Periodic Reporting for period 3 - DYNOME (Barcoding gene expression dynamics at single-molecule resolution)
Reporting period: 2023-02-01 to 2024-07-31
Gene expression is an inherently stochastic process. Random expression bursts cause cell-to-cell variations in mRNA and protein levels. The consequences can be beneficial in some instances, e.g. in cell differentiation, and harmful in others, e.g. in bacterial drug tolerance. A key interest in biology is therefore to decipher the kinetics that characterise this noise. What is the distribution of transcription rates in a cell population? Are gene expression dynamics heritable? Do gene networks communicate via the ‘Morse code’ of expression burst? Detailed answers to these questions are pending due to insufficient experimental methods to temporally resolve gene expression noise at single-molecule resolution. We need a ‘transparent cell’ for which the transcription and translation kinetics is accessible for many genes in parallel. DYNOME is our answer to this challenge. DYNOME combines (i) a super-resolution detect-and-bleach strategy, (ii) multi-colour barcoding to simultaneously monitor up to six genes, (iii) a lineage tracking tool, and (iv), lab-on-a-chip bioreactors to steer growth conditions. This innovative bio-dynamics platform will allows us to monitor gene expression dynamics at the single-molecule level for many genes in single cells at the same time over many generations. Targets of our approach are stochastic decision-making events in bacteria (B. subtilis, E. coli) and also in eukaryotic cells (yeast).
Over the past two and a half years, we designed and constructed a multi-colour TIRF-microscope including all the necessary components to parallelise bright-field imaging of cells and single-molecule tracking. Simultaneously, we established CRISPR-Cas9 as a molecular tool in our lab, which now allows us to genetically modify bacterial cells according to our experimental requirements. This particularly includes the genomic insertion of fluorescent proteins at arbitrary positions in the genome. First experiments with genetically modified E. coli have been performed and gene expression trajectories with extremely high time resolution have already been obtained. The gene expression trajectories obtained from these experiments highlight strong burst like expression dynamics and the statistics of expression burst demonstrate that simplified kinetic gene expression models are not suitable to capture the complexity of the data. Most importantly, these first experiments demonstrate that high-resolution (temporal) gene expression experiments are feasible.
In the next funding period, we will focus our efforts in establishing three major goals (i) measuring single-molecule gene expression trajectories in dividing bacterial cells to track kinetic memory across cell generations, (ii) to study the adaptation of gene expression processes to environmental conditions such as antibiotic stress, and (iii) to establish a rigorous theoretical framework to analyse gene expression trajectories using kinetic models.