Fluorescence microscopy images are inevitably contaminated by background intensity contributions. Fluorescence from out-of-focus planes and scattered light are important sources of slowly varying, low spatial frequency background, whereas background varying from pixel to pixel (high frequency noise) is introduced by the detection system. Here we present a powerful, easy-to-use software, wavelet-based background and noise subtraction (WBNS), which effectively removes both of these components. To assess its performance, we apply WBNS to synthetic images and compare the results quantitatively with the ground truth and with images processed by other background removal algorithms. We further evaluate WBNS on real images taken with a light-sheet microscope and a super-resolution stimulated emission depletion microscope. For both cases, we compare the WBNS algorithm with hardware-based background removal techniques and present a quantitative assessment of the results. WBNS shows an excellent performance in all these applications and significantly enhances the visual appearance of fluorescence images. Moreover, it may serve as a pre-processing step for further quantitative analysis.

The human heme enzyme tryptophan 2,3-dioxygenase (hTDO) catalyzes the insertion of dioxygen into its cognate substrate, l-tryptophan (l-Trp). Its active site structure is highly dynamic, and the mechanism of enzyme-substrate-ligand complex formation and the ensuing enzymatic reaction is not yet understood. Here we have studied complex formation in hTDO by using time-resolved optical and infrared spectroscopy with carbon monoxide (CO) as a ligand. We have observed that both substrate-free and substrate-bound hTDO coexist in two discrete conformations with greatly different ligand binding rates. In the fast rebinding hTDO conformation, there is facile ligand access to the heme iron, but it is greatly hindered in the slowly rebinding conformation. Spectroscopic evidence implicates active site solvation as playing a crucial role for the observed kinetic differences. Substrate binding shifts the conformational equilibrium markedly toward the fast species and thus primes the active site for subsequent ligand binding, ensuring that formation of the ternary complex occurs predominantly by first binding l-Trp and then the ligand. Consequently, the efficiency of catalysis is enhanced because O binding prior to substrate binding, resulting in nonproductive oxidation of the heme iron, is greatly suppressed.

Multi-view deconvolution is a powerful image-processing tool for light sheet fluorescence microscopy, providing isotropic resolution and enhancing the image content. However, performing these calculations on large datasets is computationally demanding and time-consuming even on high-end workstations. Especially in long-time measurements on developing animals, huge amounts of image data are acquired. To keep them manageable, redundancies should be removed right after image acquisition. To this end, we report a fast approximation to three-dimensional multi-view deconvolution, denoted 2D+1D multi-view deconvolution, which is able to keep up with the data flow. It first operates on the two dimensions perpendicular and subsequently on the one parallel to the rotation axis, exploiting the rotational symmetry of the point spread function along the rotation axis. We validated our algorithm and evaluated it quantitatively against two-dimensional and three-dimensional multi-view deconvolution using simulated and real image data. 2D+1D multi-view deconvolution takes similar computation time but performs markedly better than the two-dimensional approximation only. Therefore, it will be most useful for image processing in time-critical applications, where the full 3D multi-view deconvolution cannot keep up with the data flow.

us with a powerful tool to learn the fluorophore concentrations with high spatial resolution. FLIM analysis can be performed in either time or frequency domain. In time domain, photon arrival times are fitted to a mixture of decay profiles, which are convolutions of exponential components and the instrument response function. The current time domain approaches group the input data into multiple bins which leads to loss of information. Moreover, these techniques report sub-cellular heterogeneity with a resolution limited to the pixel size. We developed a time domain FLIM data analysis technique in a Bayesian paradigm that uses Gaussian processes to map the fluorophore concentrations with sub-pixel resolution. This method efficiently uses all the available photon arrival time information and, as such, requires a minimum number of photons to infer accurate fluorophore concentrations. The approach was validated using both empirical and simulated data.