P0659MULTIMODAL IMAGING OF MALDI MSI DATA. (6th June 2020)

• Record Type: Journal Article
• Authors: Hermann, Juliane; Brehmer, Kai; Thiele, Herbert; Jankowski, Vera; Jankowski, Joachim
• Is Part Of: Nephrology dialysis transplantation. Volume 35(2020)Supplement 3
• Publication Date: 2020-06-06
• Subjects: Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfaa142.P0659
• Languages: English
• ISSNs: 0931-0509

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