Why combine polysome profiling with proteomics instead of running RNA-seq and proteomics separately?

Running both layers on the same lysate pool removes batch and cohort variability that occurs when RNA and protein samples come from separate preparations, and it lets us directly correlate translation efficiency with measured protein abundance for each gene rather than relying on RNA-level inference alone.

What quantitative proteomics approach do you use?

We support label-free quantification as a standard approach and offer TMT-based isobaric labeling for higher-throughput multiplexed comparisons, depending on sample number, budget, and desired quantification precision.

How much starting material is required to support both the polysome and proteomics arms?

We recommend at least 2 x 10^6 cells or 100 mg of tissue per sample so that gradient fractions yield sufficient RNA for sequencing libraries and sufficient protein for trypsin digestion and LC-MS/MS.

Can this service detect cases where mRNA and protein levels disagree?

Yes, the integrated analysis explicitly flags genes where translation efficiency and protein abundance trends diverge, often the clearest signal of post-transcriptional or post-translational regulation.

What species and sample types are supported?

The service currently supports human, mouse, and rat as standard, with exploratory evaluation available for other organisms upon consultation.

How are results delivered and how should they be interpreted?

Deliverables include raw sequencing and LC-MS/MS data files, processed translation efficiency and protein abundance tables, an integrated correlation report, and GO/KEGG enrichment summaries formatted for direct interpretation and publication.

Polysome Profiling + Proteomics Integration Service - Connecting Active mRNA Translation to Measurable Protein Output

CD Genomics offers an integrated polysome profiling + proteomics workflow that pairs sucrose-gradient isolation of actively translating mRNAs with quantitative LC-MS/MS analysis of the corresponding protein products, closing the gap between the translatome and the proteome in a single study design.

Standalone RNA-seq tells you what is transcribed; standalone proteomics tells you what is present. Neither alone confirms that a specific actively translated transcript becomes a measurable protein. Our combined service tracks both layers from the same biological samples, giving you direct evidence for translational regulation rather than inferred correlation.

Key Highlights:

Isolate polysome-bound mRNA fractions and analyze matched proteomes from the same lysate pool.
Quantify translation efficiency and cross-validate it against LC-MS/MS protein abundance changes.
Detect cases where RNA and protein trends diverge, pinpointing post-transcriptional and translational control points.
Flexible add-ons for ribosome profiling, RNC-seq, and long-read translatome data to extend resolution.

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$Polysome profiling and proteomics integration showing sucrose gradient mRNA fractionation paired with LC-MS/MS protein quantification$

Why Integrate Polysome Profiling with Proteomics

mRNA abundance is a poor predictor of protein output. Multiple large-scale studies report only moderate correlation between steady-state mRNA levels and the corresponding protein concentrations across the genome, because translation initiation, elongation rate, ribosome loading, and protein turnover all reshape the final signal.

Polysome profiling resolves the translation side of this gap by separating actively translated, polysome-bound mRNAs from untranslated transcripts using sucrose-density-gradient ultracentrifugation. On its own, however, it still reports an RNA-level proxy for protein synthesis.

CD Genomics closes that proxy gap by coupling polysome fractionation directly to quantitative LC-MS/MS proteomics on the same sample set, so translation efficiency calculations can be checked against measured changes in protein abundance rather than assumed from RNA data alone.

For ribosome footprint-level resolution instead of bulk polysome fractions, see our Ribo-Seq (Ribosome Footprinting) Service.

Integrated Polysome-Proteomics vs Single-Omics Approaches

Feature	Polysome Profiling + Proteomics	Polysome Profiling Alone	Standard Proteomics Alone
Readout layer	Translatome + matched proteome from the same samples	Translatome only (RNA-level proxy)	Proteome only, no translation context
Confirms protein output	Yes — direct LC-MS/MS quantification	No — inferred from ribosome loading	Yes, but without translational mechanism
Detects translational regulation	High — flags RNA/protein divergence directly	Moderate — TE changes without confirmation	Not assessable
Identifies post-transcriptional control points	Yes, by comparing TE shifts to protein-level change	Partial	No
Sample efficiency	Single lysate pool supports both omics layers	Single layer only	Single layer only
Best suited for	Mechanistic studies of translational control, drug response, stress biology	Translation efficiency screening	Total protein abundance surveys

Technical Advantages

Matched translatome and proteome from one sample pool

Polysome fractions and proteomics aliquots are prepared from the same lysate, removing batch effects that occur when RNA and protein data are collected from separate cohorts.

Quantitative LC-MS/MS protein validation

Label-free or TMT-based quantitative proteomics confirms whether genes flagged for translational upregulation actually accumulate more protein, reducing false leads carried forward to validation.

Flexible translatome resolution

Choose bulk sucrose-gradient polysome profiling for genome-wide trends, or pair with Disome-seq and Enhanced Ribosome Profiling when codon-level or collision-level detail is needed.

Combining RNA-level translation efficiency with protein-level confirmation gives a more complete and defensible view of gene expression regulation than either omics layer can provide in isolation.

Polysome Profiling + Proteomics Workflow Overview

Our integrated workflow keeps RNA and protein streams synchronized from lysis through final reporting.

Sample Lysis and Stabilization – Cells or tissues are lysed under cycloheximide-stabilized conditions to preserve ribosome–mRNA complexes intact.
Sucrose Gradient Fractionation – Lysates are resolved on a 10–50% sucrose gradient by ultracentrifugation, separating free mRNA, monosomes, and polysomes.
Parallel Sample Split – Each fraction is split for RNA extraction (polysome-seq library prep) and for protein extraction (trypsin digestion for LC-MS/MS).
Quantitative LC-MS/MS Proteomics – Digested peptides are analyzed by high-resolution mass spectrometry for label-free or isobaric-labeled protein quantification.
Integrated Data Analysis – Translation efficiency metrics are computed alongside protein abundance changes for direct RNA–protein correlation.

$Polysome profiling and proteomics workflow diagram showing lysis, sucrose gradient fractionation, parallel RNA and protein extraction, LC-MS/MS analysis, and integrated data analysis$

Bioinformatics and Data Analysis

Analysis Type	Content Description
Polysome Profiling (Translatome)
1. Data quality control and gradient profile QC	Evaluate UV trace, fraction yield, and polysome-to-monosome ratio.
2. Read alignment and quantification	Map polysome-fraction reads to reference genome and quantify per-transcript abundance.
3. Translation efficiency (TE) calculation	Normalize polysome-bound mRNA levels to total RNA-seq abundance per gene.
4. Differential TE gene analysis	Identify genes with significant shifts in translational efficiency between conditions.
5. GO and KEGG enrichment of differential TE genes	Reveal biological processes and pathways under translational control.
Quantitative Proteomics
1. Peptide identification and protein inference	Database search of LC-MS/MS spectra with target-decoy FDR control.
2. Label-free or TMT-based quantification	Quantify relative protein abundance across fractions and conditions.
3. Differential protein abundance analysis	Detect proteins with significant abundance changes between groups.
4. Protein-fraction clustering	Group proteins by elution/fraction profile to reveal complex-level co-regulation.
Integrated RNA–Protein Analysis
1. TE-to-protein abundance correlation	Correlate translation efficiency changes with measured protein abundance changes per gene.
2. Divergence gene flagging	Highlight genes where RNA and protein trends disagree, indicating post-transcriptional control.
3. Pathway-level multi-omics integration	Map combined RNA/protein signals onto shared GO and KEGG pathway networks.

We combine polysome-derived translation efficiency with quantitative proteomics to confirm which translationally regulated transcripts actually convert into measurable changes in protein abundance.

This dual-layer analysis helps you prioritize genuine regulatory targets and avoid following leads supported only by RNA-level inference. For paired total transcriptome context, we can integrate standard RNA Sequencing data alongside the translatome.

Analytical Strategy

Gene expression regulation operates across several layers — transcription, translation, and protein stability — and each layer can shift independently. RNA abundance alone often diverges from protein output, which is why our Polysome Profiling + Proteomics design tracks both translation efficiency and protein-level confirmation from the same experiment.

1. Multi-Layer Expression Integration

By combining polysome profiling with quantitative proteomics, researchers can determine whether a change in translation efficiency actually produces a corresponding change in protein abundance, rather than relying on RNA-only assumptions.

When TE and protein trends agree

This confirms genuine translational regulation as the driver of altered protein output, strengthening confidence in candidate gene lists.

When TE and protein trends diverge

Divergence points to additional layers of control — such as protein degradation, post-translational modification, or delayed accumulation — that merit further investigation.

When prioritizing a long candidate list

Cross-referencing translation efficiency against measured protein abundance narrows broad differential expression lists to the genes most likely to show a real functional effect.

Integrated polysome profiling and proteomics analysis showing translation efficiency correlated with protein abundance for gene regulation mapping

2. Analytical Concept Flow

Input – Parallel total RNA-seq, polysome-fraction RNA-seq, and matched LC-MS/MS proteomics datasets
Processing – Normalization, translation efficiency calculation, and quantitative protein analysis
Integration – Correlation of TE shifts with protein abundance changes per gene
Output – Prioritized gene list with confirmed translational-to-protein regulation

This workflow delivers a verified view of the transcript-to-protein continuum, supporting research into translational control mechanisms, stress response biology, and drug mechanism-of-action studies.

Technical Applications

Pairing polysome profiling with quantitative proteomics extends translational analysis with direct protein-level evidence, strengthening interpretation across multiple research areas.

Validate translational regulation with protein-level evidence

Confirm whether genes showing shifted translation efficiency also show corresponding changes in quantitative protein abundance, reducing reliance on RNA-only inference.

Characterize drug response at the translation-to-protein level

Track how compounds that target translation factors or stress pathways alter both ribosome loading and downstream protein output in the same experiment.

Map ribosome- and RNA-associated protein composition

Identify proteins that co-elute with polysome and ribosomal-subunit fractions, supporting studies of translation factor complexes and RNA-binding protein interactions.

Distinguish transcriptional from translational drivers

Resolve whether expression changes originate at transcription, translation, or both, when standard RNA-seq and proteomics trends disagree.

Extend resolution with complementary translatome methods

Layer in RNC-seq, Long-Read RNC-seq, or our combined Polysome Profiling + RNA-seq Service for transcript-isoform-level translation context.

Sample Requirements

Sample Type	Minimum Requirement	Notes
Cell samples	≥ 2 × 10⁶ cells / sample	Cultured or primary cells, sufficient for RNA and protein split
Tissue samples	≥ 100 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 polysome profiling and matched LC-MS/MS proteomics

Example Results

Our Polysome Profiling + Proteomics service provides publication-ready data illustrating translation efficiency alongside matched protein quantification. Below are representative outputs from internal validation experiments.

sucrose gradient polysome profile chart showing absorbance trace with monosome and polysome peaks Figure 1. Representative polysome profile trace
The UV₂₅₄ absorbance trace shows clear separation of free mRNA, monosome (80S), and polysome fractions across the sucrose gradient, confirming sample quality before downstream RNA and protein extraction.

scatter plot correlating translation efficiency fold change with quantitative protein abundance fold change Figure 2. Translation efficiency vs protein abundance correlation
Genes with concordant shifts in translation efficiency and LC-MS/MS protein abundance cluster along the diagonal, while divergent genes highlight candidates for post-transcriptional regulation.

bar chart of differentially translated and differentially abundant proteins by functional category Figure 3. Functional categories of confirmed regulatory targets
Genes confirmed at both the translation-efficiency and protein-abundance level are grouped by GO category, prioritizing targets supported by two independent omics layers.

Gradient Separation Quality

Distinct monosome and polysome peaks confirm intact ribosome–mRNA complexes and reliable fraction collection.

Translation-to-Protein Concordance

Direct correlation analysis distinguishes genuine translational regulation from RNA-only signal.

Divergence Gene Detection

Genes with mismatched RNA and protein trends are flagged for closer mechanistic follow-up.

Prioritized Functional Targets

Two-layer confirmation narrows candidate lists to targets most likely to hold up in downstream validation.

Case Study

Background

Conventional sucrose-density-gradient (SDG) polysome fractionation is slow and introduces variability between replicates, complicating downstream proteomic analysis of isolated translation complexes. Yoshikawa et al. developed Ribo Mega-SEC, a size-exclusion-chromatography-based uHPLC method, and tested whether it could support direct mass-spectrometry-based proteomic analysis of polysome and ribosomal-subunit fractions from mouse liver tissue.

Methods

Mouse liver lysates were fractionated by Ribo Mega-SEC into 24 fractions spanning polysomes down to smaller protein complexes. Each fraction was digested with trypsin, cleaned up, and analyzed by LC-MS/MS on a Q-Exactive Plus mass spectrometer, with protein abundance estimated using the iBAQ label-free quantification algorithm.

Results

The analysis yielded more than 58,800 unique peptides mapped to 5,158 protein groups across the fractionation range. Hierarchical clustering of elution profiles produced 400 protein clusters with a mean Pearson correlation coefficient of approximately 0.95; cluster 197 contained 39 proteins predominantly from the 60S ribosomal subunit, while cluster 454 contained 30 proteins mostly from the 40S subunit, including known accessory factors. The team also detected translation initiation factors, elongation and termination factors, and Exon Junction Complex proteins distributed across the polysome and subunit fractions, consistent with their expected roles in translation.

Conclusion

This work demonstrates that polysome and ribosomal-subunit fractions isolated by chromatographic methods are directly compatible with quantitative LC-MS/MS proteomics, enabling researchers to map the protein composition of active translation complexes at proteome scale rather than relying on RNA-level data alone.

Source: Yoshikawa H, Larance M, Harney DJ, et al. Efficient analysis of mammalian polysomes in cells and tissues using Ribo Mega-SEC. eLife 2018;7:e36530. Figures 6–7. Distributed under CC BY 4.0.

FAQs – Frequently Asked Questions

- Why combine polysome profiling with proteomics instead of running RNA-seq and proteomics separately?
  - Running both layers on the same lysate pool removes batch and cohort variability that occurs when RNA and protein samples come from separate preparations, and it lets us directly correlate translation efficiency with measured protein abundance for each gene rather than relying on RNA-level inference alone.
- What quantitative proteomics approach do you use?
  - We support label-free quantification as a standard approach and offer TMT-based isobaric labeling for higher-throughput multiplexed comparisons; the choice depends on your sample number, budget, and desired quantification precision, which our team can help you select.
- How much starting material is required to support both the polysome and proteomics arms?
  - We recommend at least 2 × 10⁶ cells or 100 mg of tissue per sample so that gradient fractions yield sufficient RNA for sequencing libraries and sufficient protein for trypsin digestion and LC-MS/MS without compromising either readout.
- Can this service detect cases where mRNA and protein levels disagree?
  - Yes — our integrated analysis explicitly flags genes where translation efficiency and protein abundance trends diverge, which is often the clearest signal of post-transcriptional or post-translational regulation rather than simple transcriptional control.
- What species and sample types are supported?
  - The service currently supports human, mouse, and rat as standard, with exploratory evaluation available for other organisms; please contact our team to discuss custom sample types and specific experimental designs.
- How are results delivered and how should they be interpreted?
  - You will receive raw sequencing and LC-MS/MS data files, processed translation efficiency and protein abundance tables, an integrated correlation report, and GO/KEGG enrichment summaries, all formatted for direct interpretation and publication-ready figure production.

References:

Yoshikawa H, Larance M, Harney DJ, Sundaramoorthy R, Ly T, Owen-Hughes T, Lamond AI. Efficient analysis of mammalian polysomes in cells and tissues using Ribo Mega-SEC. eLife 2018;7:e36530.
Rodriguez-Martinez A, Young-Baird SK. Polysome profiling is an extensible tool for the analysis of bulk protein synthesis, ribosome biogenesis, and the specific steps in translation. Mol Biol Cell 2025;36(4):mr2.
Han C, Sun L, Pan Q, Sun Y, Wang W, Chen Y. Polysome profiling followed by quantitative PCR for identifying potential micropeptide encoding long non-coding RNAs in suspension cell lines. STAR Protoc 2022;3:101037.