What is LIME-seq service and how is it different from ordinary cfRNA sequencing?

LIME-seq (Low-Input Multiple Methylation Sequencing) goes beyond measuring RNA amounts by detecting site-specific modifications (like m¹A, m³C etc.), turning those modifications into identifiable mismatch signals. Ordinary cfRNA sequencing measures expression but often misses modification signals that can indicate disease earlier.

How much sample do I need and what quality should it be?

You generally need as little as ~600 µL plasma (or equivalent biofluid) or ~1.5 ng cfRNA input. Samples should be handled to avoid degradation (use RNase-free tubes, keep cold, avoid freeze-thaw), and storage at −80 °C (or lower) with dry-ice transport is required for best results.

Which RNA modifications can LIME-seq detect?

LIME-seq detects multiple modifications in one run, such as m¹A, m³C, m¹G, m²²G, m³U, and inosine. The panel can be adjusted depending on sample type, sequencing depth, or specific research needs.

Can LIME-seq analyze both human (host) and microbial cfRNA together?

Yes. LIME-seq maps reads to both host and microbial genomes so you can compare modification signatures coming from human cells vs those from microbiome. This dual-domain data can enrich biomarker discovery and clarify underlying biology.

What kind of data and figures will I receive?

You will get raw sequencing files (FASTQ), annotated tables of modification sites with confidence scores, taxonomic profiles for microbial cfRNA, visualization plots (heatmap, volcano, PCA etc.), and a technical report suitable for publication.

Is LIME-seq suitable for diagnostic / clinical decision making?

No. The LIME-seq service is provided for research use only. It is designed for biomarker discovery, mechanism studies, and preclinical research, not for clinical diagnostics or direct patient care.

What are common challenges in cfRNA based liquid biopsy and how does LIME-seq address them?

Challenges include low abundance of cfRNA, high degradation/fragmentation, high background noise, and mixed sample origins. LIME-seq uses low-input, modification-friendly chemistry, rigorous QC, and joint host−microbiome mapping to mitigate these issues. Reviews on cfRNA broadly highlight these as recurring problems.

Why consider cfRNA over cfDNA?

cfRNA offers a dynamic transcriptomic snapshot, reflecting current gene expression, cell stress, and active transcription, whereas cfDNA often reflects static mutation profiles. cfRNA can provide earlier signals in some cases. Research and reviews note cfRNA’s promise in diagnostics and disease monitoring.

How does LIME-seq compare to cfDNA tests?

LIME-seq analyzes cfRNA modifications rather than just DNA fragments; cfRNA can reflect gene expression, microbial activity, and chemical modifications which cfDNA may miss. A recent study showed that combining cfRNA with cfDNA improves sensitivity in liquid biopsy for some cancers.

Is LIME-seq validated for distinguishing colorectal cancer from healthy controls?

Yes. The LIME-seq study in Nature Biotechnology (2025) demonstrated that microbiome-derived cfRNA modification signatures can discriminate colorectal cancer patients from non-cancer controls with high accuracy.

What challenges exist for cfRNA biomarker discovery?

The main issues are technical variability, degradation of RNA, low abundance, and lack of standardised protocols. Reviews note that reproducibility is often low unless sample handling, library prep, and QC pipelines are rigorously controlled.

What sample quality or volume is needed for reliable LIME-seq results?

Reliable LIME-seq results require pure, undegraded cfRNA, free of genomic DNA or protein contaminants. Even smaller volumes (~600 µL plasma) are feasible for discovery if handled properly. Sample collection, storage, and processing steps strongly influence outcomes.

Can LIME-seq detect signals in early-stage or pre-cancer conditions?

Yes. In the LIME-seq CRC study, modification signatures were distinct even in early-stage (including Stage 0/I) colorectal cancer patients compared to controls. This suggests potential utility for early screening applications.

How reliable are microbial cfRNA signals in LIME-seq?

Microbial cfRNA constitutes ~20-40% of mapped cfRNA reads in plasma in some LIME-seq datasets, and modification levels on microbial cfRNA show consistent differences between disease and healthy states. Stability arises from using modification-aware RT enzymes and stringent QC.

Do sample handling and protocol variations affect LIME-seq results?

Definitely. Reviews emphasize that differences in extraction method, storage temperature, freeze-thaw cycles, and library preparation steps contribute to variability in cfRNA studies. Standardised protocol and QC are needed to reduce these effects.

LIME-seq Service – Low-Input Multiple Methylation Sequencing

CD Genomics offers LIME-seq (Low-Input Multiple Methylation Sequencing), a next-generation approach for profiling cell-free RNA (cfRNA) modifications with unmatched sensitivity and reproducibility. Traditional cfRNA studies are hindered by low abundance, rapid degradation, and high background noise. LIME-seq overcomes these challenges by converting RNA modification signatures into identifiable base-change signals during reverse transcription, enabling robust mapping of epitranscriptomic profiles from as little as 600 µL plasma.

We solve key research problems:

Sensitive detection of scarce cfRNA molecules
Accurate analysis of unstable and fragmented cfRNA
Integrated profiling of both host- and microbiome-derived cfRNA signals

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cfRNA modifications profiling with LIME-seq (Low-Input Multiple Methylation Sequencing)

Our advantages:

Simultaneous detection of six RNA modifications (m¹A, m³C, m¹G, m²²G, m³U, inosine)
Ultra-low input requirement: starting from ~1.5 ng cfRNA
High reproducibility (r > 0.96) across replicates
Comprehensive bioinformatics for site-specific modification analysis

Why LIME-seq Technology Overview Workflow Technical Details Bioinformatics Why CD Genomics Sample Requirements Deliverables & Demo FAQs Inquiry

Why LIME-seq Matters in cfRNA Research

Cell-free RNA (cfRNA) has emerged as a promising biomarker for liquid biopsy research, offering insights into disease states from plasma, serum, and other biofluids. However, cfRNA studies face persistent technical barriers: the molecules are highly fragmented, easily degraded by RNases, and often masked by background signals. These issues make it difficult to detect early and subtle molecular changes, especially in oncology and microbiome-related studies.

Epitranscriptomic profiling provides the next dimension.

Traditional cfRNA sequencing focuses on expression levels, which can be weak or diluted in early disease. By contrast, RNA modification patterns—the cfRNA epitranscriptome—reflect stress responses, microbial activity, and metabolic shifts with high sensitivity.

Recent research published in Nature Biotechnology has shown that analysing cfRNA modification signatures can distinguish cancer patients from healthy individuals with improved sensitivity and specificity, even at early disease stages. This breakthrough underscores why LIME-seq (Low-Input Multiple Methylation Sequencing) is a valuable addition to cfRNA research pipelines.

With its ability to convert modification events into identifiable sequencing signals, LIME-seq opens new opportunities for early biomarker discovery, host–microbe interaction studies, and multi-omics integration.

Technology Overview

What Is LIME-seq?

LIME-seq (Low-Input Multiple Methylation Sequencing) is a next-generation method designed to overcome the limitations of conventional cfRNA analysis. Instead of only measuring RNA abundance, LIME-seq detects site-specific chemical modifications that can act as highly sensitive biomarkers.

Core Principle

RNA modifications often cause reverse transcriptase to introduce mismatches or misreads. LIME-seq leverages a modification-tolerant HIV-derived reverse transcriptase, capturing these events and translating them into base-change signals detectable by sequencing. This approach preserves modification information that would otherwise be lost in standard protocols.

Key Features

Six modification types detected in one run: m¹A, m³C, m¹G, m²²G, m³U, inosine
Low sample input: works with ~600 µL plasma or ~1.5 ng cfRNA
Dual-domain analysis: enables simultaneous mapping of host-derived and microbiome-derived cfRNA
High reproducibility: consistent modification profiles with correlation values above r > 0.96

How LIME-seq Compares to Conventional cfRNA Sequencing

Conventional cfRNA-seq: Detects overall expression levels but often misses weak early signals.
LIME-seq: Adds an epitranscriptomic layer, providing site-level resolution of modifications that can distinguish subtle biological differences.

By combining sensitivity, low input requirements, and dual host–microbiome coverage, LIME-seq offers a more comprehensive picture of cfRNA biology and its role in disease and health.

Workflow

LIME-seq combines optimized cfRNA preparation with modification-aware reverse transcription and advanced sequencing analysis. Each step is carefully designed to preserve scarce cfRNA molecules and convert modification events into reliable sequencing signals.

cfRNA extraction – Enrichment of fragmented and low-abundance cfRNA from plasma or other biofluids using optimized kits.

End repair & adapter ligation – Preparation of cfRNA fragments for library construction.

HIV-derived reverse transcription – Captures modification-induced mismatches without losing signal integrity.

cDNA amplification & purification – Ensures sufficient material for sequencing while removing impurities.

Sequencing & alignment – High-throughput sequencing of host and microbial cfRNA reads.

Modification site calling & annotation – Bioinformatics pipeline for precise identification of modification events.

LIME-seq workflow diagram with plasma input, adapter ligation, reverse transcription, cDNA amplification, sequencing, and data analysis.

Technical Details

Step	Key Features	Advantage for LIME-seq users
cfRNA Extraction	Iterative low-input extraction; removes proteins, gDNA, and contaminants	Maximises recovery of scarce and fragile cfRNA
End Repair & Adapter Ligation	Repairs 5′/3′ ends, attaches sequencing adapters	Prepares short cfRNA fragments for stable library building
Reverse Transcription	Uses HIV-derived RT enzyme tolerant to modifications	Converts modification sites into identifiable base changes
cDNA Amplification & Purification	PCR amplification, primer dimer removal	Generates sufficient template for high-quality sequencing
Sequencing & Alignment	Dual host–microbiome mapping; short-read compatible	Provides comprehensive transcriptome coverage
Modification Calling	Detects m¹A, m³C, m¹G, m²²G, m³U, inosine; single-nucleotide resolution	Enables precise, site-specific quantification

Comprehensive Bioinformatics Support

CD Genomics provides a full bioinformatics pipeline tailored for LIME-seq. The analysis covers both basic data processing and advanced interpretation, ensuring that every modification site is accurately detected and annotated.

Analysis Tier	Key Components	Value for Researchers
Basic Analysis	- Raw data QC and filtering (Q30, adapter trimming) - Read mapping to host + microbial genomes - Modification site identification (m¹A, m³C, m¹G, m²²G, m³U, inosine)	Provides clean, high-quality datasets with site-level modification calls
Advanced Analysis	- Differential modification profiling (case vs. control) - Functional enrichment (GO, KEGG) - Host–microbiome comparative analysis - Visualisation: volcano plots, clustering heatmaps, Venn diagrams, pie charts	Enables biological interpretation and discovery of potential biomarkers

Applications of LIME-seq

Expanding the Scope of cfRNA Research

LIME-seq offers a powerful way to investigate cfRNA modifications beyond expression-level profiling. Its ability to capture both host and microbiome-derived cfRNA makes it an important tool for multi-dimensional research.

Oncology Research

Detect early cancer biomarkers by profiling cfRNA modification signatures that change even before strong expression signals appear.

Microbiome Studies

Explore how microbial cfRNA modification patterns act as "stress signals" in disease environments, offering new perspectives on host–microbe interactions.

Epitranscriptomics

Map dynamic changes in cfRNA modifications such as m¹A, m³C, and inosine across biological states, adding a new layer to transcriptomic studies.

Liquid Biopsy Innovation

Complement cfDNA/ctDNA methylation assays with cfRNA modification data to create a richer, multi-omics profile for disease monitoring.

Drug Response Research

Monitor how cfRNA modification profiles shift under therapeutic intervention, supporting biomarker discovery and preclinical drug evaluation.

Why choose CD Genomics for LIME-seq

Proven platform

standardized LIME-seq chemistry and validated pipelines.

End-to-end service

extraction, library prep, sequencing, bioinformatics, validation.

Integrated multi-omics

combine LIME-seq with cfDNA methylation and exosomal RNA.

Publication support

deliverables formatted for methods and figures.

Flexible projects

discovery cohorts, validation sets, or translational assay development.

Sample Requirements

To ensure high-quality results with LIME-seq , please follow the sample preparation and handling guidelines below.

Sample Type	Recommended Volume / Amount	Storage & Transport Guidelines
Plasma / Serum	≥ 2 mL	Collect in EDTA tubes; freeze at –80 °C; transport on dry ice
Urine	≥ 10 mL	Collect in sterile containers; freeze at –80 °C; transport on dry ice
Saliva	≥ 5 mL	Use RNA stabilisation tubes when possible; store at –80 °C
Cerebrospinal Fluid (CSF)	≥ 5 mL	Collect in sterile tubes; freeze immediately at –80 °C
Vitreous Fluid	≥ 500 µL	Store at –80 °C in nuclease-free tubes
Aqueous Humor	≥ 500 µL	Store at –80 °C in nuclease-free tubes
Purified cfRNA	≥ 100 ng (≥ 1 ng/µL)	Provide in RNase-free water or buffer; freeze at –80 °C

General Notes

Avoid repeated freeze–thaw cycles.
For long-term storage, liquid nitrogen is recommended.
Seal all tubes securely with parafilm before shipment.
Dry ice shipment is required for international projects.

Deliverables & Demo

Raw sequencing data: FASTQ files with QC reports

Annotated modification tables: per-site information including type, frequency, and confidence score

Microbiome profiles: taxonomic classification of cfRNA reads (Kraken2/Bracken-based)

Differential analysis outputs: volcano plots, heatmaps, and other comparative visualisations

Functional annotation results: GO terms, KEGG pathways enriched in differentially modified cfRNAs

Comprehensive project report: detailed methods, results, QC metrics, and key figures ready for publication

LIME-seq demo results showing GO KEGG pathway analysis, clustering heatmap, volcano plot, cfRNA composition, Venn diagram, and microbial genera pie chart

FAQs

- What is LIME-seq service and how is it different from ordinary cfRNA sequencing?
  - LIME-seq (Low-Input Multiple Methylation Sequencing) goes beyond measuring RNA amounts by detecting site-specific modifications (like m¹A, m³C etc.), turning those modifications into identifiable mismatch signals. Ordinary cfRNA sequencing measures expression but often misses modification signals that can indicate disease earlier.
- How much sample do I need and what quality should it be?
  - You generally need as little as ~600 µL plasma (or equivalent biofluid) or ~1.5 ng cfRNA input. Samples should be handled to avoid degradation (use RNase-free tubes, keep cold, avoid freeze-thaw), and storage at −80 °C (or lower) with dry-ice transport is required for best results.
- Which RNA modifications can LIME-seq detect?
  - LIME-seq detects multiple modifications in one run, such as m¹A, m³C, m¹G, m²²G, m³U, and inosine. The panel can be adjusted depending on sample type, sequencing depth, or specific research needs.
- Can LIME-seq analyze both human (host) and microbial cfRNA together?
  - Yes. LIME-seq maps reads to both host and microbial genomes so you can compare modification signatures coming from human cells vs those from microbiome. This dual-domain data can enrich biomarker discovery and clarify underlying biology.
- What kind of data and figures will I receive?
  - You will get raw sequencing files (FASTQ), annotated tables of modification sites with confidence scores, taxonomic profiles for microbial cfRNA, visualization plots (heatmap, volcano, PCA etc.), and a technical report suitable for publication.
- Is LIME-seq suitable for diagnostic / clinical decision making?
  - No. The LIME-seq service is provided for research use only. It is designed for biomarker discovery, mechanism studies, and preclinical research, not for clinical diagnostics or direct patient care.
- What are common challenges in cfRNA based liquid biopsy and how does LIME-seq address them?
  - Challenges include low abundance of cfRNA, high degradation/fragmentation, high background noise, and mixed sample origins. LIME-seq uses low-input, modification-friendly chemistry, rigorous QC, and joint host−microbiome mapping to mitigate these issues. Reviews on cfRNA broadly highlight these as recurring problems.
- Why consider cfRNA over cfDNA?
  - cfRNA offers a dynamic transcriptomic snapshot, reflecting current gene expression, cell stress, and active transcription, whereas cfDNA often reflects static mutation profiles. cfRNA can provide earlier signals in some cases. Research and reviews note cfRNA’s promise in diagnostics and disease monitoring.
- How does LIME-seq compare to cfDNA tests?
  - LIME-seq analyzes cfRNA modifications rather than just DNA fragments; cfRNA can reflect gene expression, microbial activity, and chemical modifications which cfDNA may miss. A recent study showed that combining cfRNA with cfDNA improves sensitivity in liquid biopsy for some cancers.
- Is LIME-seq validated for distinguishing colorectal cancer from healthy controls?
  - Yes. The LIME-seq study in Nature Biotechnology (2025) demonstrated that microbiome-derived cfRNA modification signatures can discriminate colorectal cancer patients from non-cancer controls with high accuracy.
- What challenges exist for cfRNA biomarker discovery?
  - The main issues are technical variability, degradation of RNA, low abundance, and lack of standardised protocols. Reviews note that reproducibility is often low unless sample handling, library prep, and QC pipelines are rigorously controlled.
- What sample quality or volume is needed for reliable LIME-seq results?
  - Reliable LIME-seq results require pure, undegraded cfRNA, free of genomic DNA or protein contaminants. Even smaller volumes (~600 µL plasma) are feasible for discovery if handled properly. Sample collection, storage, and processing steps strongly influence outcomes.
- Can LIME-seq detect signals in early-stage or pre-cancer conditions?
  - Yes. In the LIME-seq CRC study, modification signatures were distinct even in early-stage (including Stage 0/I) colorectal cancer patients compared to controls. This suggests potential utility for early screening applications.
- How reliable are microbial cfRNA signals in LIME-seq?
  - Microbial cfRNA constitutes ~20-40% of mapped cfRNA reads in plasma in some LIME-seq datasets, and modification levels on microbial cfRNA show consistent differences between disease and healthy states. Stability arises from using modification-aware RT enzymes and stringent QC.
- Do sample handling and protocol variations affect LIME-seq results?
  - Definitely. Reviews emphasize that differences in extraction method, storage temperature, freeze-thaw cycles, and library preparation steps contribute to variability in cfRNA studies. Standardised protocol and QC are needed to reduce these effects.

References:

Ju, CW., Lyu, R., Li, H. et al. Modifications of microbiome-derived cell-free RNA in plasma discriminates colorectal cancer samples. Nat Biotechnol (2025).
Safrastyan, A., zu Siederdissen, C.H. & Wollny, D. Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer. Hum Genomics 17, 90 (2023).
Cabús L, Lagarde J, Curado J, Lizano E, Pérez-Boza J. Current challenges and best practices for cell-free long RNA biomarker discovery. Biomark Res. 2022 Aug 18;10(1):62. doi: 10.1186/s40364-022-00409-w. PMID: 35978416; PMCID: PMC9385245.

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