RNA ac4C Modification Profile (acRIP-Seq)
An antibody-based ac4C profile. Enriched fragments give a transcript-level view of RNA cytidine acetylation, which is a different question from the one a chemical-conversion method answers.
01What the service reports
RNA ac4C Modification Profile (acRIP-Seq) is an antibody-based enrichment service for N4-acetylcytidine in RNA. An anti-ac4C antibody captures modified fragments, which are sequenced and mapped to give a region-level profile of ac4C enrichment.
The output describes where acetylated fragments came from. Reads are grouped into enriched regions and compared with input, so the profile shows which transcripts carry ac4C signal and how that signal differs between samples — a region-level readout rather than a resolved cytosine.
This matters because ac4C is also profiled by a chemical route, in which a reagent reacts with the acetylated base and the resulting signature is read at single-base resolution. The two approaches are not interchangeable, and choosing between them follows from whether the study needs breadth of comparison or position-level detail. Arraystar runs the enrichment workflow from sample QC to the analyzed profile.
02Benefits
- Breadth of coverage: the profile surveys ac4C signal across the transcriptome rather than at selected positions.
- Straightforward comparisons: enrichment data from several samples support direct comparison of ac4C status.
- Annotated regions: enriched regions are linked to transcripts, so the result reads at the gene level.
- Complementary to conversion methods: the profile works as a survey whose findings can be resolved with a chemistry-based method.
- Single-provider workflow: enrichment, library construction, sequencing and analysis come as one service.
| Service Name | RNA Class | Price |
|---|---|---|
| RNA ac4C Modification Profile (acRIP-Seq) | mRNA | |
| RNA ac4C Modification Profile (acRIP-Seq) | mRNA and lncRNA |
03Background and detection principle
RNA is fragmented, incubated with an anti-ac4C antibody and the bound fragments are captured. The captured pool and an input control are sequenced in parallel, and regions that are enriched in the captured fraction make up the profile.
Two properties of the route shape its interpretation. Enrichment localises signal to a region, so the profile does not place an individual acetylated cytosine. Capture also depends on antibody binding, so an ac4C that is recognized weakly appears less prominent than the underlying biology would suggest. Read as a comparative profile, the output is a sound map of where ac4C signal differs.
acRIP-Seq reports where acetylated fragments come from; RedaC:T-seq reports which cytosine carries the mark.Arango D. et al., Cell 2018 · DOI 10.1016/j.cell.2018.10.030
04Workflow
The service runs in five steps from sample to analyzed ac4C profile:
- Sample QC, covering integrity and purity before capture.
- Fragmentation of the RNA into a range suitable for antibody binding.
- Immunoprecipitation with an anti-ac4C antibody, against an input control.
- Library construction from the enriched and input fractions.
- Sequencing and analysis to call enriched regions and compare samples.
05Bioinformatics and deliverables
Mapped reads are compared with input to call enriched regions, which are annotated to transcripts and features. The analysis set covers region calling and annotation, distribution across features, differential enrichment and genome-browser visualization.
Deliverables
- Raw ac4C mRNA enrichment sequencing data files (FASTQ), formatted for GEO/SRA submission.
- QC reports and sorted, indexed BAM alignment files with core mapping statistics.
- ac4C enrichment region tables (XLSX) with genomic coordinates and annotated host genes.
- Region distribution plots over transcript features (PDF/PNG).
- Differential ac4C enrichment tables (XLSX) between sample groups.
- Genome-browser signal tracks (bigWig/bedGraph) and a structured project report.
06Research applications
- Translation-related studies: ac4C has been connected to translation efficiency, so conditions that shift translation are natural comparisons.
- Cancer research: tumour and normal samples can be compared for ac4C signal across transcripts.
- Writer and eraser perturbation: samples with altered NAT10 activity can be compared to see how the profile responds.
- Stress response: treated and untreated samples show which regions change at the ac4C level.
- Planning position-level work: regions that stand out in the profile are the candidates for a conversion-based, site-level method.
07Sample requirements
Storage. Cells and tissues: preserve in TRIzol or an RNA stabilisation solution, snap freeze in liquid nitrogen and store at −80 °C. RNA: resuspend in ethanol or RNase-free ultrapure water, store at −80 °C and avoid repeated freeze–thaw cycles.
Shipping. Place the sample in an RNase-free microcentrifuge tube, seal it against leakage, and ship on dry ice with adequate insulation.
| Sample type | Notes |
|---|---|
| Whole blood | EDTA tubes only; heparin is not compatible with the downstream procedure. |
| Cultured cells | Cell pellets are preferred. |
| Tissue | Fresh or frozen specimens; avoid necrotic material. |
| Total RNA | OD 260/280 ≥ 1.8 and RIN ≥ 7, with no visible degradation. Submit more than 10 µg total RNA. |
08FAQ
What if the study needs the position rather than the profile?
Then the work moves to single-base resolution. Alongside this profile, Arraystar provides ac4C sequencing at single-base resolution (RedaC:T-seq), which records the modified base as a readable signature and reports its position and level, so transcripts that change here can be resolved site by site.
How does acRIP-Seq differ from a chemical-conversion ac4C method?
The conversion method reacts with the acetylated base and reads the resulting signature per base. acRIP-Seq captures modified fragments with an antibody and reports enriched regions. One resolves position, the other surveys breadth.
Does the profile call individual acetylated cytosines?
No. Enriched fragments map to a region of a transcript, so the output is a profile rather than a base-level call.
What should I keep in mind about the antibody?
Capture depends on antibody recognition, so the profile reflects both the biology and the binding behavior of the antibody. Comparative reading across samples is the most reliable use of the data.
Can the same study include both approaches?
Yes, and doing so is common: the profile identifies the transcripts and regions that differ, and a conversion-based method then resolves the positions inside them.
Which sample types are accepted?
Total RNA, cultured cells, tissue or blood, subject to the quality thresholds on this page.
09References
- Arango D, Sturgill D, Alhusaini N, et al. Acetylation of cytidine in mRNA promotes translation efficiency. Cell 2018;175:1872–1886. DOI: 10.1016/j.cell.2018.10.030