What is DMS rG4-Seq?
RNA G-quadruplexes (G4s) are secondary structures formed by guanine-rich sequences that regulate RNA stability, splicing, and translation. However, traditional RNA structure mapping methods often miss these transient or condition-dependent formations, especially those occurring in vivo.
DMS rG4-Seq is an advanced sequencing approach that overcomes this challenge. It combines dimethyl sulfate (DMS) modification with rG4-Seq to detect RNA G-quadruplex folding at single-nucleotide resolution. DMS selectively methylates unpaired bases (adenine and cytosine) while leaving guanine residues in folded G4s protected. During reverse transcription, these folded sites generate unique RT-stop signatures that are precisely mapped through next-generation sequencing.
This dual-strategy method enables researchers to:
- Identify RNA G-quadruplexes in their natural cellular environment
- Differentiate between folded and unfolded RNA regions under varying conditions
- Quantify structural changes linked to stress, development, or drug treatment
By revealing how RNA folds dynamically in response to biological stimuli, DMS rG4-Seq provides a deeper understanding of post-transcriptional regulation and non-coding RNA function.
Workflow Overview
The DMS rG4-Seq workflow integrates chemical probing and next-generation sequencing to capture both in vitro and in vivo RNA G-quadruplex structures with single-nucleotide precision. Each step is designed to preserve native RNA conformations and generate reproducible, quantitative datasets suitable for downstream integrative analysis.
Step 1. Sample Treatment and DMS Modification
Biological samples—cells, tissues, or purified RNA—are gently treated with dimethyl sulfate (DMS) under physiological conditions. DMS selectively methylates unpaired adenine and cytosine residues while folded G4 regions remain protected, allowing in-cell structural information to be retained without the need for crosslinking or denaturation.
Step 2. RNA Purification and Reverse Transcription
After RNA extraction, reverse transcription is performed in parallel with K⁺ and Li⁺ buffers. Potassium ions promote G-quadruplex folding, whereas lithium ions do not. The reverse transcriptase enzyme stalls at folded G4 regions, producing RT-stop patterns that mark G4 positions within the transcriptome.
Step 3. Library Construction and Sequencing
Adapter-ligated cDNA fragments containing these RT stops are size-selected and amplified. Libraries are then sequenced using high-throughput Illumina platforms to obtain millions of reads for each experimental condition, ensuring quantitative reproducibility even from low RNA inputs.
Step 4. Bioinformatics and rG4 Identification
Sequencing data are processed through the rG4-Seeker pipeline, which identifies G-rich regions exhibiting DMS protection and dependent RT stops. The result is a transcriptome-wide map of in vivo G-quadruplex sites, annotated with structural stability, folding scores, and motif enrichment.
This robust workflow delivers both qualitative and quantitative insights into RNA structure formation, enabling researchers to study how G4 folding contributes to gene expression regulation and RNA–protein interaction dynamics.
Applications
RNA Structural Biology
DMS rG4-Seq enables transcriptome-wide detection of folded RNA G-quadruplexes under both physiological and stress conditions.
Researchers can use it to identify where and when rG4s form, distinguish folded versus unfolded regions, and quantify their dynamics with nucleotide-level precision.
This information provides a foundation for understanding how secondary structures shape RNA metabolism and stability in living systems.
Gene Regulation and Post-Transcriptional Control
RNA G-quadruplexes play regulatory roles in transcription, splicing, translation, and degradation.
DMS rG4-Seq allows scientists to visualize G4 folding patterns that influence ribosome pausing, alternative splicing, and mRNA turnover.
By mapping G4 structures across different stress conditions or cell types, researchers can reveal structural elements that act as molecular switches controlling gene expression.
RNA–Protein Interaction Studies
When combined with RIP-Seq or RBP profiling, DMS rG4-Seq provides structural context to RNA–protein binding events.
It helps identify RNA-binding proteins that selectively recognize or stabilize G-quadruplexes, offering insight into how RNA structure guides protein recruitment.
This integrated approach supports discovery of new RNA–protein regulatory networks relevant to cancer, neurodegeneration, and viral infection.
Epitranscriptomic and Chemical Probing Studies
Because DMS reacts specifically with unpaired bases, DMS rG4-Seq captures native RNA folding states in vivo without perturbing structure.
It can also be used alongside other chemical probing methods to assess the interplay between RNA modifications (e.g., m⁶A methylation) and G-quadruplex formation.
This makes it a valuable tool for researchers exploring RNA structure–modification crosstalk in post-transcriptional regulation.
Drug Screening and Therapeutic Target Discovery
Small molecules that stabilize or destabilize RNA G-quadruplexes are emerging as potential therapeutic agents.
DMS rG4-Seq allows high-resolution assessment of compound effects on RNA folding at a genome-wide scale.
By monitoring structural changes after treatment, researchers can identify specific G4 targets and validate structure-based drug candidates in cancer, neurobiology, and infectious disease studies.
Comparative and Evolutionary Studies
DMS rG4-Seq can also be used for cross-species comparisons, helping to identify conserved G4 structures across human, mouse, or plant transcriptomes.
This supports studies of RNA evolution, molecular adaptation, and the conservation of G4-dependent regulatory mechanisms.
Deliverables include: annotated rG4 maps, motif classification, folding index analysis, differential structure comparison under K⁺/Li⁺ conditions, and publication-ready visualizations.
Figure C. DMS rG4-Seq reveals increased folding of RNA G-quadruplexes in HEK293 cells under oxidative stress. Each row represents an individual transcript with normalized folding index values across conditions.
