Universal Sample Multiplexing for Single Cell Analysis

摘要 肿瘤的异质性对癌症的进展和对治疗的抵抗有很大的影响。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 和微环境的异质性。然而，机械地理解如何 异质性导致肿瘤进展或缺乏对治疗的反应，因为这些研究需要 多个重复、时间点和实验条件的分析。这些实验设计是 目前昂贵得令人望而却步，而且在使用最好的效果时， 和最广泛使用的scRNAseq管道。此外，对于互补性和强大的 单细胞表观遗传学分析方法，如转座酶可接近染色质单核测定 （snATACseq）和在靶标和转座下的单核切割（snCUT&Tag）。为了克服这些 障碍，并使机制的研究，使用单细胞分析需要简单，强大，廉价 使用多路复用定量比较样品的方法。 该提案的目标是推进和进一步开发MULTISEQ：一种快速、简单、廉价、可扩展， 以及用于单细胞RNA和表观遗传分析的通用样品多路复用工具。MULTISEQ集成 与最流行、性能最佳的技术无缝结合。MULTISEQ改进单细胞分析 通过将多路复用实验的成本降低5至100倍， 使单次运行中可分析的细胞数量增加3至10倍，从而可以去除伪影 例如双重峰和分批效应，避免对低RNA含量细胞的细胞类型取样偏倚，以及 使得能够设计新的实验类别，这是目前使用scRNAseq工作流程不可能实现的。 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 对技术进行了几项重大改进。在目标1中，我们将开发新的工作流程， 用于表观基因组分析的多路复用（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， 减少批量效应。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 Capsules将显著扩展MULTISEQ的可扩展性，实现强大的未来工作流程， 比较更多样化的样品类型集，并最终将MULTISEQ从商业文库中解链 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性和转移性肿瘤， 类器官这项提案的成功完成将对癌症研究产生广泛而持续的影响 通过使用单细胞转录组学和 表观基因组分析是任何基础或临床肿瘤学研究实验室可用的常规且廉价的实践。

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.