Universal Sample Multiplexing for Single Cell Analysis

用于单细胞分析的通用样品多重分析

基本信息

批准号：
10190663
负责人：
Zev Jordan Gartner
金额：
$ 39.86万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10190663
关键词：
Adopted Advanced Malignant Neoplasm Atlases Bar Codes Benchmarking Biological Assay Cancer cell line Catalogs Cell Nucleus Cells Chromatin Clinical Oncology Communities Data Data Analyses Data Compromising Data Set Detection Epigenetic Process Excision Experimental Designs Freezing Future Genes Genome Genomics Goals Guanine + Cytosine Composition Heterogeneity Human Hydrogels Libraries Lipids Malignant Neoplasms Maps Measurement Measures Membrane Proteins Messenger RNA Methods Modality Molecular Morphologic artifacts Neoplasm Metastasis Oligonucleotides Organoids Paint Patients Performance Permeability Pharmacotherapy Planet Earth Portraits Preparation Primary Neoplasm Property Protocols documentation RNA Reaction Reagent Research Research Personnel Resistance Resolution Running Sampling Sampling Biases Seeds Specimen Techniques Technology Time Tissues Transcript Transposase Tumor Cell Line anticancer research base capsule cell type combinatorial cost data quality design epigenome epigenomics experimental study histone modification improved indexing insight novel strategies patient response response single cell analysis single cell technology single-cell RNA sequencing therapy resistant tool transcriptome sequencing transcriptomics tumor tumor growth tumor heterogeneity tumor microenvironment tumor progression

项目摘要

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.

抽象的癌症的进展和对治疗的抵抗力受肿瘤异质性的强烈影响。单细胞RNA 测序（SCRNASEQ）是癌症研究的宝贵工具，因为它揭示了肿瘤的分子细节单细胞分辨率时的微环境异质性。但是，机械理解如何缺乏异质性有助于肿瘤进展或对治疗的反应，因为此类研究需要分析多个重复，时间点和实验条件。这些实验设计是目前使用最佳时，目前昂贵的昂贵，并充满了诸如双重和批处理效果之类的人工制品以及大多数广泛使用的scrnaseq管道。此外，互补和功能有类似的限制单细胞表观遗传分析方法，例如用于转座酶可访问的染色质的单核测定法（SNATACSEQ）和单核切割目标和换位（SNCUT＆TAG）。为了克服这些障碍并使用单细胞分析启用机理研究，需要简单，健壮且廉价使用多路复用定量比较样品的方法。该建议的目的是进一步发展和进一步发展Multiseq：快速，简单，廉价，可扩展的，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集成无缝采用最受欢迎，最佳的技术。 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，以端到端方式进行实验增加可以在单个运行中分析的细胞数量增加了3至10倍，从而允许移除伪影例如双线和批处理效应，避免对RNA含量低的细胞进行细胞类型采样偏置，并且实现了使用SCRNASEQ工作流程目前不可能的新的实验类别的设计。但是，Multiseq在癌症研究中具有巨大的未开发潜力，我们建议实施该技术的几个重大改进。在AIM 1中，我们将开发新的工作流程以实现样本多路复用以进行表观基因组分析（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综合分子肖像批处理效应降低。在AIM 2中，我们制定了一种可扩展的策略，将细胞转换为条形码水凝胶反应胶囊将大大扩展Multiseq的可扩展性，实现强大的未来工作流程，促进比较更多种样本类型的集合，并最终与商业图书馆的Multiseq进行比较准备平台。我们将验证和基准在三类样本上提出的方法癌症研究人员通常使用：肿瘤细胞系，闪烁的人类原发性和转移性肿瘤以及器官。该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和标本之间的比较，表观基因组分析一种常规和廉价的实践可用于任何基本或临床肿瘤研究实验室。