Gene expression profiling is a crucial technique in biology, allowing scientists to measure the activity of genes within a cell. One popular method for this is RNA sequencing (RNA-seq). Traditionally, to get accurate and quantitative results, RNA-seq requires certain preparatory steps like counting cells and adding known quantities of RNA (called spike-ins). However, these steps can be cumbersome and not always feasible for many types of samples.
To address this challenge, researchers at the Chinese Academy of Agricultural Sciences have developed a new method called siqRNA-seq. This innovative technique allows for quantitative gene expression profiling without needing spike-ins or cell counting. Instead, it uses genomic DNA (gDNA) from the same sample as an internal control, ensuring accurate measurements.
How siqRNA-seq Works
siqRNA-seq stands out because it prepares libraries of both gDNA and complementary DNA (cDNA) with equal efficiency. This means that the gDNA can act as a stable reference point against which the mRNA (messenger RNA, which is derived from cDNA) can be measured. Here’s a simplified overview of the process:
Library Construction: siqRNA-seq creates libraries for all nucleic acids in the sample, including both gDNA and mRNA.
Model Establishment: Using these libraries, a model is established to quantify gene expression levels.
Internal Reference: The gDNA sequences serve as an internal reference, allowing for precise quantification of mRNA without external spike-ins.
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
Advantages of siqRNA-seq
Compared to traditional methods, siqRNA-seq has several advantages:
Technical Reliability and Reproducibility: siqRNA-seq consistently produces reliable and reproducible results for gene expression profiling.
Accurate Differential Expression Identification: The method can accurately identify differences in gene expression between samples, even those with varying overall mRNA levels.
Efficiency with Actinomycin D: When applied to HEK293T cells treated with actinomycin D (a drug that inhibits RNA synthesis), siqRNA-seq effectively identified genes with altered expression and provided insights into how mRNA modifications affect decay rates.
Versatility Across Cell Lines: When used on various tumor cell lines, siqRNA-seq revealed significant diversity in mRNA expression levels, demonstrating its utility across different biological contexts.
Key Findings with siqRNA-seq
The researchers used siqRNA-seq to explore several interesting biological questions:
Actinomycin D Effects: By investigating the impact of actinomycin D on gene expression in HEK293T cells, they discovered that mRNA with m6A modifications decayed faster than those without. This highlights the method’s ability to provide detailed insights into mRNA stability and regulation.
Tumor Cell Line Analysis: Applying siqRNA-seq to seven different tumor cell lines, the researchers found a wide range of total mRNA expression levels. This underscores the method’s potential for studying gene expression in various types of cancer.
Conclusions
siqRNA-seq is a groundbreaking quantitative RNA sequencing method that leverages gDNA as an internal control, eliminating the need for spike-ins and cell counting. This method offers a convenient, reliable, and versatile approach to profiling the mRNA landscape in diverse samples. With siqRNA-seq, researchers can gain more accurate and comprehensive insights into gene expression, paving the way for new discoveries in biology and medicine.
Wang Z, Tao K, Ji J, Sun C, Xu W. (2024) siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape. BMC Genomics 25(1):743. [article]