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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neurosci. Author manuscript; available in PMC Nov 27, 2009.
Published in final edited form as:
PMCID: PMC2749660
NIHMSID: NIHMS120960

Transcriptional and electrophysiological maturation of neocortical fastspiking GABAergic interneurons

Abstract

Fast-spiking (FS) interneurons are important elements of neocortical circuitry that constitute the primary source of synaptic inhibition in adult cortex and impart temporal organization on ongoing cortical activity. The highly specialized intrinsic membrane and firing properties that allow cortical FS interneurons to perform these functions are due to equally specialized gene expression, which is ultimately coordinated by cell-type-specific transcriptional regulation. While embryonic transcriptional events govern the initial steps of cell-type specification in most cortical interneurons, including FS cells, the electrophysiological properties that distinguish adult cortical cell types emerge relatively late in postnatal development, and the transcriptional events that drive this maturational process are not known. To address this, we used mouse whole-genome microarrays and whole-cell patch clamp to characterize the transcriptional and electrophysiological maturation of cortical FS interneurons between postnatal day 7 (P7) and P40. We found that the intrinsic and synaptic physiology of FS cells undergoes profound regulation over the first four postnatal weeks, and that these changes are correlated with largely monotonic but bidirectional transcriptional regulation of thousands of genes belonging to multiple functional classes. Using our microarray screen as a guide, we discovered that upregulation of 2-pore K+ leak channels between P10 and P25 contributes to one of the major differences between the intrinsic membrane properties of immature and adult FS cells, and found a number of other candidate genes that likely confer cell-type specificity on mature FS cells.

Keywords: GABAergic neuron, cortex, maturation, development, transcription, excitability

Introduction

Cortical GABAergic interneurons can be segregated into distinct subtypes based on their morphology, electrophysiology, synaptic connectivity, and gene expression (Kawaguchi and Kondo, 2002; Markram et al., 2004; Nelson et al., 2006). How these complex phenotypes develop remains an open question. Given that GABAergic interneuron subtypes support diverse cortical circuit functions, such as mediating critical period plasticity (Di Cristo et al., 2007; Hensch et al., 1998; Katagiri et al., 2007), entraining network activity oscillations (Bartos et al., 2002; Bartos et al., 2007; Cobb et al., 1995, Doischer et al., 2008), and maintaining the balance of excitation and inhibition (Le Roux et al., 2008; Treiman, 2001; Wang and Kriegstein, 2008), progress towards understanding the mechanisms that promote and maintain interneuron identity is of broad importance.

Mounting evidence suggests that the location and time of birth of prospective interneurons dictates their mature phenotype in part by initiating divergent transcriptional cascades in the embryo (Butt et al., 2007; Flames et al., 2007;Butt et al., 2005; Cobos et al., 2006; Wonders and Anderson, 2006; Xu et al., 2004). While the full transcriptional networks mediating fate determination and differentiation of cortical interneurons have yet to be resolved, steady progress has been made in identifying a number of the key regulators operating at early stages of development in the embryo, such as Dlx1/2, Nkx2.1, Lhx6, and Arx (Xu et al.,2008;Butt et al., 2008, Sussel et al., 1999). However, a detailed description of the postnatal transcriptional and phenotypic maturation of interneuron subtypes is presently lacking. Knowledge at this level is essential to elucidating the function of gene networks operating downstream of early induced transcription factors, particularly as these may directly mediate specific aspects of the mature phenotype. We address this question for one particular interneuron subtype, cortical parvalbumin (PV) expressing fast-spiking (FS) GABAergic interneurons, for which a genetically labeled mouse line has afforded reliable targeting. We characterized both whole transcriptome expression and the electrophysiology of these cells at a range of postnatal ages, and found that FS cells exhibit profound developmental regulation of both gene expression and physiology over the first four postnatal weeks. Developmentally regulated genes spanned multiple functional categories, but were significantly enriched for ion channels, and showed predominantly monotonically increasing or decreasing expression trajectories. Comparing the temporal patterns of gene regulation revealed by our microarray with maturational changes in the intrinsic and synaptic properties of FS cells allowed us to correlate the expression of subsets of genes with the onset of multiple FS cell phenotypes. In particular, we demonstrate that up-regulation of 2-pore K+ leak channels contributes to the characteristic low input resistance of mature FS cells.

Methods

Transgenic Mice

We used the G42 transgenic mice described in Chattopadhyaya et. al (2004) for all experiments. All procedures were conducted in accordance with NIH guidelines for animal use and were approved by the Brandeis University animal use committee. Briefly, G42 mice were engineered by the genomic incorporation of a 200kb GAD1 BAC fused to the eGFP coding sequence. Due to a combination of promoter and position of integration effects, GFP expression is restricted to a homogenous subset of GABAergic interneurons distinguished in the adult mouse brain by their capacity to fire at high rates (&>200Hz), and by the expression of the calcium binding protein parvalbumin, which emerges between P10 and P15. Prior to the expression of parvalbumin, developing G42 cells express other cell-type specific markers (Supplemental fig. S1a). Cortical expression of GFP-positive neurons was restricted to layers 2/3, 5, and 6, with occasional expression in layer 4 and no expression in ventral regions of cortex at all studied ages (Supplemental fig. S1b). Additionally, G42 biocytin-filled cells prepared post recording at P7, P10, P15, and P25 (see methods below) were all multipolar and lacked an apical dendrite or pyramidal-shaped soma suggestive of excitatory cells. These observations, together with the results of clustering analysis performed on gene expression profiles across multiple cell types (Supplemental fig. S1c) suggest that the G42 line is a stable marker of prospective fast-spiking PV-positive cortical interneurons over the course of development.

Cell Sorting, and Microarrays

To gather sufficient cells for microarray screens, acute 400 µm coronal brain slices were prepared from G42 mice (n=3 animals for each age) over a range of maturational time points: p7, p10, p13–p15, p25, and p40–p45. Brain slices were incubated in a protease solution (1 mg/ml pronase E; Sigma-Aldrich) for approximately one hour, after which time the somatosensory cortex was microdissected under a dissecting microscope. Microdissected tissue was then triturated in ACSF using a series of three Pasteur pipettes of decreasing tip diameter. The cell suspension was then poured into a small petri dish, and, under visual control on a fluorescence dissecting microscope, GFP expressing neurons were aspirated into a micropipette with a 30–50 µm tip diameter and transferred to a clean dish. After undergoing three such ‘washes’, the sample was then lysed in 50-µl XB lysis buffer, incubated at 42 °C for 30 minutes, and then stored at −20 °C. For a more detailed discussion of the sorting procedure see Hempel, et al. 2007. The mRNA from sorted cells was subsequently isolated, amplified, labled, and hybridized to Affymetrix M430 2.0 arrays, as described in the Affymetrix GeneChip Eukaryotic Small Sample Target Labeling Assay Version 2. mRNA isolated from approximately 50–80 cells yielded sufficient quantities of labeled cRNA (10–80 µg). Arrays were scanned on an Affymetrix GeneChip scanner.

Analysis of microarray data

Scanned microarray data were processed with Affymetrix “GeneChip Operating Software” (GCOS) to convert raw image files (.DAT) to probe signal value files (.CEL). Probe signal values were then normalized and summarized using the Bioconductor affy package gcrma algorithm (http://www.bioconductor.org/packages/2.0/bioc/html/affy.html). We computed the maximum fold change for each gene by averaging the signal values across replicates for each time point and then computing the fold change between the maximum (max) and minimum (min) value, given by the formula 2(max−min). Only genes showing a minimum of 2-fold change over development were included in further analysis. In order to detect differentially expressed genes, we computed ANOVA p-values for each gene using the R statistics package (http://www.r-project.org) and custom Python code. In cases where a given gene corresponded to multiple probe sets on the array, we selected the probe set with the lowest p-value. Principal component analysis (PCA) and k-means clustering were performed using Matlab. PCA was performed on the m × n data matrix (m = 1973, n = 5), where each row corresponds to a developmentally regulated gene and each column corresponds to the signal value for each time point averaged across biological replicates and normalized to the range 0 (minimum) to 1 (maximum). The principal components are the eigenvectors derived from the n × n covariance matrix of developmental time points. P-values for Gene Ontology (GO) over-representation analysis were calculated using the hypergeometric probability distribution to compute the probability of finding a given GO term in a list of arbitrary size, given the total number of genes on the Affymetrix GeneChip annotated with that term. Agglomerative hierarchical clustering analysis (Supplemental fig. 1c) was performed in R using the 'agnes' function, with the euclidean distance between gene expression profiles used as a metric. Microarray data for additional cell types used in Supplemental figures 1a and 1c and the middle panel of Figure 6a comes from Sugino et al., 2006. G30CG and G30S1 are mostly CCK-expressing irregular spiking interneurons from cingulate and somatosensory cortex, respectively, labeled in a transgenic mouse expressing GFP under a Gad2 promoter. GIN and GINH are SST-expressing regular-spiking interneurons from cingulate cortex and hippocampus, labeled in a transgenic mouse expressing GFP under a GAD1 promoter. G42LG cells were extracted from the lateral geniculate nucleus in the thalamus. The YFPH mouse line is a transgenic line in which YFP is expressed under the control of the Thy1 promoter. YCG and YS1 are layer 5 pyramidal neurons from cingulate and somatosensory cortex, respectively. YH cells are YFPH cells extracted from the hippocampus and YA are YFPH cells extracted from the amygdala. Finally CT6 are layer six corticothalamic projection neurons labeled by injection of a retrograde tracer. Developmental G42 expression data, as well as data for additional cell types is available at >http://mouse.bio.brandeis.edu/.

Figure 6
Developmentally regulated genes related to A. transcriptional regulation, B. perineural nets, C. cell adhesion, and D. synaptic function. Heat maps denote the scaled microarray signal intensity. Given that scaling to the maximum and minimum value of a ...

Electrophysiology

Acute brain slices were prepared from G42 mice at ages corresponding to those used for microarray screens (p7,p10,p15,and p25) as previously described. Briefly, animals were deeply anesthetized with isoflurane and decapitated, and 300µm coronal sections containing S1 were prepared on a vibratome in oxygenated ice-cold ACSF containing (in mM): 126 NaCl, 3 KCl, 2 MgSO4, 1 NaH2PO4, 25 NaHCO3, 2 CaCl2, with osmolarity adjusted to ~320mOsm with dextrose (typically 10–15mM). Slices were incubated at 37°C for 10–15 minutes and then at room temperature for at least another 30 minutes. For current clamp recordings, slices were perfused continuously with 33–35°C ACSF supplemented with 50µM D,L-2-amino-5-phosphonovaleric acid (AP-V) and 20µM 6,7-dinitroquinoxaline-2,3-dione (DNQX) to block glutamatergic synaptic transmission and 50µM picrotoxin to block ionotropic GABAergic transmission. For isolation of bupivacaine-sensitive currents, TTX, NiCl, nifedipine, ZD7288, 4-AP, and TEA, were added to the recording ACSF (also maintained at 33–35°C) to block voltage-gated sodium, calcium, and potassium currents, and IH. The same cocktail of drugs with the exception of NiCl and nifedipine was used during measurement and isolation of Ca2+ currents. Miniature synaptic events were recorded with TTX in the bath and either picrotoxin during mEPSC recordings or DNQX and AP-V during mIPSC recordings. Whole-cell recordings were obtained from layer 5 G42 interneurons that were identified by GFP expression using standard epifluorescence and patched using Nomarsky optics with pipettes that had a tip resistance of 5–12MΩ when filled with internal solution containing (in mM): 100 K-gluconate, 20 KCl, 10 HEPES, 4 Mg-ATP, 0.3 Na-GTP, 10 Na-phosphocreatine, and 0.5% biocytin, adjusted to ~295mOsm with sucrose. For mIPSC recordings, we set ECl to 0mV by using an internal solution containing (in mM): 133 KCl, 10 K-HEPES, 4 Mg-ATP, 0.3 Na-GTP, 2.5 Na-phosphocreatine, 0.5 EGTA, and 0.5% Biocytin with sucrose added to adjust the osmolarity to ~295mOsm. Input resistance was measured with 250 or 500pA hyperpolarizing current injections at the beginning of every sweep in current clamp, or by −5mV voltage steps from the holding potential in voltage-clamp. Series resistance was maintained below 20MΩ or else the cell was discarded, and was compensated 75–85% in voltage clamp recordings. Recordings in which the resting membrane potential or holding current, or the input resistance, drifted more then 20% during a recording were also discarded. The membrane potential was set to −70mV with DC current injection at the beginning of each sweep during FI curves to equalize differences caused by the effect of different resting membrane potentials on inactivation of voltage-gated currents, although “natural” resting membrane potential was measured at the beginning of each current clamp recording and roughly every 10 minutes thereafter. mEPSCs and mIPSCs were recorded at a holding potential of −70mV. Signals were amplified by multiclamp 700A amplifiers (Axon Instruments) using the bridge-balance in current clamp recordings, digitized at 10kHz, and stored on a hard drive using custom IGOR Pro (Wavemetrics) software. Voltage-clamp recordings were corrected post-hoc for the measured junction potential of −10mV.

Biocytin Staining

Slices containing recorded G42 cells were fixed for less than 2 weeks in cold (4°), buffered 4% paraformaldehyde. Biocytin was visualized by incubation with 1:500 AvidinD-TxRed (Vector Labs, Burlingame, CA) in 0.5% Triton X-100 for 1 hour after permeabalizing the membrane with 1% Triton X-100 for 45 minutes and rinsing 3 times for 10 minutes each in PBS. Stained cells were examined on a dissecting microscope equipped with epifluorescence to verify the presence of both GFP and biocytin, and to make gross morphological observations. In some cases, stained cells were further imaged at 63X on a confocal microscope.

Immunohistochemistry

G42 mice at p10 and p25 were deeply anesthetized with ketamine/xylazine/acepromazine and transcardially perfused with ice-cold 0.1M PBS followed by ice-cold 4% paraformaldehyde (PFA). Brains were postfixed for 24–100 hours in 4% PFA and then embedded in agar and sectioned coronally at 50µm on a vibratome. Sections containing S1 were stored at 4°C until staining. For TASK1 immunostaining, sections were washed with PBS, blocked in 10% horse or goat serum in 0.1M PBS containing 1% Triton X-100 for an hour, and incubated overnight at 4°C with 1:500 mouse anti-GFP (Chemicon) and 1:250 rabbit anti-TASK1 (Chemicon) in 0.1M PBS supplemented with 0.1% Triton and 1% horse or goat serum. GFP and TASK antibodies were detected by incubation with 1:500 goat anti-mouse Alexa-488 and 1:500 goat anti-rabbit Alexa-568 in 0.1M PBS with 0.1% Triton and 1% horse or goat serum for 2 hours after the sections were washed 3 times in 0.1M PBS. We stained P10 and P25 sections in parallel to avoid batch effects. TASK1 antibody specificity was verified by successfully preventing staining with pre-incubation of the antibody with the epitope it was raised against (CEDEK RDAEH RALLT RNGQ). Stained sections were imaged using a 40X objective on a confocal microscope and the laser power, photomultiplier gain, pinhole, and detection filter settings were identical for p10 and p25 imaging sessions. TASK immunosignal was quantified in ImageJ by averaging the TASK signal subsumed by each GFP-positive somata in 2 animals and 2–3 FOV per age.

Results

We characterized the transcriptional and electrophysiological maturation of genetically labeled fast-spiking (FS) interneurons in mouse primary somatosensory (S1) cortex using whole genome microarrays and whole-cell recordings. mRNA was harvested from manually sorted GFP-expressing neurons dissociated from acutely prepared brain slices at a range of developmental time points (p7, p10, p15, p25, p40), and subsequently reverse transcribed, amplified, labeled, and hybridized to Affymetrix 430 2.0 whole genome microarrays (3 microarrays from 3 different animals per condition). To detect genes that are differentially expressed over the course of maturation, we performed an analysis of variance (ANOVA) across all samples for each of the roughly 45,000 probe sets represented on the chip. In order to refine our estimate of the number of differentially expressed genes, we also performed ANOVAs on shuffled datasets in which each sample was assigned to one of five triplicate groups at random (supplemental Fig. S2a). The resulting distributions of p-values for each shuffled dataset were averaged and used to compute the false discovery rate as a function of ANOVA p-value, which we defined as the ratio of the number of genes corresponding to a given p-value in the shuffled versus the original dataset (supplemental Fig. S2b). By subtracting the two distributions, we obtained the "corrected" distribution of p-values (Fig. 1a). We restricted our consideration of differentially expressed genes to those with p < 10−3, which corresponded to a false discovery rate of 1 percent. The set of differentially expressed genes was further limited to those exhibiting a minimum 2-fold change in expression over development (Fig. 1b). 1973 genes met the above criteria.

Figure 1
Number of developmentally regulated genes and dynamic range of regulation

To uncover patterns of developmental regulation, principal component analysis (PCA) and K-means clustering analyses were applied to the expression profiles of differentially expressed genes. In order to focus directly on the time course of regulation, rather than the overall expression level of a gene, expression profiles of each gene were first normalized to the range 0 (minimum) to 1 (maximum). We found that the first and second principal components accounted for approximately 77% and 12% of the total variance, respectively. The first principle component can be interpreted as the slope of the change in expression level over time (Fig. 2a). Two clusters, corresponding to increasing and decreasing sets of genes, having positive and negative slope, respectively, are plainly discernible in the plot of the first versus the second principal component (Fig. 2b). This was consistent with K-means clustering which revealed that increasing the number of clusters beyond two resulted in diminishing silhouette values (data not shown). The second principal component contributed to the concavity or convexity of the expression trajectory (Fig. 2a). Increasing genes with positive second principal component coefficients are up-regulated early in the course of maturation, whereas increasing genes with negative second principal component coefficients are up-regulated much later in development (Fig. 2b, left side). Alternatively, decreasing genes with positive second principal component coefficients were down-regulated much later than decreasing genes with negative coefficients (Fig. 2b, right side). The continuous distribution of points along the second principal component axis indicates that the interval of greatest change between time points varied continuously across individual gene expression profiles. Very few genes had large second component coefficients but small first component coefficients (Fig. 2b, middle), indicating that expression trajectories that peaked or troughed at intermediate time points and then sharply declined or increased were less frequently observed. Thus developmental regulatory patterns of gene expression were largely monotonically increasing or decreasing. Gene Ontology (GO) overrepresentation analysis revealed enrichment of specific functional categories among up and down regulated transcripts (Table 1). 7 of the 10 most significantly enriched GO terms (p < 10−5) were associated with ion channel activity. Overall, 27 ion channels were up-regulated and 13 ion channels were down-regulated over the course of maturation (Fig. 3). In particular, voltage-gated potassium channels were enriched among up-regulated genes, whereas voltage-gated sodium channels and voltage-gated calcium channels were overrepresented in the set of down-regulated genes.

Figure 2
Developmentally regulated genes exhibit mostly monotonically increasing or decreasing trajectories
Figure 3
Developmentally regulated ion channels
Table 1
GO overrepresentation analysis reveals enrichment of specific categories of molecular function among developmentally up-regulated (top) and down-regulated (bottom) genes. Hierarchical relationship of GO terms is denoted by indentation. P-values were calculated ...

These developmental changes in ion channel expression likely manifest as corresponding changes in G42 intrinsic membrane properties. We therefore characterized G42 electrophysiological maturation by performing whole-cell current-clamp recordings in the presence of synaptic blockers from identified G42 cells in acute cortical slices containing M1 and S1 at p7, p10, p15, and p25. The mature FS phenotype is characterized by high firing rates (>200Hz), narrow spikes relative to other neocortical cell types, and little or no spike-frequency adaptation (McCormick et al., 1985), Fig. 4 rightmost trace). Consistent with previous studies of FS interneuron development in layer 4 (Itami et al., 2007) and in dentate gyrus (Doischer et al., 2008), we found that these mature features emerged slowly and monotonically over development (Fig. 4), and that immature G42 neurons are electrophysiologically distinct from the mature state. The characteristic lack of adaptation during repetitive firing, for instance, emerged between p10 and p15, and G42 cells at p7 exhibited pronounced and robust spike-frequency adaptation (measured as the ratio of first to last interspike intervals or ISIs; 0.72 ± 0.12 at p7, 1.05 ± 0.30 at p15, p<0.05). Consistent with this loss of adaptation, our microarray results revealed a 15-fold down-regulation of the small conductance Ca2+-activated K+ channel Kcnn2, which contributes to the medium component of the afterhyperpolarization following an action potential in multiple cell-types (Bond et al., 2004; Liu and Herbison, 2008, Kong et al., 2008). Moreover, the kinetics of this current are consistent with the kinetics of adaptation we observed in immature G42 neurons, suggesting that expression of Kcnn2 may underlie spike-frequency adaptation in these cells. The emergence of the non-adapting firing phenotype was accompanied by an increase in maximum firing rate, which increased nearly 3-fold from p7 to p25 (p<10−4), and surprisingly increased by 20% between p15 and p25 (p<0.01), indicating that even the most prominent features of mature FS cells undergo substantial developmental regulation as late as the second postnatal week. Unsurprisingly, we also found that spike width, measured between spike threshold and the end of the spike, was inversely correlated with maximum firing rate, and decreased from 3.6ms at p7 to 2.3ms at p25 (p<10−4). It has been demonstrated that Kv3 potassium channels encoded by Kcnc1 and Kcnc2 enable sustained fast spiking by providing rapid repolarization of the membrane potential after an action potential (Du et al., 1996; Erisir et al., 1999; Lau et al., 2000; Rudy and McBain, 2001). Concordantly, we found that these transcripts were upregulated over the same developmental interval (Kcnc1: ~14-fold increase, Kcnc2: ~2-fold increase).

Figure 4
Maturation of intrinsic membrane properties and the distinctive firing type of G42 FS interneurons

In addition to the developmental loss of spike-frequency adaptation, narrowing of action potentials, and concomitant emergence of high-frequency firing, we also found a massive and highly significant decrease in the input resistance (RIn) of G42 cells over development. Although the slope and saturation point of the FI curve increased as G42 cells matured, the amount of current required to elicit action potentials also increased nearly 8-fold from p7 to p25 (p<10−6) and 2.6 fold from p15 to p25 (p<10−4). This dramatic increase in rheobase over development is consistent with previous reports in interneurons (Itami et al., 2007; Doischer et al., 2008) and pyramidal cells (Maravall et al., 2004; Zhang, 2004), and in this case is likely due in part to a decrease in resting RIn between p7 and p10 (537.4 ± 27.3MΩ at p7, 155.0 ± 27.3MΩ at p10, p<10−4) and between p15 and p25 (126.7 ± 35.3MΩ at p15, 70.6 ± 14.2MΩ at p25, p<0.01), as well as to a positive shift in the action potential voltage threshold between p7 and p15 (p<0.05). The membrane time constant (TM), a crucial determinant of a neuron’s integrative properties, also decreased precipitously from 35.4 ± 7.7ms at p7 to 2.8 ± 0.9ms at p25 (p<10−7), as would be expected given the close relationship between RIn and TM and the lack of a significant difference in whole-cell capacitance across ages (data not shown).

The parallel increase in rheobase and decrease in resting RIn suggest that both may be mediated in part by developmental upregulation of potassium leak channels, since we measured RIn by inducing small (< 5mV) hyperpolarizing voltage deflections from the resting membrane potential. In this regime, the membrane potential and input resistance are largely governed by background leak currents and voltage-gated currents such as IH and IKir that are active at relatively hyperpolarized voltages. A search of potassium leak channel transcripts that our microarray screen indicated were upregulated over development revealed that Kcnk3, which codes for the 2-pore potassium leak channel TASK1, and Kcnk1, which codes for the two-pore leak channel TWIK1 (Lesage and Lazdunski, 2000), are both highly enriched in p25 G42 interneurons compared to those isolated from younger animals (~13-fold and ~5-fold, respectively). 2-pore potassium leak conductances are blocked by the local anesthetic bupivacaine (Kindler et al., 1999; Torborg et al., 2006). We therefore directly measured TWIK1 and TASK1 function over development by pharmacologically isolating TWIK1 and TASK1-mediated leak currents in p10, p15, and p25 G42 interneurons with 40 µM bupivacaine in the presence of voltage-gated potassium, sodium, and calcium channel blockers. Bupivacaine blocked a voltage-insensitive current that reversed close to the calculated potassium equilibrium potential in cells from all three ages, and the magnitude of this current increased 5.7 fold between p7 and p25 over development (Fig. 5, p < 0.01). Moreover, the contribution of the bupivacaine-sensitive conductance to the whole-cell conductance that remained after blockade of voltage-gated K+, Na+, and Ca2+ channels also increased significantly between each age tested (Fig. 5b), indicating that 2-pore K+ leak channels become increasingly important determinants of G42 RIn over postnatal development.

Figure 5
The 2-pore potassium leak channels TWIK1 and TASK1 are upregulated in G42 FS interneurons over postnatal development and exert increasing influence on membrane excitability

If the increase in bupivacaine-sensitive current is indeed due to increased TASK1 protein expression, as one might predict given the enrichment of Kcnk3 transcript in older animals, TASK1 immunocytochemistry should differ between ages. We immunolabeled TASK1 in sections taken from p10 and p25 G42 animals and quantified the intensity of TASK1 label in GFP-labeled G42 interneurons (Fig. 5). TASK1 immunoreactivity in regions coextensive with GFP-labeled somata was almost 2-fold more frequent (overlapp10 = 0.47, overlapp25 = 0.91, p < 10−4) and stronger at p25 than at p10 (intensityp10 = 28.6 ± 4.1, intensityp25 = 50.1 ± 5.7, p < 10−6, Fig. 5b).

The observed increase in the magnitude of injected current required to elicit action potentials may also reflect the up-regulation of voltage-gated outward currents active between resting membrane potential and voltage threshold in mature versus developing FS cells. Our microarray screen detected increased expression of multiple Kv1-type potassium channel subunit transcripts known to reduce neuronal excitability. Kcna1 and Kcna6, as well as the Kcnab1, Kcnab2, and Kcnab3 modulatory subunits all show striking up-regulation over the course of maturation (~44-fold, ~61-fold, ~86-fold, ~7-fold, and ~33-fold, respectively). Together, these results show that immature G42 FS interneurons are initially highly excitable, integrate inputs over long (~35ms) time windows, and respond to sustained input with relatively low-frequency adapting spike trains, and that over the next two weeks of development FS cells become progressively and dramatically harder to excite, sensitive only to coincident input, and capable of very high-frequency repetitive firing without adaptation once driven past threshold.

While Kv3 potassium channels enable the sustained high-frequency firing of FS cells, and Kv1 potassium channels, together with an assortment of potassium leak channels, likely control their excitability, sodium channels are indispensable for membrane depolarization necessary for the initiation of action potentials. With this in mind, it is perhaps unexpected that sodium channel transcripts are overrepresented among down-regulated genes. Scn3a, Scn2a1, and Scn3b all exhibit decreasing developmental expression trajectories (~74-fold, ~20-fold, and ~35-fold, respectively). Although sodium channel genes are not significantly overrepresented in the set of up-regulated genes, the expression levels of six transcripts encoding voltage-gated sodium channel subunits increase significantly over the course of maturation: Scn4b, Scn8a, Scn1a, Scn1b, Scn2b, Scn9a (~344-fold, ~4-fold, ~5-fold, ~14-fold, ~12-fold, and ~146-fold, respectively). This bidirectional regulation suggests that the expression of different sets of sodium channels may be required for immature versus mature functions.

Just as the regulation of potassium and sodium channels plays a crucial role in determining the excitability of G42 FS cells, regulation of calcium channels and of calcium homeostasis in general also appear to be salient components of G42 FS cell maturation. 7 of the 13 down-regulated ion channel subunits form or associate with channels that flux calcium: Cacng5, Cacng4, Trpm3, Cacna1g, Trpv2, Cacna1b, Cacnb1 (~89-fold, ~84-fold, ~33-fold, ~28-fold,~10-fold, ~4-fold, ~2-fold, respectively). In particular, down-regulation of the T-type calcium channel Cav3.1d, encoded by the Cacna1g gene, by virtue of its low voltage threshold for activation, likely contributes to the maturational attenuation of excitability we observed in G42 FS cells (Iftinca and Zamponi, 2008). Indeed, we recorded TTX-insensitive humps and action potentials in p7 G42 FS cells (9/9) that were abolished by bath application of the T-type Ca2+ channel blocker nickel (100 µm, 3/3 cells), and in 2 of these cells we isolated a nickel-sensitive T-type current in voltage clamp (Supplemental fig. S3). In contrast, we never observed spiking behavior in the presence of TTX at p25.

Down-regulation of channels that are permeable to calcium may help G42 FS cells control their intracellular calcium concentration in the face of dramatic increases in firing rates over development that presumably increase calcium entry through voltage gated channels. Regulatory patterns of other genes involved in calcium homeostasis lend further support to this hypothesis. For instance, the calcium-buffering molecule parvalbumin is upregulated nearly 30 fold between p10 and p40. Additionally, the Ca2+-ATPase, encoded by the Atp2b3 gene, which extrudes calcium ions from the cell, is up-regulated 4-fold between P7 and P25. In addition to influencing cell excitability, calcium plays a critical role as a signaling molecule whose downstream effects mediate a host of cellular processes, including cell differentiation and synaptic plasticity, often by inducing transcriptional modifications (Moody and Bosma, 2005; Flavell and Greenberg, 2008). Thus mechanisms that promote calcium entry early in development may be necessary for cell differentiation and may be distinct from calcium sources required for the subsequent maintenance of mature cell identity.

Changes in the abundance of transcripts for ion channels and other genes likely reflect changing patterns of transcriptional regulation. It is known that PV-expressing FS cells derive from Nkx2.1 expressing progenitors in the MGE, and that subsequent expression of the transcription factors Dlx1, Lhx6, and Arx is necessary for proper PV-subtype migration and differentiation (Alifragis et al., 2004; Colasante et al., 2008; Friocourt et al., 2008; Liodis et al., 2007; Zhao et al., 2008). However, the downstream targets of these transcription factors, and the identity of other transcription factors arising at later stages of development, have yet to be fully elucidated (Fulp et al., 2008). Our data provide candidate transcription factors that may be essential to the maturation and maintenance of cell identity (Fig. 6a). In particular, we identified several transcription factors that were up-regulated in concert with the emergence of mature phenotype and that appear to show stable expression in the adult, such as Lmo2, Etv5, Pbx3, A630082k20Rik, Ankrd25, Zf, Nfkbie, and Cbx7. Expression of some of these is enriched in adult G42 cells with respect to other cell-types (Sugino et al., 2006). Other previously characterized transcription factors known to figure prominently in the early development of interneuron subtypes were markedly down-regulated, such as Sox11, Sox4, Myc, and Ascl1, suggesting that they play no role in the maintenance of FS interneuron identity. In accordance with the developmental function of these transcription factors, GO overrepresentation analysis showed that genes associated with nervous system development, cell proliferation, neuron differentiation, and axon guidance were enriched in the set of down-regulated genes (Supplemental Table 1). Interestingly, Dlx1, Lhx6 , and Arx show stable expression over the course of maturation, implicating them in persistent regulatory roles. Developing and mature G42 cells also stably express the transcription factor Sox2, which is expressed in several mature interneuron populations (Sugino et al., 2006) and may play a role in the development of GABAergic cells as well (Cavallaro et al., 2008).

In addition to refining their intrinsic physiological properties, G42 FS cells also refine their synaptic properties over the course of maturation. We detected transcriptional regulation of an assortment of cell adhesion molecules presumed to play a role in synaptic connectivity, as well as migration and differentiation, such as Ncam2, Clstn3, Farp2, Bcan, Hapln4, Hapln1, Vcan, Ncan, Chl1, Nxn1, Cxadr, and Fat4 (Fig. 6b and c). Among these cell adhesion genes, several are involved in the formation of perineuronal nets (PNs) (Fig. 6b). The chondroitin sulfate proteoglycans aggrecan (Agc1), versican (Vcan), neurocan (Ncan), and brevican (Bcan), together with the hyaluronan and proteoglycan binding link proteins (HAPLNs) have been identified as structural components of PNs in the rodent brain (Deepa et al., 2006). Agc1 is stably expressed by G42 cells from P7 onward (Supplemental figure S1b), however Vcan, Ncan, and Bcan, as well as HAPLN4 and HAPLN1 are all developmentally regulated (~80-fold downregulated, ~13-fold down-regulated, ~16-fold up-regulated, ~127-fold upregulated, and ~116-fold up-regulated, respectively). Additionally, ADAMTS5, ADAMTSl5, ADAMTS8, and ADAMTS20, all members of the ADAMTS family of glutamyl-endopeptidases which break down proteoglycans and thereby degrade PNs, are also developmentally regulated (~18-fold down-regulated, ~58-fold up-regulated, ~94-fold up-regulated, ~6-fold down-regulated). Given that the formation of PNs may inhibit neural plasticity (Hamel et al., 2008; McRae et al., 2007; Galtrey and Fawcett 2007), bidirectional regulation of both proteoglycans and proteoglycan-degrading genes suggests that different plasticity rules may operate at different times during development. Additionally, filtering the set of differentially expressed genes for the GO term "synaptic activity related" selected 17 genes, 10 up-regulated and 7 down-regulated (Fig. 6d). Many of the up-regulated genes are involved in the presynaptic release of neurotransmitter, such as Syt2, Syn2, Rab3a, Syngr1, and Sv2a.

We also measured developmental changes in excitatory and inhibitory synaptic input to G42 FS cells by recording mEPSCs and mIPSCs in voltage-clamp. Both the amplitude and frequency of mEPSCs and mIPSCs increased dramatically over development, although mEPSC amplitude and frequency peaked at p15 and then decreased slightly, while mIPSC amplitude and frequency increased monotonically and peaked at p25 and p15 respectively (Fig. 7 and Fig 8). The largest increases in mPSC frequency occurred between p7 and p10 for both mIPSCs (5.6-fold) and mEPSCs (5.38-fold), and at all developmental time points except p25 the frequency of mEPSCs was greater than that of mIPSCs (Fig. 7c). Interestingly, although the mEPSC decay time constant (TDecay) decreased only marginally over development (1.2-fold between p10 and p15, p<0.01), mIPSC TDecay decreased almost 10-fold between p7 (TDecay = 21.6ms) and p25 (TDecay = 2.7ms, p<10−6). This developmental decrease in mIPSCs TDecay has been previously reported in cortical pyramidal cells (Dunning et al., 1999), where it is due to upregulation of the α1 and downregulation of the α5 GABAA receptor subunits. In these neurons mIPSC TDecay is inversely related to the ratio of α1 to α5 subunits (Dunning et al., 1999). Our data are consistent with the idea that this switch also occurs in G42 FS interneurons. The gabra1/gabra5 ratio measured by our microarray screen increased 61-fold between p7 and p25, and was inversely correlated with the trajectory of mIPSC TDecay (Fig. 8c), suggesting that developmental sharpening of inhibitory transmission due to this GABAA receptor subunit switch may be a general property of multiple neocortical cell-types.

Figure 7
Excitatory synaptic transmission undergoes extensive and non-monotonic maturation between p7 and p25
Figure 8
Inhibitory transmission is also strongly developmentally-regulated in G42 FS interneurons

Discussion

We measured changes in whole-transcriptome expression and in the intrinsic and synaptic physiology of genetically labeled cortical fast-spiking parvalbumin expressing GABAergic interneurons over the first six postnatal weeks. Previous studies have assayed whole transcriptome expression in rodent neocortex over similar periods of maturation (Lyckman et al., 2008; Semeralul et al., 2006; Stead et al., 2006), however ours is the first to restrict such analysis to a homogeneous subclass of interneurons. In so doing, we were able to reveal cell-type specific transcriptional changes that are not readily extricable from the overall transcriptional profiles of complex tissues such as the cerebral cortex. We found that 1972 genes were differentially expressed over the span of p7 to P40, and that 1124 genes showed an increase in expression, whereas 848 genes showed a decrease. Non-monotonic expression trajectories were rarely observed. This is in contrast to earlier periods of development in which critical sets of genes are often transiently up-regulated and then down-regulated as their transitory developmental functions run their course (Guillemot et al., 2006; Lieuw et al., 1997; Shirasaki and Pfaff, 2002; Wonders and Anderson, 2006). Our observations likely reflect the shutdown of developmental programs initiated prior to p7, concomitant with the onset and progression of the transcriptional maturation of G42 cells.

This interpretation is further supported by examining enriched categories of gene function within the set of differentially expressed genes. GO overrepresentation analysis showed that genes associated predominately with nervous system development, cell proliferation, neuron differentiation, lipid metabolism, RNA binding, and axon guidance were enriched in the set of down-regulated genes (Supplemental Table 1). Alternatively, up-regulated genes showed enrichment for genes related to ion channel activity, voltage-gated ion channel activity, transporter activity, and glycoprotein metabolism. Thus from p7 onward there is a clear down-regulation of early developmental functions and a shift towards the regulation of processes related to mature phenotype. Most prominent among these mature features are spiking behavior, which is conferred by the expression of ion channels, and increased energy metabolism, which is required to maintain ion homeostasis and sustain spiking activity.

Down-regulated genes were not, however, restricted to those with overt developmental functions. GO overrepresentation analysis also revealed that ion channels, particularly sodium and calcium channels, were overrepresented in the set of down-regulated genes. This is consistent with an emerging perspective on the development of excitable cells (Moody and Bosma, 2005) which emphasizes that the passage from immature to mature state cannot be understood as simply the progressive expression of a fixed "basis" set of ion channels that gradually accumulate to fill out the mature complement of expressed channels. Instead, subsets of ion channels often emerge and recede, reflecting the fact that specific ion channels support temporary developmental functions that are often essential to the induction of mature phenotype, but are ultimately incompatible with the maintenance of that phenotype. Two recent studies have shown that E13.5 freshly post-mitotic interneurons express a variety of ion channels and synaptic related genes (Batista-Brito et al., 2008; Marsh et al., 2008). While some of these may persist throughout the life of these neurons, our data, which show that roughly equal numbers of ion channel and synapse-related genes are up- and down-regulated postnatally, support the hypothesis that some of these early expressed channels and receptors may in fact serve transitory functions necessary for neuronal migration or for the differentiation of cell identity, but may not directly contribute to the perseverance of the mature state.

These developmental changes in ion channel expression were reflected in changes in the electrophysiological properties of G42 cells. Over the first four postnatal weeks, G42 cells showed a sharp decline in input resistance, required greater current injection to reach spike threshold, integrated inputs over progressively smaller time windows, showed graded responses to a wider range of input currents, and achieved increasingly higher firing rates. Many of these changes characterized the maturation of other neuronal cell types (McCormick and Prince, 1987; Plotkin et al., 2005; Zhang, 2004; Doischer et al., 2008). Additionally, and consistent with previous reports (Itami et al., 2007; Doischer et al. 2008), p7 and p10 G42 cells displayed spike-frequency adaptation, a feature lost by p15. Together these data show that as late as the beginning of the third postnatal week, G42 cells are electrophysiologically distinct from their mature phenotype, which develops gradually.

Some of the molecular mechanisms underlying the hallmarks of mature FS interneurons have been described. In particular, it is well appreciated that Kv3 potassium channels enable fast spiking in these cells by providing rapid membrane repolarization following an action potential (Erisir et al., 1999; Lau et al., 2000; Rudy and McBain, 2001). Our data corroborate this, showing a coincident increase in the maximum spike rate and the expression of the Kv3 potassium channel subunit encoding genes Kcnc1 and Kcnc2. However, Kv3 channels alone do not necessarily fully account for fast spiking behavior, as they only mediate the falling phase of the action potential. Our data provide additional candidate genes that may also support this specialized function. Scn8a and Scn4b are two sodium channel subunit genes that are up-regulated and are associated with the high-frequency repetitive spiking behavior of cerebellar purkinje neurons (Grieco et al., 2005; Grieco and Raman, 2004; Levin et al., 2006). Additionally, the low input resistance and fast time constant observed in mature FS cells in part reflect upregulation of the two-pore potassium leak channels TWIK1 and TASK1, demonstrated by microarray, immunohistochemistry and pharmacology. The increase in current threshold is also paralleled by the up-regulation of several Shaker potassium channel subunits, Kcna1, Kcna6, Kcnab1, Kcnab2, and Kcnab3, that may dampen excitability.

In addition to observing dramatic changes in intrinsic physiology, we observed dynamic regulation of synaptic properties. We detected both mEPSCs and mIPSCs as early as p7, demonstrating that neocortical FS cells in somatosensory cortex receive functional inputs sometime before the close of the first postnatal week. The frequency and amplitude of mPSCs showed an overall increase between p7 and p25, consistent with this being a period of enhanced neocortical synaptogenesis. While we did not directly explore mechanisms underlying this process, our microarray screen detected regulation of an assortment of cell adhesion molecules presumed to play a role in synaptic connectivity, such as Ncam1, Ncam2, Clstn3, Farp2, Bcan, Hapln4, Hapln1, Vcan, Ncan, Chl1, Nxn1, Cxadr, and Fat4. These present good candidates for future investigation of the regulation of cell-type specific synaptic connectivity, particularly as some of these appear to be enriched or depleted in mature FS cells with respect to other cell-types (Sugino et al., 2006).

Consistent with observed changes in mPSC amplitudes and kinetics, we observed regulation of multiple GABA and glutamate receptor subunits. Gabra1 and Gria4 show approximately 9-fold and 3-fold upregulation, respectively, whereas Gabra5, Grik2, and Glra2 show 8-fold, 4-fold, and 14-fold down-regulation. While the increase in the ratio of Gabra1 to Gabra5 correlates with a developmental decrease in the decay time constant of mIPSCs in this and other cell types (Dunning et al., 1999), the functional implications of the regulation of these other receptors is not fully understood (Ali, 2003; Braga et al., 2004; Braga et al., 2003; Contractor et al., 2001; Fisahn et al., 2004; Young-Pearse et al., 2006).

Taken together, our intrinsic and synaptic results suggest that immature FS interneurons perform substantially different roles in cortical circuits than their mature counterparts. Narrow integration windows and high-frequency non-adapting firing allow mature FS cells to exert strong temporal control over the activity of their synaptic targets (Galarreta et al., 2004; Holmgren et al., 2003). Immature FS cells, however, have high input resistance, relatively long membrane time membrane constants, and exhibit spike-frequency adaptation. They therefore integrate input over broader temporal windows and transform that input with less temporal precision, and presumably produce weaker inhibition than mature FS cells due to their lower firing rate. These features are poorly suited to mediate the timing and entrainment functions typically attributed to FS interneurons in mature cortical circuits (Doischer et al., 2008).

On the other hand, the high input resistance and long integration window of immature FS cells may permit them to fire in response to the sparser and weaker synaptic inputs present as they begin to integrate into spontaneously active developing cortical circuits. In many systems spontaneous activity is transduced via Ca2+ influx into transcriptional changes required for normal maturation of intrinsic properties (Gu and Spitzer, 1995; Moody and Bosma, 2005; Spitzer, 2006) and circuit formation (Katz and Shatz, 1996). It is therefore tempting to speculate that enhanced expression of calcium channel subunits, especially Cacna1g which mediates a low-threshold calcium current, as well as other channels that flux calcium are required early in development when synaptic input is minimal, but that the need for these subunits is supplanted once synaptic inputs and firing patterns mature.

Supplementary Material

Supp1

Supplementary Figure 1:

The G42 mouse line is a stable reporter of a homogenous cell type in the neocortex. A. Microarray signal intensity of three genes that appear to be stable markers of G42 cells over the course of development. Developmental G42 expression data is plotted alongside microarray expression data for multiple cortical cell types (see methods). B. Fixed 100 micron coronal brain sections containing somatosensory cortex (shown) prepared at P7 and P25. Note that GFP expression is mostly restricted to cells residing in layer 2/3 and layers 5 and 6, with few cells labeled in layer 4 and no cells labeled in layer 1. C. Dendrogram representing the results of agglomerative hierarchical clustering analysis performed on whole genome expression profiles across multiple cell types (see methods). Each color denotes a unique sample group, be it age or cell type. Notice the developing G42 samples cluster most closely with mature G42 cells.

Supp2

Supplementary Figure 2:

Derivation of the false discovery rate (FDR). A. Frequency distribution of ANOVA p-values for ANOVAs computed for each gene across time points measured in triplicate (black bars), and for randomly shuffled triplicate datasets (gray bars). The values for randomly shuffled datasets represents the average of 30 randomizations. B. False discovery rate as a function of ANOVA p-value. The FDR was calculated by taking the ratio of the number of genes corresponding to a given ANOVA p-value in the shuffled versus the original dataset. Note that we restricted our consideration of differentially expressed genes to those with an ANOVA p-value of < 10-3 which corresponds to an FDR of < 1%.

Supp3

Supplementary Figure 3:

Immature (p7) G42 FS interneurons express T-type Ca2+ currents. A. Example voltage traces evoked by a range of current injections in the presence of TTX before (left) and after (right) 100µM NiCl application. Note the presence of Ca2+ spikes in the left panel traces and their absence in the right panel. B. Average nickel-sensitive current evoked by a family of voltage steps 10mV steps from −75mV to −5mV (left panel) and the resulting IV curve (right panel).

Supp4

Supplementary Table 1:

Same as Table 1, with the addition of GO terms associated with biological process and cellular component.

Acknowledgements

This research was supported by grants from NIMH and the Simons Foundation to SN, and NINDS to MNM. We thank Zhe Meng for technical assistance and Josh Huang for providing transgenic mice.

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