Absolute quantification of gene expression in individual bacterial cells using two-photon fluctuation microscopy

Area of Science:

• Molecular Biology
• Microscopy Techniques
• Systems Biology

Background:

• Quantifying protein expression in gene regulatory networks often relies on fluorescent protein (FP) intensity, which is subject to unknown scaling factors.
• This limitation hinders accurate analysis and interpretation of gene expression dynamics in live cells.
• Existing methods struggle with absolute concentration measurements in small cellular systems like bacteria.

Purpose of the Study:

• To adapt and validate two-photon (2p) fluorescence fluctuation microscopy, specifically scanning number and brightness (sN&B) analysis, for absolute quantification of diffusing FPs in live bacterial cells.
• To establish the detection limits and measurement uncertainty of the sN&B approach in bacteria.
• To demonstrate the utility of this method for reliably measuring transcriptional noise.

Main Methods:

• Application of two-photon (2p) fluorescence fluctuation microscopy with scanning number and brightness (sN&B) analysis.
• Utilized central pixel analysis and spatial averaging to validate the approach in small bacterial cells.
• Determined lower and upper detection limits for FP concentrations and assessed measurement uncertainty.

Main Results:

• Successfully applied sN&B analysis to determine absolute concentrations of diffusing FPs in live bacteria, overcoming previous limitations.
• Established a lower detection limit at or below 75 nM (~3 molecules/voxel) and an upper limit around 10 μM.
• Demonstrated measurement uncertainty below 5%, which is significantly lower than observed cell-cell variations in lac promoter activity.

Conclusions:

• Two-photon (2p) scanning number and brightness (sN&B) analysis provides a reliable method for absolute quantification of fluorescent protein concentrations in live bacteria.
• This technique enables accurate measurement of transcriptional noise, offering a significant advancement for studying stochastic gene expression.
• The approach is suitable for investigating the intricacies of gene regulatory networks in microbial systems.