The activity of neurons in the anterolateral cell group of the bed nucleus of the stria terminalis (BNSTALG) plays a critical role in anxiety- and stress-related behaviors. Histochemical studies have suggested that multiple distinct neuronal phenotypes exist in the BNSTALG. Consistent with this observation, the physiological properties of BNSTALG neurons are also heterogeneous, and three distinct cell types can be defined (Type I–III) based primarily on their expression of four key membrane currents, namely Ih, IA, IT, and IK(IR). Significantly, all four channels are multimeric proteins and can comprise of more than one pore-forming α subunit. Hence, differential expression of α subunits may further diversify the neuronal population. However, nothing is known about the relative expression of these ion channel α subunits in BNSTALG neurons.
We have addressed this lacuna by combining whole cell patch clamp recording together with single cell reverse transcriptase polymerase chain reaction (scRT-PCR) to assess the mRNA transcript expression for each of the subunits for the four key ion channels in Type I-III neurons of the BNSTALG. Here, cytosolic mRNA from single neurons was probed for the expression of transcripts for each of the α subunits of Ih (HCN1- HCN4), IT (Cav3.1- Cav3.3), IA (Kv1.4, Kv3.4, Kv4.1- Kv 4.3) and IK(IR) (Kir2.1-Kir2.4).
An unbiased hierarchical cluster analysis followed by discriminant function analysis revealed that a positive correlation exists between the physiological and genetic phenotype of BNSTALG neurons. Thus, the analysis segregated BNSTALG neurons into 3 distinct groups, based on their α subunit mRNA expression profile, which positively correlated with our existing electrophysiological classification (Type I–III). Furthermore, analysis of mRNA transcript expression in Type I –Type III neurons suggested that, whereas Type I and III neurons appear to represent genetically homologous cell populations, Type II neurons may be further subdivided into three genetically distinct subgroups. These data not only validate our original classification scheme, but further refine the classification at the molecular level, and thus identifies novel targets for potential disruption and/or pharmacotherapeutic intervention in stress-related anxiety-like behaviors.