Reduce the Garbage (aka Noise) in Your scRNA-seq Data to Ensure Meaningful Data
Single-cell transcriptional data sets can illuminate the path to that rare, elusive cell type lurking in the shadows, undiscovered by bulk sequencing, waiting to stand in the spotlight. To optimize the return of unique phenotypic signatures, it is important to start with a healthy cell population free of dead cells and residual debris. Unfortunately, some of the most interesting problems require tissues that contain notoriously high amounts of these contaminates, such as solid tumors, brain tissue, or any research samples that must be cryopreserved before processing.
Given that library preparation costs tend to be fixed, getting the best return on your investment means maximizing the number of usable cells and reducing the noise. In other words, steering clear of the old adage, “garbage in, garbage out.”
Since apoptotic cells, dead cells, and residual ambient RNA can all make their way into single-cell sequencing data sets, the raw data is screened via a set of standard QC metrics to help filter these contaminants out before proceeding to downstream analyses.
In this latest Research Snapshot, we have evaluated some of these quality control (QC) metrics in a single-cell data set from whole mouse brain that has undergone both viable cell enrichment on the LeviCellTM system as well as no enrichment.
We demonstrate that enrichment using Levitation Technology elevates the single-cell suspension by increasing the ratio of viable cells to dead cells and debris, improving the quality of the data set across a total of eight recommended QC parameters. Higher quality data means more usable information, which ultimately can lead to more accurate and reproducible discoveries.
