RNA quantity and quality assessment

RNA quantification and quality control methods

RNA quantification and analysis are critical steps in molecular biology and genetic research, ensuring that RNA samples meet the necessary quality parameters for downstream applications. The key RNA quality metrics are quantity, purity, integrity, and sequence. Variations in these parameters can significantly impact experimental outcomes, emphasizing the importance of thoroughly assessing RNA quality to avoid compromised data integrity or experimental failure.

Importance of RNA quantification

Molecular biology reactions require very precise amounts of RNA for optimal performance and reproducibility. 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 experimental outcomes.

RNA quantification methods

Spectrophotometry and fluorometry are two widely used techniques that offer distinct approaches to quantify RNA in your extract, each with specific advantages and limitations.

1. Spectrophotometry

Quantification is based on the principle that nucleic acids, including RNA, absorb ultraviolet (UV) light maximally at 260 nm due to their aromatic bases. This absorption follows the Beer-Lambert law, where the absorbance at 260 nm is directly proportional to the RNA concentration.
Absorbances are further used to assess the RNA purity by comparing absorbance ratios, such as A260/A280 for protein contamination and A260/A230 for salt or organic impurities.
- A260/A280: The ideal ratio is ~2.0 for pure RNA, and a ratio of ~1.8 is for pure DNA, while lower ratios indicate protein contamination. However, please note that a ratio of 1.8–2.1 at pH 7.5 in practical applications is widely accepted as indicative of highly pure RNA.
- A260/A230: The ideal ratio is >1.8 for an RNA to be considered pure. Although lower ratios suggest salt or organic contaminants, there is no consensus on the acceptable lower limits of this ratio. Additionally, this ratio depends on RNA concentration, and trace amounts of contaminants will virtually not affect the ratio if the RNA concentration is high. But will have a big impact when working with RNA at low concentration. Nevertheless, when working with a low A260/A230 ratio, it is important to consider the amount of contaminant that is transferred to the downstream reaction (e.g., cDNA synthesis), as shown in the experiment below.
Spectrophotometry is favored for its simplicity, rapid output, and non-destructive nature, allowing subsequent downstream use of the same sample.
However, it lacks specificity, as it cannot differentiate RNA from DNA or free nucleotides and is susceptible to interference from contaminants.

This experiment shows the result of mir16 detection in a SYBR® Green RT-PCR assay. RNA samples were extracted from brain tissue using the miRNeasy Micro Kit, either manually or using a QIAcube® for automated purification. All samples had an A260/A280 ratio around 1.8–2.1. The 5 mg sample had A260/A230 ratios of 1.75–1.95, whereas the 0.5 mg sample had a lower ratio of 0.8–1.5. Despite the differences in the A260/A230 ratios, no influence in the downstream amplification of mir16 was found. (A) It shows the CT values, (B) amplification plots, and (C) melting curves, which showed comparable curves and a single melting peak, indicating homogeneous amplification.

2. Fluorometry

Higher sensitivity and specificity quantification by utilizing fluorescent dyes that selectively bind to RNA. These dyes emit fluorescence upon binding, and the intensity correlates with RNA concentration.
Fluorometry is particularly advantageous for low-concentration samples and situations where distinguishing RNA from DNA is critical.
Despite requiring additional reagents and equipment calibration, it is highly accurate and essential for applications demanding precise quantification.

Comparison of the spectrophotometry and fluorometry in RNA quantification and analysis.

Choosing the right RNA quantification tools

Selecting the appropriate quantification method depends on the sample type, concentration range, and downstream applications.

RNA purity assessment

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 RNA purity. Purity is evaluated by calculating absorbance ratios, as mentioned above.

RNA integrity assessment

RNA integrity is assessed to ensure minimal degradation or fragmentation. The RNA Integrity Number (RIN) or RNA Integrity Score (RIS) quantifies RNA quality on a scale from 1 (degraded) to 10 (intact). Denaturing gel electrophoresis or automated capillary electrophoresis (e.g., QIAxcel Advanced) provides detailed size distribution analysis, identifying specific rRNA bands and ensuring accurate size ratios.

A QIAxcel gel view of total RNA extracted from rat liver samples and subjected to heat degradation (70°C, 0–60 minutes (lanes A1–A11); QX RNA size marker in lane A12; arrows indicate main ribosomal subunits. B Corresponding electropherogram and RIS values of samples in lanes A1, A7 and A11 and the superimposition of the electropherograms. This result demonstrates the loss of RNA integrity, where the ribosomal RNA subunit 28 S disintegrated due to heat treatment.

RNA sequence validation

Sequencing a sample is the ultimate RNA quality control step. It verifies and validates that the pieces of RNA you are working with are correct and that the genetic information has not been altered in any way along the workflow. Below are the common sequencing methods to validate RNA sequence fragments and their applications.

While RNA sequencing is a powerful tool for transcriptome profiling or novel RNA discovery, routine sequence validation for RNA is normally performed using a combination of versatile quality control methods to confirm that the RNA being studied is intact, accurate, and suitable for the intended experiments. These methods include reverse transcription-PCR (RT-PCR), quantitative RT-PCR (RT-qPCR), and microarray analysis, which are practical and cost-effective for routine validation of RNA integrity and sequence.

When working with RNA for cloning, transcript isoform detection, fusion gene analysis, and RNA-complex interactome studies, sequencing plays a significant role in validating the sequence of the RNA being studied. Capillary and next-generation sequencing technologies can be used for validation and quality control steps.

Troubleshooting RNA quantification and analysis

Challenges in RNA quantification and analysis can arise from various issues, leading to suboptimal results in downstream applications. Below is a guide to identifying common problems, their potential causes, and actionable solutions to ensure high-quality RNA analysis.