Molecular signature and target-specificity of inhibitory circuits formed by Martinotti cells in the mouse barrel cortex

Abstract

In the neocortex, fast synaptic inhibition orchestrates both spontaneous and sensory-evoked activity. GABAergic interneurons (INs) inhibit pyramidal neurons (PNs) directly, modulating their output activity and thus contributing to balance cortical networks. Moreover, several IN subtypes also inhibit other INs, forming specific disinhibitory circuits, which play crucial roles in several cognitive functions. Here, we studied a homogeneous subpopulation of somatostatin (SST)-positive INs, the Martinotti cells (MCs) in layer 2/3 of the mouse barrel cortex (both sexes). MCs are a prominent IN subclass inhibiting the distal portion of PN apical dendrites, thus controlling dendrite electrogenesis and synaptic integration. Yet, it is poorly understood whether MCs inhibit other elements of the cortical circuits, and the connectivity properties with non-PN targets are unknown. We found that MCs have a strong preference for PN dendrites, but they also considerably connect with parvalbumin (PV)-positive, vasoactive intestinal peptide (VIP)-expressing and layer 1 (L1) INs. Remarkably, GABAergic synapses from MCs exhibited clear cell-type-specific short-term plasticity. Moreover, whereas the biophysical properties of MC-PN synapses were consistent with distal dendritic inhibition, MC-IN synapses exhibited characteristics of fast perisomatic inhibition. Finally, MC-PN connections used α5-containing GABAARs, but this subunit was not expressed by the other INs targeted by MCs. We reveal a specialized connectivity blueprint of MCs within different elements of superficial cortical layers. In addition, our results identify α5-GABAARs as the molecular fingerprint of MC-PN dendritic inhibition. This is of critical importance, given the role of α5-GABAARs in cognitive performance and their involvement in several brain diseases.

Cell-type specific effects of somatostatin on corticocortical information processing in the anterior cingulate cortex

“…SOM weakens synaptic transmission between L1-INs and PCs. SOM-INs, on the other hand, inhibit PCs to a large extent, but not exclusively, via activation of GABA A Rs [43, 54, 57, 58] and we have shown here that SOM specifically weakens GABA B R-mediated transmission without having a significant impact on GABA A R-mediated transmission, i.e. it is likely that SOM does not affect the synaptic strength between SOM-INs and PCs.…”

Mechanisms regulating the frequency of inhibition-based gamma oscillations in primate prefrontal and parietal cortices

“…The strength of GABA A R-mediated inhibition onto L3 PNs could differ between DLPFC and PPC specifically for perisomatic GABA synapses, believed to play a crucial role in gamma oscillation production (Whittington et al, 2000; Buzsaki and Wang, 2012). GABA A R-IPSCs elicited by perisomatic synapses display faster slope of the rising phase and larger amplitudes than those elicited at more distal synapses (Xiang et al, 2002; Ali and Thomson, 2008; Donato et al, 2021). Thus, to compare synaptic currents likely originating from more proximal or more distal synapses, we sorted the GABA A R-sIPSCs using the slope of their rising phase and compared between DLPFC and PPC L3 PNs the GABA A R-sIPSC amplitudes across the range of rising phase slopes.…”

“…Separate subsets of IPSCs are selectively recruited by activation of interneurons of different subtypes. However, in the rodent neocortex, presynaptic interneurons of different subtype elicit in postsynaptic pyramidal cells GABA A R-IPSCs with similar decay kinetics (Xiang et al, 2002; Ali and Thomson, 2008; Galarreta et al, 2008; Donato et al, 2021; Campagnola et al, 2022). Likewise, different subtypes of presynaptic interneurons elicit GABA A R-IPSCs with similar decay kinetics in L3 PNs from monkey DLPFC (Gonzalez-Burgos et al, 2005).…”

“…However, in the rodent neocortex, presynaptic interneurons of different subtype elicit in postsynaptic pyramidal cells GABAAR-IPSCs with similar decay kinetics (Xiang et al, 2002;Ali and Thomson, 2008;Galarreta et al, 2008;Donato et al, 2021;Campagnola et al, 2022).…”