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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,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847259/ https://www.ncbi.nlm.nih.gov/pubmed/34750664 http://dx.doi.org/10.1007/s00418-021-02037-1 |
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author | Ritschar, Sven Schirmer, Elisabeth Hufnagl, Benedikt Löder, Martin G. J. Römpp, Andreas Laforsch, Christian |
author_facet | Ritschar, Sven Schirmer, Elisabeth Hufnagl, Benedikt Löder, Martin G. J. Römpp, Andreas Laforsch, Christian |
author_sort | Ritschar, Sven |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8847259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-88472592022-02-23 Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning Ritschar, Sven Schirmer, Elisabeth Hufnagl, Benedikt Löder, Martin G. J. Römpp, Andreas Laforsch, Christian Histochem Cell Biol Short Communication 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. Springer Berlin Heidelberg 2021-11-08 2022 /pmc/articles/PMC8847259/ /pubmed/34750664 http://dx.doi.org/10.1007/s00418-021-02037-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Short Communication 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 |
title | Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning |
title_full | Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning |
title_fullStr | Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning |
title_full_unstemmed | Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning |
title_short | Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning |
title_sort | classification of target tissues of eisenia fetida using sequential multimodal chemical analysis and machine learning |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847259/ https://www.ncbi.nlm.nih.gov/pubmed/34750664 http://dx.doi.org/10.1007/s00418-021-02037-1 |
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