Mouse Paint: A massively combinatorial approach for illuminating tumor heterogeneity in True Color

Mouse Paint：一种以真彩色阐明肿瘤异质性的大规模组合方法

基本信息

批准号：
10589030
负责人：
Joshua Clair Snyder
金额：
$ 18.27万
依托单位：
DUKE UNIVERSITY
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：
2022-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10589030
关键词：
3-Dimensional Animal Model Atlas of Cancer Mortality in the United States Atlases Benchmarking Big Data Biological Cancer Biology Cancer Model Cell Lineage Cells Color Data Analyses Developmental Biology Elements Engineering Epithelium Evolution Feasibility Studies Fluorescence Microscopy Future Genetic Genetic Research Genetically Engineered Mouse Genome Genomic approach Green Fluorescent Proteins Growth Heterogeneity Histopathology Human Image Imaging Device In Situ In Vitro Label Laboratories LacZ Genes Malignant - descriptor Malignant Neoplasms Maps Measurement Measures Methods Modeling Mus Natural History Neoplasm Metastasis Organ Output Paint Pathologist Performance Phase Proteins Proteomics Public Health Reagent Recurrence Registries Reproducibility Research Personnel Resistance Resolution System Techniques Technology Testing Therapeutic Tissue Model Tissues Training Transgenic Organisms Translating Visualization Visualization software anticancer research cancer imaging cancer invasiveness cell type combinatorial falls fitness high reward high risk human data in vivo in vivo imaging interest logarithm molecular pathology mouse model neoplastic cell premalignant programs stem cell biology therapy resistant tool transcriptomics tumor tumor heterogeneity tumor progression tumor-immune system interactions tumorigenesis

项目摘要

ABSTRACT Hundreds to thousands of malignant epithelial clones with unique genetic compositions and fitness potentials are present in lethal cancers. Each clone competes against one another and also against a hostile immune microenvironment. Remarkable selective forces establish reservoirs of therapeutically resistant tumor cells and lethal metastases. Mechanistically mapping the evolution of tumor heterogeneity is the next great challenge in cancer research. Genomics approaches have been developed for digitizing heterogeneity in tumors and inferring lineage trajectories. However, gold-standard spatial information relied upon by pathologists for centuries is lost in these techniques and the inferred lineage relationships must still be proven experimentally. For almost three decades, genetically encodable protein tags have been invaluable for lineage tracing cell-fate in vivo. The discovery of green fluorescent protein (GFP) added the benefit of fluorescence microscopy. Spectrally resolvable GFP derivatives further increased the combinatorial potential of lineage tracing. Using techniques like “confetti” labelling, four clones are now commonly traced in most laboratories. Other approaches have successfully traced a hundred clones (“Brainbow”). These status quo approaches have remained unchanged for over a decade and still fall significantly short of the thousands of colors needed for quantitatively mapping tumor heterogeneity. This proposal provides a solution in the form of a massively combinatorial lineage tracing strategy in mice, termed “MousePaint”. This high-risk and high-reward IMAT project is based upon decades of genetic research from a team with an established track-record in combinatorial lineage tracing and hyperspectral imaging. MousePaint utilizes fluorescent proteins spanning the entire visible spectrum. Downstream hyperspectral imaging is used to image thousands of lineages and theoretically approach True Color imaging of >1 million colors. The objective of this proposal is to perform feasibility studies on a “MousePaint” technology that can be used to paint and visualize thousands of tumor clones during the natural history of a cancer – including initiation, growth, and metastasis. Two Aims are proposed. (Aim 1) To optimize a MousePaint strategy for combinatorial imaging in vivo and (Aim 2) To engineer and test a genetically encodable MousePaint for quantitatively visualizing the evolution of intratumor heterogeneity. Completing these aims will deliver MousePaints for imaging tumor cell heterogeneity. Hyperspectral imaging and big data analysis will benchmark MousePaint lineage tracing and color-depth. Co-registry of MousePaint with in situ transcriptomics, molecular pathology, histopathology, and scRNAseq compatibilities will be directly tested. MousePaint is expected to transform cancer research by providing a simple mouse tool for True Color imaging tumor evolution at least 100 to 1000 times the resolution of available methods.

抽象的具有独特遗传组成和健身电位的数百至数千至数千个恶性上皮克隆存在于致命的癌症中。每个克隆人相互竞争，也与敌对的免疫力微环境。显着的选择性力建立了治疗性抗性肿瘤细胞和致命转移。机械地绘制肿瘤异质性的演变是下一个巨大的挑战癌症研究。已经开发了基因组学方法来数字化肿瘤的异质性和推断谱系轨迹。但是，病理学家依靠的金标准空间信息在这些技术中丢失了几个世纪，而推断的谱系关系仍然必须在实验中得到证明。近三十年来，遗传编码的蛋白质标签对于谱系追踪细胞命令一直是无价的体内。绿色荧光蛋白（GFP）的发现增加了荧光显微镜的好处。频谱可解析的GFP衍生物进一步增加了谱系追踪的组合潜力。使用现在，大多数实验室中通常都会追溯到“五彩纸屑”标签，四个克隆。其他方法成功地追踪了一百个克隆（“ brainbow”）。这些现状方法有十多年来保持不变，但仍然比数千种颜色所需的数千种定量映射肿瘤异质性。该提案以大规模的形式提供了解决方案小鼠中的组合谱系追踪策略称为“ Mousepaint”。这个高风险和高回报的IMAT 项目基于一个数十年来的遗传研究，该团队拥有既定的田径记录组合谱系跟踪和高光谱成像。 Mousepaint利用荧光蛋白跨越整个可见光谱。下游高光谱成像用于成像数千个谱系和理论方法的真实颜色成像> 100万颜色。该提议的目的是执行可行性研究可用于绘画和可视化成千上万种肿瘤的“慕斯”技术在癌症的自然历史期间，克隆包括倡议，生长和转移。两个目标是建议的。（目标1）优化体内组合成像的慕斯策略和（目标 2）设计和测试一般可编码的鼠标，以定量可视化肿瘤内异质性的进化。完成这些目标将带来慕斯以进行想象肿瘤细胞异质性。高光谱成像和大数据分析将基准基准慕斯谱系跟踪和颜色深入。与原位转录组学，分子病理学的共同注册组织病理学和SCRNASEQ兼容性将直接测试。预计慕斯将转变通过提供一种简单的鼠标工具来进行癌症研究，以至少100至1000 乘以可用方法的分辨率。