Ideas Lab Collaborative Research: Using Natural Odor Stimuli to Crack the Olfactory Code

该项目是在美国国家科学基金会“破解嗅觉密码”创意实验室期间开发的，由化学部的生命过程化学项目、数学科学部的数学生物学项目、物理部的生命系统物理学项目、综合有机体系统部的神经系统集群、生物基础设施部和新兴前沿部共同资助。嗅觉对于维持人类的生活质量至关重要，其衰退可能是神经退行性疾病的重要先兆。此外，由于除灵长类动物外几乎所有动物都依赖嗅觉来实现大多数生存功能，因此了解化学传感具有巨大的实用价值，例如在控制农业害虫或训练动物检测与炸弹、毒品和癌症检测相关的气味方面。尽管嗅觉很重要，但对嗅觉的理解远远落后于其他感官，部分原因是缺乏对气味的物理空间的理解。通过对视觉和听觉刺激的物理维度的研究，极大地促进了对视觉和听觉神经基础的理解。因此，对气味空间的类似深入研究——自然气味如何发生以及必须检测它们的背景——将揭示大脑中气味神经表征的新的丰富性。果蝇和蜜蜂等昆虫是这项研究的绝佳模型，因为它们的中枢神经系统易于使用，在受控实验室条件下易于使用，而且昆虫和哺乳动物大脑处理气味的功能相似。这项研究将描述花朵和水果的气味如何影响蜜蜂（食物）和果蝇（食物和产卵场所）的行为价值。进一步监测大脑早期和后期处理中的神经活动，与计算模型相结合，将揭示比迄今为止描述的更丰富的神经表征。这种新的理解将对理解健康的大脑如何编码气味的感觉和记忆以及大脑在疾病条件下如何失效产生影响。它还将对理解如何将嗅觉内置到工程设备中产生影响。最后，这两种昆虫对农业作物授粉（蜜蜂）和损害水果（果蝇）也具有重要的经济意义。 PI 将与本科生、研究生和博士后一起教学和工作，特别是招收来自科学领域代表性不足的群体的学生。这项研究将定量描述多成分自然气味场景的现实世界统计数据，并研究它们如何驱动多个大脑区域的行为和处理。重点将放在蜜蜂以及果蝇成虫和幼虫作为模型上，其中将有可能描述与多种行为输出相关的与行为学相关的自然气味库。这项工作将从定量描述特定行为学背景下自然气味场景的详细统计特性开始。这将建立在有关昆虫和哺乳动物天然气味的丰富文献的基础上。来自每种昆虫自然环境的天然植物和水果气味样本将被收集并进行化学分析。基于稀疏编码的非线性降维技术和方法将确定对行为决策最重要的气味空间的维度。这种对感觉输入的定量解构在嗅觉神经科学中是前所未有的，并且应该允许 PI 首次有效且全面地驱动嗅觉回路。假设是与动物行为最相关的刺激维度将被嗅觉系统最有效地提取。合成气味混合物将经过专门构建，以沿着相关的感官维度变化，以探测嗅觉系统中的神经代码和适应性行为。与视觉系统的研究一样，使用考虑到自然气味统计的统计方法对此类诱发的神经反应进行分析将揭示以前无法实现的新颖的嗅觉计算和行为。该项目将生成对理论生物学和数学、工程和生物学领域的科学家可立即使用且具有重要性的数据集。

This project was developed during a NSF Ideas Lab on "Cracking the Olfactory Code" and is jointly funded by the Chemistry of Life Processes program in the Chemistry Division, the Mathematical Biology program in the Division of Mathematical Sciences, the Physics of Living Systems program in the Physics Division, the Neural Systems Cluster in the Division of Integrative Organismal Systems, the Division of Biological Infrastructure, and the Division of Emerging Frontiers. The sense of smell is essential for maintaining quality of life in humans, and its decline can be an important harbinger of neurodegenerative disease. Moreover, since nearly all animals aside from primates rely on olfaction for most survival functions, understanding chemical sensing has immense practical value, for example, in the control of agricultural pests or in training animals to detect odors relevant for bomb, drug and cancer detection. In spite of its importance, the understanding of olfaction lags far behind the other senses, which is in part due to the lack of understanding of the physical space of odors. The understanding of the neural bases of vision and audition were greatly advanced by investigations of the physical dimensions of visual and auditory stimuli. It is therefore likely that a similar in-depth investigation of odor space - how natural odors occur and the backgrounds against which they must be detected - will reveal a new depth of richness of neural representations of odors in the brain. Insects such as the fruit fly and honey bee are excellent models for this research because of the accessibility of their central nervous systems, because of their ease of use under controlled laboratory conditions, and because of the functional similarity of how odors are processed in insect and mammalian brains. This research will characterize how odor flowers and fruits with respect to behavioral value for honey bees (food) and fruit flies (food and egg laying sites). Further monitoring of neural activity in early and later stage processing in the brain, when combined with computational modeling, will reveal significantly richer neural representations than have heretofore been described. This new understanding stands to have an impact on understanding how healthy brains encode sensations and memories of odors and how brains fail under disease conditions. It will also have an impact on understanding how the sense of smell may be built into engineered devices. Finally, both insects are also of economic importance to agriculture for crop pollination (honey bees) and damage to fruit (fruit flies). The PIs will teach and work with undergraduate, graduate and postdoctoral students and especially recruit students from underrepresented groups in science. This research will quantitatively characterize the real-world statistics of multi-component natural odor scenes and investigate how they drive behavior and processing in several brain regions. The focus will be on honey bee as well as fruit fly adults and larva as models, where it will be possible to characterize a library of ethologically relevant natural odors associated with a diversity of behavioral outputs. The work will begin by quantitatively characterizing the detailed statistical properties of natural odor scenes in defined ethological contexts. This will build on the rich literature on identified natural odors in insects and mammals. Naturally occurring plant and fruit odor samples from the natural environments of each insect will be collected and chemically analyzed. Nonlinear dimensionality reduction techniques and approaches based on sparse coding will determine the dimensions of odor space that are most salient for behavioral decisions. Such a quantitative deconstruction of the sensory input would be unprecedented in olfactory neuroscience, and should allow the PIs to effectively and comprehensively drive olfactory circuits for the first time. The hypothesis is that the stimulus dimensions that are most behaviorally relevant to the animal will be most efficiently extracted by the olfactory system. Synthetic odor blends will be specially constructed to vary along relevant sensory dimensions, to probe neural codes and adaptive behaviors in the olfactory system. As in research on the visual system, analysis of such evoked neural responses using statistical methods that take into account natural odor statistics will reveal novel olfactory computations and behaviors that have been previously inaccessible. The project will generate datasets of immediate use and importance to scientists in theoretical biology and mathematics, engineering and biology.