Translation in cellular context: Elucidating function, organization and regulation with near-atomic models in whole cells

Informazioni relative al progetto

TransFORM

ID dell’accordo di sovvenzione: 101119142

DOI

10.3030/101119142

Data della firma CE 18 Dicembre 2023

Data di avvio 1 Febbraio 2024

Data di completamento 31 Gennaio 2030

Finanziato da

• European Research Council (ERC)

Costo totale

€ 13 998 670,00

Contributo UE

€ 13 998 670,00

Coordinato da

EUROPEAN MOLECULAR BIOLOGY LABORATORY
Germany

Periodic Reporting for period 1 - TransFORM (Translation in cellular context: Elucidating function, organization and regulation with near-atomic models in whole cells)

Periodo di rendicontazione: 2024-02-01 al 2025-07-31

Sintesi del contesto e degli obiettivi generali del progetto

Proteins are essential molecules that carry out most functions in our cells. They are produced by ribosomes, large molecular machines that read genetic information and build proteins in a process called translation. Although ribosomes have been studied extensively in test tubes, we still know little about how millions of ribosomes work together inside human cells, how they interact with regulatory partners, and how their activity changes in response to stress or infection.
Our TransFORM project aims to answer these questions by studying protein synthesis directly in intact human cells. The project will map how ribosomes and regulatory partners are organized in different parts of the cell, how they assemble into functional complexes, and how their behavior shifts to adapt to different conditions. This will be done in a range of experimental systems, from single cells to multicellular models that mimic human tissues.
To achieve this, TransFORM is also advancing technology. The team is developing new mass spectrometry methods to identify protein partners of ribosomes inside living cells, new imaging approaches to visualize them at very high resolution, and new computational tools that integrate experimental data into detailed three-dimensional models. Together, these innovations will make it possible to generate near-atomic views of ribosomes and their partners in their natural environment.
The main outcome will be a reference atlas of ribosome states, their partners, and their locations in human cells. In addition, the methodological breakthroughs of TransFORM will provide powerful tools that can be used widely to study other essential cellular processes.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

The project has only recently started. Initial work has focused on establishing workflows and benchmarks for studying ribosome complexes directly inside human cells. Preparatory activities include optimizing methods for live-cell crosslinking to capture ribosome–protein interactions, advancing sample preparation for cryo-electron tomography and acquisition of the first large datasets, setting up computational pipelines for protein-protein interaction screens and integrative methods that combine imaging and proteomics data. The teams are aligning methodologies across sites to ensure data compatibility and reproducibility. Early progress lays the foundation for generating high-resolution maps of translation complexes in diverse cellular contexts.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

TransFORM will deliver a new level of understanding of protein synthesis by providing atomic models of ribosomes and their partners directly inside human cells. Anticipated results include: (1) a reference atlas of ribosome states and their spatial organization across cellular compartments; (2) insights into how translation is reorganized during stress and viral infection; and (3) innovative technologies that advance in-cell structural biology.
Beyond advancing knowledge, the methodological innovations – live-cell crosslinking, multi-scale cryogenic imaging, and AI-driven structural modeling – will extend the frontiers of how complex molecular systems can be studied in their native environment. For broader uptake, further research and community adoption will be supported through open data, software, and workflows, lowering barriers for other laboratories to apply these tools. Over time, this will facilitate applications ranging from fundamental cell biology to biomedical research.