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

项目总结 该项目是一个跨越两所大学和多个科学学科的合作项目，以开发新的可扩展 人脑组织内细胞类型识别的三维分子成像和分析方法。我们会 重点研究嗅觉系统，包括嗅觉上皮(OE)和嗅球(OB)。 该系统是一个理想的受限人体模型系统，用于构建和测试新的可伸缩工具套件 人类大脑图谱的生成，因为嗅觉感觉神经元与球的连接是由口述的 通过嗅觉受体的表达。我们的长远目标是为市民提供新的“物理学第一”。 提高可伸缩性、精确度和3D测量以创建像元普查并创建第一个空间的方法 OE和OB之间的连接图。我们计划通过实现两个具体目标来实现我们的目标 并行的。在我们的第一个目标中，我们将放大高分辨率、高速的单物镜光片显微镜。 用于对人类嗅球中的蛋白质和RNA进行3D成像。使用线性分解，我们将成像高达8 蛋白质。使用通过流体交换的迭代放大的RNA-FISH标记，我们将最初成像130个RNA和 详细计划扩大RNA的数量。在我们的第二个目标中，我们将开发贝叶斯非参数图像 自我一致并同时确定与所有RNA相关的概率的分析框架 在存在可变的自发荧光和可变的读出效率的情况下的位置、数字和身份。 在贝叶斯范式下，我们提出了一种新的条形码荧光纠错方法 显著减少所需轮次的实验。我们将应用这些组合的基本原理 改进OB全切面地图以确定嗅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，两者 原位杂交和原位测序可能会报告大量的假阳性或假阴性。至 减少潜在的识别错误，我们将评估双色放大标记策略结合 基于对所有图像的自洽分析来分配概率的贝叶斯非参数分析 同时堆叠所有颜色，以避免根据本地身份评估得出结论 一个亮点。结合这些方法的进步，我们将生成3D空间置信度地图 嗅觉系统中细胞类型和个体嗅觉受体表达的间隔。

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.