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

项目摘要 飞行时间多路离子束成像(MIBI-TOF)使用二次离子质谱仪和金属 结合的一次抗体在单个细胞内以亚细胞分辨率同时显示数十种蛋白质 组织切片。这项技术与档案福尔马林固定石蜡包埋组织(FFPE)后兼容 并在同行评议工作中用于同时可视化和量化36种蛋白质 人类组织队列。为了与HuBMAP财团声明的目标保持一致，该财团既要开发“高灵敏度， 高分辨率成像技术，可以快速提供大面积组织的光谱数据“和 定量成像分析工具，包括自动3D图像分割、特征提取和图像 这里概述的工作将为以下各项创建标准化、高吞吐量和用户友好的工作流 在基础和翻译研究中使用MIBI-TOF以深入了解单细胞表型和组织 在健康和疾病中，结构在功能上是相互关联的。为了实现这一点，我们将验证100个FFPE抗体 并优化冷冻干燥形式的即用多路染色面板，使其至少可以储存 两年了。用于蛋白质和信使核糖核酸靶标的多重信号放大的方案和试剂将进一步 精细化，而下一代仪器将增加样本吞吐量，以允许进行完整组织切片 在1小时内对多达40种蛋白质进行成像。标准化的试剂和更强大的仪器将 伴随着一个自动计算流水线，它利用一组标准的分割标记和 机器学习，以准确识别任何非神经性人体组织中的细胞核和细胞边界。该数据将是 用于根据形态、蛋白质将单细胞事件聚集到功能不同的群体中 表达和组织学分布。这里提出的试剂和计算流水线与 现有HuBMAP资助的平台，可以很容易地推广到几乎任何高维成像 情态。因此，这项工作不仅将为高吞吐量提供一个实用的、后向兼容的成像平台 多路成像，但也将加快其他免费成像技术的发展。

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.