A high-throughput method for simultaneous profiling of mRNA and protein levels in

一种同时分析 mRNA 和蛋白质水平的高通量方法

• 批准号: 8413560
• 负责人: MARC Wallace KIRSCHNER
• 金额: $ 26.14万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8413560
• 关键词: Bar Codes; Benchmarking; Biological; Biological Assay; Cell Count; Cell Cycle; Cell Differentiation process; Cell Separation; Cells; Characteristics; Clinical; Disease; Drops; Drug resistance; Ensure; Genetic Transcription; Health; Human Genome; Individual; Intestines; Light; Mammalian Cell; Measurement; Measures; Messenger RNA; Metabolism; Methods; Molecular; Molecular Profiling; Mus; Performance; Physiology; Population; Price; Proteins; Protocols documentation; Publishing; RNA; Race; Reading; Reverse Transcriptase Polymerase Chain Reaction; Reverse Transcription; Role; Running; Sampling; Signal Transduction; Speed; Stem cells; System; Techniques; Technology; Testing; Tissues; Transcript; Work; cancer cell; cancer stem cell; cell behavior; combinatorial; cost; flexibility; human tissue; improved; insight; interest; intestinal crypt; next generation; novel; scale up; single cell analysis; stem cell population; success; tumor

DESCRIPTION (provided by applicant): The analysis of individual cells promises to reveal insights into tissue physiology and disease that remain hidden in the study of bulk cell populations. By profiling single cells it is possible to search for rare cell sub- populations with distinct characteristics, such as drug-resistant cancer cells, which may have profound roles in health and disease. Single cell analysis can also shed light on how cell behavior is controlled, by testing for correlations between the activity of cell signaling, metabolism, cell cycle and cell differentiation within a tissue. To deliver on the promise of single cell analysis, it is necessaryto satisfy at least four technical requirements. The analysis must be carried out on a large number of cells; it should capture multiple measurements to construct a "cellular profile" or mRNA levels, protein levels, and so on; it should be sufficiently sensitive to detect changes in the profile between different cells; and it should be performed in tissues, or with cells immediately removed from tissues, to ensure that the measurements directly reflect the clinical situation. The problem of scale-up to large numbers of cells is particularly urgent. For example, if drug-resistant cancer cells constitute only 1% of a tumor, then to find even ten such cells requires analyzing 1,000 cells in total. Yet today there is a trade-off between analyzing many cells, and generating a wide cellular profile per cell. Methods that analyze many cells, for example Fluorescent Activated Cell Sorting (FACS), are restricted to a limited number of components and may therefore fail to identify rare cells of interest. Methods that can provide a more comprehensive profile are costly and labor-intensive, and therefore cannot be used on large numbers of cells. An ideal method should generate a cellular profile for <$1 per cell. We propose a method to simultaneously profile over 1,000 cells per run, using widely available sequencing technology. The method combines and adapts a number of existing molecular techniques to measure tens of proteins and 100-200 mRNA levels simultaneously in single cells. This breadth of measurement, particularly of protein levels, is not possible to achieve by any existing system at the single cell level. The method should also be more accurate than the closest comparable technology today, as it requires significantly less signal amplification. The long-term potential of the method is limited primarily by the capabilities of high-throughput sequencing technology, and as sequencing technology continues to improve dramatically in the race to deliver a $1,000 human genome, the cost-efficiency, speed and cell throughput of the method are also expected to improve. We project that this method will eventually cost significantly less than $1 per cell profile. Thus, if successful, this method would provide a profound step forward for single cell analysis. PUBLIC HEALTH RELEVANCE: A critical limitation facing single cell analysis is the current trade-off between analyzing many cells and many cellular components. We propose a method that overcomes this limitation by profiling the levels of 100-200 mRNA transcripts and tens of proteins in 1,000 or more cells at reasonable cost (~$1/cell) and with few steps. The method combines several established assays, and introduces a novel "combinatorial bar-coding" strategy to simultaneously read out the profile of all cells using next-generation sequencing.

描述（由申请人提供）：对单个细胞的分析有望揭示对大细胞群研究中隐藏的组织生理学和疾病的见解。通过分析单细胞，可以搜索稀有细胞亚群 独特的特征，例如耐药癌细胞，可能对健康和疾病具有深远的作用。单细胞分析还可以通过测试细胞信号传导活性、新陈代谢、细胞周期和细胞之间的相关性来揭示细胞行为是如何控制的。 组织内的分化。为了兑现单细胞分析的承诺，有必要满足至少四个技术要求。必须对大量细胞进行分析；它应该捕获多个测量值以构建“细胞概况”或 mRNA 水平、蛋白质水平等；它应该足够敏感以检测不同细胞之间的轮廓变化；并且应该在组织中进行，或者立即从组织中取出细胞，以确保测量结果直接反映临床情况。扩大到大量细胞的问题尤为紧迫。例如，如果耐药癌细胞仅占肿瘤的 1%，那么即使要找到 10 个这样的细胞，也需要分析总共 1,000 个细胞。然而如今，在分析许多细胞和为每个细胞生成广泛的细胞谱之间需要权衡。分析许多细胞的方法，例如荧光激活细胞分选 (FACS)，仅限于有限数量的成分，因此可能无法识别感兴趣的稀有细胞。能够提供更全面概况的方法成本高昂且劳动密集型，因此不能用于大量细胞。理想的方法应该以每个细胞 <1 美元的价格生成细胞概况。我们提出了一种使用广泛使用的测序技术，每次运行同时分析 1,000 多个细胞的方法。该方法结合并采用了多种现有分子技术，可同时测量单细胞中数十种蛋白质和 100-200 种 mRNA 水平。这种测量范围，特别是蛋白质水平的测量，是任何现有系统在单细胞水平上不可能实现的。该方法还应该比当今最接近的同类技术更准确，因为它需要的信号放大要少得多。该方法的长期潜力主要受到高通量测序技术能力的限制，并且随着测序技术在提供 1,000 美元人类基因组的竞赛中不断显着改进，该方法的成本效率、速度和细胞通量方法也有望得到改进。我们预计这种方法最终每个细胞分析的成本将大大低于 1 美元。因此，如果成功，该方法将为单细胞分析迈出深远的一步。 公共卫生相关性：单细胞分析面临的一个关键限制是当前分析许多细胞和许多细胞成分之间的权衡。我们提出了一种克服这一限制的方法，通过以合理的成本（约 1 美元/细胞）和很少的步骤对 1,000 个或更多细胞中的 100-200 个 mRNA 转录物和数十种蛋白质的水平进行分析。该方法结合了几种已建立的检测方法，并引入了一种新颖的“组合条形码”策略，以使用下一代测序同时读出所有细胞的概况。