A comparison of RNA-sequencing methods for degraded RNA

RNA sequencing (RNA-Seq) is a cutting-edge technique that’s gaining traction in clinical research and drug development. It’s a method used to study the quantity and sequences of RNA in a sample, providing insights into gene expression and helping to uncover molecular mechanisms of diseases. However, different RNA-Seq methods have unique advantages, especially when dealing with challenging samples like degraded RNA or low-input RNA (RNA collected in small amounts, often in clinical settings).

Standard RNA-Seq Method: A Quick Overview

The traditional approach to RNA-Seq involves capturing mRNA (messenger RNA) using poly(A) tails, a sequence of adenine nucleotides at the end of mRNA molecules. This method uses Oligo dT beads to bind these tails and isolate the mRNA. While effective for high-quality RNA, this technique isn’t suitable for degraded RNA, which often lacks intact poly(A) tails.

Alternative RNA-Seq Methods

To address the limitations of the standard method, researchers have developed alternative RNA-Seq library preparation kits that use random primers instead of Oligo dT beads. This study focuses on three such methods:

1. SMART-Seq
2. xGen Broad-range
3. RamDA-Seq

These methods aim to capture RNA more effectively, even when it’s degraded or present in low amounts.

Library preparation workflow

(A) SMART-Seq, (B) xGen, and (C) RamDA-Seq

Evaluating Performance

Researchers at Kanazawa Medical University Hospital compared these alternative methods to the standard RNA-Seq technique by examining several factors:

• Correlation: How closely the results match the expected gene expression patterns.
• Number of Detected Expressing Genes: The total number of genes that show activity.
• Expression Levels: The intensity of gene activity signals.

Here’s what they found:

• RamDA-Seq: This method performed similarly to the standard RNA-Seq in terms of correlation and detected gene numbers. However, its performance dropped with low-input and degraded RNA.
• xGen Broad-range: This method didn’t perform as well as SMART-Seq, especially with challenging RNA samples.
• SMART-Seq: This method stood out by showing better performance with low-input and degraded RNA samples compared to both xGen and RamDA-Seq.

Ribosomal RNA (rRNA) Depletion

Ribosomal RNA, which makes up a large portion of total RNA, can interfere with the detection of mRNA. By depleting rRNA from samples, the researchers found that the performance of both SMART-Seq and xGen improved, as this increased the expression levels of other RNA types.

Conclusion: SMART-Seq with rRNA Depletion

For RNA-Seq applications involving low-input or degraded RNA, the SMART-Seq method, especially when combined with rRNA depletion, offers significant advantages. This makes it a valuable tool in clinical settings where sample quality and quantity are often less than ideal.

In summary, understanding the strengths and limitations of different RNA-Seq methods allows researchers to choose the best approach for their specific needs. SMART-Seq, particularly with rRNA depletion, emerges as a robust choice for handling challenging RNA samples, paving the way for more accurate and insightful gene expression analysis in clinical research and drug development.