ABSTRACT
The electrical membrane potential (Vm) is one of the components of the electrochemical potential of protons across the biological membrane (proton motive force), which powers many vital cellular processes, and Vm also plays a role in signal transduction. Therefore, measuring it is of great interest, and over the years a variety of techniques has been developed for the purpose. In bacteria, given their small size, Nernstian membrane voltage probes are arguably the favourite strategy, and their cytoplasmic accumulation depends on Vm according to the Nernst equation. However, a careful calibration of Nernstian probes that takes into account the trade-offs between the ease with which the signal from the dye is observed, and the dyes’ interactions with cellular physiology, is rarely performed. Here we use a mathematical model to understand such trade-offs and, based on the knowledge gained, propose a general work-flow for the characterization of Nernstian dye candidates. We demonstrate the work-flow on the Thioflavin T dye in Escherichia coli, and identify conditions in which the dye turns from a Vm probe into an actuator.
SIGNIFICANCE STATEMENT
The phospholipid bilayer of a biological membrane is virtually impermeable to charged molecules. Much like in a rechargeable battery, cells harness this property to store an electrical potential that fuels life reactions but also transduces signals. Measuring this electrical potential, also referred to as membrane voltage, is therefore of great interest and a variety of techniques have been employed for the purpose, starting as early as the 1930s. For the case of bacteria, which are smaller in size and possess a stiffer cell wall, arguably the most popular approach to measuring membrane voltage are Nernstian probes that accumulate across the bacterial membrane according to the Nernst potential. The present study characterizes the undesired effects Nernstian probes can have on cell physiology, which can be crucial for the accurate interpretation of experimental results. Using mathematical modelling and experiments, the study provides a general, simple workflow to characterise and minimise these effects.
