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Metabolomics, i.e., the multiparallel analysis of metabolite changes occurring in a cell or an organism, has become feasible with the development of highly efficient mass spectroscopic technologies. Functional genomics as a standard tool helped to identify the function of many of the genes that encode important transporters and metabolic enzymes over the past few years. Advanced expression systems and analysis technologies made it possible to study the biochemical properties of the corresponding proteins in great detail. We begin to understand the biological functions of the gene products by systematic analysis of mutants using systematic PTGS/RNAi, knockout and TILLING approaches. However, one crucial set of data especially relevant in the case of multicellular organisms is lacking: the knowledge of the spatial and temporal profiles of metabolite levels at cellular and subcellular levels.
We therefore developed genetically encoded nanosensors for several metabolites to provide a basic set of tools for the determination of cytosolic and subcellular metabolite levels in real time by using fluorescence microscopy.
Prototypes of these sensors were successfully used in vitro and also in vivo, i.e., to measure sugar levels in fungal and animal cells.
One of the future goals will be to expand the set of sensors to a wider spectrum of substrates by using the natural spectrum of periplasmic binding proteins from bacteria and by computational design of proteins with altered binding pockets in conjunction with mutagenesis. This toolbox can then be applied for four-dimensional imaging of cells and tissues to elucidate the spatial and temporal distribution of metabolites as a discovery tool in functional genomics, as a tool for high-throughput, high-content screening for drugs, to test metabolic models, and to analyze the interplay of cells in a tissue or organ.
Despite the significant progress regarding the regulatory networks that control metabolism, we understand little of the dynamic fluctuations of metabolite levels in time and space. We have yet to identify major components of the transport machinery (especially cellular efflux mechanisms for most ions and metabolites), and we understand only roughly how regulation of transport activities of metabolites are integrated with metabolic activities. Because biochemical pathways are often distributed across several compartments and even neighboring or distant cells, metabolism and metabolite levels in groups of cells and tissues that form an organ are not necessarily uniform, as elegantly shown for glucose and lactate in liver or for nitrate concentrations in different cell types of plant leaves (1). Due to methodologic limitations in the analysis of the dynamic distribution of metabolites, measurement of target ligand concentrations has relied primarily on the analysis of the average concentration in tissues or organs. The approaches used are mostly static, have limited resolution, and often require fixation, fractionation, and disruption of tissue. Several recent studies have tried to address these limitations by using a variety of techniques. A major advance has been the development of single-cell sap sampling techniques that enabled the direct correlation of metabolite levels with gene expression in individual cells, thus calling the relevance of whole-organ concentration into question (2–4). Wobus et al. used an elegant enzymatic approach to determine glucose and sucrose levels in thin sections, thereby providing insight into the differential role of glucose and sucrose in the control of legume embryo development (5–7). To partition tissue extracts of ions and metabolites into extracellular and subcellular pools, apoplasmic wash fluids have been collected to determine the average levels of metabolites in the apoplast (8), and nonaqueous fractionation was developed for compartmental analysis within the cell (9). This technique was successfully combined with mass spectrometry for the multiplex analysis of a wide spectrum of metabolites (5).
Despite these advances, none of these methods is suitable for real-time visualization of metabolite levels. Noninvasive imaging is thus considered a potential tool to gain insight into compartmentalization, transport, and metabolite sensing. A significant advance in this area was the development of positron emission tracing imaging, which provides spatial resolution in the range of millimeters (10). Similarly, metabolite levels can also be measured in vivo by nuclear magnetic resonance microimaging (11). Using water-suppression strategies or indirect detection techniques, the in-plane resolution of nuclear magnetic resonance imaging with 1H or 13C nuclides may be extended to several hundreds of micrometers within large tissue slices (12). Imaging at this resolution provides important new perspectives but is insufficient to follow, e.g., intracellular fluctuations.
Substantially higher resolution has been achieved with fluorescence microscopy. The use of fluorescent probes has the advantage of providing subcellular resolution, high sensitivity and versatility (13). Moreover, the combination of two suitable fluorophores able to undergo fluorescence resonance energy transfer (FRET) permits ratiometric and thus quantitative analysis of the data.
FRET refers to a quantum mechanical effect between a pair of resonators: a fluorescence donor and a respective acceptor. FRET requires a number of prerequisites such as suitable distance (typically in the range up to 10 nm), a suitable geometry of donor and acceptor molecules, and an overlap between the donor emission and the acceptor excitation spectra. The nonradiative energy transfer efficiency is a function of the sixth power of the distance between the fluorophores. Thus, even small changes in distance translate into detectable changes in FRET efficiency. The Förster distance (R0) of a particular FRET pair represents the distance at which energy transfer efficacy is 50%. R0 depends on the quantum yield of the donor without an acceptor, the refractive index of the medium, the extent of spectral overlap between donor emission and acceptor absorption, and the relative orientation of donor and acceptor dipoles. Typically the R0 is 2 to 10 nm, i.e., at the scale of protein dimensions. Provided donor and acceptor are in close enough proximity, emission of the excited donor decreases, whereas emission from the sensitized acceptor increases. Steady-state FRET can be observed by exciting the sample at the donor excitation wavelength and measuring the ratio of the fluorescence intensities emitted at the emission peaks of donor and acceptor. Different factors will affect the measurement, including the possibility that chemicals in the tissue act as quenchers or directly affect the fluorophores, especially ionic conditions. Alternatively, FRET can be determined by analyzing the sensitized emission of the acceptor or by measuring the relative ratio between donor and acceptor emission intensities. Methods that exploit fluorescence decay include the determination of the decrease in the donor's fluorescence lifetime or monitoring the appearance of new components in the acceptor decay kinetics. The FRET phenomenon was deemed a “spectroscopic ruler” by Stryer and Haugland because it may be used to determine molecular distances under certain conditions (14).
Many chemical dyes have been developed for imaging variations in cellular parameters. FRET-based membrane potential dyes are among the most elegant examples (15). A set of dyes consisting of a coumarin-labeled phospholipid donor and an oxonol acceptor were synthesized chemically, providing a tool for measuring membrane potential and even action potentials as an alternative to patch clamping. However, small molecule fluorophores are not ideal because the dyes need to be loaded into a given cell, may be chemically invasive, and cannot easily be targeted to subcellular compartments or specific cell types. In contrast, genetically encoded sensors can be introduced by transformation into cells and can be targeted to the different subcellular compartments.
A major advance in cell biology was the molecular cloning of the green fluorescent protein (GFP) from the jellyfish Aequorea victoria, a protein that during maturation generates a fluorescent side chain by autocatalysis. GFP variants can be used to monitor pH changes, and fusions with a potassium channel (FlaSh-GFP) can be used to determine membrane potential by measuring fluorescence intensity changes (16). After having developed these chemical membrane potential dyes, Zhang et al. pioneered the development of a set of genetically encoded tools including zinc and calcium sensors (17). The “original” FRET-based protein sensor used blue fluorescent protein and GFP as reporters linked to a protease-sensitive peptide (18). Proteolysis leads to disruption of FRET by irreversible separation of the fused fluorophores. The cameleon makes use of a reversible conformational change of a chimeric fusion protein consisting of cyan and yellow fluorescent proteins and a reporter domain, i.e., calmodulin and the calmodulin-binding peptide M13 (17). Binding of calcium to calmodulin leads to a conformational change that generates an M13-binding interface, results in a dramatic structural rearrangement that in turn leads to a change in the relative position of the two fused GFP variants, and thus alters FRET. Using the cameleon and its improved version, pericam, with up to fivefold higher ratio changes, dynamic changes in calcium levels were visualized in a variety of animal cells and yeast (19). The cameleon was also used to determine calcium dynamics during guard cell movement in plants (20). Potentially the most striking example for the potential of in vivo imaging using genetically encoded nanosensors was the visualization of calcium oscillations during the contraction of the pharyngeal muscle in living worms (21).
On the basis of this concept, different reporters have been developed for other biochemical events, among them indicators for cyclic adenosine monophosphate (22), cyclic guanosine monophosphate (23), reporters for the activity of Ras and Rap1 protein (24), protein kinases (25), and, recently, redox potential (17,26).
Engineered versions of GFP with, e.g., improved maturation or higher solubility and of new fluorescent proteins from coelenterate organisms with different excitation and emission spectra have become available and might be used in new FRET pairs, possibly enabling the detection of two different substrates at the same time (27-29).
However, despite the ingenious concept of conformation-dependent FRET sensors, until recently, sensors for primary metabolites were unavailable because suitable domains that might be used to transduce a conformational change into an altered FRET signal were not developed.
Sensor domains have to meet several criteria to be considered as scaffolds for the engineering of FRET-based metabolite nanosensors. First, it is essential that such a sensor domain is able to undergo a conformational change large enough to transduce metabolite binding into a FRET change. The conformationalchange must be tightly coupled to substrate binding. Sensor domains with similar three-dimensional structures but different substrate binding specificities will be ideal for development of a wide spectrum of nanosensors for many different analytes. Suitable fusion sites must exist, preferably at a distance corresponding to the R0 value for the given fluorophore pair. Another important consideration is that the sensor has to have a binding constant that corresponds to the expected detection range. A high substrate binding affinity would provide an ideal starting point to engineer mutant nanosensors for different physiologic detection ranges.
In the search for candidates that meet these criteria, we have focused our attention on a class of proteins found mainly in gram-negative bacteria, the periplasmic binding proteins (PBPs). PBPs comprise a large number of diverse proteins that cover a wide range of metabolites. PBPs typically have a diameter of approximately 5 to 7 nm, an ideal range for the estimated R0 of 4.9 for the pair of enhanced cyan fluorescent protein (ECFP) and enhanced yellow fluorescent protein (EYFP) (30). PBPs typically bind ligands with affinities in the high nanomolar to low micromolar range, with a fast, one-step reversible binding scheme (31). Most importantly, PBPs undergo a significant conformational change when binding to their target ligands, thus meeting the criteria for the design of nanosensors describe above. Based on the design concept for cameleon, we tested whether PBPs could be engineered into FRET-based sensor proteins. Although unrelated at the primary sequence level, most PBPs consist of two similar globular domains. The binding site is created by specific residues in the cleft between the domains, which engulfs the ligand through a Venus flytrap-like hinge-twist motion. Crystal structures of more than a dozen PBPs, several in bound and unbound states, provide us with a detailed understanding of the mechanism of binding and the hinge motion (32).
To develop a prototype nanosensor, we fused the periplasmic maltose binding protein (MBP) from Escherichia coli between ECFP and EYFP by using short linker sequences (Fig. 1). Subsequent site-directed mutagenesis of amino acid residues involved in maltose binding led to the development of nanosensor FLIPmal-25μ (fluorescent indicator protein for maltose with a Kd of 25 μM) with a binding constant for maltose of 25 μM (Table 1) (33). FLIPmal-25μ nanosensor was characterized in vitro after expression in E.coli and was subsequently used to visualize real-time maltose uptake into the cytosol of living yeast cells.
Although the bacterial periplasmic glucose/galactose binding protein and ribose binding protein (RBP) are unrelated to MBP at the primary sequence level, both have tertiary structures similar to that of MBP (34). Although glucose/galactose binding protein and RBP had different relative positions of the N- and C-termini compared with MBP, both proteins could successfully be engineered into FRET-based nanosensors for glucose or ribose, respectively (Table 1) (35,36). As predicted from the relative change in position of the termini, FRET decreased with increasing substrate concentration for FLIPglu and FLIPrib sensors. Using site-directed mutagenesis, five ribose sensors with a range of binding constants between 400 and 11.7 mM were generated (Table 1, Fig. 2) (37). This proof of concept suggests that it will be feasible to develop a wide spectrum of nanosensors that exploit the myriad of different PBPs and their structural relatives available in nature.
The FLIP nanosensors were applied for in vivo imaging, which provided new insights into cellular sugar homeostasis. The chimeric proteins were expressed in mammalian cell culture, and cytosolic glucose and ribose levels were monitored by determination of the relative emission ratio from EYFP and ECFP using a device capable of rapid emission filter switches or by parallel imaging of both emission wavelengths with an image splitter.
At least in yeast, it seems that glucose is metabolized as soon as it enters the cell; thus, little or no glucose was detected within the intracellular space (38). In eukaryotic cells, the relative rates of uptake, phosphorylation, and release are thought to be the major factors that control the levels of free glucose in the cytosol (39). Using FLIPglu-600μ, a mutagenized glucose nanosensor with a Kd 600 μM for glucose, free glucose was detected in the cytosol of animal cells (35). Metabolism of glucose occurred at a rate that kept cytosolic glucose levels at approximately half of the externally supplied concentration in COS-7 cells. Similar results were obtained when analyzing ribose dynamics, which demonstrated that free ribose accumulates in the cytosol, where it is slowly metabolized (Fig. 3) (37). Cell lines that express the nanosensors are currently used to characterize glucose compartmentalization and intracellular transport.
The overall diameter (longitudinal elliptic axis) of PBPs is in the range of 5 to 7 nm. The distance between the N-and C-termini as taken from the crystal structure of the ligand-free form is approximately 4 to 5 nm for MBP and RBP (Fig. 4). The N- and C-termini move closer together after ligand binding by about 0.7 nm in case of MBP and farther apart by 0.2 nm in the case of RBP. Interestingly, the maximal change in ratio in all cases is approximately 0.25, suggesting that other parameters such as dipole orientation contribute to the observed ratio change (40).
The observed maximal change in ratio is relatively small, thus limiting the dynamic range for in vivo detection. Although a set of mutant nanosensors may be generated for each case that covers the full range as in the case of FLIPrib, it would be advantageous if the dynamic range of individual nanosensors could be further improved to cover a larger detection range by increasing the maximal ratio change as has been done over time for the cameleon (41). Ways to obtain sensors with larger changes of ratio after substrate binding might be site-directed mutagenesis or insertions and deletions in the linker regions that connect the binding protein to the fluorophores.
One of the major advantages of genetically encoded nanosensors is their suitability for the analysis of compartmentalization by targeting to subcellular compartments, as exemplified by the construction of a glucose nanosensor targeted to the nucleus (42). The nuclear sensor FLIPglunuc was used in a comparative study to verify that nuclear and cytosolic glucose levels are tightly coupled. As anticipated, the sensors could be targeted to different compartments to study their homeostasis individually.
Measuring ion and metabolite levels in tissues requires the determination of fluorescence intensities from several cell layers, which provides a significant technical challenge for the application of nanosensors due to signals from other focal planes. This interference is typically mediated by shading effects from overlying cells or by absorption of donor fluorophore emission by acceptor fluorophores from other tissue layers. A potential solution for this may be the restriction of signals to a fraction of the image by directing the sensors to nuclei (42). Alternatively, cell layer-specific promoters may help to improve detection in intact tissues and organs, but at the cost of having to generate transgenic lines with cell type-specific promoters. Especially in plants, autofluorescence of the chloroplasts can increase the background against which ECFP fluorescence is measured and has to be accounted for in the calibration process (20).
Sophisticated imaging technology can further improve four-dimensional imaging of FRET changes in cells and tissues. The potentials and pitfalls of four-dimensional imaging have been reviewed extensively (43,44). Because nonradiative excitation transfer alters the lifetime properties of the FRET donor, it is possible to determine the fluorescence lifetime of the combined donor/acceptor emission by fluorescence lifetime imaging microscopy (45,46). Analysis of homo-FRET, which requires imaging technology to determine anisotropic decay, may be another alternative (47). Nipkow spinning disk (for rapid kinetics), multiphoton fluorescence microscopy (for multicell layer analysis), and deconvolution-based approaches can be used to obtain images with higher spatial resolution, an aspect especially important when imaging in tissues or organs of multicellular organisms.
In conjunction with the development of genetically encoded nanosensors, other promising new technologies include in vivo confocal Raman spectroscopy, a noninvasive optical method that generates detailed information about the molecular composition with high spatial resolution (48). It has been suggested that Raman spectroscopy can be adapted to determine glucose and amino acid levels in living cells (49,50). Luminescent semiconductor nanocrystals represent a promising novel nanotechnology that may provide an alternative approach for FRET imaging of metabolites. Procedures have been developed for using quantum dots to label living cells for long-term multicolor imaging (51,52). Recently an MBP coupled to quantum dots was used successfully for FRET imaging (53). Although quantum dots may be more sensitive than the FLIP nanosensors, subcellular targeting and in vivo measurements will be more difficult due to the introduction of particles into cells. Phagocytosis and ballistic methods may be two means to introduce these nanocrystals into cells.
With the development of genetically encoded nanosensors, significant progress has been made toward real-time imaging of metabolites in living cells with cellular and subcellular resolution. FRET imaging using FLIP nanosensors represents a promising technology for the imaging of sugar levels as exemplified above and for a variety of ions and organic molecules. Further exploitation of the naturally occurring spectrum of PBPs, computational design (successfully demonstrated by a redesign of the binding sites of several of these binding proteins to recognize zinc, serotonin, lactate, or even trinitrotoluene), improvements in the dynamic range of the existing sensors, and new developments in imaging technology will provide the means to fill the gap in our understanding of cellular metabolism of metabolites and ions in multicellular organisms (54-56). The technology for the first time allows the observation of homeostasis in real-time in living cells; however, before genetically encoded nanosensors can be used for metabolomics, it will be necessary to find ways to multiplex the analyses (57).
We express our gratitude to the Faculty of Biological Sciences, Stanford University for their generous provision of laboratory space in Gilbert Hall.
Contract grant sponsor: Carnegie Institution of Washington; Contract grant sponsor: National Institutes of Health (NIH); Contract grant sponsor: Körber Foundation, Hamburg.