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Systematic Analysis of Transmitter Coexpression Reveals Organizing Principles of Local Interneuron Heterogeneity.

eNeuro | 2018

Broad neuronal classes are surprisingly heterogeneous across many parameters, and subclasses often exhibit partially overlapping traits including transmitter coexpression. However, the extent to which transmitter coexpression occurs in predictable, consistent patterns is unknown. Here, we demonstrate that pairwise coexpression of GABA and multiple neuropeptide families by olfactory local interneurons (LNs) of the moth Manduca sexta is highly heterogeneous, with a single LN capable of expressing neuropeptides from at least four peptide families and few instances in which neuropeptides are consistently coexpressed. Using computational modeling, we demonstrate that observed coexpression patterns cannot be explained by independent probabilities of expression of each neuropeptide. Our analyses point to three organizing principles that, once taken into consideration, allow replication of overall coexpression structure: (1) peptidergic neurons are highly likely to coexpress GABA; (2) expression probability of allatotropin depends on myoinhibitory peptide expression; and (3) the all-or-none coexpression patterns of tachykinin neurons with several other neuropeptides. For other peptide pairs, the presence of one peptide was not predictive of the presence of the other, and coexpression probability could be replicated by independent probabilities. The stochastic nature of these coexpression patterns highlights the heterogeneity of transmitter content among LNs and argues against clear-cut definition of subpopulation types based on the presence of single neuropeptides. Furthermore, the receptors for all neuropeptides and GABA were expressed within each population of principal neuron type in the antennal lobe (AL). Thus, activation of any given LN results in a dynamic cocktail of modulators that have the potential to influence every level of olfactory processing within the AL.

Pubmed ID: 30294668 RIS Download

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