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 随着大脑的高度发达，对大脑复杂结构的探索变得更具挑战性。 近年来，脑细胞中分子异质性的程度已经变得明显。绘制大脑在 细节将需要将大量的分子信息结合到高分辨率成像中。电流 成像方法受到可区分的检测通道的数量的限制，因此更大程度的 多路复用需要剥离和再施加探针的重复循环。这些方法会降低组织完整性 并且不能解决多路复用的另一个主要挑战- 蛋白质和RNA的标记方法和需要妥协，同时检测。我们提出 一种新的成像方法，连续切片平行免疫/荧光原位杂交（SpiFISH）， 其核心策略是将标本物理细分为比A小两个数量级的部分， 神经元细胞体每个切片都被视为一个单独的样本进行标记和成像，因此数百个 可以在给定神经元上并行执行离散标记实验。该方法是基于 切片，但与现有的电子显微镜（EM）和阵列 在断层扫描中，SpiFISH不使用EM包埋树脂。无树脂干扰，免疫标记灵敏 和RNA检测是可能的。每个切片都被单独标记和成像，因此任何给定的细胞都可以被 用许多不同的抗体和RNA探针在针对每种抗体和RNA探针优化的条件下标记。区段 存储稳定，因此可以跨时间甚至跨实验室建立大型数据集。该方法允许 技术和标记的多路复用，因此相同的样品可以用于多个成像， 染色平台。该项目的目标是开发强大的，可重复的协议和工作流程， 通过跨尺度的数据分析进行样品制备。这将包括整个啮齿动物的小样本 通过全自动化的数据收集和分析，实现全手动的智能和简化方法。