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      High precision wavefront control in Point Spread Function engineering for single emitter localization

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          Abstract

          Point Spread Function (PSF) engineering is used in single emitter localization to measure the emitter position in 3D and possibly other parameters such as the emission color or dipole orientation as well. Advanced PSF models such as spline fits to experimental PSFs or the vectorial PSF model can be used in the corresponding localization algorithms in order to model the intricate spot shape and deformations correctly. The complexity of the optical architecture and fit model makes PSF engineering approaches particularly sensitive to optical aberrations. Here, we present a calibration and alignment protocol for fluorescence microscopes equipped with a Spatial Light Modulator with the goal of establishing a wavefront error well below the diffraction limit for optimum application of complex engineered PSFs. We achieve high-precision wavefront control, to a level below 20 m\(\lambda\) wavefront aberration over a 30 minute time window after the calibration procedure, using a separate light path for calibrating the pixel-to-pixel variations of the SLM, and alignment of the SLM with respect to the optical axis and Fourier plane within 3 \(\mu\)m (\(x/y\)) and 100 \(\mu\)m (\(z\)) error. Aberrations are retrieved from a fit of the vectorial PSF model to a bead \(z\)-stack and compensated with a residual wavefront error comparable to the error of the SLM calibration step. This well-calibrated and corrected setup makes it possible to create complex `3D+\(\lambda\)' PSFs that fit very well to the vectorial PSF model. Proof-of-principle bead experiments show precisions below 10~nm in \(x\), \(y\), and \(\lambda\), and below 20~nm in \(z\) over an axial range of 1 \(\mu\)m with 2000 signal photons and 12 background photons.

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          Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy.

          Recent advances in far-field fluorescence microscopy have led to substantial improvements in image resolution, achieving a near-molecular resolution of 20 to 30 nanometers in the two lateral dimensions. Three-dimensional (3D) nanoscale-resolution imaging, however, remains a challenge. We demonstrated 3D stochastic optical reconstruction microscopy (STORM) by using optical astigmatism to determine both axial and lateral positions of individual fluorophores with nanometer accuracy. Iterative, stochastic activation of photoswitchable probes enables high-precision 3D localization of each probe, and thus the construction of a 3D image, without scanning the sample. Using this approach, we achieved an image resolution of 20 to 30 nanometers in the lateral dimensions and 50 to 60 nanometers in the axial dimension. This development allowed us to resolve the 3D morphology of nanoscopic cellular structures.
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            Far-field optical nanoscopy.

            In 1873, Ernst Abbe discovered what was to become a well-known paradigm: the inability of a lens-based optical microscope to discern details that are closer together than half of the wavelength of light. However, for its most popular imaging mode, fluorescence microscopy, the diffraction barrier is crumbling. Here, I discuss the physical concepts that have pushed fluorescence microscopy to the nanoscale, once the prerogative of electron and scanning probe microscopes. Initial applications indicate that emergent far-field optical nanoscopy will have a strong impact in the life sciences and in other areas benefiting from nanoscale visualization.
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              Optimized localization-analysis for single-molecule tracking and super-resolution microscopy

              We optimally localize isolated fluorescent beads and molecules imaged as diffraction-limited spots, determine the orientation of molecules, and present reliable formulae for the precisions of various localization methods. For beads, theory and experimental data both show that unweighted least-squares fitting of a Gaussian squanders one third of the available information, a popular formula for its precision exaggerates beyond Fisher's information limit, and weighted least-squares may do worse, while maximum likelihood fitting is practically optimal.
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                Author and article information

                Journal
                19 February 2018
                Article
                1802.06584
                c316b093-e50d-435a-9a3a-75a93e296112

                http://creativecommons.org/licenses/by-nc-sa/4.0/

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                Custom metadata
                physics.optics

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