Future Directions in Polysome Sequencing and Translational Omics

Currently, translational omics and the integration of multiple omics have become cutting-edge fields in biomedical research. With the rapid development of single-cell translational omics technology, scientists can now study the translation process within individual cells in greater depth, providing new insights into how cells respond to external signals, control gene expression, and their role in disease. The following are some future development directions for translational omics and related technologies.

Multiple translation omics technologies (Román ÁC et al., 2024)

Technological Innovation and Multidimensional Integration in Translational Omics

Translational omics, acting as a bridge between the transcriptome and proteome, can directly capture mRNA being translated by ribosomes, revealing the dynamic changes in gene expression at the translational level. Protein synthesis, modification, folding, and assembly are precisely regulated.

In recent years, translational omics technologies have made significant progress. Polysome profiling, utilizing the high sedimentation coefficient of ribosomes to separate polyribosomes, is a classic translational omics detection technique and is considered the "gold standard" for assessing translation efficiency. Compared to other methods, polysome profiling can accurately separate ribosomal subunits, monoribosomes, and polyribosomes for analyzing the overall translational activity of a sample and calculating translation efficiency (TE).

Current research suggests that integrating multi-omics data is a promising direction for future development. By combining transcriptome, proteome, and epitranscriptome (such as m6A modification) data, translational analysis enables the construction of more comprehensive gene expression maps. Increasing evidence suggests that non-coding RNAs such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) may also have translational activity and produce functional microproteins, further highlighting the importance of translational omics research.

Breakthrough in Single-Cell Translationomics

The advent of single-cell ribosome blot analysis (scRibo-seq) technology marks a new era in translationomics research. This technology enables translation detection at the single-cell level without the need for labeling or transgenic materials, making it particularly suitable for rare primary samples.

Through scRibo-seq, scientists have been able to reveal cellular heterogeneity in translation regulation at single-cell resolution. For example, in cell cycle studies, single-cell Ribo-seq technology has revealed a close relationship between translational arrest events and cell cycle progression, uncovering the dynamic translational changes of specific genes at various stages of the cell cycle.

Multi-omics integration is becoming an inevitable trend

A key direction for future translational omics research is the deep integration with data from other omics disciplines. As a "bridge" between proteomics and transcriptomics, translational omics can significantly increase the correlation between RNA and protein, revealing intermediate processes in the RNA-to-protein transition.

Combination with epitranscriptomics

The integration of translational omics with RNA modification research has become a rapidly growing field. For example, the regulatory mechanisms of RNA modifications such as m6A, m7G, and ac4C on the translation process are being widely studied. In esophageal squamous cell carcinoma research, researchers, through polysome-seq analysis, found that tRNA m7G modification affects the expression of oncogenic pathway genes by regulating the translation of m7G-related codon-enriched mRNAs.

Similarly, in stress particle research, scientists combined m7G methylated RNA immunoprecipitation sequencing (m7G meRIP-seq), QKI7 RIP-seq, and stress particle RNA sequencing (SG RNA-seq) to discover that the Quaking protein (QKI) dynamically regulates the transport and translation of mRNAs containing internal m7G modifications under stress.

Multidimensional Data Integration and Analysis

Advanced research has progressed to the simultaneous analysis of translatome, transcriptome, proteome, and epitranscriptome data. For example, in exploring the non-m6A-dependent role of METTL16, researchers combined polyribosome analysis and molecular interaction experiments, discovering that METTL16 promotes translation initiation through direct interactions with eIF3a/b and rRNA. This finding provides a new target for liver cancer treatment.

Sequential steps involved in multi-omics data representation and preparation (VanInsberghe M et al ., 2021)

Innovations in New Technologies and Methodologies

High-Resolution Structural Analysis

The Guo Qiang research group developed a novel clustering algorithm combined with cryo-electron tomography (cryo-ET) technology, achieving fine-grained analysis of the in-situ structure of intracellular polyribosomes.

This method revealed various regular arrangements of ribosomes within the cell, and that adjacent ribosomes on the same polyribosome tend to be in a consistent conformation, suggesting potential synergistic translation behavior.

High-Resolution Technology Variants

With technological advancements, a series of high-resolution technology variants are driving translationomics to deeper levels:

• Quantitative Translation Initiation Sequencing (QTI-seq): Locating translation initiation sites by chemically "freezing" and enriching initiation ribosomes while simultaneously removing elongated ribosomes from related mRNA.
• Translation Complex Mapping Sequencing (TCP-seq): Locating translation initiation sites by enriching RNA bound to the 40S ribosomal small subunit before assembly of mature ribosomes. Furthermore, because this method preserves ribosome integrity, it also allows for the analysis and comparison of the 80S ribosome portion, resulting in a more complete analysis of translational kinetics.
• Proximity-Specific Ribosome Analysis: This provides an innovative approach for assessing heterogeneous ribosome footprints and understanding translation at specific subcellular locations. This technique utilizes spatially restricted biotin ligases to label ribosomes with biotin receptor peptides in living cells.

Translationomics in Cancer Research and Precision Medicine

Unveiling the Mechanisms of Aberrant Translation in Cancer

Translationomics technologies have shown great potential in elucidating the mechanisms of aberrant translation in cancer. Studies have found specific translatome features in patients with glioblastoma or leukemia.

• For example, using ribosome analysis, researchers found that PYCR1 expression in renal cell carcinoma cells is associated with tumor progression, and that tumor progression is closely associated with the amount of proline. Codon analysis revealed how renal cell carcinoma cells depend on the amount of proline, and how tumor progression is associated with PYCR1 expression.
• Another study showed that FKBP10 promotes lung cancer progression by regulating translational elongation. This protein alters the translational elongation rate at the start of the ORF; specifically, after FKBP10 knockout, the reading frame occupancy of four proline codons was significantly higher than that of other codons. This result indicates that FKBP10 favors the elongation of proline-encoded codons.

Multi-omics Integration and Biomarker Discovery

Multi-omics integration analysis is becoming a new paradigm in cancer research. Researchers have begun to combine transcriptome, methylation, somatic mutation, and copy number variation (CNV) datasets from studies of different cancer types. This integrative approach helps to understand the interrelationships of genes under the influence of each omics feature within the human interactome.

For example, one study developed a Unified Connectivity Line (CLine) to identify specific genome-wide patterns associated with different omics in different cancer types. Across the four omics features, the identified omnigenic patterns displayed bimodal, fragmented, unimodal, and steepest descent patterns, respectively. These omics-specific patterns were found in 66.7%, 86.7%, 93.3%, and 93.3% of the malignant tumors examined (VanInsberghe M et al ., 2021).

Clinical Translation and Therapeutic Application Prospects

Tumor Subtyping and Precision Medicine

Multi-omics approaches are revolutionizing cancer molecular subtyping and treatment strategies. By integrating genomic, transcriptomic, epigenomic, and proteomic data, researchers are able to classify tumors into more refined molecular subtypes, providing a foundation for personalized treatment. For example, mutational signatures left by DNA repair defects (such as BRCA1/2 mutations) have been used to identify patients sensitive to PARP inhibitors, even if these patients do not have obvious BRCA1/2 gene mutations.

Mutation signature analysis has shown promise in predicting treatment response. Studies have found that homologous recombination defect (HRd) signatures are associated with PARP inhibitor response, while mismatch repair defect (MMRd) signatures are associated with immune checkpoint inhibitor response. Furthermore, copy number signatures have been shown to predict overall survival and the probability of platinum-resistant recurrence in ovarian cancer patients (Ma W et al., 2024).

Discovery of Novel Therapeutic Targets

Transimics technologies have facilitated the identification of novel therapeutic targets. For example, studies have found that METTL16 plays a pro-cancer role in liver cancer by promoting translation initiation through a non-m6A-dependent mechanism via direct interaction with eIF3a/b and rRNA. Another study showed that QKI proteins transport m7G-modified transcripts to stress granules and regulate mRNA metabolism under stress (Su R et al., 2021).

In esophageal squamous cell carcinoma (ESCC), studies have found that METTL1 and WDR4 are significantly upregulated and associated with poor prognosis. Mechanistically, tRNA m7G modification regulates the translation of m7G-related codon-enriched mRNAs, including negative regulators of mTOR and autophagy pathway genes. Polysome-seq analysis of METTL1 knockdown cells and control cells revealed that the more codons corresponding to m7G tRNAs on the mRNA, the more codons corresponding to m7G tRNAs the mRNA with reduced translation efficiency (TE) had (Han H et al., 2022).

To understand the difference between multimer sequencing and other translation analysis techniques, see "Comparing Polysome Sequencing with Other Translational Profiling Techniques".

To understand the challenges and limitations of multimer sequencing, see "Challenges and Limitations of Polysome Sequencing".

Navigating Technical Challenges and Future Directions in Translational Research

Translational omics continues to evolve rapidly, presenting both computational challenges and unprecedented opportunities. The integration of artificial intelligence and spatial technologies represents the next frontier in understanding protein synthesis regulation. These advances are particularly relevant for pharmaceutical companies developing targeted therapies.

Computational Integration and AI Applications

The exponential growth of multi-omics data requires sophisticated analytical approaches:

• Machine learning identifies patterns traditional statistics might miss
• Deep learning models like DeepOmix enable cancer survival prediction
• Automated pipelines process high-dimensional datasets efficiently

Satial Technologies Reshaping Research

Spatial omics technologies provide unprecedented tissue context:

• Mapping molecular distributions within tumor microenvironments
• Revealing cellular interactions and spatial heterogeneity
• Linking molecular features to specific tissue locations

These approaches help researchers understand why certain tumors respond differently to treatment. They're particularly valuable for developing localized therapeutic strategies.

Future Development Priorities

The field continues advancing toward higher resolution and clinical relevance:

• Single-cell and subcellular resolution monitoring
• Real-time translation dynamics capture
• Standardized data integration protocols
• Enhanced clinical translation pathways

Industry Applications and Impact

These technological advances deliver measurable benefits:

• Cancer precision medicine programs leverage spatial heterogeneity data
• Drug developers identify novel protein synthesis targets
• Diagnostic companies develop more accurate prognostic tools

As methodologies continue improving, translational omics will play an increasingly central role in therapeutic development. These advances help researchers understand complex gene regulation networks more completely.

People Also Ask

What is analysis of translation using polysome profiling?

Polysome profiling has been developed to infer the translational status of a specific mRNA species or to analyze the translatome, i.e. the subset of mRNAs actively translated in a cell. Polysome profiling is especially suitable for emergent model organisms for which genomic data are limited.

What is the purpose of polysome profiling?

Polysome profiling has been developed to infer the translational status of a specific mRNA species or to analyze the translatome, i.e. the subset of mRNAs actively translated in a cell.

What is translational efficiency in ribosome profiling?

Briefly, translational efficiency is calculated by dividing RPF counts for a given gene by the RNA read counts for the same gene at the same time (see Supplemental Methods for further details).

What are the advantages of ribosome profiling?

Ribosome profiling: a powerful tool in oncological research.

The prominent advantage of ribosome profiling lies in providing precise and abundant positional information that enables more detailed analysis of translational regulation.

What are the limitations of ribosome profiling?

First, only a small fraction of mRNAs is captured, limiting the ability to detect the translation landscape in cell populations. Second, the unique reads in each cell are rather scarce.

What is the difference between Ribo-Seq and RNA-seq?

RNA-Seq provides a comprehensive overview of gene activity, making it ideal for transcription studies. On the other hand, Ribo-Seq offers detailed insights into protein translation, providing critical information on how genes are turned into functional proteins.

What is translation complex profiling?

Translation complex profile sequencing (TCP-seq) is a molecular biology method for obtaining snapshots of momentary distribution of protein synthesis complexes along messenger RNA (mRNA) chains.

References:

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