Biological Structure from Precise and Accurate Estimation of Fluorophore Orientations and Distances: Proof-of-Principle using Internally Labeled dsDNA

Abstract

Structure and structural transitions are at the heart of the molecular basis of biological function. In super-resolution microscopy and single-molecule biophysics, such information is probed using fluorophores. The emitted light gives rise to diffraction-limited spots whose centers are routinely localized with nanometer precision when spots are isolated. Thus, distances shorter than the diffraction limit may be assessed by filtered imaging of differently colored fluorophores. Popular approaches assume rotational freedom of the fluorophore emission dipole moment and hence fit a 2D Gaussian intensity distribution to the image of a fluorophore. This is done using least-squares method. However, when the dipole moment is resolved in time or deliberately fixed, the resulting intensity pattern is highly anisotropic and depends radically on the dipole orientation. We recently showed that the optimal analysis to extract simultaneous orientations and positions from focused images of fixed fluorophores uses the theoretical point spread function in conjunction with maximum likelihood estimation. Here we show that this approach is mature as a structural tool: It is able to determine real, physical distances and orientations of intra- and inter-molecular domains. For proof of principle, we use dsDNA strands with two differently colored probes doubly attached to the double-helix backbone. Relative orientations and distances between probes are controlled by the number of base pairs separating them. We demonstrate that estimates of orientation and distance are accurate and precise: they scatter tightly around their true values known from structural information with a standard deviation that achieves the ultimate precision possible according to Fisher's information limit.