Odor representations in Drosophila receptor neurons analyzed by in vivo calcium imaging

Abstract

Animals use olfactory receptor neurons (ORNs) to detect odors and to get a representation of the olfactory environment around them. Many of these odors convey important information for an animals daily life and thus it is essential to reliably detect them. The olfactory environment consists of myriads of different chemicals that appear in different compositions and concentrations, yet olfactory systems of varying complexity are able extract the relevant information from it. How olfactory systems are able to encode a vast number of odors with a much smaller number of ORN types is one big question in olfactory research. A given ORN is usually activated not only by one, but by many odors. The response profile of an ORN is defined by one or a few odorant receptors (OR) it expresses. The response profiles of ORNs overlap. Thus, when stimulated with an odor usually a set of ORNs gets activated creating an odor specific ensemble response.One way to better understand how this ensemble coding works is to characterize the response profiles of individual ORNs in great detail. The ideal case would be to know the complete response profiles of all ORNs of an olfactory system and by this have access to the complete ensemble responses any odor would elicit. This knowledge about the responses produced by all possible odor–ORN combinations is the so called “complete olfactome” of a species. Of all species used in olfactory research, the best known olfactome is that of Drosophila melanogaster. In order to combine the information on the Drosophila olfactome that is scattered across different publications and not easily comparable as different studies use different recording approaches, we created the DoOR database described in Chapter 1. With DoOR it is possible to integrate all the heterogeneous response data available into one consensus model response that can then be used for further analysis or visualized in various ways. The DoOR web interface provides an easy access to the Drosophila olfactome e.g. as tool for designing physiological or behavioral experiments. In addition, the complete database including the model response as well as the raw data and all the functions is freely available and may be a useful source for computational approaches investigating olfactory coding.The Drosophila olfactome is the completest existing olfactome but it still has many gaps. In Chapter 2 we characterized the response profiles of eight ORNs. One of the ORNs investigated in this Chapter was completely uncharacterized before, here we describe a response profile consisting of 106 odor responses. Another one was published as being responsive to a single odorant alone, we found that many other odorants also elicit distinguished odor responses from these neurons. For six other ORNs we extended previously existing response profiles.The ensemble activation is only one dimension of the olfactory code. Information about odor identity are also encoded in the temporal dynamics of ORNs responses. In Chapter 3 we therefore investigated the response dynamics elicited by the > 800 odorant–ORN combinations from Chapter 2 in detail. We found that response dynamics were odorant–ORN combination specific and varied largely between odorant–ORN pairs. We analyzed several features of the response dynamics and found them to be differential distributed across ORNs. We found one feature of response dynamics to be stable across concentrations, thus it could contribute to concentration invariant odor coding. We show that odor information is still present in the ORN ensemble response after stimulus offset, mainly due to some odorant–ORN combinations that lead to especially strong and long lasting responses.While Chapters 1–3 deal with ORN responses towards monomolecular odorants, we investigated responses towards mixtures in Chapters 4 and 5. Almost all natural odors occur as mixtures. Each component of a mixture usually elicits a response pattern across ORNs and when appearing together these responses might overlap and lead to mixture interactions. In Chapter 4 we created a complex mixture of 15 components in different concentrations related to banana scent. In this mixture we found one substance, isopentyl acetate, to dominate the mixture responses elicited in Or22a ORNs. Isopentyl acetate suppressed the responses of stronger, less concentrated and weaker, higher concentrated ligands in the mixture, leading to hypoadditive mixture interactions. Further analysis revealed that the mechanism of syntopic interaction (i.e. the competition of ligands for a common receptor binding site) fully accounted for the effects we found. We discuss that syntopic interaction could account for a majority of suppressive mixture interactions found in the periphery of olfactory systems and that it might play a role in gain control and concentration invariant coding.In Chaper 5 we expanded the mixture experiments to more mixture and ORNs. We found that strong mixture interactions, like suppression and synergism were rare, most of the 20 mixtures elicited additive or hypoadditive reponses from the five ORNs tested. With a principal component analysis we calculated trajectories of ORN ensemble responses to mixtures and their components in a five-dimensional receptor space. We found that many mixture trajectories were dominated by one component, others covered distinct areas of receptor space. We discuss that these differences in physiological odor similarities might be a neural correlate of differences in perceived odor similarities.