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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结