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. 2024 Jul 30;25(1):743.
doi: 10.1186/s12864-024-10650-2.

siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape

Zhenzhen Wang #  1 , Kehan Tao #  1 , Jiaojiao Ji  1 , Changbin Sun #  2 , Wei Xu  3
Affiliations

siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape

Zhenzhen Wang et al. BMC Genomics. .

Abstract

Background: RNA sequencing (RNA-seq) is widely used for gene expression profiling and quantification. Quantitative RNA sequencing usually requires cell counting and spike-in, which is not always applicable to many samples. Here, we present a novel quantitative RNA sequencing method independent of spike-ins or cell counting, named siqRNA-seq, which can be used to quantitatively profile gene expression by utilizing gDNA as an internal control. Single-stranded library preparation used in siqRNA-seq profiles gDNA and cDNA with equal efficiency.

Results: To quantify mRNA expression levels, siqRNA-seq constructs libraries for total nucleic acid to establish a model for expression quantification. Compared to Relative Quantification RNA-seq, siqRNA-seq is technically reliable and reproducible for expression profiling but also can sequence reads from gDNA which can be used as an internal reference for accurate expression quantification. Applying siqRNA-seq to investigate the effects of actinomycin D on gene expression in HEK293T cells, we show the advantages of siqRNA-seq in accurately identifying differentially expressed genes between samples with distinct global mRNA levels. Furthermore, we analyzed factors influencing the downward trend of gene expression regulated by ActD using siqRNA-seq and found that mRNA with m6A modification exhibited a faster decay rate compared to mRNA without m6A modification. Additionally, applying this technique to the quantitative analysis of seven tumor cell lines revealed a high degree of diversity in total mRNA expression among tumor cell lines.

Conclusions: Collectively, siqRNA-seq is a spike-in independent quantitative RNA sequencing method, which creatively uses gDNA as an internal reference to absolutely quantify gene expression. We consider that siqRNA-seq provides a convenient and versatile method to quantitatively profile the mRNA landscape in various samples.

Keywords: Gene expression; Next-generation sequencing; Quantification; RNA sequencing; Transcriptome.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Design of siqRNA-seq. A Flowchart of siqRNA-seq. Total nucleic acids were extracted and two types of libraries, mRNA library (ssRNA-seq) and mRNA&gDNA library, were constructed in parallel and sequenced for each sample in siqRNA-seq. B Principle of siqRNA-seq for RCPG calculation in mRNA&gDNA library. Depth of gDNA can be assessed by intergenic read depth. RCPG is equal to four times the ratio of mRNA read depth to gDNA depth. *: gDNA of diploid with two strands. RCPG: mRNA count per genome
Fig. 2
Fig. 2
Validation of siqRNA-seq for quantitative expression profiling. A IGV showing snapshots of siqRNA-seq signals (RPCG) in the human genome. Both RNA and gDNA signals can be sequenced in the mRNA&gDNA libraries. B Scatter plots showing the correlation between mRNA&gDNA library repeats. C Scatter plots showing the correlation of ssRNA-seq with mRNA&gDNA libraries
Fig. 3
Fig. 3
Pipeline and model of siqRNA-seq for gene expression quantification. A Schematic diagram showing the pipeline for intergenic regions (IRs) screening in the gDNA library. There IRs are used for assessment of gDNA depth in siqRNA-seq quantitative analysis. B Schematic diagram showing the model of siqRNA-seq for gene expression quantification. In the mRNA&gDNA library, a set of genes was selected for constructing a linear model using RCPG values from the mRNA&gDNA library and FPKM values from ssRNA-seq. Then, the established linear model was applied to transform the FPKM values of all genes in the ssRNA-seq to RCPG. C Bar plot showing the number of mRNA molecules per cell in HEK293T and IOSE-80 cells based on siqRNA-seq quantification. D The quantitative results of siqRNA-seq were verified by RT-qPCR in HEK293T cells. n.s.: not significant
Fig. 4
Fig. 4
Analysis of factors influencing the downward trend of ActD-regulated gene expression by siqRNA-seq. A Bar plot showing absolute quantification of mRNA molecules per cell for ActD-treated and untreated HEK293T cells by siqRNA-seq. B Volcano plot showing the results of the DEGs identified by siqRNA-seq for HEK293T cells treated with ActD compared to untreated control. Genes with fold change greater than 2 and FDR less than 0.01 were assigned as DEGs. C The Sankey diagram showing the trend of genes with different fold changes(log2) of Relative Quantification RNA-seq and siqRNA-seq for HEK293T cells treated with ActD. In Relative Quantification RNA-seq, nearly 50% of genes showed an upregulation trend, while siqRNA-seq showed almost no upregulation. D Volcano plot showing the results of DEGs identified by Relative Quantification RNA-seq analysis for HEK293T cells treated with ActD compared to untreated control. Genes with a fold change greater than 2 and FDR less than 0.01 were assigned as DEGs
Fig. 5
Fig. 5
High diversity of total mRNA expression in tumor lines. A The bar chart showing the quantitative results of total mRNA content in seven tumor cell lines obtained through siqRNA-seq. B The bar charts showing the density distribution of fold changes in differential expression compared to tumor HCT116 across these tumor cell lines
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