RNA Analysis

Quantification and analysis of RNA

Depending on the downstream application, four main quality parameters can characterize the quality of your RNA samples – quantity, purity, size and sequence. Variations in these parameters can impact your experiments and affect the data quality and results interpretation. Even small variations can mean the difference between assay success or failure.
Molecular biology reactions require very precise amounts of RNA for optimal performance. Too little or too much RNA can severely impact the final assay results, so quantification should be a standard procedure following purification to ensure successful reaction outcomes.
RNA samples can become contaminated by other molecules with which they were co-extracted and eluted during the purification process or by chemicals from upstream applications. The resulting impurities can significantly decrease the sensitivity and efficiency of your downstream enzymatic reactions. Along with identifying common chemical contaminants, differentiating between different types of nucleic acids (RNA vs. DNA) is also important when assessing sample purity.

Upstream processing or contaminating nucleases can lead to degradation or fragmentation, and problems with enzymatic reactions and biochemical modifications may result in unexpected fragment sizes. Therefore, it is important to check the condition of your RNA samples prior to analysis.

RNA integrity scores (RIN or RINe: RNA integrity number or RIN equivalent and RIS: RNA integrity score) provide information on the quality of RNA before it is used in downstream applications. The quality is indicated by a score ranging from 1 (degraded RNA) to 10 (intact RNA).

Sequencing a sample is the ultimate QC step scientists should perform to verify and validate that the pieces of RNA they are working with are the correct ones and that the genetic information has not been altered in any way along the workflow.

Infographic, RNA, quantity

In this infographic, we describe the influence of these parameters on downstream applications, how to assess them and how to implement quick and simple quality control measures that can help increase your RNA research success.