QUIC-seq: Quick ultra-affordable high-throughput convenient RNA sequencing

Abstract

Quantifying transcript levels is essential for understanding the gene functions and regulatory networks. To achieve this, various techniques have been developed to measure gene transcription levels, including transcriptome sequencing (Mutz et al., 2013). Standard RNA-seq experiment involves several key steps: RNA extraction, mRNA purification, reverse transcription, second-strand cDNA synthesis, adapter ligation, and library amplification. Traditionally, RNA-seq covers the entire coding region and requires sufficient sequencing depth to produce reliable results. To address this, targeted 3′ end transcriptome sequencing methods like PAT-seq (Harrison et al., 2015) have been developed, reducing both the sequencing coverage and costs, but these methods can only process one RNA sample at a time, limiting their application in large-scale studies. Fortunately, advancements like SiPAS (Wang et al., 2022) and MP3RNA-seq (Chen et al., 2021) have made the construction of 3′ terminal libraries more convenient and suitable for high-throughput applications. Recent studies have shown that Tn5 can act on RNA/DNA hybrids (Choi et al., 2024; Lu et al., 2020), eliminating the need for cDNA second-strand synthesis. Thus, we developed QUIC-seq, a quick, ultra-affordable, high-throughput and convenient method for gene expression analysis.

The workflow for QUIC-seq library preparation is illustrated in Figure 1a. Initially, total RNA is extracted from various samples and quantified. Approximately 500 ng RNA from each sample is transferred into a 96-well plate for reverse transcription using specific reverse transcription primers (Table S1). Next, half of the RNA/DNA hybrids from each well are pooled and recovered with magnetic beads. The RNA/DNA hybrids are then fragmented using Tn5 transposase, followed by direct PCR amplification. The amplified product is selected using magnetic beads and sequenced on illumina sequencing platform.

Optimizing the QUIC-seq library conditions involved adjusting various steps to achieve the best results. First, we tested the effect of reverse transcriptase from different manufacturers (Figure S1). The AIII enzyme performed the best, followed by VIII enzyme and TIV enzyme. Despite AIII's superior performance, its cost was double that of VIII, making it less economical. We also examined the effect of inactivating the reverse transcription enzyme. The results indicated that enzyme inactivation negatively affected the subsequent data. This could be due to high temperatures causing the RNA/DNA hybrids to denature and separate, resulting in sub-optimal library construction. In addition, we found that whether SDS was added or not had little effect on the number of genes detected (Figure 1b; Figure S2).

When Tn5 fragment RNA/DNA hybrids, it creates a 9 bp gap. Previous studies (Choi et al., 2024; Lu et al., 2020) suggested adding Bst3.0 or reverse transcriptase to fill this gap. However, we found that more detected genes (Figure 1b) were achieved without adding Bst3.0 or reverse transcriptase. This indicates that Takara Ex Premier polymerase effectively fills the hybrids' gap – a novel finding in this study. In summary, we eliminated the steps of reverse transcriptase inactivation, Tn5 transposase inactivation, and Bst3.0 or reverse transcriptase gap-filling, streamlining the procedure and reducing both time and cost.

To assess the reproducibility of the library construction, we performed two independent QUIC-seq experiments. The results demonstrated a high correlation coefficient of 0.98 between the replicates (Figure 1c). TruSeq reads were evenly distributed across the entire gene, while QUIC-seq reads were concentrated at the 3′ end as expected (Figure 1d). QUIC-seq has fewer reads aligned to intergenic regions than TruSeq, improving read utilization (Figure S3). To test for sample mixing, we used RNA from Arabidopsis, rice and maize in a single experiment. Approximately 99.8% of the reads from these samples aligned with their respective reference genomes, indicating minimal cross-contamination between barcodes (Figure 1e; Figure S4). Theoretical predictions suggest that increasing read counts leads to more gene detections, but also raises costs. Our analysis showed that around 20 000 genes were detected at 1.5 × 10⁶ reads, with the number plateauing thereafter (Figure 1f).

The performances of QUIC-seq and other RNA-seq methods were compared. The correlation coefficient between the TruSeq and QUIC-seq library construction methods was 0.9 (Figure 1g), comparable to other 3′ terminal RNA-seq methods like MP3RNA-seq (Chen et al., 2021). In terms of gene identification, both methods detected 22 939 common genes (Figure 1h; Table S2). However, QUIC-seq offers significant advantages in cost and throughput. Unlike MP3RNA-seq, QUIC-seq simplifies the process by omitting RNA strand degradation and the conversion of single-stranded cDNA to double-stranded cDNA. While BOLT-seq requires the same amount of time for library preparation, it involves more complex steps such as adding Tn5 and performing PCR amplification in a 96-well plate (Choi et al., 2024). In contrast, QUIC-seq requires only reverse transcription in a 96-well plate, with Tn5 fragmentation and PCR amplification done in a few Eppendorf tubes, significantly reducing operational complexity. Additionally, QUIC-seq libraries includes three barcodes, further reducing costs. The incorporation of UMIs in the reverse transcription primers also enables more accurate gene expression quantification after UMI deduplication, a feature lacking in BOLT-seq. In summary, QUIC-seq is a simplified and efficient library preparation method that can process high-throughput samples (96 samples) within 4 h at a significantly reduced cost of only $0.823 per sample (Figure 1i; Tables S3 and S4).

Given the method's stability and convenience, we conducted several experiments using QUIC-seq. Initially, we identified 2477 differentially expressed genes (DEGs) in response to nitrogen and phosphorus stress, with 1339 genes up-regulated and 1138 down-regulated (Figure 1j; Figure S5). To further explore the role of Opaque2 (O2) and prolamin-box binding factor 1 (PBF1) in endosperm formation and to understand the expression regulatory network, we used QUIC-seq to screen target genes. In a previous transcriptome analysis using O2-GFP and GFP, 1715 up-regulated and 772 down-regulated genes were identified with FDR < 0.05 (Zhu et al., 2023). Under stricter criteria (FDR < 0.05, |log2FoldChange| > 1), QUIC-seq identified 2281 O2-up-regulated and 2913 O2-down-regulated genes, revealing more DEGs compared to PER-seq (Figure S6). Additionally, we found 539 PBF1-activated and 480 PBF1-repressed genes, with 88 genes up-regulated and 217 genes down-regulated in both O2-GFP and PBF1-GFP datasets. In analysing the expression regulatory network of BBM, we identified four transcription factor genes with functions similar to BBM. QUIC-seq was employed for library construction and sequencing, leading to the identification of DEGs in bHLH48, WRKY95, GATA28, and IFA1, ultimately uncovering 50 co-expressed regulatory genes associated with these four transcription factors (Figure 1k; Figure S7).

The authors declare they have submitted an innovation patent related to this work (application number: 202410796820X).

HW conceived and designed the project. ST, DW and HQ conducted the experiments and analysed the data. HS, MS, MX, LY, TL and QY participated in some experiments. ST wrote the manuscript. HW and REVO revised the manuscript.