Periodic Reporting for period 4 - DYNOME (Barcoding gene expression dynamics at single-molecule resolution)
Período documentado: 2024-08-01 hasta 2025-07-31
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).
The DYNOME project tackles the challenging task of monitoring gene-expression dynamics in individual bacterial cells at single-molecule resolution. Its goal is to detect the expression of single fluorescent molecules in bacteria and to use the resulting expression trajectories to extract kinetic rates for the underlying gene-expression steps—such as transcription and translation—across large cell populations.To achieve this goal, my group and I designed and built a dedicated super-resolution microscope. Standard instruments, including TIRF (total internal reflection fluorescence) microscopes, lack the long-term stability required to maintain nanometer-level precision over many hours. In addition, our system needed to image cells and track individual fluorescent molecules simultaneously, enabling us to follow gene expression in moving and potentially dividing cells.
Over the course of the project, we overcame numerous technical challenges and completed the DYNOME microscope in several phases. The first version was finished early and initial experiments yielded promising gene-expression data in bacterial cells. However, technical challenges in later funding periods necessitated major upgrades to ensure robust a performance of the system under our experimental conditions. The system is now in its final form, and new experiments with genetically engineered E. coli are currently underway.
Beyond the construction of the microscope, the DYNOME project also required the development of suitable bacterial model systems. To this end, we created a library of E. coli strains that chromosomally express two spectrally distinct fluorescent proteins. These proteins are produced either independently or in a partially coupled manner (by fusions at the mRNA or protein level), a design that will allow us to disentangle the respective contributions of transcriptional and translational noise.
Within the scope of DYNOME, we also developed a new analytical framework for photon-counting experiments (Terterov et al., 2025, Nature Communications, 16, 5537). The DYNOME project is built on the analogy between photon counting in in-vitro single-molecule fluorescence experiments and molecule counting inside living cells, and we anticipate that this new method will facilitate the computation of bias-free correlation functions in our in-cell measurements.
Over the course of the project, we overcame numerous technical challenges and completed the DYNOME microscope in several phases. The first version was finished early and initial experiments yielded promising gene-expression data in bacterial cells. However, technical challenges in later funding periods necessitated major upgrades to ensure robust a performance of the system under our experimental conditions. The system is now in its final form, and new experiments with genetically engineered E. coli are currently underway.
Beyond the construction of the microscope, the DYNOME project also required the development of suitable bacterial model systems. To this end, we created a library of E. coli strains that chromosomally express two spectrally distinct fluorescent proteins. These proteins are produced either independently or in a partially coupled manner (by fusions at the mRNA or protein level), a design that will allow us to disentangle the respective contributions of transcriptional and translational noise.
Within the scope of DYNOME, we also developed a new analytical framework for photon-counting experiments (Terterov et al., 2025, Nature Communications, 16, 5537). The DYNOME project is built on the analogy between photon counting in in-vitro single-molecule fluorescence experiments and molecule counting inside living cells, and we anticipate that this new method will facilitate the computation of bias-free correlation functions in our in-cell measurements.
Although official funding for the project has ended, our work on DYNOME continues. The past years were devoted to establishing the technological foundations of the project. We are now conducting experiments on three genetic constructs in which two fluorescent proteins (GFP and RFP) are expressed with different coupling between them. Our earlier experiments have already demonstrated that gene-expression noise depends on the coupling between transcriptional and translational processes. Our goal is to confirm these results with the upgraded version of the microscope. Once the current set of measurements is complete, the methodology will be ready for the next phase: probing gene expression in more biologically relevant natural systems. According to our original proposal, these systems include the bacterium Bacillus subtilis and its capability of stochastic phenotype switching and the regulation of a drug-efflux pump in E. coli. The latter project is particularly designed to provide a better understanding of the formation of multi-drug resistance in pathogenic bacteria, a phenomenon that requires a detailed understanding of the mechanistic processes behind the stochastic expression of genes. Building on these efforts and within the upcoming years, we envision taking the next step in complexity by adapting the DYNOME methodology for use in eukaryotic cells, such as yeast.