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Live-Cell Fluorescence Microscopy with Molecular Biosensors: What Are We Really Measuring?
Biophys J. 2012 5; 102(9):2003-2011
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15 May 2012
Good for Teaching, Technical Advance
DOI: 10.3410/f.714897823.790252978
Fluorescence microscopy with fluorescent biosensors has the potential to reveal spatiotemporal dynamics of cellular biochemical processes in living cells. Numerous biosensors have been developed to assay the concentration or chemical state of intracellular species based on molecular interactions that...Continue reading
Fluorescence microscopy with fluorescent biosensors has the potential to reveal spatiotemporal dynamics of cellular biochemical processes in living cells. Numerous biosensors have been developed to assay the concentration or chemical state of intracellular species based on molecular interactions that alter the fluorescent properties of the biosensor probe. Haugh provides a theoretical treatment of biosensor and target molecule transport and interaction, illustrating several phenomena centrally important in interpreting experiments.
Since the biosensor approach requires some interaction between the biosensor and the target molecule, the measurement process influences the dynamics of the target molecule(s). Due to biochemical kinetics and transport, the biosensor signal is not necessarily linearly proportional to target abundance or activity and changes in the signal may lag changes in the target. Similarly, transport processes and target-sensor interactions can alter both the actual and the apparent spatial profiles of target molecules. Through analyzing a series of simple models, Haugh draws two conclusions. First, affinities of biosensors “should be characterized and optimized for a particular application”. Perhaps counterintuitively, this does not mean that the dissociation constants should be minimized. Rather, they should be designed in a range that satisfies tradeoffs between saturation of readout, expected strength of signal, and the degree to which the target dynamics is perturbed by the presence of the sensor. Second, it is concluded that by modeling the interactions of the probe and the target, it may be possible to deconvolve (i.e. ’back-calculate’) the desired measurements. The model-based approach potentially solves the problems at hand, but as the author points out “requires advance knowledge or strong assumptions about the mechanisms under study.”
Since the biosensor approach requires some interaction between the biosensor and the target molecule, the measurement process influences the dynamics of the target molecule(s). Due to biochemical kinetics and transport, the biosensor signal is not necessarily linearly proportional to target abundance or activity and changes in the signal may lag changes in the target. Similarly, transport processes and target-sensor interactions can alter both the actual and the apparent spatial profiles of target molecules. Through analyzing a series of simple models, Haugh draws two conclusions. First, affinities of biosensors “should be characterized and optimized for a particular application”. Perhaps counterintuitively, this does not mean that the dissociation constants should be minimized. Rather, they should be designed in a range that satisfies tradeoffs between saturation of readout, expected strength of signal, and the degree to which the target dynamics is perturbed by the presence of the sensor. Second, it is concluded that by modeling the interactions of the probe and the target, it may be possible to deconvolve (i.e. ’back-calculate’) the desired measurements. The model-based approach potentially solves the problems at hand, but as the author points out “requires advance knowledge or strong assumptions about the mechanisms under study.”
Disclosures
None declared
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Beard D: F1000Prime Recommendation of [Haugh J, Biophys J 2012, 102(9):2003-2011]. In F1000Prime, 15 May 2012; DOI: 10.3410/f.714897823.790252978. F1000Prime.com/714897823#eval790252978
F1000Prime Recommendations, Dissents and Comments for [Haugh J, Biophys J 2012, 102(9):2003-2011]. In F1000Prime, 19 May 2013; F1000Prime.com/714897823
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Engineered protein biosensors, such as those based on Förster resonance energy transfer, membrane translocation, or solvatochromic shift, are being used in combination with live-cell fluorescence microscopy to reveal kinetics and spatial localization of intracellular processes as they occur. Progress in the application of this approach has been steady, yet its general suitability for quantitative measurements remains unclear. To address the pitfalls of the biosensor approach in quantitative terms, simple reaction-diffusion models were analyzed. The analysis shows that although high-affinity molecular recognition allows robust detection of the fluorescence readout, either of two detrimental effects is fostered. Binding of an intramolecular biosensor or of a relatively abundant intermolecular biosensor introduces observer effects in which the dynamics of the target molecule under study are significantly perturbed, whereas binding of a sparingly expressed intermolecular biosensor is subject to a saturation effect, where the pool of unbound biosensor is significantly depleted. The analysis explores how these effects are manifest in the kinetics and spatial gradients of the biosensor-target complex. A sobering insight emerges: the observer or saturation effect is always significant; the question is whether or not it can be tolerated or accounted for. The challenge in managing the adverse effects is that specification of the biosensor-target affinity to within a certain order of magnitude is required.
Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.
DOI: 10.1016/j.bpj.2012.03.055
PMID: 22824263
