Characterization Unit

波士顿人类肿瘤图谱研究中心（HTA-RC）的分子表征部门（MCU）将 全面分析500份样本（100/年），采集自黑色素瘤、结肠癌和转移性乳腺癌 生物样本单位（BSU）的癌症患者。它将使用一套互补的、经过验证的尖端技术， 细胞/核和空间技术，以建立全面的时空地图集的细胞 这三种癌症的地理分布它还将进一步开发创新技术， 验证数据分析单元（DAU）的预测，以了解肿瘤的动态变化 生态系统导致治疗抗性。更具体地说，MCU将促进整个使命， HTA-RC通过4个具体目标。在AIM 1（蜂窝/核分支）中，MCU将应用经确认的单- 细胞RNA-Seq（scRNA-Seq）和单核RNA-Seq（snRNA-Seq）方法，结合批量 全外显子组测序和批量RNA-Seq，以生成数万至数十万个基因组的基因组图谱。 单个细胞开发分子定义的细胞类型，遗传克隆和状态的综合图谱， 以及用于空间研究的签名。在AIM 2（空间分支）中，MCU将部署以下组合 空间分辨的细胞RNA（多路误差稳健荧光原位杂交（MERFISH），空间分辨的细胞RNA，空间分辨的 转录组学（ST））和蛋白质谱分析（CO表达检测（CODEX），高度多重 免疫荧光（HMIF）、多参数离子束成像（MIBI））方法来空间绘制细胞类型， 遗传克隆和细胞/核分支鉴定的细胞状态。在AIM 3中，我们将利用我们团队的 验证新兴技术（CITE-Seq）和开发创新方法以克服 具体的挑战-包括3D成像（整体安装; 3D CODEX），基因组规模的高分辨率空间 数据（Ex-SEQ和细胞的原位空间细胞标记），空间分辨表观基因组学（原位ATAC-Seq）， 和蛋白质组规模的原位测量（压缩传感）-从而加速 构建更完整的肿瘤图谱。最后，在AIM 4中，我们将测试由 通过对库存肿瘤、细胞系（恶性肿瘤和TIL）的扰动和随访分析， 类器官和器官型肿瘤球状体。MCU建立的科学家团队-他们开发了 在这项研究中使用的主要方法，在单细胞基因组学，空间 转录组学/蛋白质组学、癌症、免疫学、病理学、肿瘤学、技术开发和功能 比色法他们将与BSU和DAU密切合作，以确保适当的质量保证/质量控制 (QA/QC）措施和足够的统计功效。MCU也将灵活，以便引入 通过我们与其他人类肿瘤图谱网络（HTAN）的合作开发的技术 中心，癌症登月活动，和/或相关的地图集建设项目。这些努力将是一个核心部分， 了解肿瘤生态系统的变化如何导致癌症的治疗耐药性。

The Molecular Characterization Unit (MCU) of the Boston Human Tumor Atlas Research Center (HTA-RC) will comprehensively profile 500 samples (100/yr), collected from melanoma, colon cancer and metastatic breast cancer patients by the Biospecimens Unit (BSU). It will use a suite of complementary, validated cutting-edge cellular/nuclear and spatial technologies to build comprehensive spatiotemporal atlases of the cellular geography of these three cancers. It will also further develop innovative technologies, and perform functional validations of predictions from the Data Analysis Unit (DAU), to understand how dynamic changes in the tumor ecosystem lead to therapeutic resistance. More specifically, the MCU will facilitate the overall mission of the HTA-RC through 4 specific aims. In AIM 1 (the Cellular/Nuclear Branch), the MCU will apply validated single- cell RNA-Seq (scRNA-Seq) and single-nucleus RNA-Seq (snRNA-Seq) methods, in conjunction with bulk whole-exome sequencing and bulk RNA-Seq, to generate genomic profiles of tens to hundreds of thousands of individual cells to develop a comprehensive map of molecularly defined cell types, genetic clones, and states, as well as signatures for spatial studies. In AIM 2 (the Spatial Branch), the MCU will deploy a combination of spatially-resolved cellular RNA (multiplexed error-robust fluorescence in situ hybridization (MERFISH), Spatial Transcriptomics (ST)) and protein profiling (CO Detection of Expression (CODEX), highly multiplex immunofluorescence (HMIF), Multiparameter Ion Beam Imaging (MIBI)) methods to spatially chart cell types, genetic clones, and cell states identified by the cellular/nuclear branch. In AIM 3, we will leverage our team’s expertise to both validate emerging techniques (CITE-Seq) and develop innovative methods to overcome specific challenges—including 3D imaging (whole mount; 3D CODEX), genomic-scale high resolution spatial data (Ex-SEQ and in situ spatial cellular tagging of cells), spatially resolved epigenomics (in situ ATAC-Seq), and proteome-scale measurements in situ (with compressive sensing)—and thereby accelerate the construction of more complete tumor atlases. Finally, in AIM 4, we will test the predictive models generated by the DAU through perturbations and follow-up profiling of banked tumors, cell lines (malignant and TILs), and organoids and organotypic tumor spheroids. The MCU’s established team of scientists—who developed the major methods used in this study—have complementary expertise in single-cell genomics, spatial transcriptomics/proteomics, cancer, immunology, pathology, oncology, technology development, and functional assay. They will work closely with the BSU and DAU to ensure appropriate quality assurance/quality control (QA/QC) measures and sufficient statistical power. The MCU will also be nimble, so as to introduce technologies developed through our collaborative efforts with other Human Tumor Atlas Network (HTAN) Centers, Cancer Moonshot activities, and/or related atlas-building projects. These efforts will be a central part of the goal of understanding how changes in the tumor ecosystem lead to therapeutic resistance in cancer.