Research Project
8780513
DESCRIPTION (provided by applicant): We propose to develop and commercialize high sensitivity and accuracy RNA sequencing kits suitable for gene expression measurements in limiting input samples, such as single cells. During Phase I, we proposed the concept of using molecular barcodes in the form of DNA sequence tags to label individual molecules (or copies) of RNA for gene expression analysis in single cells. The Phase I project proved to be highly productive, and feasibility was demonstrated. Counting the barcodes provides an absolute, digital quantitative measure of the number of transcripts expressed. In addition, the barcodes are shown to be useful in correcting for PCR bias, a common challenge in small samples requiring DNA amplification protocols. We also determined a low RNA representation efficiency of 0.1-3.8% overall yield in current single cell RNA sequencing sample preparation methods, and demonstrate that with our cDNA amplification and barcoding approach, more accurate measurements are obtained at significantly higher yields of ~22.5%. These remarkable results indicate that our method can significantly improve on current techniques which suffer from large losses and have inaccuracies introduced by amplification distortions. Because measurement of gene expression levels is important in life science research and also in many clinical settings, our proposed product if successful, would contribute greatly to advancements in these areas. For Phase II, we propose to extend the validated concept into a product development program to produce high efficiency, user-friendly single-cell RNA sequencing kits. Specifically, we will perform assay development to produce validated sets of primers for inclusion in these kits. We will also optimize and validate reaction mix formulations to enable the high efficiency sampling of low abundance RNA transcripts that are missed by other methods. Analysis tools for the upstream selection of custom primer sets or gene panels, and for the downstream analysis of sequencing data will also be developed.
