Scalable 3D molecular imaging and data analysis for cell census generation

项目摘要 该项目是两所大学和多个科学学科的合作，以开发新的可扩展的 用于人脑组织细胞类型识别的3D分子成像和分析方法。我们将 将我们的努力集中在嗅觉系统，包括嗅上皮（OE）和嗅球（OB）。 该系统是一个理想的受限人体模型系统，用于构建和测试一套新的可扩展工具， 人脑图谱的生成，因为嗅觉感觉神经元与灯泡的连接组学是由 通过嗅觉受体的表达。我们的长期目标是为社区提供新的“物理优先” 这些方法提高了小区普查创建的可扩展性、精确性和3D测量，并创建了第一个空间 OE和OB之间的连接图。我们计划通过两个具体目标实现我们的目标， 并联在我们的第一个目标，我们将扩大高分辨率，高速单物镜光片显微镜 用于人类嗅球中蛋白质和RNA的3D成像。使用线性解混，我们将图像多达8 proteins.使用通过流体交换的迭代扩增RNA-FISH标记，我们将最初对130个RNA进行成像， 详细计划扩大RNA的数量。在我们的第二个目标，我们将开发一个贝叶斯非参数图像 分析框架，自洽地同时确定与所有RNA相关的概率 在存在可变自发荧光和可变读出效率的情况下的位置、数目和身份。 在贝叶斯范式内，我们提出了一种新的条形码荧光纠错方法 实验，大大减少了所需的回合数。我们将把这些基本原理 改进OB的全切片图，以确定嗅觉感觉神经元对肾小球的靶向 表达特定的嗅觉受体。由于嗅觉受体具有高度同源性和稀疏表达， 原位杂交和原位测序可能会报告大量的假阳性或假阴性。到 为了减少潜在的识别错误，我们将评估双色扩增标记策略， 贝叶斯非参数分析，根据所有图像的自洽分析分配概率 同时堆叠所有颜色，以避免基于对身份的本地评估得出结论 一个亮点。结合这些方法上的进步，我们将生成3D空间地图的信心， 细胞类型和单个嗅觉受体在整个嗅觉系统中的表达的时间间隔。

PROJECT SUMMARY This project is a collaboration across two universities and multiple scientific disciplines to develop new scalable 3D molecular imaging and analysis approaches for cell type identification within human brain tissue. We will focus our efforts on the olfactory system, comprising the olfactory epithelium (OE) and the olfactory bulb (OB). This system is an ideally confined human model system to build and test a new suite of scalable tools for the generation of a human brain atlas because the connectomics of olfactory sensory neurons to the bulb is dictated by olfactory receptor expression. Our long term goals are to provide the community with new “physics-first” methods that improve scalability, rigor, and 3D measurements for cell census creation and create the first spatial map of connections between the OE and OB. We plan to achieve our goals across two specific aims, carried out in parallel. In our first aim, we will scale up high-resolution, high-speed single objective light-sheet microscopy for 3D imaging of proteins and RNA in the human olfactory bulb. Using linear unmixing, we will image up to 8 proteins. Using iterative amplified RNA-FISH labeling by fluidic exchange, we will initially image 130 RNAs and detail plans to expand the number of RNA. In our second aim, we will develop a Bayesian nonparametric image analysis framework that self-consistently and simultaneously determines the probability associated with all RNA locations, numbers, and identities in the presence of variable autofluorescence and variable readout efficiency. Within the Bayesian paradigm, we propose a new approach to error correction in barcoded fluorescence experiments that significantly reduces the number of rounds required. We will apply these combined fundamental improvements to map full sections of the OB to determine the targeting of glomeruli by olfactory sensory neurons expressing specific olfactory receptors. As olfactory receptors have high homology and sparse expression, both in situ hybridization and in situ sequencing may report a high number of false positives or false negatives. To mitigate potential identification errors, we will evaluate dual-color amplified labeling strategies combined with Bayesian nonparametric analysis that assigns probabilities based on a self-consistent analysis of all image stacks across all colors simultaneously to avoid drawing conclusions based on local assessments of the identity of a bright spot. Combining these methodological advancements, we will generate 3D spatial maps of confidence intervals for cell types and individual olfactory receptors expression across the olfactory system.