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

Title data

Ritschar, Sven ; Schirmer, Elisabeth ; Hufnagl, Benedikt ; Löder, Martin G. J. ; Römpp, Andreas ; Laforsch, Christian:
Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning.
In: Histochemistry and Cell Biology. (2021) .
ISSN 1432-119X
DOI: https://doi.org/10.1007/s00418-021-02037-1

Project information

Project title:
Project's official titleProject's id
SFB 1357 Mikroplastik391977956

Project financing: Deutsche Forschungsgemeinschaft

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Multimodal imaging; Eisenia fetida; Random decision forest; Tissue analysis; MALDI-MSI; FTIR
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Animal Ecology I > Chair Animal Ecology I - Univ.-Prof. Dr. Christian Laforsch
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Research Institutions > Collaborative Research Centers, Research Unit > SFB 1357 - MIKROPLASTIK
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 570 Life sciences, biology
500 Science > 590 Animals (Zoology)
Date Deposited: 10 Nov 2021 10:39
Last Modified: 10 Nov 2021 10:39
URI: https://eref.uni-bayreuth.de/id/eprint/67727