Understanding how genes function and regulate biological processes requires accurate measurement of transcript levels, which represent how much of a specific RNA molecule is present in a cell. Various techniques have been developed to analyze these transcript levels, with transcriptome sequencing being one of the most prominent methods. A standard RNA sequencing (RNA-seq) experiment involves several key steps, including extracting RNA, purifying messenger RNA (mRNA), reverse transcribing it into cDNA, and amplifying the resulting library for sequencing.
Traditional RNA-seq captures the entire coding region of RNA but can be expensive and require extensive sequencing to yield reliable results. To make this process more efficient and cost-effective, targeted methods like PAT-seq have been developed. However, these methods often work with only one RNA sample at a time, limiting their application in larger studies. Recent advancements such as SiPAS and MP3RNA-seq have improved the construction of 3’ end transcript libraries, making high-throughput applications more feasible.
Researchers at the China Agricultural University have developed a new method called QUIC-seq to address these limitations. QUIC-seq is designed to be quick, affordable, and easy to use for gene expression analysis. The workflow involves several steps, starting with extracting total RNA from samples and transferring a specific amount into a 96-well plate for reverse transcription. Instead of requiring complex processing, QUIC-seq uses Tn5 transposase to fragment RNA/DNA hybrids, followed by direct amplification using polymerase, which streamlines the procedure.
QUIC-seq is a quick, ultra-affordable, high-throughputand convenient 3′-end mRNA library construction technology
(a) QUIC-seq workflow. (b) Variable library building conditions of QUIC-seq. CK: conventional method before optimization; −85 °C: reverse transcriptase not inactivated at 85 °C; -SDS: Tn5 transposase not inactivated with SDS; -Bst3.0: Bst3.0 or reverse transcriptase omitted. (c) Scatter plot showing correlation between replicates. (d) Comparison of read distribution between TruSeq and QUIC-seq. (e) Proportion of UMI counts from maize, rice and Arabidopsis samples. (f) Correlation of read numbers with detected genes in QUIC-seq. (g) Scatter plot comparing TruSeq and QUIC-seq. (h) Venn diagrams showing the overlap gene subsets detected by TruSeq and QUIC-seq. (i) QUIC-seq is highly cost-effective and time-efficient compared to other RNA library construction methods. (j) Transcriptome analysis of nitrogen and phosphorus deficiency in maize seedlings. (k) Venn diagram showing coregulatory genes of four BBM transcription factors within the regulatory network. *** corresponds to P < 0.001, ns corresponds to P > 0.05.
The results from QUIC-seq show high reproducibility, with a strong correlation between independent experiments. Unlike traditional methods that distribute reads evenly across the entire gene, QUIC-seq focuses on the 3’ end of the transcript, which is where many regulatory elements reside. The method has also demonstrated minimal cross-contamination in mixed samples, confirming its accuracy and reliability.
In comparison to other RNA-seq methods, QUIC-seq offers several advantages. It simplifies the library preparation process, reduces operational complexity, and cuts costs significantly. QUIC-seq libraries can analyze up to 96 samples in just four hours at a cost of around $0.82 per sample, making it a highly efficient option for researchers.
The researchers have already begun to explore various biological questions using QUIC-seq. For instance, it was used to identify genes that respond to nitrogen and phosphorus stress, revealing a range of genes that are up-regulated or down-regulated under these conditions. Additionally, QUIC-seq helped screen target genes related to important regulatory factors in plant development, uncovering numerous differentially expressed genes that could provide insights into regulatory networks.
In summary, QUIC-seq is a promising tool that streamlines the process of gene expression analysis, making it accessible for high-throughput studies. Its cost-effectiveness and ease of use open new avenues for research, helping scientists better understand gene functions and their roles in various biological processes.