m5C RNA-seq Services for Epitranscriptomics Research

5-methylcytosine (m5C) is a conserved RNA modification found across mRNA, tRNA, rRNA, and long non-coding RNAs. Deposited by methyltransferases of the NSUN family and DNMT2, m5C influences RNA stability, translation efficiency, and tRNA structure — with dysregulation increasingly linked to cancer, developmental disorders, and viral infection. At CD Genomics, we offer an integrated m5C RNA analysis platform covering both single-base resolution bisulfite sequencing (RNA-BS-seq) and antibody-based enrichment profiling (m5C RIP-seq/meRIP-seq), supported by expert bioinformatics. Whether your goal is to map precise m5C sites at nucleotide resolution or to survey transcriptome-wide methylation patterns for discovery, our team helps you match the right approach to your research question.

Key Highlights of Our m5C RNA-seq Services:

Dual-Method Platform: RNA-BS-seq for single-base resolution and m5C RIP-seq / meRIP-seq for transcriptome-wide enrichment — choose the approach that fits your research question.
Multi-RNA-Type Support: Optimized protocols for mRNA, tRNA, rRNA, and lncRNA — from poly(A) selection to targeted small RNA enrichment.
End-to-End Bioinformatics: From raw data QC through methylation calling, differential analysis, motif discovery, and publication-ready visualizations.
Species Flexibility: Support for model and non-model organisms, with RIP-seq compatible with de novo transcriptome assemblies.

Discuss Your m5C Analysis Project

m5C RNA-seq services illustration showing bisulfite sequencing and RIP-seq methods for 5-methylcytosine detection

Overview

Overview of Our m5C RNA-seq Services

5-methylcytosine (m5C) is a conserved RNA modification influencing RNA stability, translation efficiency, and tRNA structure. Deposited by NSUN family methyltransferases and DNMT2, its dysregulation is increasingly linked to cancer, developmental disorders, and viral infection. Profiling m5C at the transcriptome level has become an essential component of modern epitranscriptomics research. Our integrated m5C analysis platform combines single-base resolution bisulfite sequencing (RNA-BS-seq) with antibody-based enrichment profiling (m5C RIP-seq/meRIP-seq), supported by expert bioinformatics and multi-RNA-type workflows covering mRNA, tRNA, rRNA, and lncRNA.

Technologies

Core Technologies for m5C RNA Profiling

RNA Bisulfite Sequencing (RNA-BS-seq) — Single-Base Resolution

RNA bisulfite sequencing is the gold standard for m5C detection at single-nucleotide resolution. Sodium bisulfite treatment converts unmethylated cytosines (C) to uracils (U), while 5-methylcytosine remains protected and is read as C during sequencing. By comparing treated and untreated RNA, every m5C site across the transcriptome can be identified with base precision and methylation stoichiometry estimated per position.

A 2024 study in Life Science Alliance re-analyzed bisulfite RNA-seq data from five human cell lines and seven tissues, yielding 6,393 high-confidence m5C sites and defining the hierarchical contributions of NSUN2, NSUN6, and NSUN5 as mRNA m5C writers (Guarnacci et al., 2024). This level of precision — mapping individual modification sites to specific methyltransferases — is unique to BS-seq. Our RNA-BS-seq service supports poly(A)-selected mRNA, rRNA-depleted total RNA, and targeted tRNA enrichment. See our dedicated mRNA m5C BS-seq page for method-specific details.

Comparison of m5C BS-seq single-base resolution and m5C RIP-seq enrichment methods

m5C RIP-seq and m5C meRIP-seq — Transcriptome-Wide Enrichment

m5C RIP-seq (RNA Immunoprecipitation Sequencing) and m5C meRIP-seq (Methylated RNA Immunoprecipitation Sequencing) use specific antibodies raised against 5-methylcytosine to enrich m5C-containing RNA fragments, followed by sequencing to identify methylated regions transcriptome-wide. These methods provide regional resolution (~100–150 nucleotides), making them well-suited for discovery-phase profiling and comparative analysis between conditions. Compared to BS-seq, RIP-seq requires lower sequencing depth per sample and offers higher sensitivity for low-abundance transcripts due to the enrichment step — ideal for multi-sample screening studies.

Additional m5C Detection Methods

For specialized applications, we also offer miCLIP-m5C-seq (crosslinking-based single-nucleotide resolution targeting NSUN2-dependent sites) and can discuss Oxford Nanopore direct RNA-seq for native modification detection — see our ONT Direct RNA Sequencing page for details.

Decision Guide

How to Choose Between BS-seq and RIP-seq for m5C Analysis

Selecting the right m5C detection method depends on your resolution requirement, sample situation, and research goals.

Criterion	RNA-BS-seq	m5C RIP-seq / meRIP-seq
Resolution	Single-base (nucleotide)	Regional (~100–150 nt)
Coverage	Transcriptome-wide, unbiased	Transcriptome-wide, antibody-enriched
Methylation stoichiometry	Yes (C/T ratio per site)	No (enrichment fold-change)
Sensitivity for low-abundance RNA	Moderate (requires deep sequencing)	High (antibody enrichment)
Sequencing depth needed	Higher	Lower
Antibody required	No	Yes (anti-m5C)
Non-model organism support	Requires reference genome	Reference or de novo transcriptome
Best for	Precise site ID, stoichiometry, writer/substrate mapping	Discovery screening, multi-condition comparison, low-input

Selection Strategy:

Define your resolution requirement. If you need to know the exact nucleotide position and methylation level of each m5C site — for example, to map NSUN2 substrate specificity or validate a regulatory site — RNA-BS-seq is the appropriate choice. If your goal is to identify which transcripts and pathways carry m5C marks across conditions, m5C RIP-seq will be sufficient.
Consider your sample. Limited RNA amounts or low-abundance transcripts favor RIP-seq due to the enrichment step. For stoichiometric quantification per site, BS-seq is necessary.
Combine for completeness. An increasingly common strategy is RIP-seq for discovery-phase screening, followed by BS-seq validation of key sites on selected samples.
For non-model organisms. RIP-seq works with de novo transcriptome assemblies; BS-seq requires a reference genome for accurate alignment and methylation calling.

Speak with an m5C Epitranscriptomics Specialist

Workflow

End-to-End Workflow and Quality Control

Our m5C RNA-seq service follows a standardized workflow with QC checkpoints at every stage. Two parallel routes — BS-seq and RIP-seq — diverge at library preparation before converging on shared sequencing and bioinformatics.

Sample Receipt and Quality Assessment — QC Checkpoint: RNA integrity (RIN) verification via Bioanalyzer/TapeStation; concentration and purity (OD260/280) measurement; degradation and contamination assessment.
RNA Preparation — QC Checkpoint: Poly(A) selection or rRNA depletion efficiency verified by Bioanalyzer; RNA yield post-enrichment confirmed. For tRNA studies, small RNA enrichment confirmed.
Library Construction — QC Checkpoint (BS-seq): Bisulfite conversion efficiency (>99%) via spike-in controls; fragment size; library yield. QC Checkpoint (RIP-seq): Antibody enrichment efficiency by qPCR of control transcripts; fragment size; library yield.
Sequencing — QC Checkpoint: Yield and Q30 scores; duplication rate; PhiX alignment rate; read-level C→T conversion rate for BS-seq libraries.
Data Quality Control — QC Checkpoint (BS-seq): Conversion efficiency from read-level C→T rates at non-methylated positions; alignment rates; coverage uniformity. QC Checkpoint (RIP-seq): Peak enrichment signal-to-noise; FRiP; replicate correlation; library complexity.
Bioinformatics and Report Delivery — QC Checkpoint: Peak/methylation call reproducibility; differential analysis statistical metrics; deliverable completeness; final QC summary.

Sample Requirements

Sample Requirements and Preparation Guidelines

Proper sample handling is essential for m5C RNA analysis. RNA is susceptible to degradation — compromised samples can introduce bias in methylation detection.

Method	Recommended RNA Type	Input Guideline	Key QC Metrics	Notes
RNA-BS-seq (mRNA)	Poly(A)-selected mRNA	≥ 30 μg total RNA recommended; ≥ 10 μg minimum; ≥ 20 ng/μL	RIN ≥ 7; OD260/280: 1.8–2.1	Higher input recommended due to RNA loss during bisulfite treatment
RNA-BS-seq (total RNA)	rRNA-depleted total RNA	≥ 30 μg total RNA recommended; ≥ 10 μg minimum; ≥ 20 ng/μL	RIN ≥ 7; OD260/280: 1.8–2.1	rRNA depletion maximizes m5C signal from mRNA and ncRNA
m5C RIP-seq / meRIP-seq	Total RNA or poly(A)-selected	Project-specific — contact us	RIN ≥ 7; OD260/280: 1.8–2.1	Lower input feasible due to enrichment; anti-m5C antibody validated per batch
tRNA m5C BS-seq	Enriched small RNA (<200 nt)	Project-specific — contact us	Small RNA enrichment confirmed	Requires dedicated tRNA/small RNA purification

All samples should be shipped on dry ice and stored at -80°C. Avoid repeated freeze-thaw cycles.

Bioinformatics

Bioinformatics Analysis and Deliverables

All m5C RNA-seq services include standard bioinformatics processing with the option to add advanced analysis modules.

Standard Deliverables:

Deliverable	Description
Raw sequencing data	Demultiplexed read files with quality scores
Aligned reads	Reads aligned to reference genome/transcriptome
Methylation calls (BS-seq)	Per-site m5C positions with methylation ratio
Signal tracks (RIP-seq)	Normalized enrichment tracks for browser visualization
Peak calls (RIP-seq)	Enriched m5C regions with statistical significance
QC report	Conversion efficiency, alignment rates, FRiP, coverage metrics
Differential methylation analysis	Comparison between conditions with statistical tests
Motif analysis	Sequence motifs enriched around m5C sites or peaks
Peak / site annotation	Genomic context with nearest gene/transcript
GO/KEGG enrichment	Functional enrichment of m5C-modified genes

Optional Advanced Analysis:

Multi-omics integration — combined m5C data with matched RNA-seq, m6A-seq, or proteomics
Writer/reader target prediction — integration with public CLIP-seq and protein-RNA interaction databases
tRNA-specific m5C analysis — specialized pipelines for tRNA modification calling and stoichiometry
ONT direct RNA-seq analysis — native RNA modification detection without bisulfite or amplification
Custom visualization — publication-ready figures including circos plots, heatmaps, and integrative browser views
Epigenomic data analysis — see our dedicated Epigenomic Data Analysis service

Demo Results

Representative Data and Demo Results

Below are representative data types delivered with each m5C analysis project. All panels represent standard bioinformatics outputs generated by our pipeline, reflecting the quality and depth researchers can expect.

BS-seq Quality and Site-Level Outputs:

Single-base m5C methylation track — IGV-style genome browser view showing read-level C→T conversion evidence with per-site methylation ratio. Each dot represents a cytosine position; the ratio of C to T reads directly quantifies methylation stoichiometry.
m5C site distribution summary — breakdown of identified m5C sites across RNA biotypes (mRNA CDS, 5'UTR, 3'UTR, tRNA, rRNA, lncRNA), showing the global distribution of 5-methylcytosine across the transcriptome.
Sequence motif logo — consensus sequence context surrounding high-confidence m5C sites, identifying preferred nucleotide motifs associated with NSUN family methyltransferase targeting.

RIP-seq Enrichment and Differential Analysis:

m5C enrichment peak track — normalized read coverage across a representative transcript with called peak regions indicated, showing the regional nature of RIP-seq signal.
Differential m5C volcano plot — each point represents a called m5C peak; x-axis log2 fold change between conditions; y-axis -log10 adjusted p-value. Hyper- and hypo-methylated regions surpassing the significance threshold are color-highlighted.
GO/KEGG enrichment bar chart — top enriched biological process terms among genes harboring differentially methylated m5C peaks, providing functional interpretation of the m5C landscape.

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

Applications

Applications Across Research Areas

Cancer Epitranscriptomics

m5C modifications play emerging roles across multiple cancer types. In anaplastic thyroid cancer, NSUN2-mediated tRNA m5C stabilizes tRNAs and drives codon-biased oncogenic translation, contributing to chemoresistance (Li et al., Clinical and Translational Medicine, 2023). In hepatocellular carcinoma, m5C-RIP-Seq revealed NSUN2 hypermethylation of PKM2 mRNA at C773 in the 3'UTR, enhancing glycolysis (Cell Death & Disease, 2025). In non-small cell lung cancer, NSUN2-driven NRF2 mRNA m5C promotes ferroptosis resistance.

tRNA Biology and Translation Control

tRNAs are among the most heavily modified RNA species, and m5C at positions C48/C49 in the variable loop directly affects tRNA stability, aminoacylation efficiency, and codon-biased translation. RNA bisulfite sequencing targeting the tRNA fraction provides base-resolution m5C maps across the tRNA repertoire, enabling functional studies of how tRNA modifications shape the cellular translatome.

Developmental Biology and Stem Cells

The NSUN family of methyltransferases shows tissue-specific and developmental-stage-specific expression. Profiling m5C across differentiation time courses identifies regulatory methylation events controlling cell fate decisions and reveals developmental windows sensitive to epitranscriptomic disruption.

Infectious Disease and Plant Epitranscriptomics

m5C modifications influence host innate immune responses and viral replication. A 2024 review in Frontiers in Immunology highlighted the role of m5C in SARS-CoV-2-associated myocarditis. In plants, m5C is conserved across kingdoms, and profiling in crop species under stress conditions can reveal epitranscriptomic adaptation mechanisms for agricultural biotechnology.

Case Study

Case Study: NSUN2-mediated tRNA m5C Stabilization in Anaplastic Thyroid Cancer

NSUN2-mediated tRNA m5C Stabilization in Thyroid Cancer

Source: Li P. et al., Clinical and Translational Medicine, 2023. https://doi.org/10.1002/ctm2.1466

Background. Anaplastic thyroid cancer (ATC) is one of the most aggressive solid tumors, with limited treatment options and poor prognosis. While NSUN2, a primary m5C methyltransferase, is upregulated in several cancers, its specific role in ATC and its tRNA substrates were not previously characterized.

Methods. The study profiled NSUN2 expression in ATC patient samples and cell lines. tRNA bisulfite sequencing mapped m5C sites across the tRNA repertoire at single-nucleotide resolution. Functional consequences of NSUN2-mediated tRNA m5C were assessed via polysome profiling, codon-specific translation reporters, and chemosensitivity assays.

Results. NSUN2 was significantly upregulated in ATC and associated with poor prognosis. tRNA bisulfite sequencing revealed m5C primarily at C48 and C49 in the variable loop of multiple tRNA species, stabilizing tRNAs and maintaining leucine charging. This supported a pro-cancer translation program (c-Myc, BCL2, RAB31, JUNB, TRAF2). A c-Myc → NSUN2 feedback loop was identified. NSUN2 knockdown reduced tRNA stability, disrupted oncogenic translation, and sensitized ATC cells to doxorubicin and cisplatin.

Figure from Li P. et al., Clinical and Translational Medicine, 2023 — NSUN2-mediated tRNA m5C methylation at C48/C49 stabilizes tRNAs and drives codon-biased oncogenic translation in anaplastic thyroid cancer.

Conclusion. This study demonstrates that NSUN2-mediated tRNA m5C modification is a critical determinant of oncogenic translation in ATC and represents a potential therapeutic target. It highlights how single-base resolution m5C profiling (tRNA-BS-seq) can identify specific modification sites with direct functional consequences, linking epitranscriptomics to cancer therapy.

FAQ

Frequently Asked Questions

Q: What is the difference between m5C BS-seq and m5C RIP-seq?

A: m5C BS-seq uses sodium bisulfite to convert unmethylated cytosines to uracils, enabling single-base resolution mapping of all m5C sites across the transcriptome. m5C RIP-seq uses a specific antibody to enrich m5C-containing RNA fragments, providing transcriptome-wide coverage at regional resolution (~100–150 nt). BS-seq is the gold standard for precision; RIP-seq is more sensitive for low-abundance transcripts and requires less sequencing depth. Many researchers use RIP-seq for discovery followed by BS-seq for site-level validation.

Q: Can you detect m5C on all RNA types — mRNA, tRNA, rRNA, lncRNA?

Q: Do you support non-model organisms without a reference genome?

Q: What bioinformatics analysis is included?

A: Standard deliverables include raw data (FASTQ), aligned reads (BAM), methylation calls or signal tracks (bigWig), peak files (BED), a comprehensive QC report (HTML/PDF), differential methylation analysis, motif analysis, peak/site annotation, and GO/KEGG pathway enrichment. Advanced modules including multi-omics integration and custom visualization are available as add-ons. See the Bioinformatics section and our Epigenomic Data Analysis page for details.

Q: Can bisulfite sequencing distinguish m5C from other modified cytosines like hm5C?

Q: Which method is best for comparing m5C patterns between conditions?

Q: Is ONT direct RNA sequencing available for m5C detection?

References

Guarnacci et al., Life Science Alliance (2024). Substrate diversity of NSUN enzymes and links of 5-methylcytosine to mRNA translation and turnover. https://doi.org/10.26508/lsa.202402613
Li P. et al., Clinical and Translational Medicine (2023). The m5C methyltransferase NSUN2 promotes codon-dependent oncogenic translation by stabilising tRNA in anaplastic thyroid cancer. https://doi.org/10.1002/ctm2.1466
Cell Death & Disease (2025). The RNA m5C methyltransferase NSUN2 promotes progression of hepatocellular carcinoma by enhancing PKM2-mediated glycolysis. https://doi.org/10.1038/s41419-025-07414-5
Frontiers in Immunology (2024). RNA m5C methylation modification: a potential therapeutic target for SARS-CoV-2-associated myocarditis. https://doi.org/10.3389/fimmu.2024.1380697

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