P0659MULTIMODAL IMAGING OF MALDI MSI DATA

Abstract

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 results in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples The resolution information of MALDI MSI is in the range of 5-10 µm in the spatial distribution and has the ability to identify proteins, peptides, lipids and small biomolecules directly in tissue samples in one analytical step..For a more detailed analysis of MALDI MSI data and a correlation between the molecular and microscopic level, 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 from a single analysis of both methods. Therefore, MALDI MSI data sets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provide more information than a single tissue section. We developed, established and validate an algorithm for an automatic registration of MALDI data with different histological image data for the cross-process evaluation of multimodal data sets for creating 3D models. This multimodal image approach simplifies and improves molecular analyses of tissue samples clinical research and diagnosis. Method The data sets for the fusion and creating of a 3D model consist of mass spectrometric data as well as histological and Immunohistochemical staining methods. Histological tissue sections of a whole mice kidney were prepared. For MALDI MSI data the organ sections were coated and incubated with a trypsin solution were performed by using a sprayer for MALDI imaging. As matrix, α-cyano-4-hydroxycinnamic acid was used. MALDI MSI was performed using the Rapiflex. For histological staining the hematoxylin-eosin and Gomori staining were chosen. For Immunohistochemical double staining and immunofluorescence, were used for the detection of Collagen type I, smooth muscle actin and the cell nuclei. Results By using a mathematical registration, a perfect superposition of the individual histological sections mass spectrometric data was achieved. It is possible to combine mass spectrometric data, histological and Immunohistochemical data sets in a high number and to reconstruct the measured mice kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distribution can be obtained. The fusion of the data also offers a virtual incision of the organ from any 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 There is a successful fusion of MALDI MSI data and different histological and Immunohistochemical staining data sets of a whole organ