Polysome Profiling and lncRNA Sequencing Service — Integrated Translatome and Transcriptome Analysis
Standard RNA-seq measures transcript abundance, yet protein levels often diverge from mRNA expression due to translational regulation. Our integrated Polysome Profiling and lncRNA Sequencing Service bridges this gap by simultaneously capturing the translatome (polysome-bound RNA) and transcriptome (total RNA), with specialized workflows optimized for long non-coding RNA (lncRNA) analysis.
Polysome profiling separates RNA based on the number of bound ribosomes via sucrose gradient ultracentrifugation, directly quantifying translation efficiency. By combining this with lncRNA-compatible library preparation (Ribo-depletion, not polyA selection), we enable researchers to discover which lncRNAs are actively translated, identify smORF-encoded microproteins, and distinguish transcriptional from translational regulation genome-wide.
Key Highlights:
- Quantify translation efficiency (TE) genome-wide by comparing polysomal vs. total RNA fractions
- Detect polysome-associated lncRNAs and evaluate their coding potential for micropeptides
- Integrated dual-omics workflow: polysome profiling + lncRNA-seq from the same samples
- lncRNA-optimized library preparation (Ribo-depletion) captures non-polyadenylated and circular RNAs
- Comprehensive bioinformatics: TE calculation, smORF prediction, differential translation analysis
Why Integrated Polysome Profiling and lncRNA Sequencing
Understanding gene expression requires studying not only transcription but also translation — the final step that determines protein output. RNA-seq alone cannot predict translational status, and transcript–protein correlations are often poor, especially for regulatory transcripts such as lncRNAs.
Many lncRNAs associate with polysomes and encode functional microproteins via small open reading frames (smORFs). Studies have shown that over 90 lncRNAs can be exclusively regulated at the polysomal level during cellular differentiation — changes entirely invisible to standard transcriptomic analysis. Without polysome profiling, researchers risk missing this translatome layer entirely.
Our integrated service pairs polysome fractionation with lncRNA-optimized RNA-seq, providing:
- Paired translatome–transcriptome data from identical biological samples, enabling direct TE calculation
- lncRNA-compatible library prep using Ribo-depletion (not polyA selection), ensuring capture of non-polyadenylated lncRNAs, circRNAs, and other regulatory RNAs
- Full-length RNA information from polysomal fractions, preserving transcript isoform resolution for lncRNA analysis
- Dedicated bioinformatics for smORF detection, micropeptide prediction, and differential translation analysis
We combine these capabilities into a single workflow, eliminating the need to coordinate separate service providers for polysome profiling and lncRNA sequencing. For researchers focused specifically on ribosome-protected fragments, we also offer Ribo-seq (Ribosome Footprinting) and Disome-seq for codon-resolution and ribosome collision analysis respectively.
Polysome Profiling + lncRNA-seq vs. Alternative Approaches
| Feature | Polysome Profiling + lncRNA-seq | Standard Ribo-seq | RNA-seq Only |
|---|---|---|---|
| Translation readout | Full-length translatome — quantitative TE per transcript | Codon-resolution footprints (28–34 nt) | None — transcript abundance only |
| lncRNA/circRNA detection | Yes — Ribo-depletion library prep, full-length RNA preserved | Limited — RNase digestion fragments RNA; short reads miss lncRNA isoforms | Yes — but no translation status |
| smORF/microprotein detection | Yes — full-length polysomal RNA enables ORF prediction and coding-potential analysis | Partial — short footprints can infer translated regions but lack full transcript context | No |
| Translation efficiency | Directly calculated: polysomal reads vs. total RNA reads | Indirect: normalized footprints vs. RNA-seq | Not possible |
| Isoform resolution | High — full-length RNA preserves splice isoform information | Low — short fragments lose isoform identity | Moderate — depends on library type |
| RNA species captured | mRNA + lncRNA + circRNA + other non-coding (Ribo-depletion) | mRNA primarily (RNase protects ribosome-bound fragments) | Depends on library prep (polyA vs. Ribo-depletion) |
| Sample input requirement | ≥1 × 107 cells (standard); lower input upon consultation | ≥5 × 106 cells | ≥1 × 106 cells |
| Best suited for | Translational regulation studies, lncRNA function, micropeptide discovery | Translation initiation, codon occupancy, uORF discovery | Gene expression profiling |
Technical Advantages
Integrated Dual-Omics from a Single Workflow
Our service delivers paired polysomal and total RNA-seq data from the same biological samples, enabling direct translation efficiency (TE) computation without cross-experimental normalization. This integrated design eliminates batch effects and ensures that transcriptional and translational changes are distinguished with confidence.
lncRNA-Optimized Library Preparation
Standard polyA-based library preparation systematically excludes non-polyadenylated lncRNAs and circRNAs. We use Ribo-depletion (rRNA removal) instead, ensuring comprehensive capture of all RNA species — including lncRNAs, circRNAs, tRNAs, and other regulatory transcripts — from both polysomal and total RNA fractions.
Dedicated smORF and Micropeptide Discovery Pipeline
Beyond TE quantification, our bioinformatics pipeline evaluates the coding potential of polysome-associated lncRNAs using multiple algorithms (CPC2, CPAT, RNAsamba). We predict smORFs, assess conservation, and provide structural modeling support — enabling discovery of functional microproteins encoded by putative non-coding transcripts.
These technical advantages allow researchers to monitor translational regulation and lncRNA-mediated control of gene expression with greater precision than transcriptomics alone.
Polysome Profiling and lncRNA Sequencing Workflow
We follow a streamlined workflow optimized for both translatome coverage and lncRNA integrity.
- Sample Preparation — Cells or tissues are rapidly processed with translation inhibitors (cycloheximide or equivalent) to preserve native ribosome–RNA complexes. Lysates are cleared and quantified for downstream fractionation.
- Polysome Fractionation — Lysates are separated by 10–50% sucrose density gradient ultracentrifugation (270,000×g, 2 h, 4°C). The gradient is continuously monitored at A254 nm to generate a polysome profile trace. Polysomal fractions (≥2 ribosomes per transcript) and total RNA fractions are collected separately.
- RNA Extraction and Library Preparation — RNA is extracted from both polysomal and total fractions using optimized protocols. Ribo-depletion (rRNA removal) is performed for both sample types. Strand-specific RNA-seq libraries are constructed for comprehensive transcript and lncRNA analysis.
- Next-Generation Sequencing — Paired-end sequencing (PE150) is performed on Illumina platforms at a depth appropriate for both mRNA and lncRNA detection (typically 40–60 M reads per sample for total RNA and 30–50 M reads per sample for polysomal RNA).
- Bioinformatics Analysis — Reads are processed through our integrated pipeline: quality control → alignment to reference genome (STAR, HISAT2) → transcript quantification (featureCounts, Salmon) → TE calculation → differential expression analysis → smORF prediction for polysome-associated lncRNAs.
For more details on related translatomics methods, explore our Translatomics Sequencing service page.
Bioinformatics and Data Analysis
| Analysis Type | Content Description |
|---|---|
| Standard Analysis (Transcriptome — Total RNA-seq) | |
| 1. Data quality control (QC) | Read trimming, adapter removal, QC metrics report |
| 2. Reference genome alignment | STAR or HISAT2 alignment to reference genome |
| 3. Gene/transcript quantification | featureCounts or Salmon-based quantification |
| 4. mRNA differential expression | DESeq2 or edgeR — identification of differentially expressed genes |
| 5. lncRNA differential expression | Identification of differentially expressed lncRNAs between conditions |
| 6. GO enrichment analysis | Functional annotation of differentially expressed genes |
| 7. KEGG pathway enrichment | Pathway-level biological interpretation |
| Standard Analysis (Translatome — Polysomal RNA-seq) | |
| 1. Polysomal RNA alignment and quantification | STAR alignment and quantification of polysome-bound transcripts |
| 2. Translation efficiency (TE) calculation | Normalized polysomal reads / normalized total RNA reads per gene |
| 3. Differential TE analysis | Identification of genes with significant TE changes between conditions |
| 4. GO/KEGG enrichment of differential TE genes | Functional interpretation of translationally regulated genes |
| Advanced Analysis (lncRNA and Coding Potential) | |
| 1. Polysome-associated lncRNA identification | Detection of lncRNAs enriched in polysomal vs. total fractions |
| 2. smORF prediction (ORFfinder, CPAT, CPC2) | Identification of small open reading frames in polysome-associated lncRNAs |
| 3. Coding potential assessment | Multi-algorithm evaluation (CPC2, CPAT, RNAsamba) for lncRNA translation prediction |
| 4. Micropeptide sequence and structure prediction | Amino acid sequence extraction and structural modeling support |
| 5. circRNA detection and quantification | Identification of circular RNAs in Ribo-depleted libraries |
| 6. Integrative transcriptome–translatome analysis | Correlation analysis between RNA expression and translation efficiency |
Our bioinformatics team provides a comprehensive analysis report with publication-ready figures, including polysome profile traces, TE distribution plots, differential expression volcano plots, and smORF annotation tables.
Analytical Strategy — Integrating Translatome and Transcriptome
Traditional transcriptomics assumes that changes in mRNA abundance reflect corresponding changes in protein production. Yet translational regulation often decouples RNA levels from protein output — a phenomenon frequently observed for lncRNAs, stress-responsive genes, and developmental regulators. Our integrated polysome profiling and lncRNA-seq strategy addresses this gap directly.
1. Expression-Level Integration
By comparing polysomal RNA abundance with total RNA abundance for each transcript, we calculate the Translation Efficiency (TE) — defined as the ratio of polysome-bound reads to total RNA reads, normalized per transcript length and sequencing depth. This metric distinguishes:
- Transcripts regulated at the transcriptional level — total RNA changes with proportional TE (protein output driven by RNA abundance)
- Transcripts regulated at the translational level — stable total RNA but significant TE changes (protein output modulated without transcription change)
- Dual-regulated transcripts — both RNA abundance and TE change in the same or opposite direction
2. lncRNA-Specific Analytical Framework
For lncRNAs identified as polysome-associated, we apply a dedicated pipeline:
- Polysomal enrichment scoring — Statistical comparison of lncRNA abundance in polysomal vs. total fractions (FDR ≤ 5%)
- smORF scanning — ORFfinder search for smORFs (≥30 nt, ATG or near-cognate start codons) within polysome-associated lncRNA sequences
- Coding potential multi-classifier — Cross-validation across CPC2 (cutoff >0.5), CPAT (cutoff >0.3), and RNAsamba (cutoff >0.5)
- Conservation and structural analysis — Phylogenetic conservation assessment; I-Tasser structural modeling for predicted microproteins
3. Data Integration Workflow
- Input — Parallel polysomal RNA-seq and total RNA-seq datasets from matched samples
- Processing — Alignment, quantification, normalization (TMM or RPKM), TE computation, differential analysis
- Integration — Correlation of transcription and translation; identification of translationally regulated lncRNAs
- Output — Prioritized candidate list: (a) translationally regulated coding genes, (b) polysome-associated lncRNAs with coding potential, (c) smORF-encoded micropeptide predictions
This analytical architecture provides a complete view of the transcriptome-to-translatome continuum, enabling researchers to dissect gene regulatory mechanisms with greater precision than transcriptomics alone.
Figure: Representative TE distribution comparison between control and treatment conditions. Genes with significant TE changes are highlighted, revealing translationally regulated pathways.
Figure: Coding potential analysis of polysome-associated lncRNAs — CPC2, CPAT, and RNAsamba scores with smORF predictions across identified transcripts.
Applications and Research Areas
Our integrated polysome profiling and lncRNA sequencing service supports a wide range of research applications where understanding translational regulation of non-coding RNAs is critical.
Stem Cell Differentiation and Development
Polysome profiling combined with lncRNA-seq is particularly powerful for studying differentiation, where translational control plays a critical role. During adipogenesis and osteogenesis, over 90 lncRNAs are exclusively regulated at the polysomal level — demonstrating that lncRNA-mediated translational regulation shapes cell fate decisions. Our Long RNA-seq and Polysome Profiling services can be integrated to capture these dynamics.
Cancer Translational Regulation
Cancer gene expression is frequently controlled at the translation level. Many oncogenic lncRNAs modulate the translation of tumor suppressors or metabolic enzymes. Our integrated service identifies which lncRNAs shift between polysomal and non-polysomal fractions in cancerous vs. normal conditions, revealing novel regulatory nodes and potential therapeutic targets.
smORF-Encoded Micropeptide Discovery
The human genome contains thousands of unannotated smORFs within transcripts annotated as non-coding. Our polysome profiling workflow identifies which lncRNAs are truly ribosome-associated, and our dedicated bioinformatics pipeline predicts smORFs and their micropeptide products — enabling discovery of functional microproteins involved in muscle function, metabolism, and signal transduction.
Stress Response and Environmental Adaptation
Cells modulate translation globally and transcript-specifically in response to stress (hypoxia, heat shock, nutrient deprivation). Combined polysome profiling and lncRNA-seq reveals how lncRNAs participate in this translational reprogramming. RNC-seq provides an alternative yet complementary translatomics approach for these studies.
Non-Coding RNA Biology
For researchers focused on lncRNA, circRNA, or other non-coding RNA families, our Ribo-depletion-based workflow captures the full spectrum of regulatory RNAs. The ability to detect which non-coding transcripts are polysome-associated provides insights into their biological functions — whether they act as translation regulators, micropeptide precursors, or ribosome sponges. Explore our comprehensive Non-coding RNA Sequencing portfolio for additional options.
Deliverables:
- FASTQ files (polysomal RNA-seq and total RNA-seq) and processed data outputs
- Comprehensive analysis report with polysome profile traces, TE metrics, and smORF predictions
- Differential expression tables (transcriptome and translatome)
- Coding potential assessment for polysome-associated lncRNAs
- Publication-ready figures and supplementary data tables
Figure: Representative data outputs — polysome profile trace (left), TE distribution heatmap (right).
Sample Requirements
| Sample Type | Minimum Requirement | Condition |
|---|---|---|
| Cell samples | ≥1 × 107 cells / sample | Cultured or primary cells |
| Tissue samples | ≥50 mg / sample | Flash-frozen without preservatives |
| Supported species | Human, Mouse, Rat | Other species upon consultation |
Experimental Design:
- Minimum of two groups (e.g., control vs. treatment)
- Three biological replicates per group recommended
- Each sample analyzed for both polysomal and total RNA-seq
Case Study: Polysomal lncRNA Expression in Stem Cell Differentiation
A 2024 study published in the International Journal of Molecular Sciences (MDPI) by Bonilauri and colleagues used an integrated polysome profiling and RNA-seq approach to analyze lncRNA expression during early adipogenesis and osteogenesis in human adipose-derived stem cells (hASCs) — demonstrating the power of combining translatome and transcriptome analysis for non-coding RNA research.
Figure 1. Expression profile of long non-coding RNAs following 24 h of adipogenic and osteogenic differentiation.
Experimental design schematic (Panel A), volcano plots of differentially expressed lncRNAs in total and polysomal fractions (Panels B–C, E–F), and heatmaps (Panels D, G). From Bonilauri et al. 2024 (CC BY 4.0).
Figure 2. Classification and comparative analysis of differentially expressed lncRNAs between total and polysomal fractions.
Genomic locus classification (Panel A–B) and Venn diagrams showing the limited overlap between total and polysomal lncRNA responses during differentiation. From Bonilauri et al. 2024 (CC BY 4.0).
Background
Previous transcriptomic studies of stem cell differentiation focused almost exclusively on total RNA, potentially missing lncRNAs whose expression is regulated at the translational level. The authors aimed to determine whether polysome profiling could reveal a hidden layer of lncRNA regulation during the first 24 hours of lineage commitment.
Methodology
hASCs from three independent donors were induced toward adipogenic or osteogenic differentiation for 24 hours. Polysome profiling was performed using 10–50% sucrose density gradient ultracentrifugation. RNA was extracted from both total and polysomal fractions, and strand-specific RNA-seq libraries were prepared and sequenced on an Illumina HiSeq 2500. Differentially expressed lncRNAs (DELs) were identified in each fraction using edgeR (FDR ≤ 1%, |log₂FC| ≥ 1.5).
Results
Only 17–18% of upregulated lncRNAs were common between total and polysomal fractions during differentiation — meaning over 80% of translationally relevant lncRNA changes would be missed by standard RNA-seq alone. 90 lncRNAs were exclusively regulated in the polysomal fraction, including LINC01018, NEAT1, HOTAIR, H19, and DANCR. Polysome-associated lncRNAs were enriched for smORFs with predicted coding potential by CPC2, CPAT, and RNAsamba.
FAQs — Frequently Asked Questions
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- What is the difference between polysome profiling and Ribo-seq for lncRNA analysis?
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- Can polysome profiling detect translation of specific lncRNAs?
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- What type of library preparation do you use for lncRNA-seq?
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- How do you calculate translation efficiency?
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- What is the minimum sample input for this service?
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- Can you detect smORF-encoded micropeptides from lncRNAs?
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- Do you support non-human species?
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- What controls do you recommend for a polysome profiling experiment?
References:
- Bonilauri B, Ribeiro AL, Spangenberg L, Dallagiovanna B. Unveiling Polysomal Long Non-Coding RNA Expression on the First Day of Adipogenesis and Osteogenesis in Human Adipose-Derived Stem Cells. Int J Mol Sci. 2024;25(4):2013.
- Hu W, Zeng H, Shi Y, et al. Single-cell transcriptome and translatome dual-omics reveals potential mechanisms of human oocyte maturation. Nat Commun. 2022;13:5114.
- Zhang C, Wang M, Li Y, Zhang Y. Profiling and functional characterization of maternal mRNA translation during mouse maternal-to-zygotic transition. Sci Adv. 2022;8(5):eabj3967.
Research Use Only. Not for use in diagnostic or clinical procedures.
