Recently, Franke et al.introduced a way to estimate the axial position of single molecules (temporal radial-aperture-based intensity estimation (TRABI)). To this end, they compared the detected photon count from a TRABI estimation to the estimated count from Gaussian point-spread function (PSF) fitting to the data. Empirically they found that the photometric ratio is ~0.7–0.8 at points close to focus and decreases as the distance from the focal plane increases. Here we explain this reported but unexplained discrepancy and, furthermore, show that the photometric ratio as an indicator for axial position is susceptible even to typical optical aberrations.

Light microscopy, allowing sub-diffraction-limited resolution, has been among the fastest developing techniques at the interface of biology, chemistry, and physics. Intriguingly no theoretical limit exists on how far the underlying measurement uncertainty can be lowered. In particular data fusion of large amounts of images can reduce the measurement error to match the resolution of structural methods like cryo-electron microscopy. Fluorescence, although reliant on a reporter molecule and therefore not the first choice to obtain ultraresolution structures, brings highly specific labeling of molecules in a large assembly to the table and inherently allows the detection of multiple colors, which enables the interrogation of multiple molecular species at the same time in the same sample. Here, the problems to be solved in the coming years, with the aim of higher resolution, are discussed, and what polarization depletion of fluorescence at cryogenic temperatures can contribute for fluorescence imaging of biological samples, like whole cells, is described.

Light microscopy allowing sub-diffraction limited resolution has been among the fastest developing techniques at the interface of biology, chemistry and physics. Intriguingly no theoretical limit exists on how far the underlying measurement uncertainty can be lowered. In particular data fusion of large amounts of images can reduce the measurement error to match the resolution of structural methods like cryo-electron microscopy. Fluorescence, although reliant on a reporter molecule and therefore not the first choice to obtain ultra resolution structures, brings highly specific labeling of molecules in a large assemble to the table and inherently allows the detection of multiple colors, which enable the interrogation of multiple molecular species at the same time in the same sample. Here we discuss the problems to be solved in the coming years to aim for higher resolution and describe what polarization depletion of fluorescence at cryogenic temperatures can contribute for fluorescence imaging of biological samples like whole cells.

Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of > 70% and a false-positive detection rate of < 5% under conditions down to 17 photons/pixel background and 180 photons/molecule signal, which is beneficial for any kind of photon-limited application. Examples include limited brightness and photostability, phototoxicity in live-cell single-molecule imaging, and use of new labels for nanoscopy. We present simulations, experimental data, and tracking of low-signal mRNAs in yeast cells.

Resolution in optical nanoscopy (or super-resolution microscopy) depends on the localization uncertainty and density of single fluorescent labels and on the sample's spatial structure. Currently there is no integral, practical resolution measure that accounts for all factors. We introduce a measure based on Fourier ring correlation (FRC) that can be computed directly from an image. We demonstrate its validity and benefits on two-dimensional (2D) and 3D localization microscopy images of tubulin and actin filaments. Our FRC resolution method makes it possible to compare achieved resolutions in images taken with different nanoscopy methods, to optimize and rank different emitter localization and labeling strategies, to define a stopping criterion for data acquisition, to describe image anisotropy and heterogeneity, and even to estimate the average number of localizations per emitter. Our findings challenge the current focus on obtaining the best localization precision, showing instead how the best image resolution can be achieved as fast as possible.