MO435MULTIMODAL IMAGING FOR MOLECULAR TISSUE ANALYSIS. (29th May 2021) - Lellig, Michaela; Brehmer, Kai; Hohl, Mathias; Speer, Thimoteus; Schunk, Stefan; Thiele, Herbert; Jankowski, Joachim; Jankowski, Vera; Hermann, Juliane
Abstract: Background and Aims: MALDI mass spectrometric imaging (MALDI MSI) is a powerful histologic tool for the analysis of biomolecules in tissue samples. MALDI MSI measurements result in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples. For more detailed analysis of MALDI MSI data and correlation between the molecular and microscopic levels, a combination of MALDI MSI data and histological staining is essential. By combining MALDI MSI data and histological data, much more information are obtained than by analyzing both methods individually. Therefore, MALDI MSI datasets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provides more information than a single tissue section. We have developed, established and validated an algorithm for an automatic registration of MALDI data with different histological image data for cross-process evaluation of multimodal datasets to create 3D models. This multimodal imaging approach simplifies and improves molecular analyses of tissue samples in clinical research and diagnosis. Method: The datasets for fusion and creation of a 3D model consist of mass spectrometric data, histological and immunohistochemical staining methods. Histological tissue sections of a whole mouse kidney were prepared. For MALDI MSI data, organ sections were analyzed by using a Rapiflex mass-spectrometer. Results: A mathematical registration was used to achieve a perfect superposition of the individual histological sections of mass spectrometric data. It is feasible to combine mass spectrometric data, histological and immunohistochemical datasets in high numbers and reconstruct the measured mouse kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distributions can be obtained. The fusion of the data also offers a virtual incision of the organ from arbitrary angle and level. The algorithms are adapted to take the data fusion automatically offering a high-throughput approach for clinical diagnostics and the possibility to involved artificial intelligence in its interpretation in research. Conclusion: A successful fusion of MALDI MSI data and different histological and immunohistochemical staining datasets of a whole organ is performed.