Recent Publications Harnessing the Power of Translatomics

Every week we provide a digest of a small number of recent interesting papers in the field of translatomics.

In this week’s Sunday papers,

  • González et al. integrate ribosome profiling, pSILAC proteomics, and polysome profiling to map translation dynamics in liver cancer cell lines.
  • Keskin et al. provide a high-resolution temporal multi-omics dataset tracking transcription, translation, and protein abundance during human embryonic stem cell differentiation into cardiomyocytes.
  • Duan et al. uncover species-specific molecular strategies in maize and rice for coping with high-light stress.

Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines

Scientific Data, 2025

González, C., Guo, X., Aguilar, J., Lin, W., Kames, J., Kim, D., Das Sharma, S. and Jovanovic, M.

Sunday Paper 1

Over the past decade, it has become increasingly clear that mRNA levels alone are not sufficient to explain cellular protein abundance. This discrepancy underscores the importance of studying translational regulation, the process that bridges transcription and protein synthesis. This paper presents an integrated dataset aimed at understanding translational control in human cells by generating comprehensive steady-state and dynamic multi-omics data from two human liver cancer cell lines, HepG2 and Huh7.

They generated and validated three complementary datasets: (i) ribosome profiling under steady-state and harringtonine-induced translation run-off conditions to capture initiation and elongation dynamics, (ii) pulsed SILAC proteomics (pSILAC) to quantify protein synthesis rates, and (iii) polysome profiling coupled with RNA-seq to determine mean ribosome load per transcript. These data were benchmarked for reproducibility and consistency across platforms, with high correlations between replicates. The integration of ribosome footprinting and proteomics confirmed expected correlation between translation and protein abundance and accurately captured translational repression of TOP-motif genes, which encode ribosomal proteins and translation factors, during nutrient starvation.

Elongation rates derived from ribosome run-off experiments averaged 4.55–4.85 amino acids per second, consistent with prior mammalian studies. Codon enrichment analysis further reflected differential decoding speeds. The publicly available datasets expand the resources available for predicting mRNA sequence-dependent protein production, providing valuable tools for optimising protein expression systems and advancing mRNA vaccine design.

Learn more about EIRNABio’s polysome profiling, ribosome profiling and RNA-seq services here.

Temporal multiomics gene expression data of human embryonic stem cell-derived cardiomyocyte differentiation

Scientific Data, 2025

Keskin, A., Shayya, H.J., Sirabella, D., Patel, A., Corneo, B. and Jovanovic, M.

Sunday Paper 2

Understanding how human embryonic stem cells (hESCs) become specialized cell types is a key question in developmental biology. Cardiomyocyte differentiation provides an ideal model to study the regulatory mechanisms underlying early heart development. This paper presents a comprehensive multi-omics dataset capturing transcriptional, translational, and proteomic changes during the differentiation of hESCs into cardiomyocytes. The study aims to elucidate the regulatory mechanisms that govern early human development and cardiac cell fate specification.

Using the RUES2 hESC line, the authors induced cardiomyocyte differentiation through the mesodermal lineage and collected samples at 10 time points (days 0–18). They measured mRNA expression via RNA sequencing, translation activity via ribosome profiling (Ribo-seq), and protein abundance via quantitative LC-MS/MS proteomics. All experiments were performed in duplicate and underwent rigorous quality control and validation. Differentiation efficiency was confirmed by flow cytometry, with 61–84% of cells expressing the cardiomyocyte marker troponin T (TNNT2).

High reproducibility was observed across replicates and time points (Pearson’s R ≥ 0.94 for all omics layers). Expression analyses confirmed expected temporal patterns: pluripotency markers (OCT4, SOX2) declined, mesodermal markers (MIXL1, TBXT) peaked mid-course, and cardiomyocyte markers (TNNT2, MYH6) increased at later stages. Gene Ontology enrichment revealed upregulation of cardiac development pathways and downregulation of DNA replication and ribosome biogenesis.

This dataset, spanning RNA-seq, Ribo-seq, and proteomics, provides a high-resolution temporal map of gene regulation during cardiomyocyte differentiation. It serves as an invaluable resource for studying human development, multi-layer gene regulation, and translational control.

Learn more about EIRNABio’s ribosome profiling and RNA-seq services here.

Multi-omics analysis reveals distinct responses to light stress in photosynthesis and primary metabolism between maize and rice

Plant Communications, 2025

Duan, F., Li, X., Wei, Z., Li, J., Jiang, C., Jiao, C., Zhao, S., Kong, Y., Yan, M., Huang, J., Yang, J., Chen, Y., Bock, R. & Zhou, W.

Sunday Paper 3

High-light (HL) stress disrupts photosynthesis and induces oxidative damage, limiting plant growth and crop productivity. Understanding how different species detect and tolerate HL stress is essential for improving resilience and yield. This paper investigates how maize (Zea mays) and rice (Oryza sativa) respond to high-light (HL) stress using a time-resolved multi-omics approach. Integrating transcriptomic (RNA-seq), translatomic (ribosome profiling), proteomic, and metabolomic analyses with physiological measurements, the study reveals fundamental species-specific mechanisms underlying light stress tolerance.

Rice exhibited faster but more sensitive responses to HL exposure, including declines in photosynthetic efficiency, energy dissipation capacity, and increased reactive oxygen species (ROS) accumulation. In contrast, maize demonstrated greater HL tolerance, maintaining photosynthetic performance through enhanced cyclic electron flow (CEF), non-photochemical quenching (NPQ), and increased accumulation of sugars, aromatic amino acids, and antioxidants.

Multi-omics integration revealed extensive transcriptional regulation in both crops, but maize displayed more coordinated changes across translation and protein levels, suggesting tighter metabolic control. Functional validation showed that overexpression of ZmPsbS in maize improved NPQ and photoprotection, while knockout of the rice transcription factor OsbZIP18 enhanced HL tolerance, identifying it as a negative regulator of photoprotection and photosynthesis under stress.

Overall, the study highlights key molecular and physiological differences between maize and rice photosynthetic strategies under high light. By identifying central regulators such as PsbS and OsbZIP18, this work provides targets for engineering improved light stress resilience and photosynthetic efficiency in crops, contributing to future agricultural sustainability under changing climate conditions.

Learn more about EIRNABio’s ribosome profiling and RNA-seq services here.