What is RNA glycosylation, and why is it important for biological research?

RNA glycosylation refers to the covalent attachment of glycans to RNA molecules, forming GlycoRNA. This modification represents a new layer of post-transcriptional regulation influencing RNA stability, localization, immune signaling, and cell–cell communication. It offers unique opportunities to study RNA’s structural diversity and to identify novel biomarkers relevant to immune function, cancer biology, and cell stress responses.

How does CD Genomics study GlycoRNA compared with traditional RNA sequencing?

Unlike conventional RNA-seq, which focuses only on nucleotide sequences, CD Genomics’ GlycoRNA-seq platform detects glycosylated RNA species through dual labeling chemistries and high-sensitivity sequencing. These data are integrated with mass spectrometry results to reveal glycosylation patterns, linkage types, and biological pathways, providing a multidimensional understanding of RNA function.

Can GlycoRNA-seq and mass spectrometry results be combined in one study?

Yes. The sequencing and mass spectrometry modules are designed to complement each other, enabling both identification of glycosylation sites and quantitative profiling of glycan structures. Data from both assays are normalized and cross-referenced during bioinformatics processing, supporting accurate multi-omics interpretation and pathway discovery.

How is data quality ensured during RNA glycosylation analysis?

Each project follows a structured QC pipeline that includes labeling efficiency checks, replicate correlation, and internal/external standards for both sequencing and mass spectrometry. Data validation is supported by bioinformatics filtering, statistical thresholding, and reproducibility scoring, ensuring reliability for publication or downstream research.

What information will I receive in the final report?

Clients receive a comprehensive report that includes clean sequencing reads, QC metrics, identified glycans, differential expression tables, pathway enrichment analysis, and visual outputs such as volcano plots, heatmaps, and scatter charts. All results are presented in editable formats compatible with common bioinformatics and data-visualization tools.

What factors influence project pricing for RNA glycosylation research?

Costs depend on factors such as the number of samples, selected labeling method, analysis depth, and data-processing requirements. CD Genomics provides flexible pricing models that allow clients to choose standard or advanced analysis packages according to research objectives and budget considerations.

Can I request customized bioinformatics or data integration support?

Absolutely. Our bioinformatics team can design tailored pipelines for GlycoRNA data interpretation, including target gene prediction, GO/KEGG analysis, and integration with transcriptomic or proteomic datasets. This customization helps clients translate molecular data into meaningful biological insights.

Transcriptome Service
Epitranscriptomics
RNA Glycosylation Research

Explore RNA Glycosylation with GlycoRNA-seq, Mass Spectrometry, and Imaging

Uncover the hidden glycan code of RNA with CD Genomics' RNA glycosylation research comprehensive service.

Our integrated platform combines GlycoRNA-seq sequencing, GlycoRNA mass spectrometry, and GlycoRNA gel electrophoresis and blot imaging, providing a comprehensive workflow for studying RNA-linked glycan modifications.

We empower academic laboratories, CROs, and pharmaceutical R&D teams to explore how RNA glycosylation influences immune regulation, RNA stability, and intercellular communication. By combining advanced chemistry and high-throughput analytics, CD Genomics enables you to transition from glycan discovery to biological insight with precision and reproducibility.

Why choose our platform:

Dual-labeling flexibility: Ac₄ManNAz for metabolic labeling or rPAL for post-extraction coupling
High-sensitivity quantification of N- and O-linked glycans from minimal input RNA
Integrated analysis pipeline combining sequencing, LC–MS/MS, and imaging data
Applicable to cells, tissues, plasma, or purified RNA samples
Designed for research reproducibility, QC traceability, and publication-ready results

Submit Your Request Now

RNA glycosylation research platform diagram showing Ac4ManNAz and rPAL labeling workflows integrated with GlycoRNA-seq, mass spectrometry, and gel blot imaging for comprehensive RNA glycosylation analysis.

Ac₄ManNAz & rPAL Dual Labeling Techniques
RNA Glycosylation Sequencing (GlycoRNA-seq)
High-Resolution Mass Spectrometry of GlycoRNA
Gel Electrophoresis & Blot Imaging Validation

Background Labeling Strategies Service Packages Workflow Applications Advantages Sample Requirements Demo FAQ Inquiry

The Emerging Frontier of RNA Glycosylation

For decades, glycans were thought to modify only proteins and lipids. That view changed when Bertozzi's group first reported GlycoRNA in Cell (2021), identifying RNA glycosylation as a previously overlooked layer of molecular regulation.

GlycoRNAs—RNAs covalently linked to specific glycans—are now recognized as active participants in cell signaling, immune evasion, and RNA–protein interactions.

Recent discoveries have revealed that GlycoRNAs:

Regulate neutrophil inflammatory responses and cytokine signaling.
Form nanoclusters with RNA-binding proteins to mediate peptide internalization (Cell).
Contribute to immune escape by masking immunogenic RNA bases such as acp³U, preventing activation of TLR3 and TLR7 (Nature).

However, the biological functions of RNA glycosylation remain largely uncharted. Studying these low-abundance modifications requires ultra-sensitive methods and coordinated sequencing–mass spectrometry strategies.

At CD Genomics, we address this challenge by integrating GlycoRNA-seq, glycan-targeted mass spectrometry, and imaging-based validation into one cohesive workflow.

Our platform provides the technical depth needed to decode the complexity of RNA glycosylation across diverse biological systems.

Dual Labeling Strategies for GlycoRNA Detection

Understanding RNA glycosylation begins with precise and efficient labeling.

CD Genomics applies two validated chemistries—Ac₄ManNAz-based metabolic labeling and rPAL-based chemical labeling—to capture the full range of GlycoRNA molecules.

Ac₄ManNAz Metabolic Labeling

Introduces an azide-modified sugar (Ac₄ManNAz) into living cells.
Enables in-vivo incorporation of azido glycans onto newly synthesised RNA.
Enriches targets via biotin–streptavidin affinity for sequencing or LC–MS analysis.
Recommended for live-cell or dynamic metabolic studies.

rPAL Chemical Labeling

Suitable for purified RNA or tissue samples.
Uses mild oxidation to activate RNA termini, followed by alkyne–azide click chemistry.
Compatible with fixed or archived specimens when metabolic labeling is not feasible.
Provides strong signal enrichment and high reproducibility across replicates.

Both strategies support downstream GlycoRNA-seq, mass spectrometry, and gel blot imaging, ensuring consistent and cross-platform data interpretation.

Service Packages for RNA Glycosylation Analysis

Our RNA glycosylation research comprehensive service offers three complementary analytical packages—sequencing, structure mapping, and visualization—providing a complete molecular perspective from detection to validation.

Package 1 | GlycoRNA-seq (glycorna sequencing)

A next-generation sequencing workflow designed to map glycosylated RNA species, profile small RNAs, and analyse differential expression.

Performs raw read QC, genome alignment, and ncRNA database mapping.
Classifies and quantifies small non-coding RNAs (sncRNAs).
Identifies expression differences across biological replicates.
Conducts GO and KEGG enrichment to connect glycosylation with functional pathways.

Analysis Workflow Overview

Analysis Type	Main Steps	Description / Deliverables
Standard Analysis	1. Raw read filtering & quality control 2. Genome alignment 3. ncRNA database mapping 4. sncRNA length & classification statistics 5. sncRNA quantification 6. Differential expression analysis 7. Clustering of differentially expressed sncRNAs (for biological replicates)	Provides basic transcript profiling and expression quantification of GlycoRNAs and associated sncRNAs.
Advanced Analysis	1. Sequence abundance TOP20 pie chart and TOP2 bar chart 2. Custom selection (≤50 sncRNAs) for detailed analysis 3. Target gene prediction 4. GO functional annotation 5. KEGG pathway enrichment	Offers in-depth interpretation of GlycoRNA-associated pathways, gene targets, and biological relevance.

Package 2 | GlycoRNA Mass Spectrometry Analysis

Structural and quantitative profiling of RNA-linked glycans using non-targeted LC–MS/MS or MALDI workflows.

Detects both N- and O-linked glycans with femtomole-level sensitivity.
Identifies > 60 N-glycan and ≈ 10 core2-type O-glycan structures.
Quantifies acp³U, galQ, manQ, and co-occurring RNA modifications.
Provides absolute and relative quantification with integrated spectral QC.

This package is ideal for clients seeking glycosylation-site mapping, structure elucidation, or cross-validation with GlycoRNA-seq results.

Package 3 | GlycoRNA Gel Electrophoresis and Blot Imaging

A visual confirmation tool for GlycoRNA presence and abundance.
Uses formaldehyde agarose gels to separate glycosylated RNA.
Transfers RNA to nylon or NC membranes for probe-based hybridisation.
Quantifies relative expression for cross-sample comparisons.
Supports initial screening or validation of sequencing and MS results.

Recommended reading:

Enhance your results with our RNA Sequencing Solutions or explore complementary Mass Spectrometry Services.

Workflow

RNA glycosylation research workflow diagram showing sample preparation, Ac4ManNAz and rPAL labeling, enrichment, GlycoRNA-seq, LC–MS/MS, gel blot imaging, and bioinformatics analysis.

Research Applications of GlycoRNA

The discovery of RNA glycosylation has expanded our view of post-transcriptional regulation.

CD Genomics' GlycoRNA-seq, GlycoRNA mass spectrometry, and gel blot imaging platforms enable in-depth exploration of this modification across diverse biological contexts.

Scientific and Translational Applications

1. RNA Structure and Stability

Study how glycan attachment affects RNA folding, secondary structure, and degradation dynamics.

2. RNA–Protein and RNA–Glycan Interactions

Map glycosylation-dependent RNA–protein binding events and identify glycan motifs influencing signal transduction.

3. Immune and Inflammatory Regulation

Characterize how GlycoRNA modulates innate immune recognition and prevents unwanted activation of TLR3/TLR7 pathways.

4. Exosomal and Secretory RNA Studies

Trace the trafficking of glycosylated RNAs in extracellular vesicles and their roles in intercellular communication.

5. Cellular Stress and Apoptotic Clearance

Analyze RNA glycosylation changes during oxidative stress or programmed cell death to reveal protective mechanisms.

6. Biomarker and Therapeutic Discovery

Detect unique GlycoRNA signatures as potential indicators of disease progression or treatment response.

Supported Research Areas

Our integrated workflow supports multi-disciplinary investigations, including:

Immunology and Infection Biology – innate immune modulation and pathogen response
Cancer Research – tumor microenvironment and immune checkpoint regulation
Neuroscience – RNA modifications linked to neuroinflammation
Drug Discovery – glycan-related RNA targets for therapeutic development

Why Choose CD Genomics

Proven Expertise in RNA Modification Research

10+ years of RNA sequencing experience across transcriptomics, epitranscriptomics, and structural RNA analysis.
Early adopter of GlycoRNA profiling workflows, validated on diverse biological samples.
Supported by a multidisciplinary team specializing in bioinformatics, chemistry, and proteomics.
Experienced in multi-omics integration, linking RNA glycosylation data with transcriptome and proteome layers.

Quality, Reproducibility, and Data Transparency

Each project includes a dedicated project manager for communication and progress tracking.
Internal and external QC controls ensure assay consistency and cross-run comparability.
Comprehensive deliverables—from raw data to analysis reports—suitable for publication or downstream validation.

Collaborative Approach

CD Genomics supports academic groups, CROs, and pharmaceutical companies in every project phase—from experimental design and sample preparation to data interpretation.

Our scientists provide consultation to help you select the most suitable labeling strategy (Ac₄ManNAz or rPAL) and optimize sequencing–mass spectrometry integration for your research goals.

Sample Requirements & Quality Control

Reliable RNA glycosylation analysis depends on strict sample integrity and controlled workflows.

CD Genomics provides clear submission guidelines to help clients obtain reproducible GlycoRNA-seq, GlycoRNA mass spectrometry, and imaging results.

Sample Requirements

Sample Type	Recommended Labeling Method	Minimum Amount	Key Notes
Cells	Ac₄ManNAz ≥ 3 × 10⁶ cells / rPAL ≥ 2 × 10⁶ cells	—	For metabolic labeling, pre-incubate medium with 100 µM Ac₄ManNAz for 24 h.
Tissue	rPAL only (≥ 25 mg RNA later preserved)	—	~30 µg total RNA can be extracted from 25 mg tissue.
Whole Blood	rPAL recommended (3–5 mL EDTA sample)	—	Maintain samples at 4 °C; ship on dry ice.
Purified RNA	Ac₄ManNAz ≥ 50 µg / rPAL ≥ 30 µg	≥ 200 ng/µL	High-purity RNA without phenol contamination.

General Handling Guidelines

Avoid more than two freeze–thaw cycles.
Label tubes with sample ID, matrix type, and collection date.
Ship samples on dry ice to preserve glycan stability.
Include an extra 10–15 µL for QC and repeat testing if needed.

Quality Control & Validation

Every project undergoes multi-layer QC to ensure accuracy and traceability:

Labeling Efficiency Assessment – monitors azide or aldehyde incorporation efficiency.
Sequencing QC – read depth, mapping rate, and replicate correlation (> 0.9 expected).
Mass Spectrometry QC – internal standards and lock-mass calibration for mass accuracy < 5 ppm.
Imaging QC – signal-to-noise validation and cross-replicate consistency.
Bioinformatics QC – data normalization, NPX consistency check, and statistical flagging of outliers.

All QC records and sample metadata are included in the final report, supporting publication and regulatory documentation.

Demo

RNA glycosylation sequencing demo results showing GlycoRNA-seq read length distribution and RNA composition pie charts for small RNA analysis.

$GlycoRNA gel blot imaging demo showing wild-type and knockout RNA samples across total RNA and small RNA fractions for RNA glycosylation analysis.$

FAQ

- What is RNA glycosylation, and why is it important for biological research?
  - RNA glycosylation refers to the covalent attachment of glycans to RNA molecules, forming GlycoRNA. This modification represents a new layer of post-transcriptional regulation influencing RNA stability, localization, immune signaling, and cell–cell communication. It offers unique opportunities to study RNA's structural diversity and to identify novel biomarkers relevant to immune function, cancer biology, and cell stress responses.
- How does CD Genomics study GlycoRNA compared with traditional RNA sequencing?
  - Unlike conventional RNA-seq, which focuses only on nucleotide sequences, CD Genomics' GlycoRNA-seq platform detects glycosylated RNA species through dual labeling chemistries and high-sensitivity sequencing. These data are integrated with mass spectrometry results to reveal glycosylation patterns, linkage types, and biological pathways, providing a multidimensional understanding of RNA function.
- What types of samples are suitable for RNA glycosylation analysis?
  - GlycoRNA analysis can be performed on a variety of sample types, including cultured cells, tissues, plasma, or purified RNA. Both Ac₄ManNAz-based metabolic labeling and rPAL-based chemical labeling are available to accommodate living samples or extracted RNA, ensuring flexibility for basic research and preclinical applications.
- Can GlycoRNA-seq and mass spectrometry results be combined in one study?
  - Yes. The sequencing and mass spectrometry modules are designed to complement each other, enabling both identification of glycosylation sites and quantitative profiling of glycan structures. Data from both assays are normalized and cross-referenced during bioinformatics processing, supporting accurate multi-omics interpretation and pathway discovery.
- How is data quality ensured during RNA glycosylation analysis?
  - Each project follows a structured QC pipeline that includes labeling efficiency checks, replicate correlation, and internal/external standards for both sequencing and mass spectrometry. Data validation is supported by bioinformatics filtering, statistical thresholding, and reproducibility scoring, ensuring reliability for publication or downstream research.
- What information will I receive in the final report?
  - Clients receive a comprehensive report that includes clean sequencing reads, QC metrics, identified glycans, differential expression tables, pathway enrichment analysis, and visual outputs such as volcano plots, heatmaps, and scatter charts. All results are presented in editable formats compatible with common bioinformatics and data-visualization tools.
- What factors influence project pricing for RNA glycosylation research?
  - Costs depend on factors such as the number of samples, selected labeling method, analysis depth, and data-processing requirements. CD Genomics provides flexible pricing models that allow clients to choose standard or advanced analysis packages according to research objectives and budget considerations.
- Can I request customized bioinformatics or data integration support?
  - Absolutely. Our bioinformatics team can design tailored pipelines for GlycoRNA data interpretation, including target gene prediction, GO/KEGG analysis, and integration with transcriptomic or proteomic datasets. This customization helps clients translate molecular data into meaningful biological insights.

References:

Flynn, R.A. et al. (2021). Small RNAs are modified with N-glycans and display glycan-dependent localization and function. Cell, 184(12):3109–3124.e22.
Agard, N.J., Prescher, J.A., Bertozzi, C.R. (2004). A strain-promoted [3 + 2] azide–alkyne cycloaddition for covalent modification of biomolecules in living systems. J. Am. Chem. Soc., 126(46):15046–15047.
He, M., Zhou, X. & Wang, X. Glycosylation: mechanisms, biological functions and clinical implications. Sig Transduct Target Ther 9, 194 (2024).
Zhang, Y., Lu, L. & Li, X. Detection technologies for RNA modifications. Exp Mol Med 54, 1601–1616 (2022).
Hu B, Ma T, Zhou D, Jiao J, Xia X. GlycoRNA, a novel RNA modification. Discov Oncol. 2025 Oct 3;16(1):1809.

Inquiry