MO435MULTIMODAL IMAGING FOR MOLECULAR TISSUE ANALYSIS

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Michaela Lellig ◽  
Kai Brehmer ◽  
Mathias Hohl ◽  
Thimoteus Speer ◽  
Stefan Schunk ◽  
...  
Keyword(s):  
Immunohistochemical Staining ◽  
3D Model ◽  
Mouse Kidney ◽  
Mass Spectrometric ◽  
Clinical Diagnostics ◽  
Histological Image ◽  
Tissue Samples ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Histological Staining

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.

scholarly journals P0659MULTIMODAL IMAGING OF MALDI MSI DATA

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Juliane Hermann ◽  
Kai Brehmer ◽  
Herbert Thiele ◽  
Vera Jankowski ◽  
Joachim Jankowski
Keyword(s):  
Spatial Distribution ◽  
Immunohistochemical Staining ◽  
3D Model ◽  
Mass Spectrometric ◽  
Clinical Diagnostics ◽  
Data Sets ◽  
Tissue Samples ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Histological Staining

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

Application of Factor Analysis in Imaging Mass Spectrometric Data Analysis

2014 ◽  
Vol 42 (8) ◽  
pp. 1099-1103 ◽  
Author(s):  
Yi CHEN ◽  
Fei TANG ◽  
Tie-Gang LI ◽  
Jiu-Ming HE ◽  
Zeper ABLIZ ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data

scholarly journals Constructing a data-driven receptor model for organic and inorganic aerosol – a synthesis analysis of eight mass spectrometric data sets from a boreal forest site

2019 ◽  
Vol 19 (6) ◽  
pp. 3645-3672 ◽  
Author(s):  
Mikko Äijälä ◽  
Kaspar R. Daellenbach ◽  
Francesco Canonaco ◽  
Liine Heikkinen ◽  
Heikki Junninen ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Organic Aerosol ◽  
Mass Spectrometric ◽  
Chemical Components ◽  
Forest Site ◽  
Aerosol Composition ◽  
Mass Spectrometric Data ◽  
Spectrometric Data ◽  
Aerosol Mass ◽  
Inorganic Aerosol

Abstract. The interactions between organic and inorganic aerosol chemical components are integral to understanding and modelling climate and health-relevant aerosol physicochemical properties, such as volatility, hygroscopicity, light scattering and toxicity. This study presents a synthesis analysis for eight data sets, of non-refractory aerosol composition, measured at a boreal forest site. The measurements, performed with an aerosol mass spectrometer, cover in total around 9 months over the course of 3 years. In our statistical analysis, we use the complete organic and inorganic unit-resolution mass spectra, as opposed to the more common approach of only including the organic fraction. The analysis is based on iterative, combined use of (1) data reduction, (2) classification and (3) scaling tools, producing a data-driven chemical mass balance type of model capable of describing site-specific aerosol composition. The receptor model we constructed was able to explain 83±8 % of variation in data, which increased to 96±3 % when signals from low signal-to-noise variables were not considered. The resulting interpretation of an extensive set of aerosol mass spectrometric data infers seven distinct aerosol chemical components for a rural boreal forest site: ammonium sulfate (35±7 % of mass), low and semi-volatile oxidised organic aerosols (27±8 % and 12±7 %), biomass burning organic aerosol (11±7 %), a nitrate-containing organic aerosol type (7±2 %), ammonium nitrate (5±2 %), and hydrocarbon-like organic aerosol (3±1 %). Some of the additionally observed, rare outlier aerosol types likely emerge due to surface ionisation effects and likely represent amine compounds from an unknown source and alkaline metals from emissions of a nearby district heating plant. Compared to traditional, ion-balance-based inorganics apportionment schemes for aerosol mass spectrometer data, our statistics-based method provides an improved, more robust approach, yielding readily useful information for the modelling of submicron atmospheric aerosols physical and chemical properties. The results also shed light on the division between organic and inorganic aerosol types and dynamics of salt formation in aerosol. Equally importantly, the combined methodology exemplifies an iterative analysis, using consequent analysis steps by a combination of statistical methods. Such an approach offers new ways to home in on physicochemically sensible solutions with minimal need for a priori information or analyst interference. We therefore suggest that similar statistics-based approaches offer significant potential for un- or semi-supervised machine-learning applications in future analyses of aerosol mass spectrometric data.

scholarly journals Lecythomycin, a New Macrolactone Glycoside from the Endophytic Fungus Lecythophora sp

2011 ◽  
Vol 6 (5) ◽  
pp. 1934578X1100600 ◽  
Author(s):  
Noor Erma Sugijanto ◽  
Arnulf Diesel ◽  
Mostafa Rateb ◽  
Alexander Pretsch ◽  
Selma Gogalic ◽  
...  
Keyword(s):  
Antifungal Activity ◽  
Aspergillus Fumigatus ◽  
Endophytic Fungus ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Minimal Inhibitory Concentrations ◽  
Spectrometric Data ◽  
Isolated Compound

A new macrolactone glycoside, lecythomycin (1), 23-methyl-3-(1- O-mannosyl)-oxacyclotetracosan-1-one, was isolated from the endophytic fungus Lecythophora sp. (code 30.1), an endopyte of the Indonesian plant Alyxia reinwardtii. The structure of 1 was elucidated on the basis of NMR spectroscopic and mass spectrometric data. The isolated compound displayed antifungal activity against strains of Aspergillus fumigatus and Candida kruzei at minimal inhibitory concentrations (MIC) of 62.5 – 125 μg/mL.

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