Distinct Subtypes of Somatostatin-Containing Neocortical Interneurons Revealed in Transgenic Mice

Abstract

GABA-releasing inhibitory interneurons in the cerebral cortex can be classified by their neurochemical content, firing patterns, or axonal targets, to name the most common criteria, but whether classifications using different criteria converge on the same neuronal subtypes, and how many such subtypes exist, is a matter of much current interest and considerable debate. To address these issues, we generated transgenic mice expressing green fluorescent protein (GFP) under control of the GAD67 promoter. In two of these lines, named X94 and X98, GFP expression in the barrel cortex was restricted to subsets of somatostatin-containing (SOMϩ) GABAergic interneurons, similar to the previously reported "GIN" line (Oliva et al., 2000), but the laminar distributions of GFP-expressing (GFPϩ) cell bodies in the X94, X98, and GIN lines were distinct and nearly complementary. We compared neurochemical content and axonal distribution patterns of GFPϩ neurons among the three lines and analyzed in detail electrophysiological properties in a dataset of 150 neurons recorded in whole-cell, current-clamp mode. By all criteria, there was nearly perfect segregation of X94 and X98 GFPϩ neurons, whereas GIN GFPϩ neurons exhibited intermediate properties. In the X98 line, GFP expression was found in infragranular, calbindin-containing, layer 1-targeting ("Martinotti") cells that had a propensity to fire low-threshold calcium spikes, whereas X94 GFPϩ cells were stuttering interneurons with quasi fast-spiking properties, residing in and targeting the thalamo-recipient neocortical layers. We conclude that much of the variability previously attributed to neocortical SOMϩ interneurons can be accounted for by their natural grouping into distinct subtypes.

Selective, State-Dependent Activation of Somatostatin-Expressing Inhibitory Interneurons in Mouse Neocortex

“…1C), as Ma et al (2006) also observed for GIN cells. Other cells either did not have an axon projecting to layer I or the axonal destination could not be determined due to inadequate filling or high background staining.…”

“…Based on the fact that many GIN cells have dense axonal projections to layer I (Halabisky et al 2006;Ma et al 2006; this study), they can generally be categorized morphologically as Martinotti cells, although we did find some GIN cells that did not project to layer I. In addition, these cells share multiple characteristics with other described Martinotti cells, including being somatostatin expressing (Halabisky et al 2006;Ma et al 2006;Oliva Jr et al 2000;Wang et al 2004;Xu et al 2006), receiving facilitating input from pyramidal cells (Silberberg and Markram 2007), and showing spike-rate adaptation (Silberberg and Markram 2007;Wang et al 2004).…”

“…Other cells either did not have an axon projecting to layer I or the axonal destination could not be determined due to inadequate filling or high background staining. Such morphology is associated with Martinotti cells in the neocortex, which have been shown to be somatostatin positive (Ma et al 2006), show spike-rate adaptation (Ma et al 2006), and have been implicated in robust intracortical inhibition (Kapfer et al 2007;Silberberg and Markram 2007). The 16 layer 1-projecting neurons in our sample included both multipolar and bitufted cells, but there was no correlation between somadendritic patterns and axonal projections to layer 1.…”

“…In addition, these cells share multiple characteristics with other described Martinotti cells, including being somatostatin expressing (Halabisky et al 2006;Ma et al 2006;Oliva Jr et al 2000;Wang et al 2004;Xu et al 2006), receiving facilitating input from pyramidal cells (Silberberg and Markram 2007), and showing spike-rate adaptation (Silberberg and Markram 2007;Wang et al 2004). The cells discussed here were often electrically coupled with one another via gap junctions.…”

The Cerebellar Interpositus Nucleus and the Dynamic Control of Learned Motor Responses

“…It is expected that, during the acquisition of a new motor ability, both kinetic commands and kinematic parameters should present an intrinsic homogeneity. Cluster analysis is a powerful tool for the analysis of such data (Taganova and Silakov, 1980;Banfield and Raftery, 1993;Hair et al, 1998;Ma et al, 2006). This algorithm for the processing of physiological recordings provides more homogeneity in the experimental database for the subsequent dynamic correlation analysis.…”