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

Abstract

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.

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

“…Furthermore, MALDI imaging was carried out together with a number of other MS imaging modalities such as silicon nanopost array laser desorption ionization (NAPA‐LDI) [131], liquid extraction surface analysis (LESA) [132], DESI [133], and secondary ion mass spectrometry (SIMS) [134–137] resulting in synergistic spatial information. Combination of MALDI imaging with spatially resolved vibrational spectroscopy methods such as Raman and Fourier transmission infrared (FTIR) spectroscopy can achieve more specific information of the molecular classes (lipids, nucleic acids, and proteins) occurring in one region and can further specify ROIs for consecutive MALDI imaging [138–143]. Despite the many possibilities that these multimodal imaging approaches open up for sarcoma research, significant challenges remain such as the registration and data correlation between MALDI imaging and other imaging modalities [123].…”

In focus in HCB

“…However, traditional tissue analysis approaches are not only time-consuming but also of limited value. In their present work, Ritschar et al (2022) aimed to improve this situation by developing a multimodal approach and combining it with improved data acquisition and evaluation. They report a sequential multimodal imaging approach combining Fourier transform infrared spectroscopy (FTIR) followed by matrixassisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and finally H&E staining on the same 20-μm-thick cryosections prepared from sodiumcarboxymethylcellulose-embedded segments of adult Eisenia fetida (Fig.…”

Histochemistry and Cell Biology—a glance into the past and a look ahead

“…We conclude this review by referring to recent work of Ritschar and coworkers ( 2022 ) combining spectroscopic techniques with machine learning to identify target tissues in the soil model organism Eisenia fetida for application in ecotoxicological investigations. They sequentially applied Fourier transform infrared spectroscopy (FTIR) and MALDI-IMS, followed by data analysis via random decision forest classification using random forest classifiers.…”