Double transcriptomic reads in single cell RNA-seq using molecular depletion

Single cell RNA-seq is known to only capture a small fraction of the transcriptome of each cell. Often, this is due to inherent limitations of the methodology as reads ‘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 to parse out the true signal. Almost all computational algorithms have evolved to address this zero-inflation issue through a multitude of approaches, typically through various dimensionality reduction or imputation techniques. While consensus for a standardized computational approach has yet to be met, we present a turnkey molecular solution that drastically reduces dropout events attributable to technical noise, statistically enhancing biological interpretation.

 

Traditionally, single cell data processing incorporates certain filtering and normalization steps prior to canonical sequencing and downstream interpretation. Instead of removing those reads in-silico, our universally incorporated molecular solution (CRISPRclean) removes those reads in-vitro, redistributing sequencing reads 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 have exhibited the ability to redistribute 50% of reads through in-silico depletion across single cell data from 14 tissue types.

 

This webinar will review the results of our preliminary in-vitro studies. By redistributing reads to lowly expressed molecules within the sequencing library, a greater number of genes and unique molecules are identified per cell. By recovering more biologically informative reads, CRISPRclean boosts scRNA-seq sensitivity and increases the characterization of distinct cell states.

 

Learning Objectives