Universal Sample Multiplexing for Single Cell Analysis

摘要 肿瘤的异质性强烈影响肿瘤的进展和对治疗的抵抗力。单细胞RNA 测序(ScRNAseq)是癌症研究的一个有价值的工具，因为它揭示了肿瘤的分子细节 和单细胞分辨率的微环境异质性。然而，机械地理解如何 异质性导致肿瘤进展或对治疗缺乏反应，因为这样的研究需要 分析多个重复、时间点和实验条件。这些试验性设计是 目前价格高得令人望而却步，而且在使用最好的产品时，会出现双重效果和批处理效果等人工制品 和最广泛使用的scRNAseq管道。此外，互补的和强大的也存在类似的限制 转座酶可及染色质的单核分析等单细胞表观遗传学分析方法 (SnATACseq)和靶标和转座下的单核切割(SnCUT&Tag)。要克服这些 要实现使用单细胞分析的机械性研究，需要简单、可靠和廉价 使用多路复用对样品进行定量比较的方法。 这项提议的目标是推进和进一步发展MULTIseq：一种快速、简单、廉价、可扩展、 以及用于单细胞RNA和表观遗传学分析的通用样本多重工具。多工位集成 与最流行、性能最好的技术无缝连接。MULTISEQ改进了单细胞分析 端到端方式的实验通过将多路传输实验的成本降低5%至100%， 将单次运行中可以分析的单元格数量增加3到10倍，从而允许去除人工制品 例如二倍体和分批效应，避免了对低RNA含量细胞的细胞类型抽样偏差，以及 能够设计目前使用scRNAseq工作流无法实现的新实验类别。 然而，MULTISEQ在癌症研究中具有巨大的未开发潜力，我们建议实施 对这项技术进行了几项重大改进。在目标1中，我们将开发支持示例的新工作流 用于表观基因组分析的多路传输(SnATACseq和SnCUT&TAG)。当一起部署时，这些方法 将提供染色质可及性和多种组蛋白修饰的全面分子画像 减少了批处理效果。在目标2中，我们开发了一种可扩展的策略，将细胞转化为条形码水凝胶反应 将显著扩展MULTIseq的可扩展性、支持强大的未来工作流、促进 比较一组更多样化的样本类型，并最终从商业图书馆中分离出来 准备平台。我们将在三类样本上验证和基准测试所提出的方法 癌症研究人员常规使用的：肿瘤细胞系，快速冷冻的人类原发和转移肿瘤，以及 有机化合物。这项提案的成功完成将对癌症研究产生广泛和持续的影响 通过比较多个样本和使用单细胞转录和 表观基因组分析是任何基础或临床肿瘤学研究实验室提供的一种常规且廉价的做法.

ABSTRACT Cancer progression and resistance to therapy are strongly influenced by tumor heterogeneity. Single-cell RNA sequencing (scRNAseq) is a valuable tool for cancer research because it reveals the molecular details of tumor and microenvironmental heterogeneity at single-cell resolution. However, a mechanistic understanding of how heterogeneity contributes to tumor progression or response to therapy is lacking because such studies require analysis of multiple replicates, time points, and experimental conditions. These experimental designs are currently prohibitively expensive and fraught with artifacts like doublets and batch effects when using the best and most widely-used scRNAseq pipelines. Moreover, similar limitations exist for complementary and powerful single-cell epigenetic analysis methods such as single-nucleus assay for transposase accessible chromatin (snATACseq) and single-nucleus cleavage under targets and transposition (snCUT&Tag). To surmount these barriers and to enable mechanistic studies using single-cell analysis requires simple, robust, and inexpensive methods for quantitatively comparing samples using multiplexing. The goal of this proposal is to advance and further develop MULTIseq: a rapid, simple, inexpensive, scalable, and universal sample multiplexing tool for single-cell RNA and epigenetic analysis. MULTIseq integrates seamlessly with the most popular and best-performing technologies. MULTIseq improves single-cell analysis experiments in an end-to-end fashion by reducing the costs of multiplexed experiments by 5 to 100-fold, increasing the number of cells that can be analyzed in a single run by 3 to 10-fold, allowing removal of artifacts such as doublets and batch effects, avoiding cell-type sampling bias against cells with low RNA content, and enabling the design of new classes of experiments that are currently impossible using scRNAseq workflows. However, MULTIseq has tremendous untapped potential in cancer research and we propose to implement several significant improvements to the technology. In Aim 1 we will develop new workflows enabling sample multiplexing for epigenomic analyses (snATACseq and snCUT&Tag). When deployed together, these methods will provide a comprehensive molecular portrait of chromatin accessibility and multiple histone modifications with reduced batch effects. In Aim 2 we develop a scalable strategy to convert cells into barcoded hydrogel reaction capsules that will significantly extend the scalability of MULTIseq, enable powerful future workflows, facilitate comparison of a more diverse sets of sample types, and ultimately untether MULTIseq from commercial library preparation platforms. We will validate and benchmark the proposed methods on three classes of specimens used routinely by cancer researchers: tumor cell lines, flash frozen human primary and metastatic tumors, and organoids. Successful completion of this proposal will have a broad and sustained impact on cancer research by making comparisons between multiple samples and specimens using single-cell transcriptomic and epigenomic analysis a routine and inexpensive practice available to any basic or clinical oncology research lab.