A versatile approach for highly multiplexed, high-resolution imaging of endogenous molecules

一种对内源性分子进行高度多重、高分辨率成像的通用方法

• 批准号: 10505946
• 负责人: LINNAEA E OSTROFF
• 金额: $ 224.25万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10505946
• 关键词: 3-Dimensional; Antibodies; Array tomography; Biological Models; Brain; Brain Mapping; Cells; Complex; Data; Data Analyses; Data Collection; Data Set; Detection; Devices; Electron Microscopy; Ensure; Fluorescent in Situ Hybridization; Gene Expression; Goals; Heterogeneity; Image; Image Analysis; Imaging Device; Immunoelectron Microscopy; Impairment; Individual; Label; Laboratories; Manuals; Maps; Methods; Microscopy; Molecular; Neurons; Neurosciences; Pattern; Periodicity; Plant Resins; Preparation; Procedures; Process; Proteins; Protocols documentation; RNA; RNA Probes; Reagent; Reproducibility; Research; Resolution; Rodent; Sampling; Software Tools; Specimen; Stains; Structure; System; Techniques; Time; Tissue Sample; Tissues; Training; Validation; artificial intelligence algorithm; automated algorithm; base; brain cell; brain tissue; cost; design; experimental study; high resolution imaging; image registration; imaging approach; imaging modality; innovation; instrument; large datasets; microscopic imaging; molecular marker; multiplexed imaging; neuronal cell body; next generation; novel; novel strategies; routine imaging; tool; transcriptomics; user friendly software

Project Summary The quest to understand the brain’s complex structure has become more challenging as the high degree of molecular heterogeneity among brain cells has become evident in recent years. Mapping the brain in detail will require incorporating large amounts of molecular information into high-resolution imaging. Current imaging methods are limited by the number of distinguishable detection channels, so greater degrees of multiplexing entail repeated cycles of stripping and reapplying probes. These methods degrade tissue integrity and impair sensitivity, and do not address the other major challenge of multiplexing- incompatibility between protein and RNA labeling methods and the need to compromise both for simultaneous detection. We propose a novel imaging approach, Serial-section parallel immuno/ Fluorescence In Situ Hybridization (SpiFISH), whose core strategy is to physically subdivide specimens into sections two orders of magnitude smaller than a neuronal cell body. Each section is treated as a separate sample for labeling and imaging, so hundreds of discrete labeling experiments can be performed in parallel on a given neuron. The method is based on ultrathin sectioning, but unlike existing ultrathin sectioning methods such as electron microscopy (EM) and array tomography, SpiFISH does not use EM embedding resins. Without resin interfering, sensitive immunolabeling and RNA detection are possible. Each section is labeled and imaged separately, so that any given cell can be labeled with many different antibodies and RNA probes under conditions optimized for each. Sections are shelf-stable, so large datasets can be built up across time and even across laboratories. The method allows multiplexing of techniques as well as labels, so the same sample can be used with multiple imaging and staining platforms. The goal of this project is to develop robust, reproducible protocols and workflows from sample preparation through data analysis across scales. This will include small samples through whole rodent brains and streamlined methods for fully manual through fully automated data collection and analysis.

项目摘要 随着高位 近年来，脑细胞之间的分子异质性程度已成为证据。映射大脑 细节将需要将大量分子信息纳入高分辨率成像中。当前的 成像方法受可区分检测通道的数量的限制，因此更高的程度 多路复用需要重复的剥离和重新申请问题的循环。这些方法降解组织完整性 并损害敏感性，并且没有解决多重不相容性的另一个主要挑战 蛋白质和RNA标记方法，以及需要妥协的既有简单检测。我们建议 一种新型的成像方法，串行部分平行的免疫/荧光原位杂交（Spifish）， 其核心策略是将标本物理细胞尺寸分为两个比A小的数量级 神经元细胞体。每个部分都被视为用于标记和成像的单独样本，因此数百个 离散标记实验可以在给定的神经元上并行进行。该方法基于超薄 切片，但与现有的超薄分段方法（例如电子显微镜（EM）和阵列）不同 断层扫描，Spifish不使用EM嵌入树脂。没有树脂干扰，敏感的免疫标记 和RNA检测是可能的。每个部分都被标记和分别成像，因此任何给定的单元都可以是 在优化的条件下，标记了许多不同的抗体和RNA问题。部分是 货架稳定，因此可以在时间甚至实验室之间建立大型数据集。该方法允许 技术和标签的多路复用，因此可以将相同的样本用于多个成像和 染色平台。该项目的目的是从 通过跨量表进行数据分析样品制备。这将包括整个啮齿动物的小样品 通过完全自动化的数据收集和分析，用于完全手动的大脑和简化方法。