Imaging approach for monitoring cellular metabolites and ions using genetically encoded biosensors

Abstract

The spatiotemporal patterns of ion and metabolite levels in living cells are important in understanding signal transduction and metabolite flux. Imaging approaches using genetically encoded sensors are ideal for detecting such molecule dynamics, which are hard to capture otherwise. Recent years have seen iterative improvements and evaluations of sensors, which in turn are starting to make applications in more challenging experimental settings possible. In this review, we will introduce recent progress made in the variety and properties of biosensors, and how biosensors are used for the measurement of metabolite and ion in live cells. The emerging field of applications, such as parallel imaging of two separate molecules, high-resolution transport studies and high-throughput screening using biosensors, will be discussed.

Fig. 1. Various protein modules used for FP-based biosensors

FRET-based sensors based on different types of ligand-recognition modules. i) Sensors based on a chimeric peptide consisting of ligand recognition domain (green) and a peptide sequence that binds to the ligand-bound form of the recognition domain (blue). ii) Sensors whose binding domain consists of a single protein that binds to the ligand and changes its conformation. B. Single-FP based sensors. i) Variants of FPs that change fluorescent intensity and/or spectra in the presence of specific ions. ii) Circularly permutated FPs fused to an external recognition module similar to the ones in FRET-based sensors.

Fig. 2. Conceptual concentration substrate level change in the cytosol

A. Two model cases where the steady-state cytosolic levels are altered. The box above the trace indicates the time period when the substrate was externally supplied. The three shaded areas represent the working ranges of sensors with different affinities. Note that the sensors with higher affinity have the smaller absolute working range. B. The response of cytosolic sensors at higher (upper panel) and lower (lower panel) steady-state substrate concentration.

Fig. 3. Determining cellular concentration using FRET sensors

Black and red traces represent data from two independent cell types expressing the same sensor. A. Typical time-course of FRET efficiency with step-wise increase in concentration of the substrate. Boxes on top represent the period when the substrate was externally supplied. B. Relative FRET efficiency change (ΔE_x-ΔE_min)/(ΔE_max-ΔE_min) follows a hyperbolic saturation curve. C. The internal concentration in given external concentration can be calculated from the Eq. 3.