Tech Core 2

技术核心2

• 批准号: 10375193
• 负责人: Denis Wirtz
• 金额: $ 58.37万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10375193
• 关键词: 3-Dimensional; Algorithms; Anatomy; Area; Atlases; Bar Codes; Blood Vessels; Cells; Cellular biology; Clinical; Data; Development; Extracellular Matrix; Genes; Goals; Heterogeneity; Histology; Human; Image; Light; Malignant Neoplasms; Malignant neoplasm of pancreas; Mammary Neoplasms; Maps; Measures; Messenger RNA; Methods; Microfluidics; Molecular; Neoplasm Metastasis; Outcome; Play; Process; Proteins; Resolution; Role; Semantics; Site; Structure; Subcellular Anatomy; Technology; Therapeutic Intervention; Three-Dimensional Imaging; Time; Tissue Sample; Tissue imaging; Tissues; Training; Tumor Tissue; Tumor Vascular Invasion; Venous; anticancer research; base; cancer cell; cancer imaging; cancer risk; cost; deep learning; design; genome-wide; histological specimens; image registration; lymphatic Invasion; malignant breast neoplasm; meter; multiple omics; neoplastic cell; new technology; novel; pancreatic neoplasm; protein biomarkers; targeted treatment; technology development; technology validation; tissue mapping; transcriptome; tumor; tumor heterogeneity; tumor progression

Project Summary – Tech Core 2 Spatial tumor heterogeneity plays a critical role in multiple stages of cancer progression and metastasis including the venous invasion that contributes to increased risk of cancer cell dissemination. This process involves spatially distinct interaction between cancer cells and the surrounding microenvironment at multiple sites. In Tech1, PI Wirtz will develop a new 3D multiscale tumor cell mapping method, CODA, which can create a 3D large-scale tumor cell anatomy at single cell level via tissue histology image integration and trained deep-learning semantic algorithms. What is highly desired to further add to this 3D tumor cell anatomic atlas is genome-wide molecular information such as mRNAs and a large panel of proteins for unbiased discovery of cell subtype, state, and interaction, and potentially to infer new mechanisms or targets for therapeutic intervention. TECH2 PI Fan recently developed a novel technology called DBiT-seq for high-spatial-resolution multi-omics mapping via deterministic barcoding in tissue at cellular level (~10µm), whole transcriptome scale (>22,000 genes), high coverage (>2,000 genes per 10µm pixel), and multi-omics profiling (co-mapping of ~300 protein markers), which can be readily applied to FFPE tissue sections and integrated with CODA. In Tech2, we propose the following two aims: AIM 1. A high-throughput, low cost, high quality/coverage, multi-omic mapping method (DBiT-seq) with full compatibility with human PFA and FFPE tissue samples. AIM 2. Integrating CODA and DBiT-seq for 3D multi-omic tumor imaging. Successful completion of these two aims will lead to the first genome-wide multi- omics 3D view of vascular or lymphovascular invasion of human tumors and a powerful technology platform for the consortium to investigate spatial tissue heterogeneity in other human cancers.

项目摘要 – 技术核心 2 肿瘤空间异质性在癌症进展和转移的多个阶段中发挥着关键作用，包括 静脉侵入会增加癌细胞扩散的风险。这个过程涉及空间 癌细胞与周围微环境在多个部位之间存在明显的相互作用。在 Tech1、PI Wirtz将开发一种新的3D多尺度肿瘤细胞图谱方法CODA，该方法可以创建3D大规模肿瘤细胞图谱 通过组织组织学图像集成和经过训练的深度学习语义在单细胞水平上进行肿瘤细胞解剖 算法。非常需要进一步添加到这个 3D 肿瘤细胞解剖图谱中的是全基因组分子 mRNA 和大量蛋白质等信息，用于公正地发现细胞亚型、状态和 相互作用，并有可能推断出治疗干预的新机制或目标。 TECH2 PI 风扇 最近开发了一种名为 DBiT-seq 的新技术，用于通过 细胞水平组织中的确定性条形码 (~10μm)、全转录组规模（>22,000 个基因）、高 覆盖率（每 10μm 像素 >2,000 个基因）和多组学分析（约 300 个蛋白质标记的联合映射），其中 可轻松应用于 FFPE 组织切片并与 CODA 集成。在Tech2中，我们提出以下建议 两个目标： AIM 1. 一种高通量、低成本、高质量/覆盖率的多组学作图方法 (DBiT-seq) 与人体 PFA 和 FFPE 组织样品完全兼容。目标 2. 集成 CODA 和 DBiT-seq 进行 3D 多组学肿瘤成像。这两个目标的成功完成将导致第一个全基因组多 人类肿瘤血管或淋巴管侵袭的组学 3D 视图以及强大的技术平台 该联盟致力于研究其他人类癌症的空间组织异质性。