Tech Core 2

项目摘要-技术核心2 肿瘤的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 静脉侵犯会增加癌细胞扩散的风险。这个过程涉及到空间上的 癌细胞与周围多个部位的微环境之间存在明显的相互作用。在技术1中，PI Wirtz将开发一种新的3D多尺度肿瘤细胞映射方法CODA，该方法可以创建3D大范围 基于组织学图像融合和深度学习语义训练的单细胞水平肿瘤细胞解剖 算法。人们非常希望在这份3D肿瘤细胞解剖图谱中进一步增加全基因组的分子 信息，如mRNA和一大组蛋白质，用于无偏见地发现细胞亚型、状态和 相互作用，并潜在地推断新的机制或治疗干预的目标。Tech2 Pi Fans 最近开发了一种名为dbit-seq的新技术，用于通过 组织中细胞水平(~10微米)的确定性条形码，整个转录组规模(&gt；22,000个基因)，高 覆盖范围(每10微米像素2,000个基因)和多组学分析(约300个蛋白质标记的共同作图)， 可以很容易地应用于FFPE组织切片，并与CODA集成。在Tech2中，我们提出了以下建议 目标1.一种高通量、低成本、高质量/覆盖率的多基因组作图方法(DBiT-SEQ) 与人类PFA和FFPE组织样本完全兼容。目标2.集成CODA和DBiT-seq以实现3D 多组体肿瘤成像。这两个目标的成功完成将导致第一个全基因组范围的多基因 人体肿瘤血管或淋巴血管侵袭的组学3D视图和强大的技术平台 该联盟将研究其他人类癌症的空间组织异质性。

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.