Long GlycoRNA Sequencing Services
Long GlycoRNA Sequencing extends glycoRNA profiling beyond small non-coding RNAs to the transcriptome scale. By applying chemical glycoRNA enrichment — periodate oxidation-based labeling or click chemistry-based metabolic labeling — to RNA fragments longer than 200 nucleotides, followed by strand-specific library preparation and high-throughput sequencing, the workflow captures N-glycosylation events on mRNAs, lncRNAs, long snoRNAs, and mitochondrial RNAs. CD Genomics provides end-to-end support from RNA extraction through bioinformatics analysis, including novel glycoRNA transcript discovery.
Key Highlights of Our Long GlycoRNA Sequencing Service:
- Transcriptome-Scale Coverage: Targets glycosylated RNAs >200 nt — mRNA, lncRNA, long snoRNA, and mtRNA — beyond the small ncRNA focus of conventional glycoRNA analysis.
- Two Complementary Enrichment Methods: Periodate oxidation-based chemical labeling for extracted RNA from any sample type; click chemistry-based metabolic labeling for live cultured cells.
- Strand-Specific Library Preparation: Preserves transcript orientation for accurate annotation of lncRNAs and overlapping gene regions, with molecular tag deduplication for quantitative accuracy.
- Novel Transcript Discovery: Unannotated glycoRNA candidates identified through transcript assembly, with distribution statistics and expression quantification provided in the final report.
What Is Long GlycoRNA Sequencing?
GlycoRNA — RNA molecules covalently modified with N-glycans — was first reported in 2021 when Flynn and colleagues demonstrated that small non-coding RNAs carry complex N-glycan chains and are displayed on the cell surface. The subsequent identification of acp3U (3-(3-amino-3-carboxypropyl)uridine) as the covalent N-glycan attachment site on RNA provided the first direct molecular evidence of the glycan–RNA linkage.
Early glycoRNA research focused on small non-coding RNAs under 200 nucleotides (nt) — including YRNAs, tRNAs, snRNAs, and snoRNAs. However, recent evidence has extended the glycoRNA landscape: transcriptome-wide detection revealed that glycosylated RNAs are not limited to the small RNA fraction but are also present on longer transcripts, including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), long small nucleolar RNAs (snoRNAs), and mitochondrial RNAs (mtRNAs), spanning a size range from approximately 50 to over 2,000 nt.
Long GlycoRNA Sequencing addresses this expanded view of the glycoRNA landscape. The workflow selectively enriches glycosylated RNA species from total RNA using chemical labeling and streptavidin-based capture, focusing on RNA fragments longer than 200 nt. Strand-specific library preparation, high-throughput sequencing, and computational analysis then produce a transcriptome-wide catalog of glycosylated long RNAs, including identification of novel, unannotated glycoRNA candidates.
Unlike existing glycoRNA services that target small non-coding RNAs, Long GlycoRNA Sequencing is purpose-built for the longer, structurally complex transcripts that constitute the bulk of the transcriptome — opening glycoRNA profiling to mRNA and lncRNA biology, transcript assembly-based discovery, and the study of glycosylation across the full transcriptomic landscape. For small RNA glycoRNA detection (<200 nt), see our GlycoRNA-seq service.
Technology Principle & Advantages
Principle
Long GlycoRNA Sequencing adapts chemical glycoconjugate enrichment strategies to the constraints of long RNA molecules. The method proceeds through four core stages:
Total RNA is extracted from biological samples using Trizol-based protocols. RNA fragments longer than 200 nt are selectively recovered, removing the small RNA fraction and ensuring the workflow targets the long transcript population — mRNAs, lncRNAs, long snoRNAs, and mtRNAs — rather than the small ncRNAs covered by conventional glycoRNA approaches.
Purified long RNA is divided: one portion retained as the input control, the other subjected to glycoRNA-specific chemical labeling followed by streptavidin-based enrichment. Two labeling strategies are available: periodate oxidation-based labeling for extracted RNA from cells, tissues, and other sample types; and click chemistry-based metabolic labeling for live cultured cells pre-incubated with azide-modified sugar precursors. The input control is processed in parallel for background normalization.
Enriched glycoRNA and input RNA are separately converted to cDNA libraries using a strand-specific protocol that preserves transcript orientation — critical for distinguishing sense and antisense transcripts in lncRNA and overlapping gene analysis. Molecular tags are incorporated to enable PCR duplicate removal and reduce amplification bias, improving quantification accuracy for low-abundance glycoRNA species.
Libraries are sequenced to sufficient depth for transcriptome-wide glycoRNA detection. Reads are aligned to the reference genome and transcriptome. GlycoRNA-enriched transcripts are identified by comparing the enriched library signal against the input control. Identified glycoRNAs are classified by RNA type, annotated to genomic features, and analyzed for differential glycosylation. Transcript assembly detects unannotated glycoRNA candidates.
Advantages
Transcriptome-Scale GlycoRNA Coverage
By selectively recovering RNA >200 nt and applying glycoRNA-specific enrichment to this fraction, the workflow captures glycosylation events on mRNAs, lncRNAs, long snoRNAs, and mtRNAs — transcript classes excluded from small-RNA-focused glycoRNA methods. This enables study of glycoRNA biology at the scale of the full long-transcript landscape.
Two Complementary Enrichment Strategies
The periodate oxidation method works directly on extracted RNA and is compatible with cells, tissues, and other sample types — no live-cell metabolic labeling required. The click chemistry method, applied to cultured cells incubated with azide-modified sugars, provides an alternative labeling route for projects where metabolic incorporation is preferred. Both strategies converge on streptavidin-based glycoRNA capture, enabling method selection based on sample type and experimental design.
Strand-Specific Quantification with Molecular Tags
Strand-specific library construction preserves transcript orientation, improving annotation accuracy for antisense and overlapping transcripts common in lncRNA biology. Molecular tag-based deduplication reduces PCR bias, yielding more reliable quantification — particularly for low-expression glycoRNA species that may otherwise be obscured by amplification noise.
Novel GlycoRNA Discovery
Beyond annotating glycosylation on known transcripts, the analysis pipeline includes transcript assembly to identify unannotated glycoRNA candidates. These novel molecules are reported with genomic coordinates, expression estimates, and RNA-type classification, providing a discovery dimension beyond reference-based annotation alone.
GlycoRNA Enrichment Methods
Two chemical enrichment strategies are available for Long GlycoRNA Sequencing. Method selection depends on sample type and experimental design.
| Feature | Periodate Oxidation-Based Labeling | Click Chemistry-Based Metabolic Labeling |
|---|---|---|
| Chemistry | Periodate oxidizes sialic acid diols to aldehydes; biotin-amine coupling; streptavidin capture | Azide-modified sugar precursors (e.g., Ac4ManNAz) incorporated metabolically; DBCO-biotin click reaction; streptavidin capture |
| Sample types | Cells, tissues, total RNA — any sample from which RNA can be extracted | Live cultured cells only — requires metabolic incorporation during culture |
| Labeling target | Sialic acid-containing glycans on extracted RNA | Azide-labeled glycans on newly synthesized RNA in living cells |
| Advantages | Broad sample compatibility; no pre-labeling step; works on stored RNA | Labels nascent glycoRNA in the native cellular context |
| Input requirement | >1×10⁷ cells; >200 mg tissue; >25 µg total RNA | >1×10⁷ cells after metabolic labeling |
| Suitable for | Clinical specimens, banked samples, tissues, multi-sample cohorts | Cell culture models, pulse-chase experiments, nascent glycoRNA tracking |
Selection guidance:
- Choose periodate oxidation-based labeling when working with tissue samples, clinical specimens, banked RNA, or any material that cannot undergo live-cell metabolic labeling. This method is the recommended default for most projects due to its broader sample compatibility.
- Choose click chemistry-based metabolic labeling when studying glycoRNA dynamics in live cultured cells, or when preferential detection of newly synthesized glycoRNAs is desired. Note that this method requires viable cells and pre-incubation with azide-modified sugar precursors.
Experimental Workflow
The Long GlycoRNA Sequencing workflow spans six stages from RNA extraction through bioinformatics analysis, with integrated quality control at each step.
- Total RNA Extraction and Size Selection. Total RNA is extracted from samples using Trizol-based protocols. RNA fragments longer than 200 nt are selectively recovered. QC: RNA integrity and quantity assessed; size distribution verified.
- RNA Quality Control. Extracted long RNA is quantified and assessed for purity and integrity. Samples are checked for DNA and protein contamination. QC: RNA concentration and purity ratios confirmed; absence of degradation verified.
- GlycoRNA Labeling and Enrichment. Purified long RNA is divided: one portion serves as the input control; the other undergoes glycoRNA-specific chemical labeling (periodate oxidation or click chemistry, per method selection) followed by streptavidin-based enrichment of glycosylated RNA species. QC: Enrichment efficiency assessed; input control retained for downstream signal normalization.
- Strand-Specific Library Preparation. Enriched glycoRNA and input RNA are separately converted to cDNA libraries using a strand-specific protocol. Molecular tags are incorporated to enable PCR duplicate identification and removal. QC: Library size distribution, adapter content, and amplification uniformity assessed.
- Sequencing. Libraries are sequenced using paired-end 150 bp chemistry to provide sufficient read length for accurate transcript mapping and assembly. QC: Q30 scores, duplication rate, and read distribution assessed.
- Bioinformatics Analysis. Reads aligned to the reference genome. GlycoRNA-enriched transcripts identified by comparing enriched library signal against input control. Identified glycoRNAs classified by RNA type. Differential glycosylation analysis compares glycosylation levels between experimental groups. Downstream analyses include GO/KEGG enrichment, transcript assembly for novel glycoRNA discovery, and visualization.
Sample Requirements
Sample quality and quantity directly affect glycoRNA recovery, enrichment efficiency, and detection sensitivity.
| Sample Type | Recommended Amount | Notes |
|---|---|---|
| Cultured cells (periodate method) | >1×10⁷ | Pellet and snap-freeze; avoid repeated freeze-thaw |
| Cultured cells (click chemistry) | >1×10⁷ | Requires pre-incubation with azide-modified sugar precursor; contact us for protocol |
| Tissue | >200 mg | Snap-freeze in liquid nitrogen immediately after collection; store at −80°C |
| Whole blood | >20 mL | Collect in EDTA tubes (purple cap); do NOT use heparin tubes (green cap) |
| Total RNA | >25 µg | Concentration ≥ 50 ng/µL recommended; dissolved in nuclease-free water; A260/A280 ≥ 1.8 |
Shipping and storage:
- Samples should be placed in cryovials or 1.5 mL tubes, sealed with parafilm, and shipped on dry ice.
- Cell pellets and tissue samples should be snap-frozen in liquid nitrogen and stored at −80°C.
- Whole blood should be transferred to cryovials after collection and stored at −80°C; avoid repeated freeze-thaw cycles.
- Total RNA should be dissolved in nuclease-free water and stored at −80°C.
Bioinformatics Analysis Pipeline
The standard analysis pipeline covers raw data processing through functional enrichment and novel glycoRNA discovery.
- Raw data processing. Adapter trimming, low-quality read removal, read length filtering. Q30 scores and sequencing statistics reported.
- Read alignment. Clean reads aligned to the reference genome and transcriptome using splice-aware alignment.
- GlycoRNA identification and annotation. GlycoRNA-enriched transcripts identified by comparing enriched library signal against input control. Identified glycoRNAs annotated by RNA type — mRNA, lncRNA, snoRNA, mtRNA, and other categories. Genomic feature annotation (exon, intron, intergenic) provided.
- GlycoRNA classification. Pie charts display the distribution of identified glycoRNAs across RNA categories, providing an at-a-glance view of the glycoRNA landscape in each sample.
- Differential glycoRNA analysis. Glycosylation levels compared between experimental groups. Significantly differentially glycosylated transcripts identified based on fold change and statistical significance (P-value calculation requires ≥ 3 biological replicates per group). Results visualized as scatter plots, volcano plots, and hierarchical clustering heatmaps.
- GO and KEGG enrichment — mRNA targets. Genes encoding differentially glycosylated mRNAs analyzed for Gene Ontology (molecular function, cellular component, biological process) and KEGG pathway enrichment.
- GO and KEGG enrichment — lncRNA targets. For differentially glycosylated lncRNAs, neighboring protein-coding genes identified and analyzed for GO and KEGG pathway enrichment, revealing potential cis-regulatory functions.
- Novel transcript discovery (optional). Unannotated glycoRNA candidates identified through transcript assembly reported with genomic coordinates, expression estimates, and RNA-type prediction. This module supports discovery of glycoRNA species beyond current reference annotations.
Demo Results
The following representative visualizations illustrate the types of results delivered with each Long GlycoRNA Sequencing project. All panels are labeled "Representative data."
Representative Long GlycoRNA Sequencing analysis outputs delivered with each project. (A) GlycoRNA identification and annotation summary table listing genomic coordinates, RNA type, enrichment score, and expression level. (B) GlycoRNA classification pie chart showing proportion of identified glycoRNAs across mRNA, lncRNA, snoRNA, mtRNA, and novel transcript categories. (C) Volcano plot highlighting significantly hyper- and hypo-glycosylated transcripts between experimental groups. (D) Hierarchical clustering heatmap displaying glycosylation patterns across samples and conditions. (E) GO and KEGG enrichment bar charts for genes associated with differentially glycosylated transcripts — mRNA targets and lncRNA-proximal genes analyzed separately.
Data Deliverables
| Deliverable | Description |
|---|---|
| Raw sequencing data | FASTQ files for each enriched and input library |
| Quality control report | Read quality metrics, alignment statistics, enrichment efficiency assessment |
| GlycoRNA identification table | Annotated glycoRNA catalog with genomic coordinates, RNA type, enrichment scores, and expression levels |
| Classification analysis | Pie charts and summary statistics for glycoRNA distribution across RNA categories |
| Differential glycosylation results | Significantly changed glycoRNAs between conditions, with fold change and P-values |
| GO/KEGG enrichment report | Functional enrichment analysis for genes associated with differentially glycosylated mRNAs and lncRNA-proximal genes |
| Novel transcript report (optional) | Unannotated glycoRNA candidates with coordinates, expression estimates, and RNA-type predictions |
| Visualization package | Classification pie charts, volcano plots, clustering heatmaps, enrichment bar charts |
| Methods and analysis report | Detailed description of all data processing and analysis steps |
Applications
Long GlycoRNA Sequencing addresses the growing need in RNA biology: to move glycoRNA research beyond small ncRNAs and systematically characterize N-glycosylation across the transcriptome — including the protein-coding and long non-coding RNA landscape.
Long GlycoRNA Sequencing provides a comprehensive view of N-glycosylation across the long-transcript landscape — including mRNAs, lncRNAs, and other RNA species beyond the small ncRNA fraction. The workflow generates a transcriptome-scale catalog of glycosylation events, enabling investigation of which transcript classes carry glycans, in what genomic contexts, and at what relative levels.
GlycoRNAs on small extracellular vesicles (sEVs) carry disease-relevant signatures. Studies have demonstrated that sialic acid-modified glycoRNAs on sEVs can distinguish cancer patients from healthy controls with high accuracy. Long GlycoRNA Sequencing provides transcriptome-wide glycoRNA profiling to support sEV glycoRNA biomarker discovery, enabling identification of candidate glycosylated transcripts for non-invasive disease detection.
GlycoRNAs are abundant in tumor tissues and functionally involved in cancer cell proliferation. Transcriptome-wide glycoRNA profiling across tumor and matched normal samples can identify cancer-associated glycosylation changes — on both protein-coding mRNAs and regulatory lncRNAs — that may represent novel therapeutic targets or disease progression markers.
Cell-surface glycoRNAs participate in immune recognition through interactions with Siglec receptors and selectins. Long GlycoRNA Sequencing enables profiling of glycosylated transcripts in contexts such as immune cell activation, host-microbiome interaction, and intercellular communication, where glycoRNA-mediated signaling may play functional roles.
For small non-coding RNA glycoRNA detection (<200 nt), see GlycoRNA-seq. For RNA methylation analysis, MeRIP-Seq provides antibody-based detection of m6A, m5C, and other internal RNA modifications. For complementary epitranscriptomic profiling, see caRNA Modification Sequencing and cfRNA Modification Sequencing.
Method Comparison
Long GlycoRNA Sequencing, GlycoRNA-seq (small RNA), and MeRIP-Seq address different aspects of RNA modification biology. The table below summarizes the key differences to guide method selection.
| Feature | Long GlycoRNA Sequencing | GlycoRNA-seq (Small RNA) | MeRIP-Seq |
|---|---|---|---|
| Target modification | N-glycan (sialylated glycans on RNA) | N-glycan (sialylated glycans on RNA) | Internal RNA methylation (m6A, m5C, etc.) |
| RNA size range | >200 nt | <200 nt | All sizes (total RNA) |
| RNA types covered | mRNA, lncRNA, long snoRNA, mtRNA | miRNA, YRNA, tRNA, tsRNA, rsRNA, snRNA, snoRNA | mRNA, lncRNA, circRNA, and other coding/non-coding |
| Enrichment principle | Chemical labeling + streptavidin capture | Chemical labeling + streptavidin capture | Modification-specific antibody immunoprecipitation |
| Sample types | Cells, tissue, total RNA, whole blood | Cells, tissue, total RNA | Cells, tissue, total RNA |
| Strand-specific | Yes | Not standard | Not standard |
| Novel transcript discovery | Yes — transcript assembly | Limited | Not standard |
| Suitable for | Transcriptome-scale glycoRNA mapping, long RNA glycosylation biology, sEV biomarker discovery | Small ncRNA glycoRNA profiling, miRNA/YRNA/tRNA glycosylation | Transcriptome-wide internal RNA methylation profiling |
Selection guidance:
- Choose Long GlycoRNA Sequencing when your research targets glycosylation on mRNAs, lncRNAs, or other long transcripts, or when you need transcriptome-scale glycoRNA coverage beyond the small RNA fraction.
- Choose GlycoRNA-seq (small RNA) when your research focuses on glycosylation of small non-coding RNAs — miRNAs, YRNAs, tRNAs, tsRNAs, or other small ncRNA species.
- Choose MeRIP-Seq when your research targets internal RNA methylations (m6A, m5C, m1A, etc.) rather than N-glycan modifications.
- For multi-omics integration, Long GlycoRNA Sequencing can be combined with downstream mass spectrometry-based glycan structural analysis for orthogonal validation of glycoRNA findings.
Frequently Asked Questions (FAQ)
References
- Flynn RA, Pedram K, Malaker SA, et al. Small RNAs are modified with N-glycans and displayed on the surface of living cells. Cell, 2021, 184(12):3109–3124.e22.
- Xie Y, Chai P, Till NA, et al. The modified RNA base acp3U is an attachment site for N-glycans in glycoRNA. Cell, 2024, 187(19):5228–5237.e12.
- Zhu N, Yang YL, Liu YT, et al. Transcriptome-wide identification of glycoRNAs by Clier-seq pipeline. Science China Life Sciences, 2026, 69(1):72–84.
- Ren T, et al. FRET imaging of glycoRNA on small extracellular vesicles enabling sensitive cancer diagnostics. Nature Communications, 2025, 16:3391.
- Hu B, Ma T, Zhou D, et al. GlycoRNA, a novel RNA modification. Discover Oncology, 2025, 16:1809.
- Sharma S, Jiao X, Yang J, et al. Extracellular exosomal RNAs are glyco-modified. Nature Cell Biology, 2025, 27:983–991.
The services described are for research use only. They are not intended for diagnostic, therapeutic, or clinical applications.