Single-Molecule Imaging in Commercial Stationary Phase Particles Using Highly Inclined and Laminated Optical Sheet Microscopy

We demonstrate single-molecule fluorescence imaging beyond the optical diffraction limit in 3 dimensions with a wide-field microscope that exhibits a double-helix point spread function (DH-PSF). The DH-PSF design features high and uniform Fisher information and has 2 dominant lobes in the image plane whose angular orientation rotates with the axial (z) position of the emitter. Single fluorescent molecules in a thick polymer sample are localized in single 500-ms acquisitions with 10- to 20-nm precision over a large depth of field (2 microm) by finding the center of the 2 DH-PSF lobes. By using a photoactivatable fluorophore, repeated imaging of sparse subsets with a DH-PSF microscope provides superresolution imaging of high concentrations of molecules in all 3 dimensions. The combination of optical PSF design and digital postprocessing with photoactivatable fluorophores opens up avenues for improving 3D imaging resolution beyond the Rayleigh diffraction limit.

We describe a simple illumination method of fluorescence microscopy for molecular imaging. Illumination by a highly inclined and thin beam increases image intensity and decreases background intensity, yielding a signal/background ratio about eightfold greater than that of epi-illumination. A high ratio yielded clear single-molecule images and three-dimensional images using cultured mammalian cells, enabling one to visualize and quantify molecular dynamics, interactions and kinetics in cells for molecular systems biology.

Calculation of the centroid of the images of individual fluorescent particles and molecules allows localization and tracking in light microscopes to a precision about an order of magnitude greater than the microscope resolution. The factors that limit the precision of these techniques are examined and a simple equation derived that describes the precision of localization over a wide range of conditions. In addition, a localization algorithm motivated from least-squares fitting theory is constructed and tested both on image stacks of 30-nm fluorescent beads and on computer-generated images (Monte Carlo simulations). Results from the algorithm show good agreement with the derived precision equation for both the simulations and actual images. The availability of a simple equation to describe localization precision helps investigators both in assessing the quality of an experimental apparatus and in directing attention to the factors that limit further improvement. The precision of localization scales as the inverse square root of the number of photons in the spot for the shot noise limited case and as the inverse of the number of photons for the background noise limited case. The optimal image magnification depends on the expected number of photons and background noise, but, for most cases of interest, the pixel size should be about equal to the standard deviation of the point spread function.