Research Associate III,
Research & Development,
In this webinar, you will discover:
- the signal-to-noise problem of single cell RNA-seq methods;
- how to improve RNA-seq sensitivity using CRISPR technology;
- ways to boost usable data by cutting wasted sequencing by ~50%;
- how enhanced resolution allowed increased characterization of distinct cell states.
One problem faced by all single cell RNA-seq methods is that they capture only a small fraction of the transcriptome of each cell due to read “dropout” at each step of library preparation. These dropout events are then confounded with noise, outliers, and stochastic genetic variation, resulting in the daunting computational task of parsing out the true signal.
Almost all computational algorithms have evolved to address this issue through many approaches, typically using various dimensionality reduction or imputation techniques. However, there is currently no consensus for a standardized computational approach.
This webinar presents a turnkey molecular solution (CRISPRclean) that drastically reduces dropout events attributable to technical noise, statistically enhancing biological interpretation. Traditionally, single cell data processing incorporates specific filtering and normalization steps before canonical clustering and downstream interpretation.
CRISPRclean removes those reads in vitro, redistributing sequencing clusters to unique biologically relevant transcripts. By tailoring guides to deplete un-annotated genomic intervals in addition to the highest expressed ribosomal and mitochondrial genes, we show the ability to redistribute 50% of reads through in silico depletion across single cell data from 14 tissue types.
Join the webinar to see the results of our preliminary in vitro studies in immune cells, including how CRISPRclean enables the recovery of an additional 300 genes per cell (at a standard sequencing depth). In addition, this method resulted in a two-fold enrichment in unique molecular counts for ~5,000 genes, with 90% of these genes being lowly expressed. This added resolution of rare transcripts translates to the increased characterization of distinct cell states.