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

摘要 在广泛的正常人体组织中观察到克隆生长。它们也出现在 癌症演变过程，导致克隆异质性，从而促进耐药细胞的发展。 疾病克隆在已知的癌症驱动基因中携带体细胞突变，并显示出阳性的证据。 选择.然而，这些驱动突变如何改变细胞的细胞状态，使克隆能够 胜过野生型对应物仍然知之甚少。到目前为止，绘制克隆生长的努力， 正常或恶性人体组织在很大程度上限于基因分型。这是因为， 克隆通常影响样品中的少数细胞，而没有区分细胞表面标记。 为了应对这一挑战，我们开发了一系列多组学单细胞技术， 捕获多层信息（例如，基因型、转录组、甲基化组、蛋白质表达）， 相同的单细胞。此外，我们解决了scRNA-seq中基因分型的特定挑战， 通过开发靶向基因座的基因分型（GoT）以高通量培养细胞。重要的是，GoT将混合物 突变型和野生型造血从限制到优势，使直接比较 在同一个体内的突变体（“赢家”）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。我们将整合GoT与蜂窝索引， 转录组和表位测序（CITE-seq），以产生GoT-CITE，这将增加关键层， 细胞表面标志物表型鉴定为单细胞全转录组。由于剪接因子中的突变 特别是与恶性转化的风险更大，我们将制定和实施GoT- 剪接，其中将使用长读序测序来定义剪接变异作为细胞身份的函数。给定 癌症中表观遗传突变的频率很高，我们还将开发和应用靶向单细胞 在染色质可及性（GoT-ChA）的背景下进行基因分型。最后，克隆生长也将 由其与微环境的相互作用决定，以在其空间背景中定义克隆驱动基因型， 我们将调整空间转录组学（ST）以增加基因分型（GoT-ST）的关键特征。 我们的首要目标是在单细胞水平上在野生型和突变型之间进行多组学比较 细胞，以全面确定克隆生长的健身优势的基础。拟议 全面的GoT工具包将能够在高通量下将单细胞基因型与转录， 蛋白质、表观遗传和空间表型。我们预计，这些进展将改变克隆的研究 嵌合体作为癌症的预兆，以及对癌症疗法的抗性。

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.