Expanding the GoT toolkit to link single-cell clonal genotypes with protein, transcriptomic, epigenomic and spatial phenotypes

扩展 GoT 工具包，将单细胞克隆基因型与蛋白质、转录组、表观基因组和空间表型联系起来

• 批准号: 10698112
• 负责人: Dan Landau
• 金额: $ 41.08万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-07 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698112
• 关键词: Address; Admixture; Adoption; Affect; Awareness; Biological Assay; Bone Marrow; Cell physiology; Cell surface; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Chromatin; Chronic Lymphocytic Leukemia; Clonal Evolution; Clonal Expansion; Development; Disease Resistance; Epigenetic Process; Evolution; Frequencies; Genes; Genetic; Genetic Heterogeneity; Genetic Transcription; Genomics; Genotype; Goals; Growth; Hematopoiesis; Heterogeneity; Human; Human body; Immune system; Individual; Knowledge; Lead; Link; Malignant - descriptor; Malignant Neoplasms; Minority; Molecular; Mosaicism; Mutate; Mutation; Myeloproliferative disease; Nature; Normal Range; Normal tissue morphology; Output; Phenotype; Population; Proteins; RNA Splicing; Resistance; Resolution; Risk; Sampling; Signal Transduction; Slide; Somatic Mutation; Spatial Distribution; Technology; Time; Transcript; Variant; analytical method; cancer diagnosis; cancer genomics; cancer therapy; cell behavior; cell type; clinical diagnosis; driver mutation; epigenomics; experience; fitness; hematopoietic differentiation; human tissue; methylome; multiple omics; mutant; novel; phenotypic biomarker; protein expression; single cell sequencing; single cell technology; single-cell RNA sequencing; targeted treatment; therapy development; therapy resistant; transcription factor; transcriptome; transcriptomics

Abstract Clonal outgrowths are observed across a wide range of normal human tissues. They also appear during the course of cancer evolution, leading to clonal heterogeneity that fuels the development of treatment-resistant disease. Clones harbor somatic mutations in known cancer driver genes and show evidence of positive selection. Nevertheless, how these driver mutations alter the cellular states of cells to allow clones to outcompete wildtype counterparts remains poorly understood. To date, efforts to chart clonal outgrowths in normal or malignant human tissues have been largely limited to genotyping. This is due to the fact that these clones often affect a minority of cells in a sample without distinguishing cell-surface markers. To address this challenge, we developed an array of multi-omic single-cell technologies that are capable of capturing multiple layers of information (e.g., genotypes, transcriptomes, methylomes, protein expression) from the same single cells. Moreover, we addressed the specific challenge of genotyping in scRNA-seq in single cells at high throughput by developing Genotyping of Targeted loci (GoT). Importantly, GoT turns the admixture of mutant and wildtype hematopoiesis from a limitation to an advantage, enabling the direct comparison of mutant (“winner”) and wildtype (“loser”) cells within the same individual. Given the increasing adoption of our GoT platform, we now aim to extend the multi-omics single-cell toolkit to study how somatic mutations lead to clonal growth advantage. We will integrate GoT with Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) to yield GoT-CITE, which will add the critical layer of cell surface marker phenotyping to single-cell whole transcriptomes. As mutations in splicing factors are specifically associated with greater risk of malignant transformation, we will develop and implement GoT- Splice, where long-read sequencing will be used to define splicing variation as a function of cell identity. Given the high frequency of epigenetic mutations in cancer, we will also develop and apply targeted single-cell genotyping in the context of chromatin accessibility (GoT-ChA). Finally, as clone growth will also be determined by its interaction with the microenvironment, to define clonal driver genotypes in its spatial context, we will adapt spatial transcriptomics (ST) to add the critical feature of genotyping (GoT-ST). Our overarching goal is to invoke multi-omic comparisons at the single-cell level between wildtype and mutant cells to comprehensively identify the underpinnings of fitness advantage in clonal outgrowth. The proposed comprehensive GoT toolkit will enable the linking, at high throughout, single-cell genotypes with transcriptional, protein, epigenetic and spatial phenotypes. We anticipate that these advances will transform the study of clonal mosaicism as a harbinger of cancer, as well as resistance to cancer therapies.

抽象的 在广泛的正常人体组织中观察到克隆产物。他们也出现在 癌症进化的过程，导致克隆异质性，助长治疗的发展 疾病。克隆在已知的癌症驱动基因中含有体细胞突变，并显示了阳性的证据 选择。然而，这些驱动器突变如何改变细胞的细胞状态以允许克隆 胜过野生型对应物仍然很少理解。迄今为止，努力绘制克隆出生的生长 正常或恶性的人体组织在很大程度上限于基因分型。这是由于这些事实是 克隆通常会影响样品中的少数细胞，而不会区分细胞表面标记。 为了应对这一挑战，我们开发了一系列能够 从中捕获多层信息（例如，基因型，转录组，甲基组，蛋白质表达） 相同的单个单元格。此外，我们解决了单个SCRNA-SEQ中基因分型的具体挑战 通过发展靶点基因座的基因分型（GOT），处于高通量的细胞。重要的是，变成混合物 从限制到优势的突变体和野生型造血作用 突变体（“赢家”）和野生型（“失败者”）细胞内。 鉴于我们的GOT平台的采用越来越多，我们现在旨在将多族单细胞工具包扩展到 研究体细胞突变如何导致克隆生长的优势。我们将与蜂窝索引集成 通过测序（cite-seq）进行转录组和表位以产生cite，这将添加临界层的临界层 细胞表面标记表型型至单细胞整体转录组。由于剪接因素的突变是 特别与更大的恶性转变风险相关，我们将开发和实施 - 剪接，其中将使用长阅读测序来定义剪接变化作为细胞身份的函数。给出 癌症表观遗传突变的高频率，我们还将开发和应用目标单细胞 基因分型在染色质可及性（GOT-CHA）的背景下进行。最后，因为克隆的增长也将是 取决于其与微环境的相互作用，以在其空间环境中定义克隆驱动器基因型 我们将适应空间转录组学（ST），以添加基因分型（GOT-ST）的关键特征。 我们的总体目标是在WildType和突变体之间在单细胞水平上调用多OMIC比较 细胞全面识别克隆出生中健身优势的基础。提议 综合GoT Toolkit将使链接在整个过程中都可以链接到具有转录， 蛋白质，表观遗传和空间表型。我们预计这些进步将改变克隆的研究 镶嵌作为癌症的预兆，以及对癌症疗法的抵抗力。