Tech Core 2

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起着关键作用，包括 导致癌细胞扩散风险增加的静脉侵入。这一过程涉及空间 癌细胞与周围微环境在多个部位的不同相互作用。在Tech 1，PI 维尔茨将开发一种新的3D多尺度肿瘤细胞绘图方法CODA，它可以创建一个3D大尺度的肿瘤细胞图像。 通过组织组织学图像集成和训练的深度学习语义，在单细胞水平上进行肿瘤细胞解剖 算法高度期望进一步添加到该3D肿瘤细胞解剖图谱的是全基因组分子标记。 信息，如mRNA和一个大的面板蛋白质的细胞亚型，状态， 相互作用，并可能推断新的机制或治疗干预的目标。102 PI风扇 最近开发了一种名为DBiT-seq的新技术，用于高空间分辨率的多组学映射， 组织中细胞水平的确定性条形码（~10µm），全转录组规模（> 22，000个基因）， 覆盖率（> 2，000个基因/10µm像素）和多组学分析（约300个蛋白质标记物的共定位）， 可以容易地应用于FFPE组织切片并与CODA整合。在Tech 2中，我们提出以下建议 两个目标：目标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PFA和FFPE组织样本完全相容。AIM 2.集成CODA和DBiT-seq用于3D 多组肿瘤成像。这两个目标的成功完成将导致第一个全基因组的多- 组学3D视图的血管或淋巴管侵犯的人类肿瘤和一个强大的技术平台， 研究其他人类癌症的空间组织异质性的联盟。

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.