Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust—a software tool for analysis GC–MS and LC–MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC–MS and LC–MS alignment data sets.

A methodology is described for the quantitative determination of Förster resonance energy transfer (FRET) in live cells using the rise time of acceptor fluorescence as determined with fluorescence lifetime imaging microscopy (FLIM). An advantage of this method is that only those molecules that are involved in the energy-transfer process are monitored. This contrasts with current methods that measure either steady-state fluorescence of donor and acceptor molecules or time-resolved fluorescence of donor molecules, and thereby probe a mixture of donor molecules that are involved in FRET and those that are fluorescent but not involved in FRET. The absence of FRET can, for instance, be due to unwanted acceptor bleaching or incomplete maturing of visible proteins that should act as acceptor molecules. In addition, parameters describing the rise of acceptor fluorescence and the decay of donor fluorescence can be determined via simultaneous global analysis of multiple FLIM images, thereby increasing the reliability of the analysis. In the present study, plant protoplasts transfected with fusions of visible fluorescent proteins are used to illustrate the new data analysis method. It is demonstrated that the distances estimated with the present method are substantially smaller than those estimated from the average donor lifetimes, due to a fraction of non-transferring donor molecules. Software to reproduce the presented results is provided in an open-source and freely available package called “TIMP” for “The R project for Statistical Computing”.

Microfibers are reported as the most abundant microparticle type in the environment. Their small size and light weight allow easy and fast distribution, but also make it challenging to determine their chemical composition. Vibrational microspectroscopy methods as infrared and spontaneous Raman microscopy have been widely used for the identification of environmental microparticles. However, only few studies report on the identification of microfibers, mainly due to difficulties caused by their small diameter. Here we present the use of Stimulated Raman Scattering (SRS) microscopy for fast and reliable classification of microfibers from environmental samples. SRS microscopy features high sensitivity and has the potential to be faster than other vibrational microspectroscopy methods. As a proof of principle, we analyzed fibers extracted from the fish gastrointestinal (GIT) tract, deep-sea and coastal sediments, surface seawater and drinking water. Challenges were faced while measuring fibers from the fish GIT, due to the acidic degradation they undergo. However, the main vibrational peaks were still recognizable and sufficient to determine the natural or synthetic origin of the fibers. Notably, our results are in accordance to other recent studies showing that the majority of the analyzed environmental fibers has a natural origin. Our findings suggest that advanced spectroscopic methods must be used for estimation of the plastic fibers concentration in the environment.