Periodic Reporting for period 1 - ChOICE (Chance Or Intentional: Cellular decisions Explored)
Période du rapport: 2023-03-01 au 2025-08-31
Cells are incredibly complex out-of-equilibrium systems that constantly react to changing environments in an efficient and strategic manner. As a consequence, it is necessary for cells to make fast and accurate decisions about their functional roles to fit their needs both at short and long timescales. Throughout the decision-making process fluctuations in protein levels, called noise, play a pivotal role. A high amount of noise allows probabilistic decision-making and enhances fitness when cells find themselves in variable environments. However, noise can be detrimental for commitment to cellular decisions, requiring cells to implement strategies to minimise noise when it is unfavourable. Due to the prevalence and importance of cellular-decision making in healthy and pathogenic cells, it is highly important to identify the molecular events that drive and modulate noise throughout the decision-making process. This requires single-cell approaches that identify the molecular drivers, modulators, and enforcers of cellular decisions at both short and long timescales.
In Chance Or Intentional: Cellular decisions Explored, we will identify the molecular drivers of cellular decision, the feedback architectures that modulate commitment to decisions, and physical properties that enforce memory of decisions. We propose an ambitious research program that maps the mechanisms underlying cellular decision-making by combining single-molecule imaging, single-cell sequencing, and time-lapse microscopy with mathematical models. The results will provide a much-needed quantitative characterization of the decision-making process at three distinct timescales. Since protein noise has been associated with various pathological conditions, including infections and cancer, the results from ChOICE will have a wide-ranging impact.
In Chance Or Intentional: Cellular decisions Explored, we will identify the molecular drivers of cellular decision, the feedback architectures that modulate commitment to decisions, and physical properties that enforce memory of decisions. We propose an ambitious research program that maps the mechanisms underlying cellular decision-making by combining single-molecule imaging, single-cell sequencing, and time-lapse microscopy with mathematical models. The results will provide a much-needed quantitative characterization of the decision-making process at three distinct timescales. Since protein noise has been associated with various pathological conditions, including infections and cancer, the results from ChOICE will have a wide-ranging impact.
This project investigates how cells control gene expression noise to influence cellular decisions. Using stem cells as a model, we aim to understand how molecular processes and nuclear organization contribute to gene expression noise in seemingly identical cells. This research is fundamental in nature and aims to uncover basic principles that guide how cells adopt specific identities, with implications for developmental disorders and disease.
Over the course of the project, we developed and implemented an innovative method to measure mRNA and protein levels simultaneously in single cells. This included the successful creation of a high-resolution imaging platform and analysis software that allows quantification of both mRNA and protein levels in the same cell.
We also designed a large-scale screening approach to identify genes that regulate gene expression noise. This screen revealed several promising candidates that appear to modulate noise without changing average gene expression levels—offering a new way to think about how cells might regulate gene expression. Follow-up experiments are underway to better understand how these regulators and their target genes function in the context of cell fate decisions.
Together, these findings lay the groundwork for a deeper understanding of how gene expression noise is controlled. The tools and insights developed in this project will continue to benefit future research in cell and developmental biology.
Over the course of the project, we developed and implemented an innovative method to measure mRNA and protein levels simultaneously in single cells. This included the successful creation of a high-resolution imaging platform and analysis software that allows quantification of both mRNA and protein levels in the same cell.
We also designed a large-scale screening approach to identify genes that regulate gene expression noise. This screen revealed several promising candidates that appear to modulate noise without changing average gene expression levels—offering a new way to think about how cells might regulate gene expression. Follow-up experiments are underway to better understand how these regulators and their target genes function in the context of cell fate decisions.
Together, these findings lay the groundwork for a deeper understanding of how gene expression noise is controlled. The tools and insights developed in this project will continue to benefit future research in cell and developmental biology.
The findings mark a major step forward in our understanding of gene regulation by systematically identifying proteins that modulate gene expression noise without altering mean expression levels. This shifts the focus from traditional gene regulation models, which centre on mean expression, to a broader understanding where noise itself is an actively regulated feature. The use of an integrative approach—combining single-cell transcriptomics, proteomics, and computational analysis—provides a powerful and generalizable framework for uncovering regulators of gene expression noise.
The potential impacts of these findings are far-reaching. Noise in gene expression plays a crucial role in cell fate decisions during development and disease progression (i.e. cancer and infections). This work provides both a conceptual breakthrough and a practical roadmap for exploring gene expression noise in diverse biological contexts.
The potential impacts of these findings are far-reaching. Noise in gene expression plays a crucial role in cell fate decisions during development and disease progression (i.e. cancer and infections). This work provides both a conceptual breakthrough and a practical roadmap for exploring gene expression noise in diverse biological contexts.