A contamination focused approach for optimizing the single-cell RNA-seq experiment

A contamination focused approach for optimizing the single-cell RNA-seq experiment

Blog Article

Summary: Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations Jams caused by nucleic acid material released by dead and dying cells.This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio.Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain.Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics.

Through a series of controlled experiments, we report improvements Wild Bird Supplies that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms.We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.