Lydia J. McCartney

In vivo glucose sensing for diabetes management: progress towards non-invasive monitoring

A device for continuous in vivo monitoring of glucose concentration in people with diabetes has been a clinical and research priority for many years but now has an urgency which is probably unquestioned in diabetes care. The purpose of this article is to explain recent advances in technology that are bringing glucose sensors closer to routine use and to highlight some of the remaining problems. Important new technologies include artificial receptors for glucose, tissue fluid sampling techniques, and new approaches to non-invasive sensing, such as fluorescence lifetime measurements.

A time-resolved near-infrared fluorescence assay for glucose: opportunities for trans-dermal sensing.

We report a time-resolved near-infrared fluorescence assay for glucose detection that incorporates pulsed diode laser excitation. Reduction in fluorescence resonance energy transfer to a malachite green-Dextran complex from allophycocyanin bound to concanavalin A (ConA) due to displacement of the complex by glucose from ConA provides the basis of the assay. The fluorescence quenching kinetics are analysed and discussed in detail. The change in fluorescence decay kinetics in the presence of glucose is found from dimensionality studies to be brought about by a change in the distribution of malachite green-Dextran acceptors. Glucose concentrations are measured in solution to within +/- 10% over the range 0-30 mM.

A method of determining donor–acceptor distribution functions in Förster resonance energy transfer

We describe a method of determining the distribution function associated with acceptors partaking in Forster resonance energy transfer (FRET) from excited donors. Unlike previous approaches to this problem the method described makes no a priori assumption about the nature of the distribution. The use of an acceptor distribution function in analyte sensing is demonstrated for the first time. In the case of glucose sensing via FRET from Concanavalin A labelled with allophycocyanin to malachite green labelled dextran, the change in the acceptor distribution is shown to be a more sensitive sensing parameter than either the mean fluorescence lifetime or Forster γ value.

FLUORESCENCE RESONANCE ENERGY TRANSFER FROM ALLOPHYCOCYANIN TO MALACHITE GREEN

Abstract The near-infrared fluorescence resonance energy transfer kinetics of the phycobiliprotein allophycocyanin (APC) to malachite green (MG) have been investigated. A model is proposed to account for the fluorescence decay whereby MG binds to APC with a donor–acceptor site distribution which can be best described by 2D quenching kinetics. The results highlight a potential fallacy when interpreting the dimensionality of complex systems or the location of binding sites from Forster decay kinetics. The use of APC in trans-dermal measurements is proposed.

Molecular distribution sensing in a fluorescence resonance energy transfer based affinity assay for glucose.

A newly developed method for determining molecular distribution functions is applied to a widely researched glucose affinity sensor. The reduction in fluorescence resonance energy transfer (FRET) to a malachite green (MG)-dextran complex from allophycocyanin (APC) bound to concanavalin A (ConA) due to displacement of the complex by glucose from ConA provides the basis of the assay. The higher sensitivity and specificity of a new approach to fluorescence decay analysis, over the methods based on conventional Forster-type models, is demonstrated and critical parameters in competitive binding FRET sensing derived.

Sensing metabolites using donor-acceptor nanodistributions in fluorescence resonance energy transfer

Before fluorescence sensing techniques can be applied to media as delicate and complicated as human tissue, an adequate interpretation of the measured observables is required, i.e., an inverse problem needs to be solved. Recently we have solved the inverse problem relating to the kinetics of fluorescence resonance energy transfer (FRET), which clears the way for the determination of the donor–acceptor distribution function in FRET assays. In this letter this approach to monitoring metabolic processes is highlighted and the application to glucose sensing demonstrated.

Fluorescence nanotomography using resonance energy transfer: demonstration with a protein-sugar complex

A new approach to structural sensing, fluorescence resonance energy transfer nanotomography, which interprets fluorescence decay measurement in terms of site density analysis of molecular distributions, has been applied to a glucose sensor based on competitive binding with malachite green labelled dextran to the sugar binding protein concanavalin A labelled with allophycocyanin. Opportunities for structural sensing in clinical medicine are highlighted.

Near-infrared assay for glucose determination

A new glucose sensing system based on near infra-red fluorescence resonance energy transfer (FRET) from CocanavalinA-allophycocyanin to dextran labelled malachite green is demonstrated. Single-photon timing fluorescence lifetime measurements have enabled us to investigate and understand the quenching kinetics in terms of the dimensionality of energy transfer.

A new approach to fluorescence lifetime sensing based on molecular distributions

Fluorescence resonance energy transfer (FRET) from donor to acceptor molecules is one of the most powerful techniques for monitoring structure and dynamics. This is because FRET has a strong spatial dependence with angstroms resolution. This dependence includes the simplest case of a random distribution of acceptors for which an analytical solution exists for the fluorescence impulse response I(t). However, in general the acceptor distribution function p(r) is not random and a unique solution cannot be found for I(t). In many important applications of FRET eg in proteins, the simple random treatment is quite inappropriate and yet the information concerning conformation changes is preserved in p(r). One approach, which as been applied to the problem of determining p(r), is to make some assumptions as to its form eg Gaussian and then try to use this to describe I(t).