Widespread receptor-driven modulation in peripheral olfactory coding

Abstract

Olfactory responses to single odors have been well characterized but in reality we are continually presented with complex mixtures of odors. We performed high-throughput analysis of single-cell responses to odor blends using Swept Confocally Aligned Planar Excitation (SCAPE) microscopy of intact mouse olfactory epithelium, imaging ~10,000 olfactory sensory neurons in parallel. In large numbers of responding cells, mixtures of odors did not elicit a simple sum of the responses to individual components of the blend. Instead, many neurons exhibited either antagonism or enhancement of their response in the presence of another odor. All eight odors tested acted as both agonists and antagonists at different receptors. We propose that this peripheral modulation of responses increases the capacity of the olfactory system to distinguish complex odor mixtures.

Antagonistic odor interactions in olfactory sensory neurons are widespread in freely breathing mice

“…Since these interactions were observed at the earliest stages of odor encoding, in OSNs, the most likely source of mixture interactions is odorant receptors themselves. Evidence of antagonistic interactions in OSNs has been reported previously [16][17][18]54,55 , and we advance these prior observations by providing evidence for widespread occurrence of this phenomenon. Going beyond binary mixtures, we used odor blends of increasing complexity, up to 12 components, to ask how mixture responses compared with the expected linear summation of individual responses (below saturation).…”

“…Beyond our theoretical observations, there are other more recent experimental studies that report related effects [18][19][20] . Our work is unique in the following regard: (1) we report responses from a large set of OSNs in intact, freely breathing mice, responding to vapor phase odorants inhaled physiologically, (2) we report fast, real-time responses with a variety of odorants and using both OSN and glomerular imaging and (3) we report responses to mixtures of high complexity (up to 12 odors), well beyond just binary mixtures.…”

“…1b, c). Experimental work in vitro in OSNs has suggested the existence of nonlinear interactions, especially antagonism or partial antagonism [12][13][14][15][16][17][18][19][20] . However, the prevalence of these interactions, especially in living animals within the constraints of natural sniffing dynamics, has not been explored.…”

Mixture coding and segmentation in the anterior piriform cortex

“…Several mechanisms may contribute to the sublinear summation of odorant responses. These include interactions between odorants at the olfactory epithelium (Kurahashi et al, 1994;Duchamp-Viret et al, 2003;Oka et al, 2004;Grossman et al, 2008;Takeuchi et al, 2009;Reddy et al, 2018;Xu et al, 2019;Zak et al, 2019), processing in the olfactory bulb (Giraudet et al, 2002;Linster and Cleland, 2004;Tabor et al, 2004;Lin et al, 2006), and further normalization within piriform cortex, implemented by local inhibition. Inhibitory circuits within the piriform cortex have indeed been shown to play a major role in shaping piriform representations Isaacson, 2009, 2011;Franks et al, 2011;Bolding and Franks, 2018).…”

“…Several studies have reported sublinear mixture responses in olfactory cortex, thereby limiting the possible response space, however they have not provided models to predict mixture responses (Lei et al, 2006;Yoshida and Mori, 2007;Stettler and Axel, 2009). Sublinearlity of mixture responses is probably inherited to some extent from the olfactory epithelium (Kurahashi et al, 1994;Duchamp-Viret et al, 2003;Oka et al, 2004;Takeuchi et al, 2009;Xu et al, 2020;Zak et al, 2020), as well as from the olfactory bulb where cross odorant inhibition may contribute (Yokoi et al, 1995;Urban, 2002;Aungst et al, 2003;McGann et al, 2005;Arevian et al, 2008;Fantana et al, 2008). The piriform cortex integrates these non-linear odor representations from the olfactory bulb and utilizes local recurrent circuitry to generate representations that presumably support segmentation Isaacson, 2009, 2011;Franks et al, 2011;Miura et al, 2012;Suzuki and Bekkers, 2012;Roland et al, 2017;Bolding and Franks, 2018).…”