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

用于细胞普查生成的可扩展 3D 分子成像和数据分析

• 批准号: 10369885
• 负责人: Steve Presse
• 金额: $ 223.43万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10369885
• 关键词: 3-Dimensional; Address; Atlases; BRAIN initiative; Bar Codes; Biochemical; Biochemistry; Biological Models; Biology; Brain; Brain region; Cells; Censuses; Collaborations; Color; Communities; Complex; Confidence Intervals; Data; Data Analyses; Data Set; Development; Discipline; Disease; Dyes; Environment; Epithelial; Fluorescence; Generations; Goals; Health; Human; Human Genome; Human body; Image; Image Analysis; Imaging Device; In Situ Hybridization; Individual; Label; Learning; Light; Liquid substance; Location; Maps; Measurement; Messenger RNA; Methodology; Methods; Microscopy; Molecular; Molecular Analysis; Morphology; Odors; Olfactory Epithelium; Olfactory Pathways; Olfactory Receptor Cells; Olfactory Receptor Neurons; Optics; Pattern; Physics; Probability; Problem Solving; Proteins; RNA; RNA amplification; Receptor Gene; Reporting; Resolution; Rodent; Sensory; Speed; Spottings; System; Testing; Three-Dimensional Imaging; Three-dimensional analysis; Training; Universities; Work; base; brain tissue; cell type; connectome; deep learning; experimental study; human model; imaging approach; improved; in situ sequencing; innovation; insight; molecular imaging; molecular scale; novel strategies; olfactory bulb; olfactory bulb glomeruli; olfactory receptor; olfactory sensory neurons; receptor expression; scale up; single molecule; spatiotemporal; tool; transcriptome; transcriptomics

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.

项目摘要 该项目是两所大学的合作和多个科学学科，以开发新的可扩展性 3D分子成像和分析方法，用于人脑组织内的细胞类型鉴定。我们将 将我们的精力集中在嗅觉系统上，完成嗅觉上皮（OE）和嗅球（OB）。 该系统是一个理想限制的人类模型系统，用于构建和测试一套新的可扩展工具 人脑大脑的产生是因为嗅觉感觉神经元与灯泡的连接组被指示 通过嗅觉受体表达。我们的长期目标是为社区提供新的“物理优先” 改善细胞普查创建细胞普查的可伸缩性，严格和3D测量的方法并创建第一个空间 OE和OB之间的连接图。我们计划在两个特定目标中实现我们的目标 并联。在我们的第一个目标中，我们将扩展高分辨率高速单一目标灯页显微镜 用于人类嗅球中蛋白质和RNA的3D成像。使用线性Umixing，我们最多将映像8 蛋白质。使用流体交换使用迭代放大的RNA-FISH标记，我们最初将图像130 RNA和 详细说明扩大RNA数量的计划。在我们的第二个目标中，我们将开发贝叶斯非参数图像 分析框架自兼而有，轻松地确定与所有RNA相关的概率 在存在可变自动荧光和可变读数效率的情况下，位置，数字和身份。 在贝叶斯范式中，我们提出了一种新的透明荧光误差校正方法 显着减少所需的回合数的实验。我们将应用这些合并的基本 改进映射OB的完整部分，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。由于嗅觉受体具有较高的同源性和稀疏的表达，因此 原位杂交和原位测序可能会报告大量的假阳性或假否定性。到 减轻潜在识别错误​​，我们将评估双色放大标签策略与 贝叶斯非参数分析，该分析基于对所有图像的自洽分析而分配了可能性 轻松地堆叠所有颜色，以避免根据身份的本地评估得出结论 一个亮点。结合这些方法论的进步，我们将生成3D空间图的信心 细胞类型和各个嗅觉受体在嗅觉系统中的表达的间隔。