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A robust platform for multiplexed, subcellular proteomic imaging in human tissue

用于人体组织多重亚细胞蛋白质组成像的强大平台

基本信息

批准号：
9894465
负责人：
Robert michael Angelo
金额：
$ 59万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-11 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9894465
关键词：
Allergic Antibodies Archives Area Atlases Back Basic Science Biopsy Cell Nucleus Cells Clinical Cloud Computing Communities Data Data Set Decidua Development Disease Equipment Event Extramural Activities Feedback First Pregnancy Trimester Formalin Foundations Freeze Drying Funding Goals Granuloma Health Hippocampus (Brain)Histologic Hour Human Image Image Analysis Imaging Techniques Imaging technology Immune Immunosuppression Individual Institutes Ions Letters Link Machine Learning Medical center Messenger RNA Metals Morphology Multiplexed Ion Beam Imaging Noninfiltrating Intraductal Carcinoma Optics Organ Paraffin Embedding Pathology Peer Review Phenotype Population Proteins Proteomics Protocols documentation Pulmonary Tuberculosis Readiness Reagent Reproducibility Resolution Resources Sampling Scanning Signal Transduction Site Spectrometry, Mass, Secondary Ion Stains Standardization Structure Technology Three-Dimensional Image Time Tissue Embedding Tissues Translational Research United States National Institutes of Health Work cancer immunotherapy cohort computational platform computerized tools design graphical user interface high dimensionality high resolution imaging human imaging human tissue imaging Segmentation imaging modality imaging platform insight instrumentation ion source learning strategy multiplexed imaging new technology next generation programs protein expression quantitative imaging reagent standardization technology validation tool tumor microenvironment user-friendly virtual

项目摘要

Project Summary Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) uses secondary ion mass spectrometry and metal conjugated primary antibodies to simultaneously visualize dozens of proteins at subcellular resolution in a single tissue section. This technology is back compatible with archival formalin fixed, paraffin embedded tissue (FFPE) and has been used in peer-reviewed work to simultaneously visualize and quantify 36 proteins in retrospective human tissue cohorts. In line with the stated goals of the HuBMAP consortium to develop both “High-sensitivity, high-resolution imaging techniques that can rapidly provide spectral data over large areas of tissue” and “Quantitative imaging analysis tools, including automated 3D image segmentation, feature extraction, and image annotation,” the work outlined here will create a standardized, high throughput, and user-friendly workflow for using MIBI-TOF in basic and translational research to gain insight into how single cell phenotype and tissue structure are functionally-linked in health and disease. To achieve this, we will validate 100 FFPE antibodies and optimize ready-to-use multiplexed staining panels in lyophilized format that will permit storage for at least two years. Protocols and reagents for multiplexed signal amplification of protein and mRNA targets will be further refined, while next generation instrumentation will increase sample throughput to permit full tissue section imaging of up to 40 proteins in 1 hour. Standardized reagents and more robust instrumentation will be accompanied by an automated computational pipeline that utilizes a standard set of segmentation markers and machine learning to accurately identify nuclei and cell borders in any non-neural human tissue. This data will be used to cluster single cell events into functionally distinct populations according to morphology, protein expression, and histological distribution. The reagents and computational pipeline proposed here synergize with existing HuBMAP-funded platforms and could be readily generalized to virtually any high dimensional imaging modality. Thus, this work will not only provide a practical, back compatible imaging platform for high throughput multiplexed imaging, but will also accelerate development of other complimentary imaging technologies as well.

项目总结