APR 19, 2022 1:30 PM PDT

Boosting Signal-to-Noise in Single Cell RNA-Seq Through Molecular Depletion

Sponsored by: Jumpcode Genomics
C.E. Credits: CEU


Regardless of method, single cell RNA-seq only captures 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 clustering and downstream interpretation. Instead of removing those reads in-silico, our universally incorporated molecular solution (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 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 in immune cells. At a standard sequencing depth, CRISPRclean enables the recovery of an additional 300 genes per cell. In addition, roughly 5,000 genes have a two-fold enrichment in unique molecular counts. Of those 5,000 genes, greater than 90% are lowly expressed. This added resolution of rare transcripts translates to increased characterization of distinct cell states. In fact, in PBMCs, CRISPRclean identifies two additional cell types, including a rare dendritic subtype. Furthermore, by redistributing 50% of sequenced reads to biologically relevant transcripts, an additional 2,600 ligand-receptor interactions are observed as a part of the cell-to-cell communication. Consequently, there is a two-fold enrichment in the immune interaction network. In essence, CRISPRclean recovers more information per cell. In essence, CRISPRclean recovers more information per cell.  CRISPRclean enhances signal to noise and provides additional confidence in finding solutions to biologically relevant problems.

Learning Objectives:

1. Discuss the signal-to-noise problem of single cell RNA-seq methods.

2. Explain how to improve RNA-seq sensitivity using CRISPR technology.

3. Explain how enhanced resolution allowed increased characterization of distinct cell states.

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