Understanding Gene Expression in Single Cells: A New Approach
When scientists study how genes are expressed in cells, they often look at the RNA molecules produced by those genes. This process, called transcriptomics, helps us understand what’s happening inside cells at any given moment. However, one of the biggest challenges in this field is figuring out the patterns and mechanisms behind these gene activities, especially since the snapshots we get from single-cell studies don’t capture changes over time.
In simpler terms, imagine trying to understand a movie by looking at just one frame. You might get an idea of what’s happening, but you’d miss the entire story. Similarly, scientists need to understand how gene activity changes over time, especially as cells grow and divide. But how can we capture this dynamic process when our tools mostly give us still pictures?
A New Model for Understanding Gene Activity
To address this challenge, researchers at the Imperial College London have developed a new model that helps us understand how genes are expressed over time in single cells. This model takes into account two key factors: the size of the cell and its stage in the cell cycle (the process a cell goes through as it grows and divides).
The researchers used a technique called stochastic modeling, which is a way of predicting how gene expression might vary due to random, unpredictable factors. They combined this with advanced methods like Bayesian computation—a statistical approach that helps them make sense of the data and correct for any technical errors.
By applying this model to single-cell RNA sequencing data, the scientists were able to track how often genes are “turned on” (burst frequency), how much RNA is produced when they are (burst size), and how quickly that RNA is broken down (degradation rate). What’s groundbreaking about this approach is that it allowed them to do this across thousands of genes in many cells at once, providing a comprehensive view of gene activity throughout the cell cycle.
Overview of modeling and inference framework
Key Findings: How Cells Control Gene Expression
One of the most important discoveries from this study is the relationship between cell size and gene expression. The researchers found that as cells grow, their transcription rates (how quickly they make RNA) scale up. This means that larger cells tend to produce more RNA, which could be a way for cells to maintain balance as they grow.
Additionally, the study revealed that gene regulation happens in waves during the cell cycle. This means that different genes are activated or deactivated at specific stages of the cell cycle, helping the cell manage its growth and division processes.
Why This Matters
Understanding how cells control gene expression over time is crucial for many areas of biology and medicine. For example, knowing how genes are regulated during the cell cycle can provide insights into how cancer cells grow uncontrollably or how stem cells differentiate into various types of cells in the body.
This new approach to studying gene expression dynamics opens up new possibilities for research, allowing scientists to explore the mechanisms that drive these processes in greater detail. As we continue to develop and refine these models, we’ll gain a deeper understanding of how life operates at the most fundamental level, potentially leading to new treatments and therapies for various diseases.
This study represents a significant step forward in our ability to understand the dynamic nature of gene expression in single cells. By using advanced modeling techniques, researchers are beginning to unravel the complex patterns that govern how genes are activated and regulated, offering new insights into the inner workings of cells.
Volteras D, Shahrezaei V, Thomas P. (2024) Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing. Cell Syst [Epub ahead of print]. [article]