O8G-Seq Services - RNA 8-oxoG Profiling

Overview

Overview of Our O8G-Seq Services

8-oxo-7,8-dihydroguanine (8-oxoG, or O8G) is one of the most abundant oxidative lesions in RNA, formed when reactive oxygen species (ROS) attack the C8 position of guanine. Unlike random chemical damage, 8-oxoG deposition is selective — it preferentially accumulates in guanine-rich regions, miRNA seed sequences, and specific transcript classes under oxidative stress. Once formed, 8-oxoG alters base-pairing specificity (G→T misreading), disrupts translation fidelity, and is recognized by dedicated RNA-binding proteins — including PNPase, AUF1/HNRNPD, PCBP1, and YB-1 — that route oxidized transcripts toward degradation or stabilization.

O8G-Seq maps these modifications transcriptome-wide. The method uses a highly specific anti-8-oxoG antibody to enrich oxidized RNA fragments via immunoprecipitation (IP), with a parallel input control sequenced for background normalization. By comparing IP to input, O8G-enriched transcripts and regions are identified — providing a comprehensive view of where RNA oxidation occurs, which transcripts are most affected, and how O8G landscapes shift between experimental conditions.

At CD Genomics, we offer a standardized O8G-Seq platform covering mRNA, lncRNA, circRNA, and total transcriptome workflows — each with paired IP and input library construction, sequencing, and full bioinformatics support.

Technologies

Core Technologies: O8G mRNA, lncRNA, circRNA, and Total Transcriptome Sequencing

O8G mRNA-Seq — Transcriptome-Wide mRNA Oxidation Profiling

O8G mRNA-seq focuses O8G profiling on the polyadenylated transcriptome — the most functionally annotated RNA fraction. mRNA is enriched via poly(A) selection, fragmented, and subjected to anti-8-oxoG IP alongside an input control. This is the standard entry point for most studies, as it directly links RNA oxidation to protein-coding gene expression.

O8G mRNA-seq is well-suited for:

Mapping oxidation across the coding transcriptome under defined stress conditions
Identifying mRNAs with selective 8-oxoG enrichment for follow-up functional studies
Comparing O8G landscapes between treatment and control, or between disease and normal tissue
Integration with matched RNA-seq data to correlate oxidation with transcript abundance changes

A 2025 study in Frontiers in Cell and Developmental Biology applied O8G RIP-seq to mRNA from normal and senescent CaCO2 colon cancer cells, identifying distinct oxidation profiles that distinguish proliferating from senescent cancer cells (Huang et al., 2025).

O8G lncRNA-Seq — Long Non-Coding RNA Oxidation Detection

Long non-coding RNAs participate in diverse regulatory processes — chromatin remodeling, transcriptional control, post-transcriptional regulation — and their functions can be altered by oxidative modifications. O8G lncRNA-seq enriches for oxidized lncRNAs using an antibody-based approach compatible with rRNA-depleted or poly(A)-minus RNA fractions.

This workflow is suited for studies investigating how oxidative stress reshapes the lncRNA regulatory network, or whether specific lncRNAs acquire O8G modifications that alter their stability, structure, or protein-binding capacity.

O8G circRNA-Seq — Circular RNA Oxidation Profiling

Circular RNAs are covalently closed, typically more stable than linear transcripts, and enriched in the brain and other oxidative-metabolism-active tissues. Their extended half-life makes them particularly informative as cumulative records of oxidative exposure. circRNA-specific O8G-Seq requires RNase R treatment to deplete linear RNA before IP, enriching the circular fraction for oxidation profiling.

A 2025 Molecular Cancer study demonstrated that ROS-induced O8G modification of circPLCE1 recruits AUF1, destabilizes the circRNA, and relieves its tumor-suppressive activity in lung cancer — establishing circRNA oxidation as functionally relevant to cancer progression (Zhao et al., 2025).

O8G Total Transcriptome Sequencing

For studies requiring a complete picture — mRNA + lncRNA + circRNA simultaneously — O8G total transcriptome sequencing combines rRNA depletion with anti-8-oxoG IP to profile oxidation across all major RNA biotypes in a single experiment. This is the most comprehensive option, recommended when the full scope of RNA oxidation is unknown or when comparing global O8G landscapes between conditions.

Selection Guide

Method Selection Guide: Choosing the Right O8G-Seq Approach

Criterion	O8G mRNA-Seq	O8G lncRNA-Seq	O8G circRNA-Seq	O8G Total Transcriptome
RNA fraction	Poly(A)-selected mRNA	rRNA-depleted, poly(A)-minus	RNase R-treated circular RNA	rRNA-depleted total RNA
Transcripts profiled	Protein-coding mRNAs	Long non-coding RNAs	Circular RNAs	mRNA + lncRNA + circRNA
O8G detection	Anti-8-oxoG IP vs Input	Anti-8-oxoG IP vs Input	Anti-8-oxoG IP vs Input (+ RNase R)	Anti-8-oxoG IP vs Input
Resolution	Transcript/region-level (~100–200 nt)	Transcript/region-level	CircRNA-level	Transcript/region-level across biotypes
Functional annotation	Most complete — GO, KEGG, reactome	Moderate — lncRNA-specific databases	Limited — circRNA databases emerging	Full — across all annotated biotypes
RNA input	Moderate	Moderate to high	Higher (RNase R step + IP)	Highest (all biotypes)
Best for	mRNA-focused oxidation studies; stress response; translation fidelity; disease biomarker screening	lncRNA regulatory network oxidation; chromatin-associated lncRNA O8G profiling	Cumulative oxidative exposure; brain/neuron studies; circRNA functional oxidation	Global O8G landscape discovery; unknown oxidation targets; multi-RNA-type comparisons

Selection Strategy:

Start with your biological question. If you study how oxidation affects protein-coding gene expression, O8G mRNA-seq is the standard entry point. If your interest is in regulatory RNA oxidation, choose lncRNA-seq or circRNA-seq. If the scope is unknown — choose total transcriptome.
Consider the functional annotation depth. mRNA oxidation profiles map directly to known pathways (GO, KEGG). lncRNA and circRNA oxidation profiles provide positional and quantitative information but functional interpretation relies on more limited annotation databases.
RNase R efficiency matters for circRNA. circRNA-Seq requires efficient linear RNA removal. We verify RNase R digestion efficiency by qPCR of linear control transcripts before proceeding to IP.
Combine for completeness. An efficient strategy is total transcriptome for discovery, followed by targeted validation using individual RNA-type workflows on key samples.

Speak with an O8G-Seq Specialist

Workflow

End-to-End Workflow and Quality Control

Our O8G-Seq service follows a standardized workflow with QC checkpoints at each stage. The defining feature is the paired IP and input library design — two parallel sequencing libraries from the same RNA sample, enabling background-corrected O8G enrichment calling.

Sample Receipt and Quality Assessment — QC Checkpoint: RNA integrity (RIN) by Bioanalyzer/TapeStation; concentration and purity (OD260/280, OD260/230); degradation assessment. Anti-oxidation precautions: samples handled in O₂-minimized buffers during QC.
RNA Preparation and Fragmentation — QC Checkpoint: poly(A) selection / rRNA depletion / RNase R treatment efficiency verified; RNA fragmentation size distribution confirmed (Bioanalyzer); RNA yield post-enrichment. Fragmentation is performed in O₂-depleted buffer with antioxidants to prevent artifactual oxidation during processing.
Anti-8-oxoG Immunoprecipitation — QC Checkpoint: anti-8-oxoG antibody specificity validated against m6A, 8-oxoA, 5-hydroxycytosine, and unmodified RNA; IP enrichment efficiency monitored by qPCR of known oxidized and non-oxidized control transcripts; input aliquot retained for parallel library construction.
Library Construction and Sequencing — QC Checkpoint: library yield and fragment size; Q30 scores; duplication rate. Both IP and input libraries sequenced on the same flow cell to minimize batch effects.
Data QC and Bioinformatics — QC Checkpoint: IP vs input read distribution; peak enrichment signal-to-noise (FRiP); replicate correlation; peak reproducibility between biological replicates; deliverable completeness.

Sample

Sample Requirements and Anti-Oxidation Preparation Guidelines

RNA is chemically more susceptible to oxidation than DNA — guanine bases in single-stranded RNA are particularly exposed. Preventing artifactual oxidation during sample preparation, storage, and shipping is essential for O8G-Seq data quality.

Service Tier	Recommended RNA Type	Input Guideline	Key QC Metrics	Notes
O8G mRNA-Seq	Poly(A)-selected or total RNA	≥ 30 μg total RNA recommended; ≥ 10 μg minimum; ≥ 20 ng/μL	RIN ≥ 7; OD260/280: 1.8–2.1; OD260/230 ≥ 1.5	Standard entry option; total RNA submitted, poly(A) selection performed in-house under anti-oxidation conditions
O8G lncRNA-Seq	Total RNA (rRNA-depleted)	≥ 30 μg total RNA recommended; ≥ 10 μg minimum; ≥ 20 ng/μL	RIN ≥ 7; OD260/280: 1.8–2.1	Requires rRNA depletion before IP; higher total input than mRNA-seq
O8G circRNA-Seq	Total RNA (RNase R-treated)	≥ 40 μg total RNA recommended; ≥ 15 μg minimum; ≥ 20 ng/μL	RIN ≥ 7; OD260/280: 1.8–2.1	RNase R digestion reduces linear RNA; input validated by qPCR before IP
O8G Total Transcriptome	Total RNA (rRNA-depleted)	≥ 40 μg total RNA recommended; ≥ 15 μg minimum; ≥ 20 ng/μL	RIN ≥ 7; OD260/280: 1.8–2.1; OD260/230 ≥ 1.5	Highest input requirement; single experiment covers mRNA, lncRNA, and circRNA O8G profiles

Anti-Oxidation Preparation Notes:

Isolate RNA in the presence of antioxidants (e.g., deferoxamine mesylate) and under O₂-minimized conditions where feasible
Avoid RNA fragmentation before shipping — intact RNA is less susceptible to oxidation than fragmented RNA
Use nuclease-free water or TE buffer (pH 7.0) pre-treated to remove trace metals that catalyze oxidation
Ship on dry ice; store at -80°C; avoid repeated freeze-thaw cycles
Include a small aliquot of control RNA (e.g., untreated reference sample) shipped in parallel to help distinguish biological oxidation from handling-related background

Bioinformatics

Bioinformatics Analysis and Deliverables

O8G-Seq generates paired IP and input datasets per sample. Our pipeline integrates peak calling with input-normalized enrichment quantification, transcript annotation, and differential analysis.

Standard Deliverables:

Deliverable	Description
Raw sequencing data	Demultiplexed read files for IP and input libraries with quality scores
Aligned reads	Reads aligned to reference genome/transcriptome for IP and input
O8G enrichment peaks	Statistically significant O8G-enriched regions called from IP vs input comparison
Normalized signal tracks	Input-normalized IP enrichment tracks for genome browser visualization
Peak annotation	Peaks annotated to transcripts, genomic features (CDS, 5'UTR, 3'UTR), and RNA biotype
QC report	IP enrichment efficiency, FRiP, read distribution, library complexity, replicate correlation
Differential O8G analysis	Statistically significant differentially oxidized regions between user-defined condition groups
Motif analysis	Sequence motifs enriched around O8G peak summits
GO/KEGG enrichment	Functional enrichment of O8G-modified transcripts (mRNA and total transcriptome workflows)

Optional Advanced Analysis:

O8G stoichiometry estimation — relative oxidation level per transcript, accounting for transcript abundance and IP efficiency
Multi-omics integration — combined O8G data with matched RNA-seq, m6A-seq, or proteomics to link RNA oxidation to expression and translation changes
G-rich region oxidation bias analysis — assessment of whether observed oxidation correlates with predicted susceptibility based on G-content and sequence context
circRNA-specific O8G analysis — specialized annotation and differential analysis for circRNA oxidation profiles
Custom visualization — publication-ready figures including genome browser tracks, heatmaps, volcano plots, and circos plots
Epigenomic data analysis — see our dedicated Epigenomic Data Analysis service

Demo

Representative Data and Demo Results

The composite image below illustrates the data types delivered with each O8G-Seq project. All panels reflect standard bioinformatics output formats from our pipeline.

O8G Enrichment and Genomic Distribution:

IP vs Input enrichment track (Panel A) — IGV-style genome browser view showing three parallel tracks: IP coverage, input coverage, and normalized enrichment ratio across a representative genomic locus. Peaks called above the significance threshold are highlighted, revealing O8G-enriched regions at single-transcript resolution.
O8G peak distribution across RNA biotypes (Panel B) — horizontal stacked bar chart or pie chart showing the distribution of called O8G peaks across mRNA CDS, 5'UTR, 3'UTR, lncRNA, and circRNA — providing a global view of where oxidation occurs in the transcriptome.
Metagene profile of O8G enrichment (Panel C) — average O8G IP/input enrichment signal plotted across the gene body from 5'UTR through CDS to 3'UTR. Reveals whether oxidation preferentially accumulates in specific transcript regions.

Differential Analysis and Functional Interpretation:

Differential O8G volcano plot (Panel D) — each point represents an O8G-enriched transcript or region; x-axis shows log2 fold change of IP/input ratio between conditions; y-axis shows -log10 adjusted p-value. Hyper-oxidized transcripts in red, hypo-oxidized in blue.
Sequence motif logo (Panel E) — consensus sequence motif enriched around O8G peak summits, identifying preferred nucleotide contexts for 8-oxoG deposition. G-rich motifs are characteristic of genuine oxidation signal.
GO/KEGG enrichment bar chart (Panel F) — top enriched biological process terms among O8G-modified transcripts, linking oxidation patterns to specific pathways and providing functional context for differential analysis results.

All demo results are generated from representative datasets and reflect the standard analysis depth delivered with each project. Actual figures are customized to your experimental design and research question.

Applications

Applications Across Research Areas

Cancer and Tumor Biology

8-oxoG deposition is linked to cancer through multiple mechanisms. In miRNA seeds, position-specific 8-oxoG redirects target recognition — 4o8G-miR-124 suppresses glioma while 3o8G-miR-122 promotes hepatocellular carcinoma (Eom et al., Nature Cell Biology, 2023). In circRNAs, ROS-induced O8G modification of circPLCE1 relieves tumor suppression by recruiting AUF1 for degradation (Zhao et al., Molecular Cancer, 2025). O8G-Seq enables transcriptome-wide mapping of these functionally significant oxidation events across cancer types, stages, and treatment conditions.

Cellular Senescence and Aging

RNA oxidation accumulates with age, and senescent cells exhibit distinct O8G landscapes from their proliferating counterparts. Huang et al. (2025) applied O8G RIP-seq to compare mRNA oxidation profiles in normal versus senescent CaCO2 colon cancer cells, identifying transcripts and pathways selectively oxidized during senescence — including focal adhesion and RNA binding pathways. O8G-Seq provides a direct readout of the RNA oxidation component of the aging process.

Environmental Stress and Toxicology

Air pollution exposure induces transcriptome-wide RNA oxidation. Gonzalez-Rivera et al. (Communications Biology, 2020) identified 707 oxidized transcripts after low-level pollution exposure and 555 after high-level exposure in human bronchial epithelial cells, with oxidized transcripts enriched in cholesterol biosynthesis, fatty acid elongation, and cancer pathways. O8G-Seq is the direct approach for mapping how environmental exposures leave chemical marks on the transcriptome.

Neurodegenerative Disease

The brain is a high-oxidative-metabolism tissue with abundant RNA oxidation. 8-oxoG accumulation in neuronal transcripts is implicated in Alzheimer's disease, Parkinson's disease, and ALS — where oxidative stress and mitochondrial dysfunction converge on RNA as a target. O8G-Seq can map which transcripts are most vulnerable in specific brain regions, cell types, or disease models.

Cardiovascular Disease

RNA guanine is more readily oxidized than DNA under the oxidative conditions prevalent in cardiovascular disease. A 2024 review in Pharmacological Research highlighted 8-oxoG in both DNA and RNA as contributors to atherosclerosis, ischemia-reperfusion injury, and cardiac hypertrophy (Li et al., 2024). O8G-Seq enables transcript-specific mapping of RNA oxidation in cardiac and vascular tissues under disease-relevant conditions.

Infectious Disease and Host-Pathogen Interaction

Viral RNA genomes and host transcripts both undergo oxidation during infection-associated oxidative stress. In respiratory syncytial virus (RSV), the DNA/RNA repair enzyme OGG1 binds 8-oxoG in viral RNA, concealing oxidized guanine to maintain genetic fidelity — and inhibiting this interaction reduces viral progeny production (Pan et al., PLOS Pathogens, 2024). O8G-Seq can simultaneously profile oxidation on host and pathogen RNA during infection time courses.

Case

Case Study: O8G-Seq Reveals mRNA Oxidation Profiles Distinguishing Normal from Senescent Cancer Cells

O8G RIP-Seq of Normal vs Senescent Cancer Cell mRNA

Source: Huang J, Lin Y, Zhao Y, Wei L, Frontiers in Cell and Developmental Biology, 2025. https://doi.org/10.3389/fcell.2025.1443888

Background. Cellular senescence plays a dual role in cancer — it can suppress tumor growth by halting proliferation of damaged cells, but senescent cells also secrete pro-inflammatory factors that promote tumor progression. While RNA methylation (m6A) had been studied in the context of cancer senescence, the role of 8-oxoguanine — a distinct and functionally different RNA modification — was largely unexplored.

Methods. The study constructed a senescent cancer cell model by knocking down ADAR1 in CaCO2 colon cancer cells. RNA immunoprecipitation sequencing (O8G RIP-seq) was performed on mRNA from both normal proliferating and senescent CaCO2 cells, using an anti-8-oxoguanine antibody to enrich oxidized mRNA fragments with paired input controls. Peak calling and differential analysis compared O8G landscapes between the two cell states.

Results. Distinct O8G modification profiles were identified on mRNA in senescent versus normal cancer cells. O8G-modified mRNAs in senescent cells were enriched in pathways including focal adhesion, cellular component organization, and RNA binding. KEGG analysis further identified enrichment in focal adhesion, small cell lung cancer, and proteoglycans in cancer pathways — linking 8-oxoG deposition to processes directly relevant to cancer biology and senescence-associated secretory phenotype regulation.

Figure from Huang et al., Frontiers in Cell and Developmental Biology, 2025 — O8G RIP-seq identifies distinct mRNA oxidation profiles distinguishing normal proliferating from senescent CaCO2 colon cancer cells.

Conclusion. This study establishes that 8-oxoguanine modification of mRNA is not uniform or random — it is selectively deposited on specific transcripts and pathways in a cell-state-dependent manner. O8G-Seq identified functionally relevant oxidation events that distinguish senescent from proliferating cancer cells, suggesting that RNA oxidation profiling could serve as a tool for characterizing senescence states in cancer and potentially identifying oxidation-based vulnerabilities.

Method Comparison

Method Comparison: O8G-Seq vs Alternative RNA Oxidation Detection Approaches

Feature	O8G-Seq (Antibody IP)	ChLoRox-Seq	G>T Mutation Signature	OAbSeq
Principle	Anti-8-oxoG antibody enriches modified RNA fragments	Chemical labeling (K₂IrBr₆ + biotin) of 8-oxoG, streptavidin enrichment	8-oxoG→T misincorporation during reverse transcription detected as G>T variants	Aniline-induced strand scission at abasic sites (8-oxoG oxidation products)
Antibody-free	No — requires anti-8-oxoG antibody	Yes	Yes	Yes
Resolution	Transcript/region (~100–200 nt)	Exon-level	Single-base (site of G>T substitution)	Single-base (site of strand scission)
Detects	Stable 8-oxoG in RNA	Stable 8-oxoG in RNA	8-oxoG + other G oxidation products that cause G>T misreading	Abasic sites (terminal oxidation products); indirect 8-oxoG inference
Specificity	Antibody-dependent; validated against m6A, 8-oxoA	94-fold over unmodified RNA (Burroughs et al., 2024)	Cannot distinguish 8-oxoG from other G→T-causing lesions	Detects abasic sites — 8-oxoG is a precursor, not directly detected
Input requirement	Moderate	Moderate	High (requires deep coverage for variant calling)	Low to moderate
Maturity	Established — published since 2020, multiple independent studies	Emerging — published 2024	Conceptually straightforward but limited adoption	Emerging — published 2025
IP/Input paired design	Yes — background correction via input control	Yes — streptavidin vs input comparison	No — relies on sequencing depth and variant caller sensitivity	No — relies on unique ligation-competent fragment formation
Best for	Transcriptome-wide O8G discovery; multi-sample comparison; established workflows with validated QC	Antibody-free labs; exon-level resolution; lower cost per sample	Site-level O8G mapping; miRNA seed oxidation studies with sufficient depth	Mapping terminal oxidation damage products; nucleotide-resolution abasic site detection

O8G-Seq using antibody-based IP remains the most established method for transcriptome-wide 8-oxoG profiling, with paired input controls that directly correct for non-specific background — an advantage for multi-sample comparative studies where consistent normalization is critical. Newer antibody-free methods (ChLoRox-Seq, OAbSeq) offer higher resolution and are important developments, but their adoption is still emerging and each detects a different subset of the oxidation landscape (stable 8-oxoG vs abasic sites).

FAQ

Frequently Asked Questions

Q: What is 8-oxoguanine (O8G) and why is it important in RNA?

A: 8-oxo-7,8-dihydroguanine (8-oxoG, O8G) is the most abundant oxidized base lesion in RNA, formed when reactive oxygen species (ROS) attack the C8 position of guanine. Unlike random chemical damage, O8G deposition is selective — it accumulates in G-rich regions, alters base-pairing (causing G→T misreading during translation), and is specifically recognized by RNA-binding proteins (PNPase, AUF1, PCBP1, YB-1) that route oxidized transcripts for degradation or stabilization. RNA is more susceptible to oxidation than DNA because it is predominantly single-stranded, exposing guanine bases to solvent. O8G is implicated in cancer, neurodegeneration, aging, and environmental stress response.

Q: How does O8G-Seq work?

A: O8G-Seq uses an anti-8-oxoguanine antibody to immunoprecipitate (IP) RNA fragments containing 8-oxoG modifications. In parallel, an input library (non-immunoprecipitated RNA) is sequenced as a background control. By comparing IP to input, O8G-enriched regions are identified transcriptome-wide. The input control is critical — it corrects for transcript abundance, non-specific antibody binding, and sequencing bias. The final output is a set of statistically called O8G peaks annotated to specific transcripts and genomic features.

Q: What resolution does O8G-Seq provide? Can it detect single-base 8-oxoG sites?

A: Standard antibody-based O8G-Seq provides transcript/region-level resolution (~100–200 nucleotides), comparable to other RIP-seq and MeRIP-seq methods. The resolution is limited by RNA fragment size during IP, not by sequencing. For researchers requiring single-base resolution, alternative methods exist — ChLoRox-Seq (chemical labeling, exon-level) and G>T mutation signature analysis (single-base, requires deep sequencing). However, these methods trade resolution for other constraints (emerging technology, high sequencing depth, or detection of different oxidation products). Contact us to discuss which resolution level is appropriate for your experimental question.

Q: How do you prevent artifactual oxidation during sample preparation?

A: Artifactual oxidation is a key concern for any O8G-Seq experiment. We use O₂-depleted buffers with metal chelators (e.g., deferoxamine mesylate) during RNA fragmentation and IP steps, minimize handling time, and include antioxidants in all processing solutions. We recommend that customers also use anti-oxidation precautions during RNA isolation and to ship samples on dry ice immediately after extraction. A control RNA aliquot shipped alongside experimental samples can help distinguish biological oxidation from handling-induced background.

Q: What is the difference between the four O8G-Seq options — mRNA, lncRNA, circRNA, and total transcriptome?

A: The core anti-8-oxoG IP method is the same for all four options. The difference is the RNA fraction enriched before IP: poly(A)-selected mRNA, rRNA-depleted/poly(A)-minus lncRNA, RNase R-treated circRNA, or rRNA-depleted total RNA (total transcriptome — covering all three biotypes simultaneously). Choose based on which RNA class is biologically relevant to your study. Total transcriptome provides the broadest view but requires the most input RNA and sequencing depth.

Q: How much RNA do I need for O8G-Seq?

Q: How are O8G peaks called and differential analysis performed?

A: Peak calling compares IP read coverage to input read coverage using established statistical frameworks. We use input-normalized enrichment models that account for transcript abundance — a transcript with high expression will have more reads in both IP and input, and the enrichment ratio controls for this. Differential O8G analysis between conditions uses statistical models designed for IP-based count data, with multiple testing correction. Full parameter records and software versions are documented in the QC report.

Q: Can O8G-Seq distinguish 8-oxoG from other oxidized guanine products?

A: The anti-8-oxoG antibody is validated against other RNA modifications (m6A, 8-oxoA, 5-hydroxycytosine) and shows high specificity for 8-oxoguanine. However, closely related guanine oxidation products (guanidinohydantoin, spiroiminodihydantoin) that share structural features with 8-oxoG may show cross-reactivity. The antibody does not detect abasic sites — which a 2025 Nature Communications study identified as the more abundant terminal product of guanosine oxidation. This means O8G-Seq captures the stable 8-oxoG pool specifically, not the full spectrum of oxidation-derived RNA damage.

Q: Is O8G-Seq compatible with non-model organisms?

A: Yes. The anti-8-oxoG antibody recognizes 8-oxoguanine independent of sequence context or species. For species with an annotated reference genome/transcriptome, standard peak annotation and functional enrichment pipelines apply. For species with limited annotation, peak calling and motif analysis are still informative, but GO/KEGG enrichment will be limited to homology-based inference. Contact us to evaluate feasibility for your species of interest.

References

Gonzalez-Rivera JC, Baldridge KC, Wang Y, Patel A, Contreras LM, et al. Post-transcriptional air pollution oxidation to the cholesterol biosynthesis pathway promotes pulmonary stress phenotypes. Communications Biology, 2020, 3:480. https://doi.org/10.1038/s42003-020-01118-6
Eom S, et al. Widespread 8-oxoguanine modifications of miRNA seeds differentially regulate redox-dependent cancer development. Nature Cell Biology, 2023, 25:1369-1383. https://doi.org/10.1038/s41556-023-01209-6
Huang J, Lin Y, Zhao Y, Wei L. Overview of distinct 8-oxoguanine profiles of messenger RNA in normal and senescent cancer cells. Frontiers in Cell and Developmental Biology, 2025, 13:1443888. https://doi.org/10.3389/fcell.2025.1443888
Zhao Q, Cai D, Xu H, et al. O8G-modified circPLCE1 inhibits lung cancer progression via chaperone-mediated autophagy. Molecular Cancer, 2025, 24:82. https://doi.org/10.1186/s12943-025-02283-0
Pan L, Wang K, Hao W, et al. 8-Oxoguanine DNA Glycosylase 1 conceals oxidized guanine in nucleoprotein-associated RNA of respiratory syncytial virus. PLOS Pathogens, 2024, 20(10):e1012616. https://doi.org/10.1371/journal.ppat.1012616
Burroughs MR, Sweet PJ, Contreras LM. Optimized chemical labeling method for isolation of 8-oxoG-modified RNA, ChLoRox-Seq, identifies mRNAs enriched in oxidation and transcriptome-wide distribution biases of oxidation events post environmental stress. RNA Biology, 2024, 21:1209-1225. https://doi.org/10.1080/15476286.2024.2427903
Li Y, et al. The role of DNA and RNA guanosine oxidation in cardiovascular diseases. Pharmacological Research, 2024, 204:107187. https://doi.org/10.1016/j.phrs.2024.107187

O8G-Seq for Transcriptome-Wide RNA Oxidation Mapping