We introduce a novel and universal method for fast optical high -as well as superresolution imaging. Our method is based on reconstructing super-resolved images from conventional image sequences containing rapid random signal fluctuations. Such sequences could be obtained from either wide-field single-molecule blinking experiments or rapid image sequences with fluorophores undergoing random intensity fluctuations. By calculating the local entropy (H) and cross-entropy (xH) values pixel-by-pixel, weighted with higher order-statistics (HOS), a new image with pixel intensities representing the true information content in the time series is obtained. We show that analyzing image sequences by this formalism enables the reconstruction of super-resolved images, where the optical resolution that can be achieved depends only on the number of input frames and the higher order moments used for the calculation. We find that the acquisition of <100 frames per sequence is sufficient to reconstruct super-resolved images of entire cells. We also demonstrate that not only on-off switching of the fluorescent dyes, but also other dynamic events, i.e. photobleaching, can be exploited for

Cellular lipid droplets are the least studied and least understood cellular organelles in eukaryotic and prokaryotic cells. Despite a significant body of research studying the physiology of lipid droplets it has not yet been possible to fully determine the composition of individual cellular lipid droplets. In this paper we use Raman spectroscopy on single cellular lipid droplets and least-squares fitting of pure fatty acid spectra to determine the composition of individual lipid droplets in cells after treatment with different ratios of oleic and palmitic acid. We validate the results of the Raman spectroscopy-based single lipid droplet analysis with results obtained by gas chromatography analysis of millions of cells, and find that our approach can accurately predict the relative amount of a specific fatty acid in the lipid droplet. Based on these results we show that the fatty acid composition in individual lipid droplets is on average similar to that of all lipid droplets found in the sample. Furthermore, we expand this approach to the investigation of the lipid composition in single cellular peroxisomes. We determine the location of cellular peroxisomes based on two-photon excitation fluorescence (TPEF) imaging of peroxisomes labeled with the green fluorescent protein, and successive Raman spectroscopy of peroxisomes. We find that in some cases peroxisomes can produce a detectable CARS signal, and that the peroxisomal Raman spectra exhibit an oleic acid-like signature.

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We introduce a robust and relatively easy-to-use method to evaluate the quality of two-color (or more) fluorescence coincidence measurements based on close investigation of the coincidence correlation-matrix. This matrix contains temporal correlations between the number of detected bursts in individual channels and their coincidences. We show that the Euclidian norm of a vector Γ derived from elements of the correlation matrix takes a value between 0 and 2 depending on the relative coincidence frequency. We characterized the Γ-norm and its dependence on various experimental conditions by computer simulations and fluorescence microscopy experiments. Single-molecule experiments with two differently colored dye molecules diffusing freely in aqueous solution, a sample that generates purely random coincidence events, return a Γ-norm less than one, depending on the concentration of the fluorescent dyes. As perfect coincidence sample we monitored broad autofluorescence of 2.8 μm beads and determined the Γ-norm to be maximal and close to two. As in realistic diagnostic applications, we show that two-color coincidence detection of single-stranded DNA molecules, using differently labeled Molecular Beacons hybridizing to the same target, reveal a value between one and two representing a mixture of an optimal coincidence sample and a sample generating random coincidences. The Γ-norm introduced for data analysis provides a quantifiable measure for quickly judging the outcome of single-molecule coincidence experiments and estimating the quality of detected coincidences.

Activating transcription factor 3 (ATF3) is a member of the mammalian activation transcription factor/cAMP, physiologically important in the regulation of pro- and anti-inflammatory target genes. We compared the induction of ATF3 protein as measured by Western blot analysis with single-molecule localization microscopy dSTORM to quantify the dynamics of accumulation of intranuclear ATF3 of triglyceride-rich (TGRL) lipolysis product-treated HAEC (Human Aortic Endothelial Cells). The ATF3 expression rate within the first three hours after treatment with TGRL lipolysis products is about 3500/h. After three hours we detected 33,090 ± 3,491 single-molecule localizations of ATF3. This was accompanied by significant structural changes in the F-actin network of the cells at ~3-fold increased localization precision compared to widefield microscopy after treatment. Additionally, we discovered a cluster size of approximately 384 nanometers of ATF3 molecules. We show for the first time the time course of ATF3 accumulation in the nucleus undergoing lipotoxic injury. Furthermore, we demonstrate ATF3 accumulation associated with increased concentrations of TGRL lipolysis products occurs in large aggregates.

One of the most challenging tasks in microscopy is the quantitative identification and characterization of molecular interactions. In living cells this task is typically performed by fluorescent labeling of the interaction partners with spectrally distinct fluorophores and imaging in different color channels. Current methods for determining colocalization of molecules result in outcomes that can vary greatly depending on signal‐to‐noise ratios, threshold and background levels, or differences in intensity between channels. Here, we present a novel and quantitative method for determining the degree of colocalization in live‐cell fluorescence microscopy images for two and more data channels. Moreover, our method enables the construction of images that directly classify areas of high colocalization. (© 2013 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)

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The sensitive and rapid detection of pathogenic DNA is of tremendous importance in the field of diagnostics. We demonstrate the ability of detecting and quantifying single-and double-stranded pathogenic DNA with picomolar sensitivity in a bead-based fluorescence assay. Selecting appropriate capturing and detection sequences enables rapid (2 h) and reliable DNA quantification. We show that synthetic sequences of S. pneumoniae and M. luteus can be quantified in very small sample volumes (20 µL) across a linear detection range over four orders of magnitude from 1 nM to 1 pM, using a miniaturized wide-field fluorescence microscope without amplification steps. The method offers single molecule detection sensitivity without using complex setups and thus volunteers as simple, robust, and reliable method for the sensitive detection of DNA and RNA sequences.