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

摘要 克隆突起广泛存在于正常人体组织中。他们也会出现在 癌症进化过程，导致克隆异质性，从而促进治疗耐药的发展 疾病。克隆包含已知癌症驱动基因的体细胞突变，并显示阳性证据 选择。然而，这些驱动基因突变如何改变细胞的细胞状态，从而允许克隆人 在竞争中击败野生型的同行们仍然知之甚少。到目前为止，绘制克隆结果的努力 人类正常或恶性组织在很大程度上仅限于基因分型。这是因为这些 克隆通常影响样本中的少数细胞，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体单细胞技术，能够 获取多层信息(例如，基因类型、转录本、甲基组、蛋白质表达) 同样的单细胞。此外，我们还解决了单细胞scrna-seq基因分型的特殊挑战。 通过发展靶基因座(GOT)基因分型，获得高通量的细胞。重要的是，得到了混合剂 突变和野生型造血从限制到优势，使直接比较 同一个体内的突变(“赢家”)和野生型(“输家”)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带来克隆生长优势。我们将把GET与蜂窝索引集成在一起 转录本和表位通过测序(CITE-SEQ)产生GET-CITE，这将增加关键的一层 单细胞转录本的细胞表面标记表型。因为剪接因子的突变是 特别是与更大的恶变风险相关的，我们将开发和实施GOT- 剪接，其中长读测序将被用来将剪接变异定义为细胞特性的函数。vt.给出 在表观遗传突变频率较高的癌症中，我们还将开发和应用靶向单细胞 染色质可及性背景下的基因分型(GOT-CHA)。最后，由于克隆人的生长也将是 由其与微环境的相互作用决定，以在其空间环境中定义克隆驱动基因类型， 我们将采用空间转录学(ST)来增加基因分型的关键特征(GOT-ST)。 我们的首要目标是在单细胞水平上对野生型和突变型进行多组比较。 细胞，以全面识别克隆生长中适应性优势的基础。建议数 全面的GET工具包将使单细胞基因类型与转录的、 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 马赛克是癌症的先兆，也是癌症治疗的抵抗力。

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.