Jeliyah Clark

We can learn a lot about human health by measuring changes in gene expression that occur in response to signaling molecules, drugs or during disease. Single-cell analysis is important because heterogeneity within a population of cells is often overlooked in a population-based measurement. Through measuring changes in gene expression in individual cells, we can learn how stimulants of interest orchestrate cellular responses. Gene expression is measured by quantifying messenger RNA molecules, which serve as templates for protein production. Reverse transcription reactions copy information from degradation sensitive RNA molecules to more stable cDNA molecules, which can be processed for sequencing, thus revealing gene expression profiles. Recent advances in lowering the input requirement of these reactions opened up the possibility to quantify the limited RNA molecules an individual cell typically holds.

Most single-cell protocols have used polyT primers to initiate the reverse transcription reactions, followed by exponential amplification. Extensive amplification induces dropout of lowly expressed transcripts and contributes technical noise to the data. Here, we explore random priming that aims to extend the coverage of mRNA molecules by using random primers to initiate reverse transcription. To assess efficacy of random primed reverse transcription, we extracted messenger RNA equivalent to 10,000 cells from a total RNA stock using dT beads, and compared the results to polyT primed reverse transcription. We find that random priming yields significantly higher amounts of cDNA at lengths appropriate for Illumina sequencing. We aim to improve the quality of single-cell data through limiting the need for amplification by a more efficient priming strategy, early pooling, and linear amplification methods.

PROJECT: Random priming strategy for single-cell RNA-seq

Mentor: Jellert Gaublomme, Regev Lab