Highly accurate small-RNA sequencing of single cells (RealSeq-SC)

摘要 这项赠款申请的目标是开发第一个商业化的图书馆准备工作 使用NGS方法从单细胞中分析小RNA的试剂盒。信使核糖核酸的单细胞分析 使得能够识别原本被认为是细胞之间的关键差异 一模一样。这些发现表明，在基因调控中存在着内在的“噪音”。 在一组细胞中的表达，在决定细胞命运方面起着重要作用。 不幸的是，目前缺乏关于细胞间水平的可变性的信息 作为基因表达调节因子的microRNAs也可能起到关键作用。事实上，没有 市场上可获得的miRNAs和其他小RNA的文库准备试剂盒 单细胞。我们建议使用先进的、专有的“低”来量化单个细胞中的miRNAs 输入单适配器和循环“技术，可实现灵敏和无偏检测。 对于更高的输入量，展示了核心的单适配器和环化技术 对基准Universal miRNA池中超过70%的miRNA进行无偏检测 从最好的竞争对手套件提高到~35%。我们进一步开发了这项用于单细胞的技术 通过创建一种新的“低输入版本”进行分析，该版本即使在单一输入模式下也能保持检测精度。 细胞水平。我们第一阶段研究的数据显示，这种“低输入适配器”最大限度地减少了辍学 事件(单细胞分析中的关键和常见问题)通过提高 MiRNA检测。单细胞miRNA测序的另一个主要障碍是形成 在文库制备过程中缺少miRNA插入的适配二聚体，严重减少了量 有用的miRNA测序读数。我们采用了三种不同的策略来大幅降低 文库中存在适配二聚体。此外，我们的方案执行细胞裂解的所有步骤 最终纯化单管中的扩增文库，以减少单细胞miRNA的损失 并减少环境RNA污染单细胞样本的可能性。在……里面 第一阶段，我们通过检测来自单个细胞的小RNA来演示原理证明 不同的细胞系。在第二阶段，我们将进一步开发和优化我们的技术，以显著 提高来自单个细胞的miRNAs和其他小RNA的灵敏度和检测准确性 商业可行性。我们还将开发一种单细胞小rna-seq文库制备试剂盒。 (RealSeq-SC)。

Abstract The goal of this grant application is to develop the first commercially available library preparation kit for profiling small RNAs from single cells using NGS methods. Single-cell analyses of mRNA have allowed the identification of crucial differences between cells that were otherwise considered identical. These findings have shown that there is intrinsic “noise” in the regulation of gene expression within a population of cells that plays an important role in determining cell fates. Unfortunately, there is currently a lack of information about the cell-to-cell variability of levels of microRNAs that as gene expression regulators may also play a critical role. Indeed, there is no commercially available library preparation kit for miRNAs and other small RNAs that can profile single cells. We propose to quantify miRNAs from single cells using an advanced, proprietary “low input single adapter and circularization” technology that allows sensitive and unbiased detection. The core single adapter and circularization technology, for higher input quantities, demonstrated unbiased detection of over 70% of all miRNAs in a benchmark Universal miRNA pool, compared to ~35% from the best competitor kit. We have further developed this technology for single-cell analysis by creating a novel “low input version” that retains the detection accuracy even at single- cell levels. Data from our Phase I studies show that this “low input adapter” minimizes dropout events (a critical and common problem in single cell analysis) by increasing the efficiency of miRNA detection. Another major obstacle for single-cell miRNA sequencing is formation of adapter-dimers lacking miRNA inserts during library preparation that critically reduces the amount of useful miRNA sequencing reads. We employ three separate strategies to dramatically reduce the presence of adapter-dimers in the library. Also, our protocol performs all steps from cell lysis to final purification of amplified libraries in a single tube to reduce loss of miRNA from single-cells and to reduce the possibility of contamination of single-cell samples by environmental RNA. In Phase I we demonstrated proof-of-principle by detecting small RNAs from single-cells for three different cell lines. In Phase II, we will further develop and optimize our technology to significantly increase sensitivity and detection accuracy of miRNAs and other small RNAs from single cells for commercial viability. We will also develop a kit for single-cell small RNA-seq library preparation (RealSeq-SC).