types of I_SA currents in CCK+INs”.

3) Activation and inactivation curves of several ion channels are known to be affected by patch configuration and time-dependent changes, e.g. shifts to more negative values over time (Donnan effects may contribute to these changes). This raises the question whether the curves were similar in different recording configurations and affected by nonstationarities. At the very least, statements should be added to the Materials and methods section.

We agree that long-lasting hyperpolarization may cause changes in the ionic milieu. However, we devised our experiments to avoid this effect. We applied large hyperpolarization only in a few current experiments and in these cases, it lasted only for 3 seconds. We highlight this aspect in the manuscript (subsection “A large portion of CCK+INs show state-dependent firing in the CA3 area” and Materials and methods section). Furthermore, in voltage experiments we did not observe substantial reduction in I_SA currents due to shifts caused by hyperpolarization below the potassium reversal (see most left points on the inactivation curves Figure 3B, F and Figure 3—figure supplement 2C). We avoided larger hyperpolarization than -100 mV in current clamp experiments. As stated in the Materials and methods section, we typically quantified the TOR phenomenon at membrane potentials that are above the reversal potentials of potassium, which does not artificially shift potassium concentrations inside or outside the cells. The preceding hyperpolarization or depolarization lasted for 3 seconds in both RS and TOR cells. Between the traces, the membrane potential was kept at rest. Thus, depletion of potassium ions (below -90mV, the potassium reversal) was probably negligible and affected all cells similarly.

The two distinct firing patterns appeared in physiologically plausible voltage ranges, which required about only a hundred pA of hyperpolarization or depolarization from the resting membrane potential (subsection “A large portion of CCK+INs show state-dependent firing in the CA3 area”). Furthermore, not only the presence of the TOR phenomenon was stable, but the timing of the first APs was also maintained similarly in individual cells during long (>40 minutes) recordings. We now include additional experiments where we specifically investigated the stability of the firing patterns of TOR and RS cells during an hour recording period (45-64 minutes, mean: 58 min, n = 7 cells, see new text in subsection “A large portion of CCK+INs show state-dependent firing in the CA3 area”). Furthermore, we would like to point to the experiments where we investigated the effects of Kv4-inhibitor, HpTX on the firing of both TOR and RS cells (Figure 4A). In these recordings, the gap in the TOR firing recovered to control levels after the washout of the toxin. These recordings lasted for 25-58 minutes. Whereas, in RS cells the firing was stable throughout the experiments including control, toxin and washout periods, which altogether lasted for 20-58 minutes. Similarly, firing patterns remained stable during the application of TEA or low concentrations of 4-AP (Figure 3—figure supplement 1D) that do not affect Kv4-mediated potassium currents. These recordings also lasted typically for 30-50 minutes. We mention these observations in subsection “Kv4 channels are responsible for both types of I_SA currents in CCK+INs”.

We would like to emphasize that I_SA currents from TOR and RS cells were compared under the same conditions, including whole-cell current- and voltage-clamp, outside-out patches and nucleated patches. The differences were consistent, and the properties of individual cells were stable. Thus, it is unlikely that the recording configuration directly influenced these two types of firing. Our simulations also confirm that the observed differences in the I_SA currents alone are sufficient for generating TOR and RS firing phenotype.

4) The authors conclude that fine-tuning in gating and conductance density of K⁺ channels is critically important for determining the action potential phenotype. This seems in contrast to the previous demonstration that the same firing pattern can be generated by several combinations of conductances (Marder and Goaillard, 2006). The authors should better discuss these opposing views.

Thank you for this suggestion. We have placed our findings into the suggested context. See Discussion section.

5) The evidence for an involvement of alternatively spliced auxiliary subunits is compelling, but remains correlative. The authors should clearly mention the limitations of the correlative approach in both Abstract and Discussion section.

We have revised the Discussion section as suggested to highlight that our findings provide correlative evidence between the availability of different KChIPs and firing properties of TOR and RS CCK+INs. The correlative nature of the evidences is also clearly emphasized in the abstract: “the firing phenotypes were correlated with the presence of distinct isoforms of Kv4 auxiliary subunits” and “alternative splicing of few genes…can underlie distinct cell-type identity”.

We added the following text to the Discussion section to clarify this question:

“The presence of KChIP4e in TOR cells and KChIP1 in RS cells is consistent with their different I_SA kinetics and densities that underlie the different functionality of these cells. Future studies are expected not only to confirm that these alternatively spliced variants are solely responsible for the two firing types, but can also address whether these differences in subunits are determined by the destiny of the cells from early of their development or whether these subunits are actively regulated throughout the life span and may underlie activity-dependent modification of the CCK+INs population.”

Furthermore, this question is also mentioned Materials and methods section:

“…, all differences that we observed between RS and TOR cells (i.e., the higher channel density, faster inactivation kinetics and faster recovery from inactivation of I_SA in RS compared to TOR cells) are consistent with the differential expression of KChIP1 and KChIP4e subunits.”

We also modified sentence Discussion section which now reads as:

“We found that a potential explanation for this paradox is the differential expression of auxiliary subunits of Kv4.3 channels”.

6) The modeling section is unclear. Inclusion of a table with the main free parameters may provide the necessary clarification. Furthermore, the Methods section addressing this part needs to be rewritten. Finally, it is not entirely clear whether two populations of channels are indeed required to describe I_SA channel gating in TOR cells. A single population with intermediate gating properties may do the same job.

We have added a new supplementary table that summarizes the passive membrane properties and densities of active conductences in different subcellular compartments of the simulated CCK+INs (Figure 4—figure supplement 1). Here we specify which parameters were (1) variable among the ten simulated cells, (2) fixed or were responsible for the TOR and RS phenotypes. Please note that each cell was simulated both as TOR and RS phenotypes by only changing these gTOR and gRS conductances. We also added further description to the Materials and methods section to clarify these aspects.

We included both gTOR and gRS because outside out patch recordings from TOR cells showed large variability in the kinetics of the currents. Furthermore, preliminary results showed that simulations with the combined gTOR+gRS better reproduced the recorded firing patterns. We specify this in the manuscript text (subsection “Realistic models of TOR and RS firing”).

7) Surely, mRNA and protein expression are important, but acute modifications (e.g. by phosphorylation) may be also relevant. At the very least, appropriate caveat sentences should be added.

Thank you for this suggestion. We added the following text to the Discussion section to highlight posttranslational modification of Kv4 protein complexes: “In addition to the direct modulation, Kv4 protein complexes can be phosphorylated by various kinases and are involved in complex post-phosphorylation signaling, which require the presence of auxiliary subunits and modify the mediated currents. Thus, in spite of the large number of potential mechanisms that can modulate Kv4.3 functions.”

8) Finally, the paper is too long in relation to the novel information it contains. It should be shortened to approximately 60 to 70%. A scheme of the proposed mechanisms at the end of the paper might increase clarity and help in shortening the text.

Reorganization of the manuscript allowed shortening the main text. However, due to addition of new sections the overall length did not change significantly.

Because we used several approaches to unequivocally understand the underlying mechanisms the manuscript is a little bit longer than usual papers. However, without the details of the methods the results cannot be interpreted. Nevertheless, we tried to shorten and make the manuscript more concise. To facilitate this effort, majority of the requested experiments were placed into the supplementary materials and the introduction was shortened by more than hundred words. We combined Figure 3 and Figure 5 of the original manuscript into a single figure, which allowed the shortening the text and reduced the number of main figures to five.

https://doi.org/10.7554/eLife.58515.sa2

Article and author information

Author details

Viktor János Oláh
1. Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Budapest, Hungary
2. János Szentágothai School of Neurosciences, Semmelweis University, Budapest, Hungary
Contribution
Conceptualization, Formal analysis, Investigation, Visualization, Writing - review and editing

Competing interests
No competing interests declared
David Lukacsovich

Laboratory of Neural Connectivity, Brain Research Institute, University of Zurich, Zurich, Switzerland

Contribution
Formal analysis, Investigation

Competing interests
No competing interests declared
Jochen Winterer

Laboratory of Neural Connectivity, Brain Research Institute, University of Zurich, Zurich, Switzerland

Contribution
Investigation, Writing - review and editing

Competing interests
No competing interests declared
Antónia Arszovszki

Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Budapest, Hungary

Contribution
Formal analysis, Investigation

Competing interests
No competing interests declared
Andrea Lőrincz

Laboratory of Cellular Neurophysiology, Institute of Experimental Medicine, Budapest, Hungary

Contribution
Conceptualization, Formal analysis, Investigation, Writing - review and editing

Competing interests
No competing interests declared
Zoltan Nusser

Laboratory of Cellular Neurophysiology, Institute of Experimental Medicine, Budapest, Hungary

Contribution
Conceptualization, Funding acquisition, Writing - review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-7004-4111
Csaba Földy

Laboratory of Neural Connectivity, Brain Research Institute, University of Zurich, Zurich, Switzerland

Contribution
Conceptualization, Funding acquisition, Writing - review and editing

Competing interests
No competing interests declared
János Szabadics

Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Budapest, Hungary

Contribution
Conceptualization, Formal analysis, Funding acquisition, Writing - original draft

For correspondence
szabadics.janos@koki.mta.hu

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-4968-2562

Funding

Wellcome Trust International Senior Research Fellowship (087497)

János Szabadics

Hungarian National Brain Research Program (KTIA_13_NAP-A-I/15)

János Szabadics

European Research Council Consolidator Grant (ERC-CoG 772452)

János Szabadics

Stephen W. Kuffler Research Foundation

Viktor János Oláh

Swiss National Science Foundation (CRETP3_166815)

Csaba Földy

Swiss National Science Foundation (31003A_170085)

Csaba Földy

Dr. Eric Slack-Gyr Foundation (Switzerland)

Zoltan Nusser

European Research Council Advance Grant (ERC-AdG 787157)

Zoltan Nusser

Hungarian National Brain Research Program Grant (NAP2.0)

Zoltan Nusser

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

Acknowledgements

We thank Professors Henry Jerng, Paul Pfaffinger and Tõnis Timmusk for advices on DPP6 and KChIP isoforms and Kenneth Harris for suggestions about the gene profiles of hippocampal CCK+INs. We are thankful for the computational resources provided by the Neuroscience Gateway. We thank Andrea Szabó, Dóra Rónaszéki, Dóra Kókay and Andrea Juszel for their their excellent technical assistance and László Barna for the kindly provided microscopy support at the Nikon Microscopy Center at the IEM HAS, which is sponsored by Nikon Europe, Nikon Austria and Auro-Science Consulting. This work was supported by Wellcome Trust International Senior Research Fellowship 087497, Hungarian Brain Research Program KTIA_13_NAP-A-I/5, European Research Council Consolidator Grant (ERC-CoG 772452) to JS, Stephen W Kuffler Research Foundation to VJO, Swiss National Science Foundation (CRETP3_166815 and 31003A_170085) and from the Dr. Eric Slack-Gyr Foundation (Switzerland) to CF. ZN is the recipient of a European Research Council Advanced Grant (ERC-AdG 787157), and a Hungarian National Brain Research Program (NAP2.0) grant.

Ethics

Animal experimentation: Animal protocols and husbandry practices were approved by the Institute of Experimental Medicine Protection of Research Subjects Committee (MÁB-7/2016 for slice recording and anatomy experiments and MÁB-2/2017 for immunolabelling experiments in perfusion fixed brains) and by the Veterinary Office of Zurich Kanton (single cell RNAseq experiments).

Senior Editor

Gary L Westbrook, Oregon Health and Science University, United States

Reviewing Editor

Katalin Toth, Université Laval, Canada

Reviewer

Lisa Topolnik, Université Laval, Canada

Version history

Received: May 2, 2020
Accepted: May 20, 2020
Version of Record published: June 3, 2020 (version 1)
Version of Record updated: June 8, 2020 (version 2)

Copyright

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.