Analyzing how genes are expressed provides answers relevant for diagnostics, therapeutics and fundamental biology. The transcriptome, the entirety of a cell’s transcripts, is much more complex than the genome it is transcribed from, due to its modular nature and reaction to internal and external stimuli. It changes constantly over time and during development, reflecting the “programming” required by the cell in any given situation.

This complexity sustains incredible coding power, but the dynamic nature of the transcriptome makes it hard to find a sample-independent baseline to compare gene expression results. So-called housekeeping genes that do not vary in expression between samples have proven difficult to use in practice as accurate sets must be redefined when comparing different tissues. Furthermore, these internal standards cannot sufficiently reflect certain technical aspects of library preparation such as sensitivity and isoform complexity.

In response, scientists are increasingly turning to external controls, comprising sets of in vitro produced spike-in transcripts. These controls provide the means to determine overall validity as well as sensitivity, precision, and repeatability of a given gene expression assay. The controls reflect concentration gradients, splicing and transcription complexity, miRNA diversity, and other aspects of cellular transcripts. The limited number of transcripts that need to be analyzed, together with their known concentration and sequence provides precise and rapid feedback on the RNA sequencing experiment.

The recently published feature in Nature Methods (Vivien, M., 2019) covers the topic of spike-in controls, iterating the need for their use in RNA sequencing experiments. Lexogen is at the forefront of developing external spike-in controls for RNA sequencing experiments and produces the Spike-In RNA Variant (SIRV) control mixes that enable experimenters to perform RNA-Seq experimental validation and comparison analyses:

Validation – Failures, biases, variations and limitations immediately become obvious allowing to determine the quality of an experiment and to improve workflows as well as data analysis tools.

Comparison – The “fingerprint” obtained by analysis of the spike-in controls alone is sufficient to decide whether data sets should be compared. The technical variation can be known to be able to determine the biological one which is at the focus of the experiment. Data sets obtained e.g. by a collaborator, by sequencing on different NGS platforms or simply by using different algorithm versions of otherwise identical data analysis pipelines will result in differences in gene expression values. Only external controls can establish a ground truth to determine the comparability of data sets.

The SIRV Isoform Mixes, only available from Lexogen, reflect the transcriptional and splicing complexity of the transcriptome. SIRVs are also available in combination with the non-isoform ERCC spike-ins to provide an additional measure of sensitivity and dose-response.

Reference:
Marx, V. (2019) Controls let genomics experimenters drive with a dashboard. Nature Methods 16, 29–32. DOI: 10.1038/s41592-018-0265-y.