We report superresolution fluorescence microscopy in an intact living organism, namely Caenorhabditis elegans nematodes expressing green fluorescent protein (GFP)-fusion proteins. We also superresolve, by stimulated emission depletion (STED) microscopy, living cultured cells, demonstrating that STED microscopy with GFP can be widely applied. STED with GFP can be performed with both pulsed and continuous-wave lasers spanning a wide wavelength range from at least 556-592 nm. Acquiring subdiffraction resolution images within seconds enables the recording of movies revealing structural dynamics. These results demonstrate that numerous microscopy studies of live samples employing GFP as the marker can be performed at subdiffraction resolution.

Abstract:Biological hazardous substances such as certain fungi and bacteria represent a high risk for the broad public if fallen into wrong hands. Incidents based on bio-agents are commonly considered to have unpredictable and complex consequences for first responders and people. The impact of such an event can be minimized by an early and fast detection of hazards. The presented approach is based on optical standoff detection applying laser-induced fluorescence (LIF) on bacteria. The LIF bio-detector has been designed for outdoor operation at standoff distances from 20 m up to more than 100 m. The detector acquires LIF spectral data for two different excitation wavelengths (280 and 355 nm) which can be used to classify suspicious samples. A correlation analysis and spectral classification by a decision tree is used to discriminate between the measured samples. In order to demonstrate the capabilities of the system, suspensions of the low-risk and non-pathogenic bacteria Bacillus thuringiensis, Bacillus atrophaeus, Bacillus subtilis, Brevibacillus brevis, Micrococcus luteus, Oligella urethralis, Paenibacillus polymyxa and Escherichia coli (K12) have been investigated with the system, resulting in a discrimination accuracy of about 90%.

The reversible photoswitching of the photochromic fluorescent protein Padron0.9 involves a cis-trans isomerization of the chromophore. Both isomers are subjected to a protonation equilibrium between a neutral and a deprotonated form. The observed pH dependent absorption spectra require at least two protonating groups in the chromophore environment modulating its proton affinity. Using femtosecond transient absorption spectroscopy, we elucidate the primary reaction steps of selectively excited chromophore species. Employing kinetic and spectral modeling of the time dependent transients, we identify intermediate states and their spectra. Excitation of the deprotonated trans species is followed by excited state relaxation and internal conversion to a hot ground state on a time scale of 1.1-6.5 ps. As the switching yield is very low (Φtrans→cis = 0.0003 ± 0.0001), direct formation of the cis isomer in the time-resolved experiment is not observed. The reverse switching route involves excitation of the neutral cis chromophore. A strong H/D isotope effect reveals the initial reaction step to be an excited state proton transfer with a rate constant of kH = (1.7 ps)(-1) (kD = (8.6 ps)(-1)) competing with internal conversion (kic = (4.5 ps)(-1)). The deprotonated excited cis intermediate relaxes to the well-known long-lived fluorescent species (kr = (24 ps)(-1)). The switching quantum yield is determined to be low as well, Φcis→trans = 0.02 ± 0.01. Excitation of both the neutral and deprotonated cis chromophores is followed by a ground state proton transfer reaction partially re-establishing the disturbed ground state equilibrium within 1.6 ps (deuterated species: 5.6 ps). The incomplete equilibration reveals an inhomogeneous population of deprotonated cis species which equilibrate on different time scales.

In an effort to reduce the potential risk of human exposure to chemical and biological hazardous materials, the demand increases for a detection system which rapidly identifies possible threats from a distance to avoid direct human contact to these materials. In this scope, we present a novel detection system which is able to measure simultaneously spectrally and temporally resolved laser induced fluorescence (LIF) signals excited by two consecutive laser pulses with different central wavelengths at 266 nm and 355 nm. As shown in this paper, the setup enables fast data acquisition that provides a complete dataset in less than a few milliseconds at repetition rates of 100 Hz. Furthermore, with its modular design it can be transported easily for operation at different locations. First measurements indicate a high performance with an accuracy of more than 97% in the distinguishability of bacterial specimen within a limited set of three representative bacterial species, namely Bacillus thuringiensis, Micrococcus luteus and Oligella urethralis. Together with the consecutive classification procedure, the setup promises to become a valuable tool for standoff detection of bio-hazards.

Chemical contamination of objects and surfaces, caused by accident or on purpose, is a common security issue. Immediate countermeasures depend on the class of risk and consequently on the characteristics of the substances.Laser-based standoff detection techniques can help to provide information about the thread without direct contact of humans to the hazardous materials.This article explains a data acquisition and classification procedure for laserinduced fluorescence spectra of several chemical agents. The substances are excited from a distance of 3.5 m by laser pulses of two UV wavelengths (266 and 355 nm) with less than 0.1 mJ per laser pulse and a repetition rate of 100 Hz. Each pair of simultaneously emitted laser pulses is separated using an optical delay line. Every measurement consists of a dataset of 100 spectra per wavelength containing the signal intensities in the spectral range from 250 to 680 nm, recorded by a 32-channel photo multiplying tube array. Based on this dataset, three classification algorithms are trained which can distinguish the samples by their single spectra with an accuracy of over 98%. These predictive models, generated with decision trees, support vector machines, and neural networks, can identify all agents (eg, benzaldehyde, isoproturon, and piperine) within the current set of substances.