Fluorescent Diarylethene Photoswitches-A Universal Tool for Super-Resolution Microscopy in Nanostructured Materials.

Super-resolution fluorescence microscopy allows for unprecedented in situ visualization of biological structures, but its application to materials science has so far been comparatively limited. One of the main reasons is the lack of powerful dyes that allow for labeling and photoswitching in materials science systems. In this study it is shown that appropriate substitution of diarylethenes bearing a fluorescent closed and dark open form paves the way for imaging nanostructured materials with three of the most popular super-resolution fluorescence microscopy methods that are based on different concepts to achieve imaging beyond the diffraction limit of light. The key to obtain optimal resolution lies in a proper control over the photochemistry of the photoswitches and its adaption to the system to be imaged. It is hoped that the present work will provide researchers with a guide to choose the best photoswitch derivative for super-resolution microscopy in materials science, just like the correct choice of a Swiss Army Knife's tool is essential to fulfill a given task.

Three-dimensional single-molecule localization with nanometer accuracy using Metal-Induced Energy Transfer (MIET) imaging.

Our paper presents the first theoretical and experimental study using single-molecule Metal-Induced Energy Transfer (smMIET) for localizing single fluorescent molecules in three dimensions. Metal-Induced Energy Transfer describes the resonant energy transfer from the excited state of a fluorescent emitter to surface plasmons in a metal nanostructure. This energy transfer is strongly distance-dependent and can be used to localize an emitter along one dimension. We have used Metal-Induced Energy Transfer in the past for localizing fluorescent emitters with nanometer accuracy along the optical axis of a microscope. The combination of smMIET with single-molecule localization based super-resolution microscopy that provides nanometer lateral localization accuracy offers the prospect of achieving isotropic nanometer localization accuracy in all three spatial dimensions. We give a thorough theoretical explanation and analysis of smMIET, describe its experimental requirements, also in its combination with lateral single-molecule localization techniques, and present first proof-of-principle experiments using dye molecules immobilized on top of a silica spacer, and of dye molecules embedded in thin polymer films.

Super-Resolution Optical Fluctuation Bio-Imaging with Dual-Color Carbon Nanodots.

Success in super-resolution imaging relies on a proper choice of fluorescent probes. Here, we suggest novel easily produced and biocompatible nanoparticles-carbon nanodots-for super-resolution optical fluctuation bioimaging (SOFI). The particles revealed an intrinsic dual-color fluorescence, which corresponds to two subpopulations of particles of different electric charges. The neutral nanoparticles localize to cellular nuclei suggesting their potential use as an inexpensive, easily produced nucleus-specific label. The single particle study revealed that the carbon nanodots possess a unique hybrid combination of fluorescence properties exhibiting characteristics of both dye molecules and semiconductor nanocrystals. The results suggest that charge trapping and redistribution on the surface of the particles triggers their transitions between emissive and dark states. These findings open up new possibilities for the utilization of carbon nanodots in the various super-resolution microscopy methods based on stochastic optical switching.

Dead-time correction of fluorescence lifetime measurements and fluorescence lifetime imaging.

We present a comprehensive theory of dead-time effects on Time-Correlated Single Photon Counting (TCSPC) as used for fluorescence lifetime measurements, and develop a correction algorithm to remove these artifacts. We apply this algorithm to fluorescence lifetime measurements as well as to Fluorescence Lifetime Imaging Microscopy (FLIM), where rapid data acquisition is necessarily connected with high count rates. There, dead-time effects cannot be neglected, and lead to distortions in the observed lifetime image. The algorithm is quite general and completely independent of the particular nature of the measured signal. It can also be applied to any other single-event counting measurement with detector and/or electronics dead-time.

Ultra-stable and versatile widefield cryo-fluorescence microscope for single-molecule localization with sub-nanometer accuracy.

We developed a stand-alone cryostat with optical access to the sample which can be adapted to any epi-fluorescence microscope for single-molecule fluorescence spectroscopy and imaging. The cryostat cools the sample to a cryogenic temperature of 89 K, and allows for imaging single molecules using an air objective with a numerical aperture of 0.7. An important property of this system is its excellent thermal and mechanical stability, enabling long-time observations of samples over several hours with negligible drift. Using this system, we performed photo-bleaching studies of Atto647N dye molecules, and find an improvement of the photostability of these molecules by more than two orders of magnitude. The resulting increased photon numbers of several millions allow for single-molecule localization accuracy of sub-nanometer.

Fourier interpolation stochastic optical fluctuation imaging.

Stochastic Optical Fluctuation Imaging (SOFI) is a super-resolution fluorescence microscopy technique which allows to enhance the spatial resolution of an image by evaluating the temporal fluctuations of blinking fluorescent emitters. SOFI is not based on the identification and localization of single molecules such as in the widely used Photoactivation Localization Microsopy (PALM) or Stochastic Optical Reconstruction Microscopy (STORM), but computes a superresolved image via temporal cumulants from a recorded movie. A technical challenge hereby is that, when directly applying the SOFI algorithm to a movie of raw images, the pixel size of the final SOFI image is the same as that of the original images, which becomes problematic when the final SOFI resolution is much smaller than this value. In the past, sophisticated cross-correlation schemes have been used for tackling this problem. Here, we present an alternative, exact, straightforward, and simple solution using an interpolation scheme based on Fourier transforms. We exemplify the method on simulated and experimental data.

Simultaneous Measurement of the Three-Dimensional Orientation of Excitation and Emission Dipoles.

The emission properties of most fluorescent emitters, such as dye molecules or solid-state color centers, can be well described by the model of an oscillating electric dipole. However, the orientations of their excitation and emission dipoles are, in most cases, not parallel. Although single molecule excitation and emission dipole orientation measurements have been performed in the past, no experimental method has so far looked at the three-dimensional excitation and emission dipole geometry of individual emitters simultaneously. We present the first experimental study, using defocused imaging in conjunction with radially polarized excitation scanning, to measure both the excitation as well as emission dipole orientations of single molecules, which allows us to sample the distribution of their mutual orientation. We find an unexpectedly broad distribution of the angle between both dipoles which we attribute to the interaction between the observed molecules and the substrate they are immobilized on.

Morpho-Phonetic Effects in Speech Production: Modeling the Acoustic Duration of English Derived Words With Linear Discriminative Learning.

Recent evidence for the influence of morphological structure on the phonetic output goes unexplained by established models of speech production and by theories of the morphology-phonology interaction. Linear discriminative learning (LDL) is a recent computational approach in which such effects can be expected. We predict the acoustic duration of 4,530 English derivative tokens with the morphological functions DIS, NESS, LESS, ATION, and IZE in natural speech data by using predictors derived from a linear discriminative learning network. We find that the network is accurate in learning speech production and comprehension, and that the measures derived from it are successful in predicting duration. For example, words are lengthened when the semantic support of the word's predicted articulatory path is stronger. Importantly, differences between morphological categories emerge naturally from the network, even when no morphological information is provided. The results imply that morphological effects on duration can be explained without postulating theoretical units like the morpheme, and they provide further evidence that LDL is a promising alternative for modeling speech production.

Durational Differences of Word-Final /s/ Emerge From the Lexicon: Modelling Morpho-Phonetic Effects in Pseudowords With Linear Discriminative Learning.

Recent research has shown that seemingly identical suffixes such as word-final /s/ in English show systematic differences in their phonetic realisations. Most recently, durational differences between different types of /s/ have been found to also hold for pseudowords: the duration of /s/ is longest in non-morphemic contexts, shorter with suffixes, and shortest in clitics. At the theoretical level such systematic differences are unexpected and unaccounted for in current theories of speech production. Following a recent approach, we implemented a linear discriminative learning network trained on real word data in order to predict the duration of word-final non-morphemic and plural /s/ in pseudowords using production data by a previous production study. It is demonstrated that the duration of word-final /s/ in pseudowords can be predicted by LDL networks trained on real word data. That is, duration of word-final /s/ in pseudowords can be predicted based on their relations to the lexicon.

TrackNTrace: A simple and extendable open-source framework for developing single-molecule localization and tracking algorithms.

Super-resolution localization microscopy and single particle tracking are important tools for fluorescence microscopy. Both rely on detecting, and tracking, a large number of fluorescent markers using increasingly sophisticated computer algorithms. However, this rise in complexity makes it difficult to fine-tune parameters and detect inconsistencies, improve existing routines, or develop new approaches founded on established principles. We present an open-source MATLAB framework for single molecule localization, tracking and super-resolution applications. The purpose of this software is to facilitate the development, distribution, and comparison of methods in the community by providing a unique, easily extendable plugin-based system and combining it with a novel visualization system. This graphical interface incorporates possibilities for quick inspection of localization and tracking results, giving direct feedback of the quality achieved with the chosen algorithms and parameter values, as well as possible sources for errors. This is of great importance in practical applications and even more so when developing new techniques. The plugin system greatly simplifies the development of new methods as well as adapting and tailoring routines towards any research problem's individual requirements. We demonstrate its high speed and accuracy with plugins implementing state-of-the-art algorithms and show two biological applications.

Perceptual influence of elementary three-dimensional geometry: (2) fundamental object parts.

Objects usually consist of parts and the question arises whether there are perceptual features which allow breaking down an object into its fundamental parts without any additional (e.g., functional) information. As in the first paper of this sequence, we focus on the division of our world along convex to concave surface transitions. Here we are using machine vision to produce convex segments from 3D-scenes. We assume that a fundamental part is one, which we can easily name while at the same time there is no natural subdivision possible into smaller parts. Hence in this experiment we presented the computer vision generated segments to our participants and asked whether they can identify and name them. Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name-ability. In addition, we observed that using other image-segmentation methods will not yield nameable entities. This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities. It appears that other or further subdivisions do not carry such a strong semantical link to our everyday language as there are no names for them.