Tilted light sheet microscopy with 3D point spread functions (TILT3D) combines a novel, tilted light sheet illumination strategy with long axial range point spread functions (PSFs) for low-background, 3D super-localization of single molecules as well as 3D super-resolution imaging in thick cells. Because the axial positions of the single emitters are encoded in the shape of each single-molecule image rather than in the position or thickness of the light sheet, the light sheet need not be extremely thin. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The result is simple and flexible 3D superresolution imaging with tens of nm localization precision throughout thick mammalian cells. We validate TILT3D for 3D super-resolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed tetrapod PSFs for fiducial bead tracking and live axial drift correction.

Nanoscale localization of single molecules is a crucial function in several advanced microscopy techniques, including single-molecule tracking and wide-field super-resolution imaging 1. To date, a central consideration of such techniques is how to optimize the precision of molecular localization. However, as these methods continue to push toward the nanometre size scale, an increasingly important concern is the localization accuracy. In particular, single fluorescent molecules emit with an anisotropic radiation pattern of an oscillating electric dipole, which can cause significant localization biases using common estimators 2-5. Here we present the theory and experimental demonstration of a solution to this problem based on azimuthal filtering in the Fourier plane of the microscope. We do so using a high efficiency dielectric metasurface polarization/phase device composed of nanoposts with sub-wavelength spacing 6. The method is demonstrated both on fluorophores embedded in a polymer matrix, and in dL5 protein complexes that bind Malachite green 7, 8.

Abstract:We report the use of a phase retrieval procedure based on maximum likelihood estimation (MLE) to produce an improved, experimentally calibrated model of a point spread function (PSF) for use in three-dimensional (3D) localization microscopy experiments. The method estimates a global pupil phase function (which includes both the PSF and system aberrations) over the full axial range from a simple calibration scan. The pupil function is used to refine the PSF model and hence enable superior localizations from experimental data. To demonstrate the utility of the procedure, we apply it to experimental data acquired with a microscope employing a tetrapod PSF with a 6 µm axial range. The phase-retrieved model demonstrates significant improvements in both accuracy and precision of 3D localizations relative to the model based on scalar diffraction theory. The localization precision of the phase-retrieved model is shown to be near the limits imposed by estimation theory, and the reproducibility of the procedure is characterized and discussed. Code which performs the phase retrieval algorithm is provided. References and Links1. H. Qian, M. P. Sheetz, and E. L. Elson, "Single particle tracking. Analysis of diffusion and flow in twodimensional systems," Biophys. J. 60(4), 910-921 (1991). 2. M. J. Saxton, "Single-particle tracking: the distribution of diffusion coefficients," Biophys.

The development of imaging techniques beyond the diffraction limit has paved the way for detailed studies of nanostructures and molecular mechanisms in biological systems. Imaging thicker samples, such as mammalian cells and tissue, in all three dimensions, is challenging due to increased background and volumes to image. Light sheet illumination is a method that allows for selective irradiation of the image plane, and its inherent optical sectioning capability allows for imaging of biological samples with reduced background, photobleaching, and photodamage. In this review, we discuss the advantage of combining single-molecule imaging with light sheet illumination. We begin by describing the principles of single-molecule localization microscopy and of light sheet illumination. Finally, we present examples of designs that successfully have married single-molecule super-resolution imaging with light sheet illumination for improved precision in mammalian cells.

Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3D localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a general method to rapidly estimate the background underlying the image of a point source with excellent accuracy, even when point spread function (PSF) engineering is in use to create complex PSF shapes. We trained BGnet to extract the background from images of various PSFs and show that the identification is accurate for a wide range of different interfering background structures constructed from many spatial frequencies. Furthermore, we demonstrate that the obtained background-corrected PSF images, both for simulated and experimental data, lead to a substantial improvement in localization precision. Finally, we verify that structured background estimation with BGnet results in higher quality of super-resolution reconstructions of biological structures. Significance:A main factor that degrades image quality in fluorescence microscopy is unwanted background fluorescence. Background is almost never uniform, especially in complex samples. Rather, background usually exhibits some structure, making it very difficult to distinguish from the signal of interest. Due to this challenging problem, background fluorescence is often assumed to be uniform even though it is not. This assumption leads to deteriorated image quality, e.g. in localization-based superresolution microscopy, and to the introduction of uncharacterized biases. To overcome this challenge, we developed a general framework rooted in deep learning to accurately and rapidly estimate arbitrarily structured background using several distinct image shapes for the single emitter. Proper background estimation allows for critical performance improvement of various optical microscopy methods. Main Text: