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

摘要

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).