By Joseph Zak

Imagine the aroma of your favorite blend of coffee when you wake up in the morning. The smell is intricate, much more than simply “coffee”. Perhaps you identify subtle olfactory notes of cocoa, nuttiness, and a hint of vanilla. Together, the interactions between these notes transform the experience of your morning cup into something beyond the sum of its parts.

Within your nose, the olfactory notes of coffee each bind to, and activate, specialized sensory neurons that recognize the molecular shape of an odor molecule. However, different odor molecules, or coffee notes, may compete for the same receptor neurons, thereby complicating the interpretation of sensory neuron activity by the brain.

In a collaborative study between the Vergassola Lab at the Ecole Normale Supérieure in Paris and the Murthy Lab at Harvard, we sought to answer the question: How are blends of odors both detected and encoded by olfactory receptor neurons? We hypothesized that through competition between odors for the same receptor types, mixtures of odors will generate neural representations in sensory neurons that are distinct from the addition of neural representations associated with each of the mixture’s components alone.

To answer this question, we developed a method that allowed us to directly measure the neural activity of olfactory receptor neurons in the nose of live mice using genetically-encoded calcium indicators. We chose a panel of 16 monomolecular odors that each had unique chemical properties and delivered them to freely-breathing mice. While each odor alone activated an ensemble of sensory neurons, most sensory neurons were activated by more than one odor in our panel.

We then mimicked natural odors, like coffee, by constructing odor blends from our panel. These blends varied in complexity. Some were simple, containing only one or two unique odors, while others were intricate, containing up 12 different odors. By using the sensory neuron responses to individual odors, we could make predictions about how mixtures containing any of the individual odors might be represented.

A key finding from our experiments was that the sensory neuron mixture responses were far below the linear prediction from the summed responses to individual odors. This observation is consistent with the idea that odors compete for the same receptors through a process called antagonism. Further analysis of our data revealed that the prevalence and magnitude of these antagonistic interactions between odors is related to the complexity of an odor mixture – more complex mixtures typically led to weaker than predicted responses.

While it may seem intuitive that coffee has a complex aroma that is defined by the subtle olfactory notes, these experiments are the first time that such interactions between odor molecules have been measured in live animals within the context of their natural respiration dynamics. These studies also provide new evidence that antagonistic interactions between odors reformat how odor mixtures or blends are encoded by sensory neurons. Our studies revealed that, while the aroma of coffee is perceived to exceed the sum of its parts, at the level of peripheral stimulus encoding the neural representation of coffee is in fact less than the sum of its parts.

Joseph Zak is a Postdoctoral Fellow in the lab of Venkatesh Murthy in the Department of Molecular & Cellular Biology at Harvard University.

Learn more in the original research article:
Zak JD, Reddy G, Vergassola M, Murthy VN. Antagonistic odor interactions in olfactory sensory neurons are widespread in freely breathing mice. Nat Commun. 2020;11(1):3350. Published 2020 Jul 3. doi:10.1038/s41467-020-17124-5