Smile cfRNA-seq: Comprehensive cfRNA Sequencing and Quantitative Profiling Service
When a biofluid contains scarce, fragmented, and degradation-prone RNA, splitting the sample across separate long- and small-RNA libraries can increase sample loss and batch variation. Quantitative interpretation also becomes difficult when amplification duplicates, reverse-transcription efficiency, and sample indexing are not controlled together.
Smile cfRNA-seq is a single-library whole-transcriptome workflow available through CD Genomics for simultaneous profiling of 10 cell-free RNA classes. It combines molecular identifiers, known-concentration spike-in standards, and unique dual indexes with class-aware bioinformatics to support biomarker discovery from plasma, serum, urine, cerebrospinal fluid, and other qualified biofluids.
Core features of Smile cfRNA-seq
One library simultaneously profiles mRNA, lncRNA, microRNA, piRNA, tRNA-derived RNA, Y RNA, small nuclear RNA, small nucleolar RNA, ribosomal RNA fragments, and signal-recognition-particle RNA.
Known-concentration spike-in standards are introduced before reverse transcription to calibrate downstream enzymatic and library-processing variation.
Molecular identifiers label original cDNA molecules before amplification for duplicate-aware molecule counting.
Unique dual indexes reduce sample misassignment during multiplexed sequencing.
End-to-end analysis covers mRNA, lncRNA, circular RNA, and small noncoding RNA result streams.
Cell-free RNA can reflect transcriptional activity from blood, immune, and solid-organ cell types, but it circulates as a complex mixture of free ribonucleoprotein-associated molecules, lipoprotein-associated RNA, and extracellular-vesicle cargo. Many fragments are short and present near the detection limit.
Smile cfRNA-seq addresses this measurement problem with a single-library, long-and-short-RNA-compatible workflow. Optimized adapter and reverse-transcription conditions capture long RNA molecules above 200 nucleotides and small RNA molecules at or below 200 nucleotides in the same reaction system. This avoids dividing a limited sample into separate long- and small-RNA libraries and reduces cross-library batch effects.
The product name summarizes five connected design elements:
S — Spike-in standards: known-concentration, non-homologous RNA controls added before reverse transcription.
M — Molecular identifiers: molecule-level labels added before amplification for duplicate-aware counting.
I — Indexing: unique dual indexes for two-ended sample identity checks in multiplexed runs.
L — Level evaluation: calibrated abundance matrices generated from accepted standards and QC criteria.
E — Empowering biomarker discovery: one coordinated data matrix for multi-class candidate screening and downstream validation planning.
For projects centered on direct long extracellular RNA profiling without vesicle enrichment, our HEBER-seq cfRNA sequencing service may be the more focused choice. For multi-analyte studies combining RNA with circulating DNA, methylation, or nucleosome signals, explore our liquid biopsy solutions.
Service Overview
Single-Library Coverage of 10 cfRNA Classes
Smile cfRNA-seq uses a single-library whole-transcriptome strategy to profile long and short cfRNA molecules together. The same sample and library preparation batch can therefore support a wider biomarker search while conserving limited biofluid material.
Long RNA
mRNA and lncRNA profiling supports identification, annotation, differential abundance, clustering, pathway enrichment, gene-set enrichment, and lncRNA cis-regulatory exploration.
Canonical Small RNA
MicroRNA and piRNA are quantified alongside long RNA in the same Smile library. Small-RNA annotation, differential analysis, target prediction, and target-gene enrichment can be included.
Structured and Fragment-Derived RNA
tRNA-derived RNA, Y RNA, small nuclear RNA, small nucleolar RNA, and ribosomal RNA fragments are analyzed with class-specific references and matching rules.
Signal-Recognition-Particle RNA
Signal-recognition-particle RNA completes the 10-class panel. Circular RNA is available as an additional back-splice-junction analysis stream, but it is not counted as one of the 10 named Smile classes.
When to Choose Smile cfRNA-seq
Smile cfRNA-seq is designed for broad, quantitative cfRNA biomarker discovery. The boundaries below distinguish it from more specialized extracellular-RNA services.
Approach
Best for
Not for / boundary
Smile cfRNA-seq
Single-library profiling of 10 long and small cfRNA classes, calibrated quantitative matrices, biomarker discovery, and multi-center research designs
Not a diagnostic assay or a substitute for independent targeted validation
HEBER-seq
Direct long extracellular RNA profiling, especially mRNA and lncRNA, without extracellular-vesicle enrichment
Not the primary option when simultaneous 10-class quantitative profiling is required
cfRNA modification sequencing
Research questions centered on RNA modification sites and modification abundance
Not required when the endpoint is unmodified transcript abundance
Extracellular-vesicle RNA sequencing
Studies that specifically require a purified vesicle-enriched compartment
Does not represent the complete whole-biofluid cfRNA pool
If your program extends from cfRNA discovery into tissue or cellular transcriptomics, our transcriptome sequencing services can provide a matched broader expression context.
Workflow
Smile cfRNA-seq Project Workflow
The service covers biofluid separation, cfRNA purification, single-library construction, sequencing, and analysis under one documented workflow.
Study design and sample plan — Confirm biofluid, cohort structure, comparison groups, confounders, target RNA classes, and the intended quantitative output.
Biofluid separation — Apply sample-specific centrifugation and handling to minimize residual cellular RNA. Optimized procedures are available for plasma, serum, urine, cerebrospinal fluid, saliva, vitreous humor, and aqueous humor.
cfRNA extraction — Purify low-abundance and fragmented cfRNA with a biofluid-compatible commercial isolation method while removing proteins, salts, and other inhibitors.
Single-library construction — Add known-concentration spike-in standards before reverse transcription, attach molecular identifiers before amplification, and apply unique dual indexes. Optimized adapter and reverse-transcription conditions capture the 10 Smile RNA classes in one library.
High-throughput sequencing — Sequence the pooled libraries at a project-specific depth selected for cohort size, biofluid, and biomarker-discovery goals.
Quantification and bioinformatics — Perform data QC, duplicate-aware molecule counting, standard-curve evaluation, class-specific annotation, differential analysis, and biological interpretation.
Each project includes decision gates after sample QC and library QC. Samples that do not meet the agreed criteria are reviewed before proceeding rather than silently combined with passing samples.
Quantitative Design
How the S-M-I-L-E Architecture Supports Quantitative Profiling
Smile cfRNA-seq connects five technical elements rather than relying on amplification duplicate removal alone. The stage and function of every control are documented in the project design.
S — Spike-in Standards
Non-homologous RNA standards with known concentrations are added to each purified cfRNA sample before reverse transcription. They provide a common reference for reverse transcription, library preparation, sequencing, and standard-curve evaluation. Because they are added after extraction, they do not by themselves measure extraction recovery.
M — Molecular Identifiers
Original cDNA molecules receive unique labels before amplification. Reads sharing the same label and insert identity can be consolidated, reducing amplification redundancy and supporting molecule-aware abundance estimates.
I — Unique Dual Indexing
Two-ended unique sample indexes provide an additional identity check during multiplexed sequencing and reduce index-related sample misassignment. Indexing controls lower risk but do not replace negative controls or batch-balanced design.
L — Level Evaluation
Accepted spike-in standards and molecular counts are used to build calibrated abundance matrices. Copy-number estimates are reported only when the standard curve, QC behavior, and model assumptions meet the agreed criteria; otherwise the report distinguishes relative and spike-normalized abundance.
E — Empowering Biomarker Discovery
The same single library yields coordinated abundance matrices across the 10 named RNA classes, reducing sample splitting and supporting multi-class candidate screening, cross-batch comparison, and follow-up validation planning.
Bioinformatics
Smile cfRNA-seq Bioinformatics and Data Outputs
Analysis follows four result streams—mRNA, lncRNA, circular RNA, and small noncoding RNA—after shared raw-data QC and duplicate-aware processing.
Raw-read quality assessment, adapter trimming, and low-quality read filtering.
Molecular-tag extraction, consensus handling, and duplicate-aware counting where applicable.
mRNA: identification and annotation, differential abundance, GO and KEGG enrichment, gene-set enrichment, clustering, scatter plots, and volcano plots.
lncRNA: identification and annotation, differential abundance, neighboring-gene enrichment, clustering, scatter plots, volcano plots, and cis-regulatory exploration.
Small noncoding RNA: novel-candidate prediction, class-specific annotation, differential analysis, target prediction, target-gene enrichment, clustering, scatter plots, and volcano plots.
Cross-sample correlation, principal-component analysis, batch/covariate assessment, and calibrated abundance-matrix delivery.
Optional modules include tissue/cell-of-origin modeling, longitudinal analysis, classifier development with nested validation, and integration with cfDNA or other omics layers. Classifier outputs are research models and require independent validation before translational use.
Typical deliverables include FASTQ files, processed count matrices, annotation tables, QC summaries, differential-analysis tables, enrichment results, and publication-oriented figures. Project files include analysis-method notes so downstream teams can trace main parameters and reference versions.
For studies moving from broad signals to candidate prioritization and validation planning, our biomarker discovery solutions can extend the analysis framework.
Applications
Applications of Comprehensive cfRNA Profiling
Broad cell-free transcript profiling can support multiple research scenarios when cohort design and technical controls are aligned with the biological question.
Biomarker Discovery
Compare cfRNA signatures across disease models, response groups, or longitudinal time points while monitoring pre-analytical and batch effects.
Tissue and Cell of Origin
Integrate cell-free transcript profiles with reference atlases. Results are interpreted as model-based estimates that depend on reference coverage and cohort context.
Pharmacodynamic Research
Track pathway-level and cell-type-associated changes across time points using paired sampling, balanced batches, and prespecified contrasts.
Extracellular Compartments
Compare whole biofluid and extracellular-vesicle-enriched fractions with matched processing and compartment-aware interpretation.
Multi-Omics Programs
Combine cfRNA with cfDNA fragmentomics, methylation, proteomics, or tissue transcriptomics under a shared cohort and statistical plan.
Sample Requirements
Sample Requirements and Pre-analytical Guidance
The amounts below reflect the current Smile cfRNA-seq submission guidance. Final acceptance remains project-specific because collection method, biofluid quality, cohort design, and prior sample handling can affect feasibility.
Sample type
Planning guidance
Handling priorities
Total RNA
At least 150 ng; concentration at least 1 ng/µL
Dissolve in RNase-free sterile water, store at -80 °C, avoid repeated freeze-thaw cycles, and ship on dry ice
Plasma or serum
At least 8 mL
Transfer to sealed cryovials, store at -80 °C, avoid repeated freeze-thaw cycles, and ship on dry ice
Urine
At least 18 mL
Document collection timing, stabilization, centrifugation, and storage
Cerebrospinal fluid
At least 8 mL
Use low-binding, RNase-free consumables and minimize transfers
Vitreous humor or aqueous humor
At least 800 µL
Use RNase-free cryovials and maintain a consistent frozen chain
Saliva
At least 8 mL
Use the project-approved collection and stabilization procedure
Other biofluids
Project-specific feasibility review
Provide collection, processing, storage, and prior-use metadata
Do not begin collection based only on this table. We confirm the project-specific submission plan before sample generation. Balanced collection order, standardized processing, negative controls, and complete metadata are particularly important for large or multi-center cohorts.
Case Study
Case Study: Whole-Transcriptome cfRNA Biomarker Discovery With Quantitative Controls
Source: Larson M.H., Pan W., Kim H.J., et al. A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nature Communications. 2021;12:2357. https://doi.org/10.1038/s41467-021-22444-1
Mutation-centered liquid-biopsy studies can be limited by low tumor shedding and may provide little transcript-level tissue or subtype context. The published study evaluated whole-transcriptome plasma cfRNA for tissue- and subtype-associated cancer biomarker discovery.
The authors analyzed plasma from 165 participants: 46 with stage III breast cancer, 30 with lung cancer, and 89 without cancer. External ERCC RNA controls were added to purified cfRNA, and custom adapters carried eight-base molecular identifiers for duplicate-aware analysis. Because the external controls were added after extraction, they informed library-stage calibration rather than extraction recovery.
Among 57,820 annotated genes, 39,564 were not detected in non-cancer plasma, creating low-background regions in which the authors identified recurrent tissue- and cancer-associated transcripts. The cfRNA profiles also supported tissue-of-origin and subtype-oriented biomarker research.
The study supports the value of broad cfRNA profiling, external standards, and molecule-aware counting for biomarker discovery. It is not a direct analytical validation of Smile cfRNA-seq, does not validate extraction-loss correction, and should not be treated as a performance claim for this service.
Advantages
Why Choose CD Genomics
One Library, 10 RNA Classes
Long and small cfRNA classes are captured within one coordinated library to conserve sample and reduce cross-library batch variation.
Controls With Defined Meaning
Control placement is tied to the process step it monitors, supporting more defensible interpretation.
Integrated Planning
RNA classes, reference databases, contrasts, covariates, and deliverables are aligned before sequencing.
Transparent Boundaries
Relative abundance and calibrated estimates are reported according to the available evidence, without converting one into the other by wording.
Q1. Which RNA classes are included in the single Smile library?
Smile cfRNA-seq simultaneously profiles mRNA, lncRNA, microRNA, piRNA, tRNA-derived RNA, Y RNA, small nuclear RNA, small nucleolar RNA, ribosomal RNA fragments, and signal-recognition-particle RNA. Class-specific capture efficiency can still differ, so cross-class interpretation uses class-aware QC and calibration.
Q2. Does adding a synthetic RNA control correct extraction loss?
Only when the control is added before extraction and behaves appropriately for the extraction chemistry. A control added to purified RNA or during library preparation monitors downstream steps, not extraction recovery.
Q3. How does Smile cfRNA-seq support absolute quantification?
Known-concentration spike-in standards, duplicate-aware molecule counts, and accepted standard curves support calibrated abundance estimates. Absolute copy-number reporting is released only when calibration and QC criteria are met; extraction recovery requires a separate control added before extraction.
Q4. How should plasma samples be collected and stored?
Collection tubes, plasma-separation timing, centrifugation, aliquoting, and storage must be standardized across the cohort. Samples should be protected from cellular contamination and repeated freeze-thaw cycles.
Q5. Can whole plasma and extracellular-vesicle-enriched RNA be compared directly?
They can be studied together, but they represent different operational compartments and can have different RNA-class distributions. A paired design with matched processing and compartment-aware interpretation is preferable.
Q6. What validation is recommended after discovery?
Candidate features should be tested in an independent cohort and, where practical, with an orthogonal assay. Models should use prespecified splits or nested validation and avoid using the same samples for feature selection and final performance estimation.
