Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning

Acquiring comprehensive knowledge about the uptake of pollutants, impact on tissue integrity and the effects at the molecular level in organisms is of increasing interest due to the environmental exposure to numerous contaminants. The analysis of tissues can be performed by histological examination, which is still time-consuming and restricted to target-specific staining methods. The histological approaches can be complemented with chemical imaging analysis. Chemical imaging of tissue sections is typically performed using a single imaging approach. However, for toxicological testing of environmental pollutants, a multimodal approach combined with improved data acquisition and evaluation is desirable, since it may allow for more rapid tissue characterization and give further information on ecotoxicological effects at the tissue level. Therefore, using the soil model organism Eisenia fetida as a model, we developed a sequential workflow combining Fourier transform infrared spectroscopy (FTIR) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) for chemical analysis of the same tissue sections. Data analysis of the FTIR spectra via random decision forest (RDF) classification enabled the rapid identification of target tissues (e.g., digestive tissue), which are relevant from an ecotoxicological point of view. MALDI imaging analysis provided specific lipid species which are sensitive to metabolic changes and environmental stressors. Taken together, our approach provides a fast and reproducible workflow for label-free histochemical tissue analyses in E. fetida, which can be applied to other model organisms as well. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00418-021-02037-1

Comparison of two rapid automated analysis tools for large FTIR microplastic datasets

AbstractOne of the biggest issues in microplastic (MP, plastic items  &lt;5 mm) research is the lack of standardisation and harmonisation in all fields, reaching from sampling methodology to sample purification, analytical methods and data analysis. This hampers comparability as well as reproducibility among studies. Concerning chemical analysis of MPs, Fourier-transform infrared (FTIR) spectroscocopy is one of the most powerful tools. Here, focal plane array (FPA) based micro-FTIR (µFTIR) imaging allows for rapid measurement and identification without manual preselection of putative MP and therefore enables large sample throughputs with high spatial resolution. The resulting huge datasets necessitate automated algorithms for data analysis in a reasonable time frame. Although solutions are available, little is known about the comparability or the level of reliability of their output. For the first time, within our study, we compare two well-established and frequently applied data analysis algorithms in regard to results in abundance, polymer composition and size distributions of MP (11–500 µm) derived from selected environmental water samples: (a) the siMPle analysis tool (systematic identification of MicroPlastics in the environment) in combination with MPAPP (MicroPlastic Automated Particle/fibre analysis Pipeline) and (b) the BPF (Bayreuth Particle Finder). The results of our comparison show an overall good accordance but also indicate discrepancies concerning certain polymer types/clusters as well as the smallest MP size classes. Our study further demonstrates that a detailed comparison of MP algorithms is an essential prerequisite for a better comparability of MP data.</jats:p

From the Well to the Bottle: Identifying Sources of Microplastics in Mineral Water

Microplastics (MP) have been detected in bottled mineral water across the world. Because only few MP particles have been reported in ground water-sourced drinking water, it is suspected that MP enter the water during bottle cleaning and filling. However, until today, MP entry paths were not revealed. For the first time, this study provides findings of MP from the well to the bottle including the bottle washing process. At four mineral water bottlers, five sample types were taken along the process: raw and deferrized water samples were filtered in situ; clean bottles were sampled right after they left the bottle washer and after filling and capping. Caustic cleaning solutions were sampled from bottle washers and MP particles isolated through enzymatic and chemical treatments. The samples were analyzed for eleven synthetic and natural polymer particles ≥11 µm with Fourier-transform infrared imaging and random decision forests. MP were present in all steps of mineral water bottling, with a sharp increase from &lt;1 MP L−1 to 317 ± 257 MP L−1 attributed to bottle capping. As 81% of MP resembled the PE-based cap sealing material, abrasion from the sealings was identified as the main entry path for MP into bottled mineral water

The role of sea ice for plastic pollution in the Arctic

Marine plastic pollution is a growing worldwide environmental concern as recent reports indicate that increasing quantities of litter disperse into secluded environments, including Polar Regions. Plastic degrades into smaller fragments under the influence of sunlight, temperature changes, mechanic abrasion and wave action resulting in small particles < 5mm called microplastics (MP). Sea ice cores, collected in the Arctic Ocean have so far revealed extremely high concentrations of very small microplastic particles, which might be transferred in the ecosystem with so far unknown consequences for the ice dependant marine food chain. Sea ice has long been recognised as a transport vehicle for any contaminates entering the Arctic Ocean from various long range and local sources. The Fram Strait is hereby both, a major inflow gateway of warm Atlantic water, with any anthropogenic imprints and the major outflow region of sea ice originating from the Siberian shelves and carried via the Transpolar Drift. The studied sea ice revealed a unique footprint of microplastic pollution, which were related to different water masses and indicating different source regions. Climate change in the Arctic include loss of sea ice, therefore, large fractions of the embedded plastic particles might be released and have an impact on living systems. By combining modeling of sea ice origin and growth, MP particle trajectories in the water column as well as MPs long-range transport via particle tracking and transport models we get first insights about the sources and pathways of MP in the Arctic Ocean and beyond and how this might affect the Arctic ecosystem