Characterization unit

摘要 表征单位的首要目标是捕捉分子和细胞 儿童三大肿瘤--高级别胶质瘤的异质性和克隆演化 (PHGG)、神经母细胞瘤(NB)和极高风险急性淋巴细胞白血病(VHR- 全部)。尽管许多研究已经描述了DNA之间的相互作用 使用大量肿瘤样本的甲基化、染色质可及性和转录组 表观基因组和转录组在单细胞水平上的关系以及这种关系是如何发生的 关系对癌症异质性的影响还没有被很好地理解。除了……之外 恶性细胞的内在因素，肿瘤微环境中的外在因素也 需要在单细胞分辨率下更好地表征。有了这些考虑，在 请记住，我们将在这个项目中应用双管齐下的空间多组学方法。我们的 该方法由多种尖端的单细胞和原位阵列实现 由我们的调查团队开发的技术，包括：大规模并行单 核RNA序列，单细胞转座体超敏位点测序，多重 单分子RNA荧光原位杂交和图像质量细胞术。 多维和纵向数据集将由 数据分析单位构建肿瘤生态系统前后图谱 心理治疗。我们提出了以下四个具体目标：1)体细胞和生殖系的鉴定 使用大量肿瘤样本和血液样本的全基因组测序来检测突变。 对于每种癌症类型，我们将收集20名患者在治疗前后的肿瘤样本 药物治疗。突变数据将与单细胞组学数据相结合，以 构建治疗过程中肿瘤进化的系统树；2)进行 大宗和单细胞肿瘤样本的转录组图谱。VHR-ALL 样本，我们将只分析恶性细胞。对于Nb和PHGG样品，我们将配置 肿瘤微环境中的恶性细胞、免疫细胞和间质细胞； 对大宗和单细胞肿瘤样本进行表观基因组图谱分析。我们将为您介绍 全基因组亚硫酸氢法测定5-甲基胞嘧啶和染色质的可及性 和转座体超敏位点测序；4)进行靶向 原位RNA和蛋白质检测。每种肿瘤的一组标记基因将被提名 基于对AIMS 1-3收集的组学数据的计算分析。

Abstract The overarching goal of the Characterization Unit is to capture the molecular and cellular heterogeneity and clonal evolution of three major pediatric cancers, high-grade glioma (pHGG), neuroblastoma (NB), and very high-risk acute lymphoblastic leukemia (VHR- ALL). Although many studies have characterized the interplay between DNA methylation, chromatin accessibility and transcriptome using bulk tumor samples, how the epigenome and transcriptome relate at the single-cell level and how such relationships contribute to cancer heterogeneity is not well understood. In addition to factors intrinsic to malignant cells, extrinsic factors in the tumor microenvironment also need to be better characterized at single-cell resolution. With these considerations in mind, we will apply a two-pronged, spatial multi-omics approach in this project. Our approach is enabled by the diverse array of cutting-edge single-cell and in situ technologies developed by our team of investigators, including: massive parallel single nucleus RNA-Seq, single-cell transposome hypersensitivity site sequencing, multiplexed single molecule RNA fluorescence in situ hybridization and imaging mass cytometry. The multidimensional and longitudinal data set will be analyzed and integrated by the Data Analysis Unit to construct atlases of the tumor ecosystems before and after therapy. We propose the following four specific aims: 1) To identify somatic and germline mutations using whole genome sequencing of bulk tumor samples and blood samples. For each cancer type, we will collect tumor samples from 20 patients before and after medical therapy. The mutation data will be integrated with single-cell omics data to construct phylogenetic tree of cancer evolution during therapy; 2) To conduct transcriptome profiling of both bulk and single-cell tumor samples. For VHR-ALL samples, we will profile malignant cells only. For NB and pHGG samples, we will profile malignant cells, immune cells, and stromal cells in the tumor microenvironment; 3) To conduct epigenomic profiling of both bulk and single-cell tumor samples. We will profile 5-methylcytosine and chromatin accessibility using whole-genome bisulfite sequencing and transposome hypersensitive site sequencing, respectively; 4) To conduct targeted in situ RNA and protein assays. A panel of marker genes for each tumor will be nominated based on computational analysis of the omics data collected in Aims 1-3.