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.

抽象的数百至数千个具有独特遗传组成和适应潜力的恶性上皮克隆存在于致命的癌症中。显着的选择力建立了治疗耐药肿瘤细胞的储存库和机械地绘制肿瘤异质性的演变是下一个巨大挑战。基因组学方法已被开发用于数字化肿瘤和癌症的异质性。然而，病理学家依赖金标准空间信息来推断谱系轨迹。这些技术已经浪费了几个世纪的时间，而推断出的谱系关系仍然必须通过实验来证明。近三十年来，基因编码的蛋白质标签对于谱系追踪细胞命运具有无价的价值绿色荧光蛋白（GFP）的发现增加了荧光显微镜的优势。光谱可分辨的 GFP 衍生物进一步增加了谱系追踪的组合潜力。通过“五彩纸屑”标记等技术，现在大多数实验室都可以追踪到四种克隆。方法已成功追踪一百个克隆（“Brainbow”）。十多年来一直没有变化，但仍然远远低于所需的数千种颜色该提案以大规模的形式提供了一种解决方案。小鼠组合谱系追踪策略，称为“MousePaint”，这种高风险和高回报的 IMAT。该项目基于一个团队数十年的基因研究，该团队在 MousePaint 组合谱系追踪和高光谱成像利用荧光蛋白跨越。整个可见光谱用于对数千个谱系进行成像。理论上接近> 100万种颜色的真彩色成像。 “MousePaint”技术的可行性研究，该技术可用于绘制和可视化数千个肿瘤癌症自然史中的克隆——包括起始、生长和转移有两个目标。提出（目标 1）优化体内组合成像的 MousePaint 策略和（目标） 2) 设计并测试可基因编码的 MousePaint，以定量可视化肿瘤内异质性的进化完成这些目标将为成像提供MousePaints。高光谱成像和大数据分析将为 MousePaint 谱系进行基准测试。 MousePaint 与原位转录组学、分子病理学的共同注册。组织病理学和 scRNAseq 兼容性将有望直接进行改造。通过提供简单的鼠标工具对肿瘤演变进行真彩色成像至少 100 到 1000 进行癌症研究倍于可用方法的分辨率。