Microplastic particles have been found in drinking water sources worldwide and, thus, also in our food and beverages. Especially small microplastics, with sizes of 1 mm and less, cannot be identified reliably without spectroscopic means such as Fourier transform infrared spectroscopy (FTIR) or Raman spectroscopy, usually applied to the particles extracted from the samples. However, for drinking and tap water, with its comparatively low biological loads, direct observation may be possible and allows a point-of-entry monitoring for beverages and food to ensure uncontaminated drinking water is being used. In a proof of concept, we apply Raman spectroscopy to observe individual microplastic particles in tap water with added particulate and fluorescent contaminants streaming with 1 L/h through a custom-made flow cell. We evaluated several tubing materials for compatibility with microplastic suspensions containing three different polymers widely found in microplastic surveys worldwide. The experiment promises the monitoring of streaming tap water and even clear surface waters for microplastics smaller than 0.1 mm.

The extraction of plastic microparticles, so-called microplastics, from sludge is a challenging task due to the complex, highly organic material often interspersed with other benign microparticles. The current procedures for microplastic extraction from sludge are time consuming and require expensive reagents for density separation as well as large volumes of oxidizing agents for organic removal, often resulting in tiny sample sizes and thus a disproportional risk of sample bias. In this work, we present an improved extraction method tested on return activated sludge (RAS). The treatment of 100 ml of RAS requires only 6% hydrogen peroxide (HO) for bleaching at 70 °C, followed by density separation with sodium nitrate/sodium thiosulfate (SNT) solution, and is completed within 24 h. Extracted particles of all sizes were chemically analyzed with confocal Raman microscopy. An extraction efficiency of 78 ± 8% for plastic particle sizes 20 µm and up was confirmed in a recovery experiment. However, glass shards with a diameter of less than 20 µm remained in the sample despite the density of glass exceeding the density of the separating SNT solution by 1.1 g/cm. This indicates that density separation may be unreliable for particle sizes in the lower micrometer range.

The temperature-sensitive gating of human Connexin 26 (hCx26) was analyzed with confocal Raman microscopy. High-resolution Raman spectra covering the spectral range between 400 and 1500 rel. cm(-1) with a spectral resolution of 1 cm(-1) were fully annotated, revealing notable differences between the spectrum recorded from solubilized hCx26 in Ca(2+)-buffered POPC at 10°C and any other set of protein conditions (temperature, Ca(2+) presence, POPC presence). Spectral components originating from specific amino acids show that the TM1/EL1 parahelix and probably the TM4 trans-membrane helix and the plug domain are involved in the gating process responsible for fully closing the hemichannel.

Resonance Raman microscopy is well suited to examine living bacterial samples without further preparation. Therefore, comparatively little thought has been given to its compatibility with common fixation methods. However, fixation of cell samples is a very important tool in the microbiological sciences, allowing the preservation of samples in a specific condition for further examination, future measurements, transport, or later reference. We examined the effects of three common fixatives-ethanol, formaldehyde solution, and gentle heat--on the resonant Raman spectrum of three generic bacteria species, Rhodobacter sphaeroides DSM 158(T), Rhodopseudomonas palustris DSM 123(T), and Rhodospirillum rubrum DSM 467(T), holding carotenoid- and heme-chromophores in confocal Raman microscopy. In addition, we analyzed the effect of poly-L-lysine coating of microscope slides, widely used for mounting biological and medical samples, on subsequent confocal Raman measurements of native and fixed samples. The results indicate that ethanol is preferable to formaldehyde as fixative if applied for less than 24 h, whereas heat fixation has a strong, detrimental effect on the resonant Raman spectrum of bacteria. Formaldehyde fixation excels at fixation times above 24 h, but causes an overall reduction in signal intensity. Poly-L-lysine coating has no discernable effect on the Raman spectra of samples fixed with ethanol or heat, but it further decreases the signal intensity, especially at higher wavenumbers, in the spectra of samples fixed with formaldehyde.

Resonance Raman microspectroscopy in combination with hierarchical cluster analysis (HCA) is one of the most promising tools for the rapid examination of complex biological and medical samples. HCA is a ready, computerized tool for examining large sets of data for common characteristics, and a multitude of algorithms for this purpose have been developed over the years. However, resonance Raman spectra obtained from complex biological samples may originate from different chromophores as well as from a common chromophore found in different host environments, i.e., bacteria. Therefore, algorithms applied to resonance Raman spectra must handle data of high intrinsic similarity, i.e., spectra originating from a common chromophore, and data with highly dissimilar features, i.e., spectra from different chromophores, in the same unsupervised analysis. We examined the performance of eight widely used algorithms for hierarchical cluster analysis in clustering resonance Raman spectra of bacteria: Single-Linkage (Nearest-Neighbor), Complete-Linkage (Farthest-Neighbor), Average-Linkage, Weighted-Average-Linkage, Centroid, Median, and the Ward algorithm. Algorithm performance was evaluated by comparing the results of clustering a set of high-quality reference spectra with the results obtained when clustering a set of spectra recorded from single cells. References were formed by averaging 100 spectra of individual cells. While all algorithms returned highly similar results when clustering the reference spectra, their performance differed significantly when applied to single spectra. The best-performing algorithm, Weighted-Average-Linkage, correctly grouped single spectra with a reliability of above 95% while the spectral distances between the clusters deviated less than 10% from the results obtained with reference spectra. In contrast, the algorithm performing worst showed no similarity to the reference clustering at all. The widely used Ward algorithm deviated up to 30% from the reference in the spectral distances and returned a different spectral relation between bacteria expressing the same chromophore.