RNA sequencing is widely used to measure RNA molecules and understand how cells regulate gene activity. However, small RNA sequencing can be technically challenging. During library preparation, unwanted primer dimers can form, and reverse transcriptase enzymes can stop early when they encounter RNA modifications or complex RNA structures.

Researchers at the Department of Biological Engineering, Massachusetts Institute of Technology have developed a gel-free library preparation strategy to make next-generation RNA sequencing more efficient. The article was published in Communications Biology in 2026.

The team focused on improving absolute quantification RNA sequencing, or AQRNA-seq, a method used to measure small RNAs with high accuracy. Small RNAs, such as tRNAs, can be difficult to analyze because they often contain chemical modifications and structured regions that interfere with sequencing workflows.

Experimental design and workflow

Four variations of the AQRNQ -seq library preparation protocol (designated V1 -V4) were developed to achieve a full -factorial design based on the original AQNRA -seq method (V1), enabling direct comparison between the original and novel approaches for mitigating reverse transcription polymerase fall -off (PF) and minimizing primer dimers (PD). Protocols V1 and V3 mitigate PF by using AlkB treatment to remove selected methylations (ALKB), whereas protocols V2 and V4 use a high -processivity reverse transcriptase to improve read -through across diverse modifications (SS). Protocols V1 and V2 mitigate PD through gel extraction (GE), whereas protocols V3 and V4 incorporate post -RT digestion of excess RT primers via exonuclease I treatment (EXOI). Gray boxes in the center column depict the protocol workflow. For each protocol variant, an arrow traverses a series of gray boxes indicating sequential steps. The left and right columns detail the strategies for mitigating PF and reducing PD implemented in different approaches.

One major problem is primer dimer formation. Primer dimers are small unwanted products that can take up sequencing space and reduce useful data. To address this, the researchers added a post-reverse-transcription digestion step using E. coli exonuclease I, an enzyme that removes excess primers. This reduces the need for gel purification, which can be time-consuming and difficult to automate.

The second challenge is polymerase fall-off, where the reverse transcriptase enzyme stops before reaching the end of an RNA molecule. This can lead to incomplete reads and lower sensitivity. The researchers used a high-processivity reverse transcriptase, which is better able to read through difficult RNA regions and generate more full-length reads.

Together, these changes improved AQRNA-seq sensitivity by more than tenfold. By reducing primer dimer artifacts and increasing target RNA reads, the workflow can produce more useful sequencing data from smaller sample inputs.

This improvement also makes the method more practical for automation. Removing the need for gel electrophoresis simplifies library preparation, increases throughput, and may make small RNA quantification more accessible for larger studies.

Overall, this work shows how careful improvements to RNA sequencing library preparation can make small RNA analysis more sensitive, scalable, and reproducible. These advances may support studies of RNA modifications, bacterial small RNAs, tRNA biology, and other areas where accurate small RNA measurement is important.