1 Brain and Mind Institute, EPFL, Lausanne 1015, Switzerland, 2 Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel, 3 Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA, 4 Department of Physiology, Medical School, Tel Aviv University, Tel Aviv 69978, Israel, 5 Department of Internal Medicine, University of Nevada, Reno, NV 89557, USA
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Abstract |
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Key Words: cortex, electrophysiology, ion channel, neuron, single-cell RT-PCR
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Introduction |
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Only a few studies have established strong evidence for a causal relationship between the expression of a single ion channel gene and the electrical behavior using, for example, dynamic clamp in hippocampal neurons (Lien and Jonas, 2003), antisense oligonucleotides in hippocampal neurons (Du et al., 2000
), transfection with dominant negative mutants in cerebellar granule cells (Shibata et al., 2000
), single-cell pharmacology and transfection of HEK cells (Baranauskas et al., 2003
) and modeling combined with immunohistochemical and pharmacological approaches in neocortical interneurons (Erisir et al., 1999
).
More commonly, attempts have been made to correlate the frequency of detecting an expressed gene in a specific cell with an electrophysiological feature (Yan and Surmeier, 1996; Martina et al., 1998
; Plant et al., 1998
; Song et al., 1998
; Lien et al., 2002
). In some cases it has been possible to establish a correlation between the expression of specific ion channel subunits and an aspect of electrical behavior (Martina et al., 1998
; Plant et al., 1998
; Seifert et al., 1999
; Liss et al., 2001
; Lien et al., 2002
). However, causal relationships have often been much more difficult to establish. This difficulty probably results from the enormous complexity. Since a large number of ion channel genes may be expressed in different combinations to generate the electrical behavior, a first essential step is to establish quantitatively the degree to which profiles of expressed genes are correlated to the electrical behavior.
Although there are several published reports of the simultaneous expression of a large number of receptors using single-neuronal RT-PCR (Porter et al., 1998; Cauli et al., 2000
), a maximum of only five ion channel genes were simultaneously investigated. Mermelstein et al. (1999
) studied the expression of Ca
1A and its four auxiliary subunits Caß14 from acutely dissociated cortical pyramidal neurons; Franz et al. (2000
) studied the co-expression of the four Na+/K+ channels HCN14 in layer V pyramidal neurons; and Foehring et al. (2000
) reported the expression of Ca
1E from acutely dissociated neocortical pyramidal neurons.
The detection of specific mRNA transcripts from a single cell is a technical challenge due to the minute quantities of mRNA in a single neuron. There are several strategies to overcome this limitation. The most straightforward strategy is to use the entire cells cytoplasm to test the expression of a single gene (Martina et al., 1998). A second strategy is to split the cells cytoplasm into as many reactions as genes to be tested before amplifying each gene independently (Surmeier et al., 1996
; Yan and Surmeier, 1996
). A third strategy, useful when the genes of interest show a high degree of sequence similarity, is to design a single pair of degenerated primers located in regions identical or nearly identical for all the genes (Plant et al., 1998
; Lien et al., 2002
). Subsequently, the identity of each gene can be determined by: (i) differences in the size of the PCR products; (ii) use of specific restriction enzymes that cut only one amplified PCR product (Plant et al., 1998
); (iii) Southern blot using gene specific probes (Lien et al., 2002
); or (iv) second PCR using gene specific nested primers (Lien et al., 2002
). A fourth method is non-specific pre-amplification of all the neurons mRNA before the gene specific PCR, using amplification methods based on either PCR (Brady et al., 1990
; Dulac and Axel, 1995
; Dixon et al., 1998
; Lin et al., 1999
) or T7 mRNA polymerase (Eberwine et al., 1992
; Ginsberg and Che, 2002
). Although T7 mRNA amplification is routine when starting from micrograms of mRNA or cDNA, it remains technically challenging when starting from the picogram-levels of mRNA, available from single neurons. Although a few studies attempted cDNA microarray analysis on single-cell aspirates (Chiang and Melton, 2003
; Kamme et al., 2003
; Tietjen et al., 2003
), the success rate was very low and this method is therefore not yet practical for profiling of a large number of individual neurons.
Finally, a fifth strategy to detect specific mRNA transcripts from a single-cell is based on gene specific pre-amplification employing multiplex-PCR (Edwards and Gibbs, 1994; Cauli et al., 1997
; Wang et al., 2002
). Unfortunately, the number of genes that can be simultaneously investigated using multiplex, although large, is still limited. The main limitation of multiplex PCR is interference among the multiple primers used for amplifying the different genes, requiring a lengthy calibration procedure in which the optimal combination and relative concentrations of primers and temperatures must be determined (Edwards and Gibbs, 1994
).
A major drawback of all single-cell gene expression approaches is the false negative rate (genes expressed but not detected). The best current solution to this problem is to obtain a large dataset and perform statistical modeling on a population of neurons. In this study, we determined the profiles of genes expressed by 203 neocortical neurons characterized electrically using patch clamp recordings combined with the single-cell multiplex RT-PCR method (Lambolez et al., 1992; Monyer and Jonas, 1995
; Monyer and Lambolez, 1995
; Sucher and Deitcher, 1995
; Cauli et al., 1997
, 2000; Wang et al., 2002
; Fig. 1). Neurons were screened for the expression of one house-keeping gene (GAPDH), three Ca2+ binding proteins (CaBPs) and 26 ion channel genes for which the biophysical properties have been established in the literature. We then used statistical modeling approaches to determine the correlations between profiles of gene expression and profiles of electrical properties and tested the validity of the derived correlation maps. This approach revealed the relationship between electrical phenotype and a modest but physiologically relevant subset of the neuronal transcriptome.
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Materials and Methods |
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Slicing procedure was described previously (Gupta et al., 2000; Markram et al., 1997
). In brief, Wistar rats (1316 days old) were rapidly decapitated and neocortical slices (sagittal, 300 µm thick) were sectioned on a vibratome (Microslicer; DSK, Japan). Slices were incubated for 30 min at 34°C and then at room temperature until transferred to the recording chamber. The extra-cellular solution contained (mM): 125 NaCl, 2.5 KCl, 25 glucose, 25 NaHCO3, 1.25 NaH2PO4, 2 CaCl2 and 1 MgCl2. Neurons in somatosensory cortex were visually identified using infrared differential interference contrast microscopy.
Electrical Recording
This was performed as previously described (Markram et al., 1997; Gupta et al., 2000
; Wang et al., 2002
). In brief, somatic whole-cell recordings (pipette resistance 13 M
) were made. Signals were sampled at intervals of 10400 µs, filtered at 3, 10 or 30 kHz, digitized using an ITC-18 interface (Instrutech, Great Neck, NY) and stored on the computer hard disk for off-line analysis (Igor Wavemetrics, Lake Oswego, OR). Voltages were recorded with pipettes containing RNAse-free (mM) 100 K+ gluconate, 20 KCl, 4 ATPMg, 10 phosphocreatine, 0.3 GTP, 10 Hepes (pH 7.3, 310 mosmol/l, adjusted with sucrose) and 0.5% biocytin (Sigma). Neurons were filled with biocytin by diffusion during the 2030 min recordings. Strengths of current injection were normalized across all cells according to the minimal step current required to reach AP threshold. Somatic current injections to reach threshold ranged from 30 to 150 pA.
Analysis of Electrophysiological Recordings
Intrinsic properties: input resistances were approximated by linear regression of voltage deflections from holding potential (70 ± 1 mV) in response to 2 s current steps of four to eight different amplitudes after reaching steady state. Membrane time constants were determined by fitting a mono-exponential to the decay phases of hyperpolarizing delta-pulses (1 ms duration, voltage deflections of <10 mV), or from fitting a mono-exponential to the rising phases of the voltage traces used for determining the input resistances. AP analysis was performed on the first and second APs elicited by supra-threshold depolarizations. Values of the AP amplitude, duration, half duration (time from AP half amplitude to the same voltage during offset), rise time and fall time (duration from peak to the offset when Vm reaches that of onset) were determined by averaging three to five values. Values of the fAHP were determined by averaging three to five traces. Maximum rise and fall rates were obtained as peak values after differentiating the single AP.
Histological Procedures and Morphological Identification
These were performed as previously described in (Markram et al., 1997; Gupta et al., 2000
). In brief, after recording, slices were fixed for 24 h in cold 0.1 M phosphate buffer (PB, pH 7.4) containing 2% paraformaldehyde, 1% glutaraldehyde and 0.3% saturated picric acid, then rinsed several times in PB and transferred into phosphate-buffered 3% H2O2 for 30 min. After rinsing in PB, slices were incubated overnight at 4°C in biotinylated horseradish peroxidase conjugated to avidin (2% A, 2% B and 1% Triton-100, ABC-Elite; Vector Labs, Peterborough, UK). Sections were then washed several times in PB, developed with diaminobenzadine, washed and then mounted. Subsequently neurons were morphologically classified according to the axonal morphology (reviewed in, for example, Toledo-Rodriguez et al., 2002
).
Cytoplasm Harvesting and Single-Cell Reverse Transcription
These procedures were performed as previously described (Cauli et al., 1997; Wang et al., 2002
). In brief, at the end of the recording, cell cytoplasm was aspirated into the recording pipette under visual control by applying gentle negative pressure. Only cells in which the seal was intact throughout the recording and whose nucleus was not harvested were further processed. The electrode was then withdrawn from the cell to form an outside-out patch that prevented contamination as the pipette was removed. The tip of the pipette was broken and the contents of the pipette expelled into a test tube by applying positive pressure. mRNA was reverse transcribed using an oligo-dT primer (25 ng/ul) and 100 U of MMLV reverse transcriptase (Gibco, BRL). After 50 min incubation at 42°C, the cDNA was frozen and stored at 20°C before further processing.
Multiplex PCR
Multiplex-PCR conditions were optimized using total RNA purified from rat neocortex, so that a PCR product could be detected from (250 pg1 ng) of total RNA without contamination caused by non-specific amplification. For the lists of the primer pairs included into the different multiplexes, the name and accession number of the genes amplified and the length of the PCR product, see Table 1. Three different multiplex-PCR reactions were performed for testing the expression of 30 mRNA species from each cell. The genes co-amplified in each of three multiplex-PCR reactions were Pool I (CB, PV, CR and GAPDH), Pool II
(Kv1.1, Kv1.2, Kv1.6, Kv2.1, Kv2.2, Kv3.1, Kv3.2, Kv4.2, Kvß1, Kvß2, HCN1 and HCN2) and Pool III
(Kv1.4, Kv3.3, Kv3.4, Kv4.3, HCN3, HCN4, Ca
1A, Ca
1B, Ca
1G, Ca
1I, Caß1, Caß3, Caß4 and SK2). Pool 1 was already calibrated to give PCR products for each gene with even intensity (Cauli et al., 1997
; Wang et al., 2002
). Pools 2 and 3 were calibrated to give PCR products for each gene with even intensity starting from 1 ng of brain total mRNA. During calibration different combinations of genes were distributed between the two pools (2 and 3) and different primer pairs were tested until an even amplification of all genes included in the pool was obtained.
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Controls for the RT-PCR
For each PCR amplification, controls for contaminating artifacts were performed using sterile water instead of cDNA. A control for non-specific harvesting of surrounding tissue components was randomly employed by advancing pipettes into the slice and retrieving without seal formation and suction. Both types of controls gave negative results throughout the study. Amplification of genomic DNA could be excluded by the intron-overspanning location of many of the primers and by the fact that the cell nucleus was never harvested. Moreover controls in which the RT was omitted were performed giving negative results.
Pre-processing Expression and Electrical Data
Electrophysiological measurements were considered outliers if the value was six or more standard deviations from corresponding mean each of the 61 electrophysiological parameters. These outliers comprised only 1% of all measurements. Missing values were ignored in computing means and standard deviations and deleted pairwise for correlation analysis (below). For all analyses described below, each electrophysiological parameter was z-normalized (mean of zero and unit standard deviation) in order to provide a common scale of comparison.
The Operator
The operator consists of a family of multivariate linear regression models, one for each of the 61 z-normalized electrophysiological variables, fit by minimizing the least squares error (SYSTAT, Richmond, CA; R Statistical System, v. 1.6.2, http://www.r-project.org). We used 10-fold cross-validation to estimate mean prediction error and to obtain scatter plots of actual-versus-estimated values. To test the statistical significance of the operator, we compared its prediction error to a distribution of errors obtained by generating 500 random cell-wise g-Profile permutations, each of which was used to fit and cross-validate a complete operator. To determine the most independent subsets of predictor genes, we applied a bounds-and-branch exhaustive search for best subsets (LEAPS algorithm; R Statistical System; Miller, 2002). Searching for best subsets comprising 129 genes was computationally feasible for full model and bootstrapped modelling. We found no changes in best subset distributions when increasing from 400 to 800 bootstrap replications. Genes that survived the bootstrap test are defined as appearing in at least 80% of bootstrapped searches.
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Results |
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In order to correlate detailed electrical properties with gene expression profiles, we performed whole-cell patch-clamp recordings from neocortical neurons located in layers 26 of the somatosensory cortex of rats (P1316, Fig. 1a), applied a comprehensive protocol for testing their electrical properties (Fig. 1b) and harvested their cytoplasm (Fig. 1c) for subsequent multiplex non-quantitative RT-PCR (Fig. 1d,e). We studied the simultaneous expression of 30 genes: 26 ion channels, including the voltage activated K+ channels [Kv1.1/2/4/6 (Table 2), Kvß1/2, Kv2.1/2, Kv3.1/2/3/4, Kv4.2/3] (Rettig et al., 1994; Coetzee et al., 1999
); the K+/Na+ permeable hyperpolarization activated channels (HCN1/2/3/4) (Santoro and Tibbs, 1999
); the Ca2+ activated K+ channel (SK2) (Vergara et al., 1998
); the voltage activated Ca2+ channels (Ca
1A/B/G/I, Caß1/3/4) (Moreno Davila, 1999
); three CaBPs [Calbindin (CB), parvalbumin (PV) and calretinin (CR)]; and the house-keeping gene GAPDH (as a quality control of the harvested mRNA). While >100 ion channel genes are potentially expressed in neurons, we focused on 26 thought to be crucial for the active and passive electrical properties of neurons (Fig. 1e; see also Table 1 and Materials and Methods). This profile of expressed genes by a specific neuron is referred to as g-Profile. During recordings, neurons were also loaded with the dye biocytin for subsequent histochemical staining to establish their morphological identity (Fig. 1f).
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From a total of 703 neuronal recordings, 601 were harvested (those not harvested were used for pharmacological tests and controls). Of these, we selected the neurons expressing four or more genes (including GAPDH and a minimum of two ion channels; n = 203). Overall, 198 of these 203 neurons (97.5%) expressed three or more of ion channel genes (Fig. 1g).
Electrical Profiles
Neurons were submitted to a series of somatic current injection protocols, during whole-cell patch clamp recordings, designed to capture their key active and passive electrical properties (Fig. 2). We focused on the discharge responses to step current pulses (Fig. 2a), the shape of the first two action potentials (APs) generated just above threshold (Fig. 2b), the neuronal response to ramp current injection (Fig. 2c), the change in the spiking behavior with time (Fig. 2d), the after-depolarization generated by APs (Fig. 2e), the hyperpolarization after a burst of APs (Fig. 2f), the subthreshold current-voltage relationship (Fig. 2g), the membrane time constant at different potentials (Fig. 2h), the membrane time constant for brief hyperpolarizing current pulses (Fig. 2i) and the resting membrane potential. A numerical breakdown of the electrical behavior was obtained by measuring various aspects of the voltage responses to these stimulation protocols yielding 61 key electrical parameters (EPs) (Table 3). This profile of 61 EPs representing the electrical behavior of each neuron is referred to as the electrical profile or e-Profile of the neuron.
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To determine whether it is possible to derive correlations between gene expression and the phenotype, we first examined whether any relationship can be detected at all by comparing corresponding gene expression and electrical profiles across neurons. For each cell, the detected expression of a gene was coded as one and the absence as zero, obtaining what we refer to as the neurons gene expression profile or g-Profile (does not include GAPDH in the analysis because all the cells expressed this house-keeping gene). In this way, the g-Profile of each cell was represented by a vector of 29 ones (gene expressed) and zeros (gene not expressed; Fig. 3a). The g-Profile of each cell (vector of binary values) was correlated with the g-Profile of each of the other cells, thereby obtaining a correlation matrix of similarity representing the degree too which an expression profile of a cell compares with that of others (Pearson correlation coefficient; Fig. 3a). On the colour scale, colours towards red indicate cell pairs that are more similar in terms of their gene expression profile. The same correlation was performed for the electrical profiles (e-Profile) except that the values were normalized analog values (Fig. 3b; see Table 3). A z-normalization was performed across cells to obtain a standard deviation of 1 around 0. Subsequently, the e-Profile of each cell (vector of 61 analog z-normalized EP values) was correlated with all other cells, producing another similarity matrix for the electrophysiological behavior (Fig. 3b; see Materials and Methods).
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Clusters of Expressed Genes
Before deriving the correlations between the gene expression profile and the phenotype, we wanted to know whether there is any detectable structure within the patterns of gene expression. We therefore carried out an unsupervised gene cluster analysis (Ward, 1963; Fig. 3c) to determine co-expression tendencies. Because we did not know a priori the number of clusters to expect, we used a hierarchical technique that begins by assigning each gene to its own class. The class of each gene was represented by a vector or ones (expressed) and zeros (not expressed) across all 203 cells. We computed the Euclidian distance between each class (sum of squared differences) and then combined classes as we gradually relaxed this distance criterion (Fig. 3c).
Four major clusters of genes that tend to co-express were discovered. Interestingly, three of these clusters each contained one of the three CaBPs widely used for classifying neocortical neurons and therefore we named them according to the CaBP they included. The CR cluster contained SK2, Kv3.4, CR and Ca1B; the CB cluster contained CB, Caß4, HCN3, Kv1.4, Ca
1G, Caß1, HCN4, Kv3.3 and Caß3; and the PV cluster contained HCN2, Kv3.1, Kv1.2, Kv1.6, Kv1.1, PV, Kv3.2, HCN1, Kvß1 and Ca
1A. These three clusters are also consistent with the known biophysical properties of the different ion channels and CaBPs, which may complement each other to generate a broad class of discharge behaviors: the CR cluster is associated with accommodation of discharge (Vergara et al., 1998
); the CB cluster is associated with bursting behavior (Ertel and Ertel, 1997
), and the PV cluster is associated with high frequency discharge (Martina et al., 1998
; Chow et al., 1999
; Rudy and McBain, 2001
). These three clusters are also consistent with the known expression of CB, PV and CR in different types of neocortical neurons, further validating the sufficient level of accuracy of the expression profiling carried out in this study as well as the sufficient numbers of cells included in the data set for statistical modeling.
The Linear Operator
While many methods can be applied to derive the correlations between two vectors, as a first step we chose linear regression because this would allow reversible translation between profiles of expressed genes and the electrical phenotype. This operator provides coefficients, Ck, for the relative correlation of the expression or non-expression of each gene, (mRNAk, 1 or 0, respectively) with the final value of each EP (EPi) (see Materials and Methods; Fig. 3d):
where Ni is the normalization factor used to z-normalize EPi. A profile of gene coefficients (PGC = {C1, C2, . . ., C29}) was independently obtained for each EPi by fitting to the multiple regression model with a least-squares error function. These regression coefficients provide a novel quantification of the relative correlation between the expression and non-expression of individual genes in the context of co-expressed genes with the value of each EP. The coefficients were represented on an analog scale where the highest coefficient predicts the maximal EP value recorded in any of the 203 cells (Fig. 4a).
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The performance of the operator was tested in a number of different ways. The first approach was to generate the operator using only 90% of the cells and test the predictions of EP values using the remaining 10%. Figure 4b shows an example where several features of the AP waveform are predicted with >90% accuracy for one cell belonging to the 10% test group. The accuracy of using the coefficients to predict all 61 electrical parameters for a representative cell is shown in Figure 4c. We then generated 10 operators each time leaving out a different 10% of cells for the test set. The accuracy of predicting a single EP (e.g. AP duration) in all 203 cells is shown in Figure 4d. To evaluate the performance of the operator as a whole, for all parameters and all cells, we compared its average prediction error with the average prediction errors of operators generated by randomly shuffling the g- and e-Profiles (P < 0.0001; Fig. 4d). In another test we measured the variance accounted for by the coefficients to make the predictions. The mean was 14% (corrected R2 = 0.1443) for all the cells and all EPs, but the corrected R2 was much higher for many EPs, some even greater than 0.3, or 30%. The SD in the permutations is small, such that 3 SD is 0.1 R2 units. Therefore predictions with corrected R2 > 0.1, or 10%, are statistically meaningful (35 of the 61 EPs). An average operator was also generated from the 10 partial operators with SDs that give an indication of the significance of the coefficient (data not shown).
We further tested the significance of the coefficients by searching for the minimal set of genes (best gene subsets, bGs) that independently predict the EPs (open circles in Fig. 4a; see Materials and Methods) and then tested which of these genes would survive a bootstrap re-sampling of the bGs modeling process, which tests for bias caused by under-sampling (solid circles in Fig. 4a). The bGs and the subset of genes surviving the bootstrap, overlay on virtually all strong, some moderate and a few weak coefficients, supporting the significance of the main predictions of the operator using these coefficients as well as identifying the limitations of the derived operator due to the methodological noise, nonlinearities, multicolinearities and limited sample size.
The Operator: General Observations
Figure 4a is a pseudocolored display of the regression coefficients (weights) for all 29 genes in predicting each of the 61 EPs. The first important observation is that each gene contributes significantly (adjusted for chance) to the prediction of each EP. Secondly, best-subsets analysis showed that no simple subset of genes can be used to predict all EPs, nor even EPs with moderate correlation; on average an overlapping 42% of genes independently predicts any given EP. Thirdly, the average correlation among sets of gene-wise weights was very low (mean, 0.02 ± 0.291), indicating that genes tend to contribute independently in predicting the electrical profile.
We did, however, notice a few strong deviations from this rule. Two gene trios were found to be strongly positively correlated (Kvß2PV, 0.7445; Kv1.4PV, 0.6795; Kv1.4Kvß2, 0.5886) and (SK2CR, 0.6336; SK2Kv2.1, 0.597; Kv2.1CR, 0.4954) indicating they are similarly correlated with the electrical profiles. The genes in each trio were also strongly negatively correlated with the genes in the other group. This indicates that the expression of each of these two trios correlates with nearly opposite electrical profiles. Further gene pairs were found to be highly negatively correlated (Kv1.2Kv3.1, 0.679 and Kv1.2Kv3.2, 0.636; HCN4PV, 0.694 and HCN4Kvß1, 0.63; Caß4SK2, 0.596 and Caß4CR, 0.614).
We then examined how these gene pairs and trios are co-expressed and found four principles of co-expression: (i) some gene pairs such as SK2CR, which have similar correlation profiles, can be co-expressed (see Fig. 3c), while (ii) other genes pairs, such as Kv1.1Kv1.4 and PVKv1.4, which also have similar correlation profiles, are seldom co-expressed; (iii) gene pairs such as Kv1.2Kv3.1 and Kv1.2Kv3.2, which are correlated with opposite electrical values, are often co-expressed; while (iv) other gene pairs such as HCN4PV, KCN4Kvß1, Caß4SK2 and Caß4CR, which are also correlated with opposite electrical values are seldom co-expressed. The apparent conflict between these finding is discussed further bellow.
We noticed that some differences and similarities between the correlation profiles were surprising given their known biophysical properties. For example, the two low threshold Ca2+ channel genes, Ca1I and Ca
1G, that both generate rather similar T-type Ca2+ currents were found to be nearly oppositely correlated with the frequency of the initial burst (see Fig. 5a2). The correlation profiles for the delayed rectifiers Kv1.1 and Kv1.2 with very similar biophysical properties (Wang et al., 1999
) are also nearly opposite. Similarly for the two hyperpolarization activated Na+/K+ channels HCN2 and HCN4. We also found examples of genes with similar correlation profiles that produce ion channels with very different biophysical properties. These include Kv4.3 (an A type channel) and Kv2.1 (a delayed rectifier) as well as Kv3.3 (another type of delayed rectifier) and Kv4.3. However, when we checked the co-expression of these genes pairs, we found that they were expressed in different neurons, indicating that the electrical properties with which an expressed gene is correlated, is strongly influenced by the type of neuron in which it is expressed.
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The statistical validation of the operator provides confidence in the correlation coefficients allowing a more detailed analysis of the specific gene profile correlated with electrical properties. Four categories of electrical properties are discussed: the AP waveform, after hyperpolarization, passive properties and discharge behavior.
The profile of expression that correlated positively with brief APs, as found in high frequency discharging interneurons (genes whose coefficients were more than twice the standard deviation from the mean), includes PV, Kv1.4, Kvß2, Kvß1, Kv1.1, Kv3.2, Kv3.1 and Caß4 and the profile that is negatively correlated includes CR, HCN1, HCN4, Ca1I, Ca
1B, Kv1.2, SK2 and Kv2.1 (Fig. 4a, E7 Average time from 1st AP onset). A similar profile of expression is also correlated with the 2nd AP (Fig. 4a, E15 Average time from 2nd AP onset).
Many interneurons, especially the high frequency spiking interneurons, exhibit large afterhyperpolarizations immediately after an AP (fAHP), which serves to remove Na+ channels from inactivation permitting high frequency discharge. The profile positively correlated with large amplitude fAHPs includes PV, Kv1.6, Kv3.1, Kv3.2 Ca1G and Caß4 and the negatively correlated profile includes CR, Ca
1I, Kv2.2, HCN4 and SK2 (Fig. 4a, E13 Amplitude from 1st AP onset to minimum voltage). The amplitude of the intermediate AHP following a burst of APs (measured as the amplitude at 100 ms after a burst) is positively correlated with the expression of SK2, HCN1, PV, Ca
1B, CR, Kv1.2 and Kvß1 and negatively with Kv3.3, Kv3.1, Kv4.2 and Kv3.2 (Fig. 4a, E37 Amplitude of the AHP at 100 ms after the end of a burst of APs).
Interneurons can differ greatly in terms of their input resistances (Chitwood et al., 1999). Figure 4a [E28 Maximum input resistance (peak voltage response to current injection) and E29 Input resistance at steady-state (steady-state of voltage response to current injection)] illustrates the gene expression profiles that are positively correlated with the input resistance at the peak and steady state following a step current injection. The genes whose expression correlated positively with low input resistances include PV, Kvß2, Kv3.2 and Ca
1G and the expression profile correlated with high input resistances include CR, SK2, Kv4.3 and Caß1. Another important passive property is a non-linear change in the membrane resistance (rectification) as a function of voltage; the expression profile correlated with high indices of refraction included HCN1, SK2, HCN2, HCN3, Kv2.2, Caß1, CB and Kv1.1 and the absence of Kv3.3, Ca
1B, Ca
1A, Kvß2 and Ca
1I (Fig. 4a, E30 Change in input resistance at peak voltage and E31 Change in input resistance at steady-state voltage). The resting membrane potential may also vary >10 mV; the expression profile correlated with positive potentials includes SK2, CR, Ca
1G, Kvß1 and Kv2.1 and the absence of PV, Kvß2, Kv2.2, Ca
1B, Kv1.4 and HCN3 (Fig. 4a, E1 Membrane potential at the onset of whole-cell). Finally, interneurons can differ strikingly in terms of their thresholds for AP generation. When submitted to a standard pulse (where the duration was fixed and the amplitude strength was scaled as described in Materials and Methods) high thresholds are positively correlated with PV, HCN1, Kv2.2, Ca
1B, Ca
1G and CR expression and negatively correlated with CB, Kv4.2, Kv3.4 and HCN4 expression (Fig. 4a, E34 Threshold to discharge APs during a ramp depolarization).
Figure 5a1 shows the representative responses for delayed (d-, left) and rapid (b-, right) onset responses. EPs that distinguish these behaviors are the delay to spiking onset (Fig. 4a, E42 The average delay for the cell to generate a 2nd AP) and the mean inter-spike interval as the cell begins to discharge (Fig. 4a, E44 Average interspike interval for the first 3 APs). While differences in these two behaviors have been noted previously (Kawaguchi and Kubota, 1997; Gupta et al., 2000
) it was not suspected that these two behaviors were opposite phenotypic classes of fast reacting and slowly reacting neurons until we noticed a near perfect inversion of the correlated gene expression profiles (Fig. 5a2).
The Operator as a Road Map
The linear operator is a correlation map that can now be used to translate the profiles of genes expressed into the electrical phenotype. These correlations do not imply any causality, but they can be used as a road map to explore the numerous potential causal steps between gene expression and the emergent behaviour. As an illustration, we tested for the presence of the Kv1.1 protein whose mRNA expression was positively correlated with an index of stuttering discharge (STUT, see Gupta et al., 2000) and which is supported by the best subsets analysis (Fig. 4a, E52 median of the distribution of interspike intervals). The presence of Kv1.1 protein in STUT cells was confirmed by immunohistochemical (Fig. 5b1) and pharmacological analysis. Indeed, application of dendrotoxin I (DtxI, 20 nM, n = 9), which blocks Kv1.1/2/6 or dendrotoxin K (DtxK, 10 nM, n = 9) which preferentially blocks Kv1.1 (Harvey, 1997
), blocked stuttering and allowed unhindered fast spiking and non-adapting discharges in all cells tested (Fig. 5b2). This finding is also consistent with the involvement of Kv1.1 in a related firing pattern, the irregular discharging neuron (Porter et al., 1998
).
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Discussion |
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The causal relationship between profiles of genes expressed and the electrical phenotype could be quantifiable in future studies by using the correlation map to generate hypotheses to knock-out, express, or silence multiple genes in different combinations and applying causally oriented statistical modeling. Using the techniques applied here, it would be possible to derive precise information about developmental changes in ion channel and calcium binding protein expression, firing patterns and alterations that occur in cortical circuits in human brain disorders and related animal models. Moreover, the use of DNA microarrays with hundreds or thousands of simultaneously-measured mRNAs could make it possible to expand the descriptive and predictive accuracies of our approach to the whole transcriptome; this could facilitate identification of key nodes in complex networks, which can serve as therapeutic drug targets.
Gene Coefficients
Although our multiplex contains the largest collection of ion channels genes simultaneously studied from single electrically and morphologically characterized neocortical neurons, this is only a subset of the genes that are relevant to the neurons electrical behavior. Since the number of genes that can be included in the multiplex is limited, we chose to include only those genes that produce ion channels: (i) whose activity may add significantly to the electrical behavior of neurons; (ii) whose biophysics are also well known; (iii) shown to be expressed in the neocortex (based on previous RT-PCR, immunohistochemical, in situ hybridization and pharmacological studies); and (iv) known to have some causal relationship with the behavior (Tanaka et al., 1995; Coetzee et al., 1999
; Talley et al., 1999
; Du et al., 2000
; Shibata et al., 2000
; Stocker and Pedarzani, 2000
; Lien and Jonas, 2003
). Aside from not selecting Na+ channel genes, our strategy was to continue adding K+ and Ca2+ channel genes to the multiplex until the calibration became too complex.
The usual measure employed when correlating gene expression with electrical behavior of neurons is the frequency of expression. This measure, however, is susceptible to methodological flaws in sensitively detecting mRNA expression and/or accurately characterizing specific phenotypes. Our approach to minimizing these biases was to quantify multivariate correlations between the expression of a gene and the value of a large repertoire of specific electrical parameters. We did not attempt to perform quantitative single-cell multiplex RT-PCR for two reasons. First, technical difficulties increase as a function of the number of genes included in the multiplex due to non-specific interactions and competition for the reagents. Secondly, mRNA quantification will provide information about only a small part of the molecular cascade that gives rise to the phenotype. In order to unravel the causal chain of events leading from gene expression to electrical behavior one would also need to know the amount of protein translated from each mRNA species, the degree of ion channel heteromerization, the types of posttranslational modifications the protein undergoes, the functional state of each protein, the targeting of the protein and the rate of protein turnover. Although the correlations and associations established in our study can not prove causality they are required to support a causal hypothesis.
Reliability of Correlation Coefficients
Any method that attempts to detect genes expressed in a single-cell is likely to suffer from a significant incidence of false negatives. In this study the number of false negatives depends on the specific gene analyzed because each gene gives rise to a different number of mRNA molecules. We directly addressed this handicap by obtaining large numbers of neurons and performing statistical modeling.
Several findings indicate that this approach is valid and reliable. Firstly, we found that cells with similar electrical behavior also have statistically significantly similar gene expression profiles. This finding together with the fact that these are two methodologically independent measures indicates that the expression and electrical profiling was sufficiently accurate to justify statistical modeling.
Secondly, we found three main clusters of ion channel gene co-expression, each one defined by a CaBP: CB, PV and CR. It is known from immunohistochemical, in situ hybridization and RT-PCR studies that these three CaBPs are largely expressed in different types of neurons (reviewed in, for example, DeFelipe, 1997; Kawaguchi and Kubota, 1997
; Chow et al., 1999
; Toledo-Rodriguez et al., 2002
) further supporting the accuracy of the expression profiles as well as validating the sufficient number of neurons obtained for this study. The expected biophysical properties of the produced ion channels in each cluster are also largely consistent with those supporting the three broad classes of electrical behaviors found in neocortical neurons (regular, bursting and fast firing). For example, several delayed rectifiers, between them the two members of the Kv3 family Kv3.1 and KV3.2, which have been previously shown to be highly correlated with narrow action potentials and high-frequency discharging (Martina et al., 1998
; Erisir et al., 1999
) were found in the PV cluster that correlates with fast firing neurons.
Thirdly, a cross-validation analysis indicated that many electrical parameters can be predicted with very low errors and that the entire e-Profile can be predicted with an accuracy that is statistically significant. Moreover, cross-validation was performed on cells not involved in constructing the operator, indicating that the coefficients allow predictions of electrical parameters from gene expression profiles for new cells that will be recorded in the future in this same species, age and brain region. How these coefficients will generalize to other species, ages and brain regions is an interesting issue for future study.
Fourthly, the correlation coefficients verify many past studies in terms of previously reported gene expression in specific neocortical interneurons using single-cell RT-PCR and other methods of ion channel localization, such as immunohistochemistry and in situ hybridization and are consistent with biophysical properties of the ion channels that would be expected to result from this expression and their expected impact on the electrical behavior. While very little is known about which specific genes are expressed in single identified neurons in the neocortex, our data confirms the findings of those previous studies that reported a high correlation between the expression of the CaBP PV, the powerful delayed rectifiers Kv3.1 and Kv3.2 and fast spiking neocortical interneurons with brief action potentials (see Martina et al., 1998; Erisir et al., 1999
; Rudy and McBain, 2001
). We further extended these findings by showing that while less frequent, Kv3.1 and Kv3.2 are also expressed by neocortical pyramidal neurons [which is in agreement with a similar finding in hippocampal pyramidal neurons (Martina et al., 1998
)].
In terms of consistency with predicted biophysical impact, the coefficients reveal: (i) a high correlation between the expression of the hyperpolarization activated Na+/K+ channels HCN1, HCN2 and HCN3 and voltage rectification, as seen in the hippocampus (see Magee, 1998); (ii) expression of the low threshold Ca2+ channel Ca
1I (see Lee et al., 1999
) and the auxiliary subunit Caß3 (which accelerates the rate of inactivation of the voltage activated Ca2+ channels to facilitate a transient Ca2+ influx; Castellano et al., 1993
), as in burst firing neurons in the hypothalamus (Fan et al., 2001
); (iii) the expression of Kv4.3 and Kvß1 (which probably modifies Kv1.2 to produce a transient A type channel) and high threshold Ca2+ channels in delayed discharge neurons, as in neurogliaform cells in the neocortex (Kawaguchi and Kubota, 1997
); (iv) the ion channels predicted to favor accommodation of the firing rate are biophysically consistent (expression of SK2); (v) the predicted Ca2+ channels that favor burst firing are biophysically consistent (alpha subunit Ca
1G in addition to its three beta subunits Caß1, Caß3 and Caß4, whose combined activity enables the explosive increase in the intracellular Ca2+ concentrations underlying the initial burst); and (vi) the predicted ion channels that favor non-accommodation are also biophysically consistent and tend to co-express with PV.
In summary, the main the reliability of the PCR methodology is supported by: (i) the significant correlation among expression and electrical profiles; (ii) the predictive power of the operator prediction electrical behavior from gene expression; (iii) the consistency of our findings with previous reports of expression in single neocortical neurons; and (iv) with the known biophysical properties of the ion channels.
Novel Correlations
This study reveals the simultaneous expression 26 of ion channels and 3 CaBPs in neocortical neurons and a significant and quantifiable relationship between a small subset of the transcriptome and the electrical phenotype. This relationship exposes the specific genes that are correlated with specific electrical properties in neocortical neurons. For example, we found, in addition to Kv3.1, Kv3.2 and PV that expression of another delayed rectifier, Kv1.6, as well as the high threshold Ca2+ channel gene Ca1G and its ß subunit, Caß4 are also highly correlated with fast spiking, while CR, Ca
1I, Kv2.2, HCN4 and SK2 expression are anti-correlated with this firing behavior. The operator also indicates that the precise pacing of the discharge to minimize accommodation in fast spiking neurons is correlated with the expression of the hyperpolarization activated channels HCN1 and HCN2 and of Kvß1 which is an auxiliary subunit of the Kv1 gene family that transforms these delayed rectifiers into transient A type channels (Rettig et al., 1994
) that have important pacemaker properties (Adamson et al., 2002
). Indeed, we also found that blockade of HCN channels with the specific blocker ZD7288 (100 nM) converts high frequency evenly spaced firing into an interrupted pattern (data not shown).
This study further provides a cluster analysis of co-expression of multiple genes across a large number of neurons revealing the specific ion channels that co-express with the three major CaBPs. By combining the co-expression results with the gene correlation coefficients, novel principles that govern expression in these electrically diverse neocortical neurons were found.
Gene Expression Principles
The coefficient matrix provides two measures of the relationship between gene expression and the electrical phenotype. The first is a multi-gene coefficient profile to evaluate the correlation of each gene with the value of a specific electrical parameter and the second is a single-gene coefficient profile that evaluates the correlation of one gene with the values of multiple electrical parameters. Comparison of single-gene coefficient profiles, revealed three features of the single-gene correlations. First, most genes displayed unique single-gene coefficients profiles, which indicates low potential functional redundancy. Secondly, the expression of some genes, even with different biophysical properties can demonstrate remarkably similar correlations with the electrical profile. Thirdly, the expression of some genes, even very similar biophysically, can demonstrate nearly opposite correlations with the electrical profile.
When we further combined these findings with the results from the co-expression, we found evidence to suggest four possible co-expression principles that may underlie the electrical diversity: synergizing (genes expressed that correlate in the same manner and found in the same cells, e.g. SK2-CR); antagonizing (genes expressed that correlate oppositely but found in the same cells, e.g. Kv1.2Kv3.1 and Kv1.2Kv3.2); homogenizing (genes expressed that correlate in the same manner but are expressed in different cells, e.g. Kv1.1Kv1.4 and PVKv1.4); and heterogenizing (genes that correlate in the opposite manner and expressed in different cells, e.g. HCN4PV, KCN4Kvß1, Caß4SK2 and Caß4CR). These governing principles may enable the large diversity of electrical types and may explain how different morphological types of neurons can express the same electrical behavior.
Inversion of Gene Expression Profiles
Typically interneurons are classified into fast, burst and regular firing. Recently a more detailed classification scheme was proposed in which fast firing cells are sub-classified according to their onset response: one sub-class where the neuron responds to a depolarizing current pulse with a delayed discharge, one sub-class in which the neuron begins with a high frequency burst and one sub-class that does not display any special onset response. The current study demonstrates that the entire expression profile that was tested is inverted when comparing delayed onset to burst onset neurons. This finding indicates that these two subclasses of neurons, which have previously been classified into the same broad group as fast spiking, are essentially opposite types of neurons. To our knowledge, such a phenomenon has never been reported before and alludes to upstream control of the entire gene expression profile possibly by a few transcription factors. Understanding how many upstream genes control the expression of entire profiles of genes could solve a long-standing problem of whether electrical behavior is expressed either to form a continuous diversity or a finite number of classes.
Expression Principles in Different Morphological and Electrophysiological Types
The present study purposefully avoided the problem of classifying electrical behavior according to the gross and subjectively defined electrical classes because the statistical modeling approach requires an unbiased numerical breakdown of the electrical phenotype. We recorded from as many different types of neocortical neurons (including pyramidal neurons) as possible so that the maximal ranges of values for the different electrical parameters would be represented. The diversity in this study is therefore an advantage rather than an intractable problem. The study also included neurons with diverse morphologies and since a correlation map could be derived despite this morphological diversity, these correlations are independent of morphology. This does not mean that morphology is irrelevant, just that the coefficients reflect the morphology-independent component of the electrical behavior. Taking morphology into account in future studies could add greatly to understanding the relationship between ion channel genes, morphology and electrical behavior. Nevertheless, solely based on frequency of expression Kv2.1, HCN4, Kv4.2, Kv1.1 and some of the voltage activated Ca2+ channels were the ion channels with the highest expression in pyramidal neurons, Kv3.1, Kv3.2, Kv3.3, PV, HCN1, HCN2 and HCN3 were the ion channels with the highest expression in large basket cells and Kv2.1, Kv3.3, Kv4.2, HCN4 and Kv3.1 were amongst the channels with the highest expression in Martinotti cells. This measure is not, however, optimal and further studies will be needed for correlating gene expression with morphology and electrophysiology.
Reverse Prediction
While we did not focus on the gene profiles for subjective classes of neurons, this approach can be adapted to derive such profiles. As an illustration, we derived a reverse operator to predict the profile for any single cell (Fig. 6). Although there is no simple analytical solution (expression is binary) and the ratio of available neuronal exemplars to the number of EPs is modest, we can use an iterative, stepwise optimization technique to accurately predict the gene expression profile. For example, we found that at least 17 EPs are required to make a reliable prediction (data not shown). The best EPs that reliably predict >10 genes (EP1, EP11, EP14, EP17, EP30, EP31, EP37, EP46, EP50, EP58; data not shown) were also isolated. The same approach could in principle be used in the future to predict the gene profile for any subjectively defined type of neuron from its averaged electrical profile.
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Address correspondence to Henry Markram, Brain and Mind Institute, EPFL, Lausanne, 1015 Switzerland. Email: henry.markram{at}epfl.ch.
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Aranda-Abreu G, Behar L, Chung S, Furneaux H, Ginzburg I (1999) Embryonic lethal abnormal vision-like RNA-binding proteins regulate neurite outgrowth and tau expression in PC12 cells. J Neurosci 19:69076917.
Baranauskas G, Tkatch T, Surmeier DJ (1999) Delayed rectifier currents in rat globus pallidus neurons are attributable to Kv2.1 and Kv3.1/3.2 K(+) channels. J Neurosci 19:63946404.
Baranauskas G, Tkatch T, Nagata K, Yeh JZ, Surmeier DJ (2003) Kv3.4 subunits enhance the repolarizing efficiency of Kv3.1 channels in fast-spiking neurons. Nat Neurosci 6:258266.[CrossRef][ISI][Medline]
Brady G, Barbara M, Iscove NN (1990) Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol Cell Biol 2:1725.
Castellano A, Wei X, Birnbaumer L, Perez-Reyes E (1993) cloning and expression of a third calcium channel beta subunit. J Biol Chem 268:34503455.
Cauli B, Audinat E, Lambolez B, Angulo MC, Ropert N, Tsuzuki K, Hestrin S, Rossier J (1997) Molecular and physiological diversity of cortical nonpyramidal cells. J Neurosci 17:38943906.
Cauli B, Porter JT, Tsuzuki K, Lambolez B, Rossier J, Quenet B, Audinat E (2000) Classification of fusiform neocortical interneurons based on unsupervised clustering. Proc Natl Acad Sci USA 97:61446149.
Chiang MK, Melton DA (2003) Single-cell transcript analysis of pancreas development. Dev Cell 4:383393.[ISI][Medline]
Chitwood R, Hubbard A, Jaffe D (1999) Passive electrotonic properties of rat hippocampal Ca3 interneurones. J Physiol 515:743756.
Chow A, Erisir A, Farb C, Nadal MS, Ozaita A, Lau D, Welker E, Rudy B (1999) K(+) channel expression distinguishes subpopulations of parvalbumin- and somatostatin-containing neocortical interneurons. J Neurosci 19:93329345.
Coetzee WA, Amarillo Y, Chiu J, Chow A, Lau D, McCormack T, Moreno H, Nadal MS, Ozaita A, Pountney D, Saganich M, Vega-Saenz de Miera E, Rudy B (1999) Molecular diversity of K+ channels. Ann N Y Acad Sci 868:233285.
DeFelipe J (1997) Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex. J Chem Neuroanat 14:119.[CrossRef][ISI][Medline]
Dixon AK, Richardson PJ, Lee K, Carter NP, Freeman TC (1998) Expression profiling of single cells using 3 prime end amplification (TPEA) PCR., Nucleic Acids Res 26:44264431.
Du J, Haak LL, Phillips-Tansey E, Russell JT, McBain CJ (2000) Frequency-dependent regulation of rat hippocampal somato-dendritic excitability by the K+ channel subunit Kv2.1. J Physiol 522:1931.
Dulac C, Axel R (1995) A novel family of genes encoding putative pheromone receptors in mammals. Cell 83:195206.[ISI][Medline]
Eberwine J, Yeh H, Miyarisho KCY (1992) Analysis of gene expression in single live neurons. Proc Natl Acad Sci USA 89:30103014.[Abstract]
Edwards MC, Gibbs RA (1994) Multiplex PCR: advantages, development, and applications. PCR Methods Appl 3:S65S75.[ISI]
Erisir A, Lau D, Rudy B, Leonard CS (1999) Function of specific K(+) channels in sustained high-frequency firing of fast-spiking neocortical interneurons. J Neurophysiol 82:24762489.
Ertel S, Ertel E (1997) Low-voltage-activated T-type Ca2+ channels. Trends Pharmacol Sci 18:3742.[CrossRef][ISI][Medline]
Fan Y, Horn E, Waldrop T (2001) Biophysical characterization of rat caudal hypothalamic neurons: calcium channel contribution to excitability. J Neurophysiol 84:28962903.[ISI]
Foehring RC, Mermelstein PG, Song WJ, Ulrich S, Surmeier DJ (2000) Unique properties of R-type calcium currents in neocortical and neostriatal neurons. J Neurophysiol 84:22252236.
Franz O, Liss B, Neu A, Roeper J (2000) Single-cell mRNA expression of HCN1 correlates with a fast gating phenotype of hyperpolarization-activated cyclic nucleotide-gated ion channels (Ih) in central neurons. Eur J Neurosci 12:26852693.[CrossRef][ISI][Medline]
Ginsberg S, Che S (2002) RNA amplification in brain tissues. Neurochem Res 27:981992.[CrossRef][ISI][Medline]
Glasgow E, Kusano K, Chin H, Mezey E, Young WS III, Gainer H (1999) Single cell reverse transcriptionpolymerase chain reaction analysis of rat supraoptic magnocellular neurons: neuropeptide phenotypes and high voltage-gated calcium channel subtypes. Endocrinology 140:53915401.
Gupta A, Wang Y, Markram H (2000) Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science 287:273278.
Harvey A (1997) Recent studies on dendrotoxins and potassium ion channels. Gen Pharmacol 28:712.[CrossRef][Medline]
Hille B (2001) Ionic channels of excitable membranes. Sunderland, MA: Sinauer.
Johnston D, Wu SM-S (1995) Fundations of cellular neurophysiology. Cambridge, MA: MIT Press.
Kamme F, Salunga R, Yu J, Tran DT, Zhu J, Luo L, Bittner A, Guo HQ, Miller N, Wan J, Erlander M (2003) Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J Neurosci 23:36073615.
Kawaguchi Y, Kubota Y (1997) GABAergic cell subtypes and their synaptic connections in rat frontal cortex. Cereb Cortex 7:476486.[Abstract]
Lambolez B, Audinat E, Bochet P, Crepel F, Rossier J (1992) AMPA receptor subunits expressed by single Purkinje cells. Neuron 9:247258.[ISI][Medline]
Lee JH, Daud A, Cribbs L, Lacerda A, Pereverzev A, Klockner U, Schneider T, Perez-Reyes E (1999) Cloning and expression of a novel member of the low voltage-activated T-type calcium channel family. J Neurosci 19:19121921.
Lien C, Jonas P (2003) Kv3 potassium conductance is necessary and kinetically optimized for high-frequency action potential generation in hippocampal interneurons. J Neurosci 23:20582068.
Lien CC, Martina M, Schultz JH, Ehmke H, Jonas P (2002) Gating, modulation and subunit composition of voltage-gated K(+) channels in dendritic inhibitory interneurones of rat hippocampus. J Physiol 538:405419.
Lin SL, Chuong CM, Widelitz RB, Ying SY (1999) In vivo analysis of cancerous gene expression by RNA-polymerase chain reaction. Nucleic Acids Res 27:45854589.
Liss B, Franz O, Sewing S, Bruns R, Neuhoff H, Roeper J (2001) Tuning pacemaker frequency of individual dopaminergic neurons by Kv4.3L and KChip3.1 transcription. EMBO J 20:57155724.
Magee J (1998) Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. J Neurosci 18:76137624.
Markram H, Lubke J, Frotscher M, Roth A, Sakmann B (1997) Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J Physiol 500:409440.[Abstract]
Martina M, Schultz JH, Ehmke H, Monyer H, Jonas P (1998) Functional and molecular differences between voltage-gated K+ channels of fast-spiking interneurons and pyramidal neurons of rat hippocampus. J Neurosci 18:81118125.
Mermelstein PG, Foehring RC, Tkatch T, Song WJ, Baranauskas G, Surmeier DJ (1999) Properties of Q-type calcium channels in neostriatal and cortical neurons are correlated with beta subunit expression. J Neurosci 19:72687277.
Miller A (2002) Subset selection in regression. New York: Chapman & Hall/CRC.
Monyer H, Jonas P (1995) Polymerase chain reaction analysis of ion channel expression in single neurons of brain slices. In: Single-channel recordings (Sakmann B, Neher E, eds), pp. 357373. New York: Plenum Press.
Monyer H, Lambolez B (1995) Molecular biology and physiology at the single-cell level. Curr Opin Neurobiol 5:382387.[CrossRef][ISI][Medline]
Moreno Davila H (1999) Molecular and functional diversity of voltage-gated calcium channels. Ann N Y Acad Sci 868:102117.
Plant TD, Schirra C, Katz E, Uchitel OD, Konnerth A (1998) Single-cell RT-PCR and functional characterization of Ca2+ channels in motoneurons of the rat facial nucleus. J Neurosci 18:95739584.
Porter JT, Cauli B, Staiger JF, Lambolez B, Rossier J, Audinat E (1998) Properties of bipolar VIPergic interneurons and their excitation by pyramidal neurons in the rat neocortex. Eur J Neurosci 10:36173628.[CrossRef][ISI][Medline]
Rettig J, Heinemann SH, Wunder F, Lorra C, Parcej DN, Dolly JO, Pongs O (1994) Inactivation properties of voltage-gated K+ channels altered by presence of beta-subunit. Nature 369:289294.[CrossRef][ISI][Medline]
Rudy B, McBain CJ (2001) Kv3 channels: voltage-gated K+ channels designed for high-frequency repetitive firing. Trends Neurosci 24:517526.[CrossRef][ISI][Medline]
Sakmann B, Neher E (1995) Single-channel recordings. New York: Plenum Press.
Santoro B, Tibbs GR (1999) The HCN gene family: molecular basis of the hyperpolarization-activated pacemaker channels. Ann N Y Acad Sci 868:741764.
Seifert G, Kuprijanova E, Zhou M, Steinhauser C (1999) Developmental changes in the expression of Shaker- and Shab-related K(+) channels in neurons of the rat trigeminal ganglion. Brain Res Mol Brain Res 74:5568.[ISI][Medline]
Shibata R, Nakahira K, Shibasaki K, Wakazono Y, Imoto K, Ikenaka K (2000) A-type K+ current mediated by the Kv4 channel regulates the generation of action potential in developing cerebellar granule cells. J Neurosci 20:41454155.
Song WJ, Tkatch T, Baranauskas G, Ichinohe N, Kitai ST, Surmeier DJ (1998) Somatodendritic depolarization-activated potassium currents in rat neostriatal cholinergic interneurons are predominantly of the A type and attributable to coexpression of Kv4.2 and Kv4.1 subunits. J Neurosci 18:31243137.
Stocker M, Pedarzani P (2000) Differential distribution of three Ca(2+)-activated K(+) channel subunits, SK1, SK2, and SK3, in the adult rat central nervous system. Mol Cell Neurosci 15:476493.[CrossRef][ISI][Medline]
Sucher NJ, Deitcher DL (1995) PCR and patch-clamp analysis of single neurons. Neuron 14:10951100.[ISI][Medline]
Surmeier DJ, Song WJ, Yan Z (1996) Coordinated expression of dopamine receptors in neostriatal medium spiny neurons. J Neurosci 16:65796591.
Talley E, Cribbs L, Lee J, Daud A, Perez-Reyes E, Bayliss D (1999) Differential distribution of three members of a gene family encoding low voltage-activated (T-type) calcium channels. J Neurosci 19:18951911.
Tanaka O, Sakagami H, Kondo H (1995) Localization of mRNAs of voltage-dependent Ca(2+)-channels: four subtypes of alpha 1- and beta-subunits in developing and mature rat brain. Brain Res Mol Brain Res 30:116.[CrossRef][ISI][Medline]
Tietjen I, Rihel J, Cao Y, Koentges G, Zakhary L, Dulac C (2003) Single-cell transcriptional analysis of neuronal progenitors. Neuron 38:161175.[ISI][Medline]
Toledo-Rodriguez M, Gupta A, Wang Y, Wu CZ, Markram H (2002) Neocortex, basic neuron types. In: The handbook of brain theory and neural networks (Arbib MA, ed.), pp. 719725. Cambridge, MA: MIT Press.
Vergara C, Latorre R, Marrion NV, Adelman JP (1998) Calcium-activated potassium channels. Curr Opin Neurobiol 8:321329.[CrossRef][ISI][Medline]
Wang F, Parcej DN, Dolly JO (1999) Alpha subunit compositions of Kv1.1-containing K channel subtypes fractionated from rat brain using dendrotoxins. Eur J Biochem 263:230237.
Wang Y, Gupta A, Toledo-Rodriguez M, Wu CZ, Markram H (2002) Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex. Cereb Cortex 12:395410.
Ward JHJ (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236244.[ISI]
Yan Z, Surmeier DJ (1996) Muscarinic (m2/m4) receptors reduce N- and P-type Ca2+ currents in rat neostriatal cholinergic interneurons through a fast, membrane-delimited, G-protein pathway. J Neurosci 16:25922604.[Abstract]