Final Report Summary - INVIVOTRNSREG (In Vivo Observation of Transcriptional Regulation in Bacilli by Fluorescence Correlation Spectroscopy)
In the 18 month long project INVIVOTRNSREG, number and brightness analysis (N&B) was adapted to count the number of green fluorescent protein (GFP) labeled molecules in single living cells of Bacillus subtilis (B. subtilis). We demonstrated N&B as a useful technique in systems biology to quantify gene expression and regulation (Ferguson et al., Analytical biochemistry, 2011). Quantitative determination of gene expression combined with stochastic modeling allowed us to discriminate between molecular mechanisms of transcriptional regulation (Ferguson et al., PNAS, 2011).
Quantification of promoter activity or protein expression in gene regulatory networks is generally achieved via measurement of green fluorescent protein (GFP) intensity, which is related to the true GFP concentration by an unknown scaling factor, thereby limiting analysis and interpretation. Using approaches originally developed for eukaryotic cells, we showed that two-photon (2p) fluorescence fluctuation microscopy, specifically scanning number and brightness (sN&B) analysis, can be applied to determine the absolute concentrations of diffusing GFPs in live bacterial cells. First, we demonstrate the validity of the approach, despite the small size of the bacteria, using the central pixels and spatial averaging. We established the lower detection limit at or below 75 nM (~ three molecules of GFP per excitation volume) and the upper detection limit at approximately 10 µM, which can be extended using intensity measurements. We found that the uncertainty inherent in our measurements (< 5 %) was smaller than the high cell-cell variations observed for stochastic leakage from GFP fusions of the lac promoter in the repressed state or the ten to 25 % variation observed on induction. This demonstrates that a reliable and absolute measure of transcriptional noise can be made using our approach, which makes it particularly appropriate for the investigation of stochasticity in gene expression networks.
Assessing gene expression noise in order to obtain mechanistic insights requires accurate quantification of gene expression on many individual cells over a large dynamic range. We used our unique method based on 2p fluorescence fluctuation microscopy to measure directly, at the single cell level and with single-molecule sensitivity, the absolute concentration of fluorescent proteins produced from the two B. subtilis promoters that control the switch between glycolysis and gluconeogenesis. We quantified cell-to-cell variations in GFP concentrations in reporter strains grown on glucose or malate, including very weakly transcribed genes under strong catabolite repression. Results revealed strong transcriptional bursting, particularly for the glycolytic promoter. Noise pattern parameters of the two antagonistic promoters controlling the nutrient switch were differentially affected on glycolytic and gluconeogenic carbon sources, discriminating between the different mechanisms that control their activity. Our stochastic model for the transcription events reproduced the observed noise patterns and identified the critical parameters responsible for the differences in expression profiles of the promoters. The model also resolved apparent contradictions between in vitro operator affinity and in vivo repressor activity at these promoters. Finally, our results demonstrated that negative feedback is not noise-reducing in the case of strong transcriptional bursting.
Quantification of promoter activity or protein expression in gene regulatory networks is generally achieved via measurement of green fluorescent protein (GFP) intensity, which is related to the true GFP concentration by an unknown scaling factor, thereby limiting analysis and interpretation. Using approaches originally developed for eukaryotic cells, we showed that two-photon (2p) fluorescence fluctuation microscopy, specifically scanning number and brightness (sN&B) analysis, can be applied to determine the absolute concentrations of diffusing GFPs in live bacterial cells. First, we demonstrate the validity of the approach, despite the small size of the bacteria, using the central pixels and spatial averaging. We established the lower detection limit at or below 75 nM (~ three molecules of GFP per excitation volume) and the upper detection limit at approximately 10 µM, which can be extended using intensity measurements. We found that the uncertainty inherent in our measurements (< 5 %) was smaller than the high cell-cell variations observed for stochastic leakage from GFP fusions of the lac promoter in the repressed state or the ten to 25 % variation observed on induction. This demonstrates that a reliable and absolute measure of transcriptional noise can be made using our approach, which makes it particularly appropriate for the investigation of stochasticity in gene expression networks.
Assessing gene expression noise in order to obtain mechanistic insights requires accurate quantification of gene expression on many individual cells over a large dynamic range. We used our unique method based on 2p fluorescence fluctuation microscopy to measure directly, at the single cell level and with single-molecule sensitivity, the absolute concentration of fluorescent proteins produced from the two B. subtilis promoters that control the switch between glycolysis and gluconeogenesis. We quantified cell-to-cell variations in GFP concentrations in reporter strains grown on glucose or malate, including very weakly transcribed genes under strong catabolite repression. Results revealed strong transcriptional bursting, particularly for the glycolytic promoter. Noise pattern parameters of the two antagonistic promoters controlling the nutrient switch were differentially affected on glycolytic and gluconeogenic carbon sources, discriminating between the different mechanisms that control their activity. Our stochastic model for the transcription events reproduced the observed noise patterns and identified the critical parameters responsible for the differences in expression profiles of the promoters. The model also resolved apparent contradictions between in vitro operator affinity and in vivo repressor activity at these promoters. Finally, our results demonstrated that negative feedback is not noise-reducing in the case of strong transcriptional bursting.