Polysome Sequencing and Multi-Omics Integration: Linking Translation with Transcriptomics and Proteomics

Quick Overview

01 Multi-omics Integration Framework 02 Multi-omics Integration Strategy 03 Challenges and Future Directions 04 Summary 05 People Also Ask

Polysome sequencing (Polysome-seq) is the gold standard technique for studying translation efficiency. It separates active translational mRNAs bound to ≥2 ribosomes through sucrose density gradient centrifugation. Unlike Ribo-seq (which captures ribosome-protected fragments, RPFs), Polysome-seq focuses on polysome-bound mRNAs, directly assessing translational activity and the translational capacity of non-coding RNAs.

Key features of Polysome-seq:

Quantification of translation efficiency: The polyribosome score (PS) is calculated based on the ratio of polysome-bound mRNA to total mRNA, reflecting translational activity.

Dynamic monitoring: Captures ribosome arrest, collision events, and stress-induced translational changes (e.g., oxidative stress increases the proportion of monosomal mRNAs).

Non-coding RNA detection: Detects the translation of atypical regions (e.g., lncRNA/circRNA) through ribosome binding.

Figure 1.Example of Polysome Profiling. Example of Polysome Profiling (Ye Y et al., 2021)

Multi-omics Integration Framework

To connect transcription, translation, and post-translational regulatory layers, Polysome-seq needs to be integrated with RNA-seq (transcriptomics) and proteomics (such as LC-MS/MS or DIA-MS) to construct a systems biology analysis pipeline and elucidate gene expression bottlenecks and regulatory networks.

Integration Strategies

Data Standardization:
- RNA-seq: Provides transcript abundance and alternative splicing information.
- Polysome-seq: Quantifies translation efficiency (TE = polyribosomal mRNA / total mRNA).
- Proteomics: Measures protein abundance and post-translational modifications (PTMs).

Key Analytical Methods

Correlation Analysis: Compares mRNA levels from RNA-seq with protein abundance from proteomics to identify post-transcriptional regulation.

Co-expression Networks: Correlates Polysome-seq TE values with RNA-seq and proteomics data to map highly translationally active pathways (such as stress responses).

Structural equation modeling (SEM): Quantifying the contributions of transcriptional and translational regulation to protein expression.

Multi-omics Integration Strategy

(1) Combined Analysis with Transcriptome

Wu J et al. isolated ribosome-bound mRNAs (non-polysome, light polysome, and heavy polysome) using polysome profiling. Combined with RNA-seq analysis, they found that EPRS inhibition (e.g., using the PRS-specific inhibitor HF) significantly reduced the proportion of proline-rich genes (PRRs, such as collagen, LTBP2, and SULF1) in heavy polysomes (high translational activity), demonstrating that their translation efficiency is regulated by EPRS.

Eighty-three PRR genes were identified (online Table IX/X). These genes are downregulated by HF at the post-transcriptional level and constitute a major part of the ECM and secretory signaling molecules, making them key targets for myocardial fibrosis.

Integrated analysis of polysome-seq and RNA-seq showed that EPRS, by regulating the translation efficiency of PRR genes (such as collagen, LTBP2, and SULF1), becomes a key driver of cardiac fibrosis, providing a basis for anti-fibrotic therapy targeting EPRS or its downstream effectors (such as SULF1).

Figure 2.Pro-rich genes are preferential translational targets of EPRS. Pro-rich genes are preferential translational targets of EPRS (Wu J et al., 2020)

Wang Z et al., using multimer analysis (sucrose gradient centrifugation to separate multimers) combined with RNA-seq, found that the ospus1-1 mutant (loss of OsPUS1 function) exhibited the following at low temperatures:

Abnormal chloroplast ribosome assembly (multimer profile showing ribosome defects), and decreased translational activity (reduced proportion of multiple multimers);

Reduced mature rRNA, accumulated precursor rRNA, and inhibited ribosome biosynthesis.

RNA-seq and differential expression analysis (edgeR) showed that in the ospus1-1 mutant, the expression of photosynthesis-related genes was downregulated (growth inhibition), while the expression of stress-response genes was upregulated (ROS accumulation-induced);

ROS detection revealed severe accumulation of reactive oxygen species (ROS) in the mutant at low temperatures, disrupting cellular homeostasis and further disturbing the gene expression network (through retrograde signaling regulation of nuclear genes). SeqHB-LMPCR sequencing confirmed the binding of OsPUS1 to chloroplast precursor rRNA (pre-rRNA);

Quantitative RT-PCR validated differentially expressed genes (such as stress genes);

Gene ontology analysis (TBtools) revealed that OsPUS1 regulates pathways such as ribosome biosynthesis and translation.

Integration of polysome sequencing and multi-omics technologies showed that OsPUS1, by mediating Ψ modification of chloroplast rRNA, maintains ribosome function and translation efficiency, balancing growth and stress response, and is a key regulator of rice's low-temperature adaptation. This discovery provides a new target for improving crop cold tolerance (such as regulating OsPUS1 expression or rRNA modification).

The transcriptome and translatome homeostasis are affected in the rice ospus1-1 mutants at low temperature (Wang Z et al., 2022)

(2) Proteomic Association

Li Q et al., through multimer analysis (sucrose gradient centrifugation to separate ribosome-bound mRNA), combined with qRT-PCR and proteomics, found that: when YTHDF1 was overexpressed, ATG2A/ATG14 mRNA was enriched in multimers (high translational activity), and translation efficiency was significantly increased; after YTHDF1 knockdown/removal, ATG2A/ATG14 translation efficiency decreased, and the expression of autophagy-related protein (LC3) decreased.

ChIP assays and dual luciferase reporter assays confirmed that under hypoxic conditions, HIF-1α directly binds to the YTHDF1 promoter, activating its transcription (YTHDF1 is highly expressed in HCC tissues and is associated with poor prognosis).

MeRIP-seq and RIP showed that YTHDF1 enhances the stability and translation of ATG2A/ATG14 mRNA by recognizing the m6A modification site (RNA stability analysis showed that the half-life of ATG2A/ATG14 mRNA was shortened after YTHDF1 knockdown). Polysome sequencing and multi-omics integration indicate that YTHDF1 is a key regulator of hypoxia-induced autophagy in HCC. It activates ATG2A/ATG14 through m6A-dependent translation, promoting malignant progression. YTHDF1 could serve as a prognostic biomarker and therapeutic target for HCC (e.g., inhibiting YTHDF1 or its downstream autophagy genes).

Figure 4.MeRIP-seq and proteomics identified potential targets of YTHDF1 in HCC. MeRIP-seq and proteomics identified potential targets of YTHDF1 in HCC (Li Q et al., 2021)

Bhaduri U et al., using polysome sequencing and RNA-seq, found that TRIM8 silencing resulted in:

significant upregulation of multisomal-binding MALAT1 lncRNA (MALAT1 interacts with miR-561 to regulate TOP2A, and its knockdown induces G0/G1 arrest);

altered translational activity, affecting the translation efficiency of autophagy genes such as ATG2A/ATG14 (protein expression changes were verified by LC-MS/MS).

ScRNA-seq and differential expression analysis showed that TRIM8 depletion disrupts the "chromosomal replication cell cycle regulation" pathway, affecting gene expression in the G0/G1/S/G2/M phases (such as TOP2A and MCM complex proteins);

Flow cytometry (BrdU/7-AAD) confirmed that TRIM8 silencing led to cell cycle heterogeneity and accumulation in the G0/G1 phase (consistent with the arrest induced by MALAT1 upregulation). LC-MS/MS proteomics identified differentially expressed proteins regulated by TRIM8 (such as CEP170 and MCM complex components), enriched in the centrosome/spindle pathway.

RT-qPCR+ChIP further validated TRIM8's transcriptional/translational regulation of target genes (such as MALAT1 and TOP2A), and wound healing assays showed that TRIM8 influences cell migration.

Polysome sequencing and multi-omics integration revealed that TRIM8 acts as a key regulator of multiple stages of mitosis (centrosome replication-chromosome replication-cytokinesis) by regulating the translation efficiency of polysome-binding RNAs (such as MALAT1), maintaining cell cycle gene (TOP2A/MCM) expression, and primary ciliary assembly, providing a basis for cancer therapy (such as targeting the TRIM8-mitotic regulatory axis).

Figure 5.Differential proteomic (LC-MS/MS) and translatomic (polysome profiling with RNA-seq) study upon silencing of TRIM8 in RPE cells. Differential proteomic (LC-MS/MS) and translatomic (polysome profiling with RNA-seq) study upon silencing of TRIM8 in RPE cells (Bhaduri U et al., 2025)

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MeRIP-seq

ChIP assays

RIP

Polysome sequencing

RNA-seq

(3) Integration with the Epigenetic Modifier Group

Chen Z et al., through multi-ribosome-associated mRNA sequencing (TE = FPKM of multi-ribosome mRNA / FPKM of total RNA) combined with qPCR, found that:

After METTL1/WDR4 knockdown, the proportion of target mRNAs (such as genes containing m7G-related codons) decreased in multi-ribosomes (high translational activity), and translation efficiency was significantly reduced;

Overexpression of wild-type METTL1 (non-catalytic death mutant) increased the translation efficiency of target mRNAs and promoted HCC progression.

LC-MS quantification of tRNA modification: After METTL1 knockdown, the percentage of tRNA m7G modification decreased (e.g., reduced m7G modification in tRNA-LysCTT);

TRAC-seq (tRNA m7G reduction and cleavage sequencing) identified m7G modification sites (e.g., positions 46-48 in tRNA-LysCTT), and the enrichment of m7G-modified tRNAs was verified by anti-m7G immunoprecipitation qPCR.

Multi-omics integration: TCGA data analysis showed that METTL1/WDR4 is highly expressed in HCC and is associated with advanced stage and low survival; RNA-seq/proteomics validated changes in the expression of target genes (such as proliferation and migration-related genes).

Polysome sequencing and multi-omics integration indicate that METTL1/WDR4 enhances the translation efficiency of mRNAs containing m7G-preferred codons by catalyzing tRNA m7G modification, driving the malignant phenotype of HCC, and providing a basis for HCC therapy targeting m7G tRNA modification.

Figure 6.METTL1 regulates m7G tRNA methylome, tRNA expression and global mRNA translation. METTL1 regulates m7G tRNA methylome, tRNA expression and global mRNA translation (Chen Z et al., 2021)

Lu X et al., through multisomal profiling (combined with qRT-PCR), found that: when YTHDF1 was overexpressed, SLC7A11 mRNA was enriched in multisomal complexes (high translational activity), and translation efficiency was significantly increased; after YTHDF1 knockdown, SLC7A11 translation efficiency decreased, expression of the anti-ferroptosis protein GPX4 decreased, and ferroptosis levels increased.

Dual-luciferase reporter assay and ChIP-PCR confirmed that under hypoxic conditions, HIF-1α directly binds to the YTHDF1 promoter, activating its transcription (YTHDF1 is highly expressed in NPCs and is associated with anti-ferroptosis).

RIP assays showed that YTHDF1 recognizes the m6A modification site of SLC7A11 mRNA (predicted by SRAMP) through its YTH domain, enhancing mRNA stability and translation.

RNA stability assays (actinomycin D treatment) indicated that YTHDF1 knockdown shortened the half-life of SLC7A11 mRNA (accelerated degradation).

Protein stability assays (CHX treatment) showed that YTHDF1 promotes SLC7A11 protein accumulation (enhanced translation offsets degradation).

Polysome sequencing and multi-omics integration revealed that YTHDF1 is a key downstream translational regulator of HIF-1α, activating SLC7A11 through m6A-dependent translation and maintaining the systemic Xc⁻-GSH-GPX4 anti-ferroptosis pathway, providing a novel target for IVDD therapy (such as regulating the HIF-1α-YTHDF1-SLC7A11 axis).

Figure 7.YTHDF1 promotes translation of SLC7A11 mRNA upon binding to it. YTHDF1 promotes translation of SLC7A11 mRNA upon binding to it (Lu X et al., 2024)

Challenges and Future Directions

Technical Limitations:

Polysome-seq relies on a large number of cells (≥1×10⁷), limiting its application in rare samples.

Short-read sequencing (such as Ribo-seq) is susceptible to ribosome distribution bias, requiring the combination with long-read techniques to analyze complex ORFs.

Frontier Directions:

Spatial Translationomics: Combining spatial transcriptomics techniques to analyze translational heterogeneity in the tissue microenvironment.

Artificial Intelligence Integration: Utilizing deep learning to predict translational regulatory networks, such as multi-omics data fusion models based on GNNs.

Summary

Polysome sequencing and multi-omics integration provide a panoramic perspective for analyzing translational regulation, demonstrating strong analytical capabilities from molecular mechanisms (such as RNA modification and ribosome dynamics) to physiological and pathological processes (such as stress response and tumorigenesis). Future efforts need to overcome technological bottlenecks and deepen the development of cross-omics algorithms to fully reveal the complexity of translational regulation.