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 在多种正常人体组织中都可以观察到克隆生长，它们也出现在克隆过程中。 癌症进化过程，导致克隆异质性，从而促进耐药性的发展 克隆体内含有已知癌症驱动基因的体细胞突变，并显示出阳性证据。 然而，这些驱动突变如何改变细胞的细胞状态以使克隆能够进行选择。 迄今为止，人们对如何在竞争中胜出野生型对仍知之甚少。 正常或恶性的人体组织在很大程度上仅限于基因分型。 克隆通常会影响样品中的少数细胞，而不区分细胞表面标记。 为了应对这一挑战，我们开发了一系列多组学单细胞技术，这些技术能够 捕获多层信息（例如基因型、转录组、甲基化组、蛋白质表达） 此外，我们还解决了单细胞 scRNA-seq 基因分型的具体挑战。 通过开发目标位点 (GoT) 的基因分型，以高通量检测细胞。重要的是，GoT 可以改变混合物。 突变型和野生型造血功能从限制变为优势，从而能够直接比较 同一个体内的突变型（“获胜者”）和野生型（“失败者”）细胞。 鉴于我们的 GoT 平台越来越多地被采用，我们现在的目标是将多组学单细胞工具包扩展到 研究体细胞突变如何导致克隆生长优势我们将把 GoT 与细胞索引结合起来。 通过测序 (CITE-seq) 进行转录组和表位生成 GoT-CITE，这将添加关键层 细胞表面标记表型对单细胞全转录组的影响 特别是与更大的恶性转化风险相关的，我们将制定并实施 GoT- 剪接，长读长测序将用于定义剪接变异作为细胞身份的函数。 针对癌症表观遗传突变的高频率，我们还将开发和应用靶向单细胞 最后，克隆生长也将在染色质可及性（GoT-ChA）的背景下进行基因分型。 由其与微环境的相互作用决定，在其空间背景下定义克隆驱动基因型， 我们将采用空间转录组学（ST）来添加基因分型（GoT-ST）的关键特征。 我们的首要目标是在野生型和突变体之间在单细胞水平上进行多组学比较 细胞以全面识别克隆生长中适应性优势的基础。 全面的 GoT 工具包将能够以高通量将单细胞基因型与转录、 我们预计这些进展将改变克隆的研究。 镶嵌现象是癌症的先兆，以及对癌症治疗的抵抗力。