
Single-molecule total internal reflection fluorescence (TIRF) microscopy constitutes an umbrella of powerful tools that facilitate direct observation of the biophysical properties, population heterogeneities, and interactions of single biomolecules without the need for ensemble synchronization. Due to the low signal/noise ratio in single-molecule TIRF microscopy experiments, it is important to determine the local background intensity, especially when the fluorescence intensity of the molecule is used quantitatively. Here we compare and evaluate the performance of different aperture-based background estimators used particularly in single-molecule Förster resonance energy transfer. We introduce the general concept of multiaperture signatures and use this technique to demonstrate how the choice of background can affect the measured fluorescence signal considerably. A new, to our knowledge, and simple background estimator is proposed, called the local statistical percentile (LSP). We show that the LSP background estimator performs as well as current background estimators at low molecular densities and significantly better in regions of high molecular densities. The LSP background estimator is thus suited for single-particle TIRF microscopy of dense biological samples in which the intensity itself is an observable of the technique.
Models, Molecular, Photons, EFFICIENCY, Surface Properties, CHARGE-COUPLED-DEVICES, DNA, Single-Stranded, BIOMOLECULES, Fluorescence, Single Molecule Imaging, ALTERNATING-LASER EXCITATION, RESOLUTION, Microscopy, Fluorescence, Fluorescence Resonance Energy Transfer, Computer Simulation, FLUORESCENCE, LOCALIZATION MICROSCOPY, SUPERRESOLUTION MICROSCOPY, Algorithms
Models, Molecular, Photons, EFFICIENCY, Surface Properties, CHARGE-COUPLED-DEVICES, DNA, Single-Stranded, BIOMOLECULES, Fluorescence, Single Molecule Imaging, ALTERNATING-LASER EXCITATION, RESOLUTION, Microscopy, Fluorescence, Fluorescence Resonance Energy Transfer, Computer Simulation, FLUORESCENCE, LOCALIZATION MICROSCOPY, SUPERRESOLUTION MICROSCOPY, Algorithms
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