Project Summary It has long been observed that certain diseases may be diagnosed by smell alone. There is mounting evidence supporting these observations, showing that the metabolic changes brought about by disease, expressed in biospecimens such as sweat, breath, urine and blood, can be accurately identified through olfaction. This is the case not only for metabolic diseases such as diabetes, but most notably cancer, Alzheimer’s, Parkinson’s, and many types of infection, including COVID-19. But it remains a mystery how olfactory systems achieve this ability, especially when faced with the stark levels of variance in healthy populations, and the challenge of identifying a complex odor object against irrelevant background components. This project will investigate the neural mechanisms of odor-based disease diagnostics in the olfactory system of the mouse. Initial experiments will image the responses of olfactory sensory neurons in the olfactory bulb of the awake mouse. Using mouse models of disease, we will collect urine samples corresponding to both disease and healthy states, with controlled between-sample variability. We will image glomeruli, with each glomerulus aggregating the axons of sensory neurons expressing the same class of receptor. Linear and nonlinear dimensionality reduction methods will be developed to analyze the complex spatiotemporal patterns of glomerular activity elicited by disease and healthy control samples. From this analysis, the key features of neural activity that underpin disease detection will be identified, and related to specific glomeruli. Glomeruli of interest will then be used to isolate the volatile organic compounds of relevance, through gas chromatography-olfactometry in parallel with gas-chromatography/mass-spectrometry. Additionally, quantitative methods will be developed for the alignment of neural spaces across multiple mice, using a minimal number of odors. This will render odor features translatable across animals, allowing for the decoding of disease in mice without extensive training data collection. The developed experimental and computational pipeline will be then applied to detect and decipher odorprints of multiple human diseases. Understanding how olfactory systems detect disease has the potential to revolutionize medical diagnostics, particularly with respect to early and noninvasive screening. But it will also constitute progress in ‘cracking the olfactory code’, with our understanding of olfaction currently lagging behind vision and audition. From an evolutionary perspective, the natural stimuli of olfaction were the metabolic states of food, mates, peers, and predators, rarely the monomolecular odorants commonly used in olfaction research today. While this project has an applied aim of medical diagnostics, the path to that aim proceeds via a deep understanding of some of the fundamental, yet still mysterious, principles of olfaction.