In vision, two mixtures, each containing an independent set of many different wavelengths, may produce a common color percept termed "white." In audition, two mixtures, each containing an independent set of many different frequencies, may produce a common perceptual hum termed "white noise." Visual and auditory whites emerge upon two conditions: when the mixture components span stimulus space, and when they are of equal intensity. We hypothesized that if we apply these same conditions to odorant mixtures, "whiteness" may emerge in olfaction as well. We selected 86 molecules that span olfactory stimulus space and individually diluted them to a point of about equal intensity. We then prepared various odorant mixtures, each containing various numbers of molecular components, and asked human participants to rate the perceptual similarity of such mixture pairs. We found that as we increased the number of nonoverlapping, equal-intensity components in odorant mixtures, the mixtures became more similar to each other, despite not having a single component in common. With ∼30 components, most mixtures smelled alike. After participants were acquainted with a novel, arbitrarily named mixture of ∼30 equal-intensity components, they later applied this name more readily to other novel mixtures of ∼30 equal-intensity components spanning stimulus space, but not to mixtures containing fewer components or to mixtures that did not span stimulus space. We conclude that a common olfactory percept, "olfactory white," is associated with mixtures of ∼30 or more equal-intensity components that span stimulus space, implying that olfactory representations are of features of molecules rather than of molecular identity.odor | sensory perception | smell

Odor identity is coded in spatiotemporal patterns of neural activity in the olfactory bulb. Here we asked whether meaningful olfactory information could also be read from the global olfactory neural population response. We applied standard statistical methods of dimensionality-reduction to neural activity from 12 previously published studies using seven different species. Four studies reported olfactory receptor activity, seven reported glomerulus activity, and one reported the activity of projection-neurons. We found two linear axes of neural population activity that accounted for more than half of the variance in neural response across species. The first axis was correlated with the total sum of odor-induced neural activity, and reflected the behavior of approach or withdrawal in animals, and odorant pleasantness in humans. The second and orthogonal axis reflected odorant toxicity across species. We conclude that in parallel with spatiotemporal pattern coding, the olfactory system can use simple global computations to read vital olfactory information from the neural population response.

To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction.

Procollagen C-proteinase enhancer-1 (PCPE-1) is an extracellular matrix (ECM) glycoprotein that can stimulate procollagen processing by procollagen C-proteinases (PCPs) such as bone morphogenetic protein-1 (BMP-1). The PCPs can process additional extracellular protein precursors and play fundamental roles in developmental processes and assembly of the ECM. The stimulatory activity of PCPE-1 is restricted to the processing of fibrillar procollagens, suggesting PCPE-1 is a specific regulator of collagen deposition. PCPE-1 consists of two CUB domains that bind to the procollagen C-propeptides and are required for PCP enhancing activity, and one NTR domain that binds heparin. To understand the biological role of the NTR domain, we performed surface plasmon resonance (SPR) binding assays, cell attachment assays as well as immunofluorescence and activity assays, all indicating that the NTR domain can mediate PCPE-1 binding to cell surface heparan sulfate proteoglycans (HSPGs). The SPR data revealed binding affinities to heparin/HSPGs in the high nanomolar range and dependence on calcium. Both 3T3 mouse fibroblasts and human embryonic kidney cells (HEK-293) attached to PCPE-1, an interaction that was inhibited by heparin. Cell attachment was also inhibited by an NTR-specific antibody and the NTR fragment. Immunofluorescence analysis revealed that PCPE-Flag binds to mouse fibroblasts and heparin competes for this binding. Cell-associated PCPE-Flag stimulated procollagen processing by BMP-1 several fold. Our data suggest that through interaction with cell surface HSPGs, the NTR domain can anchor PCPE-1 to the cell membrane, permitting pericellular enhancement of PCP activity. This points to the cell surface as a physiological site of PCPE-1 action.