Steinbeis Innovation Center for Scientific Computing in Life science

Bremen, Germany

Steinbeis Innovation Center for Scientific Computing in Life science

Bremen, Germany
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Holscher D.,Nuclear Magnetic Resonance Research Group | Holscher D.,University of Kassel | Dhakshinamoorthy S.,Laboratory of Tropical Crop Improvement | Alexandrov T.,University of Bremen | And 27 more authors.
Proceedings of the National Academy of Sciences of the United States of America | Year: 2014

The global yield of bananas - one of the most important food crops - is severely hampered by parasites, such as nematodes, which cause yield losses up to 75%. Plant-nematode interactions of two banana cultivars differing in susceptibility to Radopholus similis were investigated by combining the conventional and spatially resolved analytical techniques 1H NMR spectroscopy, matrixfree UV-laser desorption/ionization mass spectrometric imaging, and Raman microspectroscopy. This innovative combination of analytical techniques was applied to isolate, identify, and locate the bananaspecific type of phytoalexins, phenylphenalenones, in the R. similiscaused lesions of the plants. The striking antinematode activity of the phenylphenalenone anigorufone, its ingestion by the nematode, and its subsequent localization in lipid droplets within the nematode is reported. The importance of varying local concentrations of these specialized metabolites in infected plant tissues, their involvement in the plant's defense system, and derived strategies for improving banana resistance are highlighted.


Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center for Scientific Computing in Life science | Meding S.,Helmholtz Center Munich | Trede D.,University of Bremen | And 7 more authors.
Journal of Proteomics | Year: 2011

In the last decade, imaging mass spectrometry has seen incredible technological advances in its applications to biological samples. One computational method of data mining in this field is the spatial segmentation of a sample, which produces a segmentation map highlighting chemically similar regions. An important issue for any imaging mass spectrometry technology is its relatively low spatial or lateral resolution (i.e. a large size of pixel) as compared with microscopy. Thus, the spatial resolution of a segmentation map is also relatively low, that complicates its visual examination and interpretation when compared with microscopy data, as well as reduces the accuracy of any automated comparison. We address this issue by proposing an approach to improve the spatial resolution of a segmentation map. Given a segmentation map, our method magnifies it up to some factor, producing a super-resolution segmentation map. The super-resolution map can be overlaid and compared with a high-res microscopy image. The proposed method is based on recent advances in image processing and smoothes the "pixilated" region boundaries while preserving fine details. Moreover, it neither eliminates nor splits any region. We evaluated the proposed super-resolution segmentation approach on three MALDI-imaging datasets of human tissue sections and demonstrated the superiority of the super-segmentation maps over standard segmentation maps. © 2011 Elsevier B.V.


Trede D.,Steinbeis Innovation Center for Scientific Computing in Life science | Trede D.,University of Bremen | Schiffler S.,Steinbeis Innovation Center for Scientific Computing in Life science | Schiffler S.,University of Bremen | And 18 more authors.
Analytical Chemistry | Year: 2012

Three-dimensional (3D) imaging has a significant impact on many challenges of life sciences. Three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging label-free bioanalytical technique capturing the spatial distribution of hundreds of molecular compounds in 3D by providing a MALDI mass spectrum for each spatial point of a 3D sample. Currently, 3D MALDI-IMS cannot tap its full potential due to the lack efficient computational methods for constructing, processing, and visualizing large and complex 3D MALDI-IMS data. We present a new pipeline of efficient computational methods, which enables analysis and interpretation of a 3D MALDI-IMS data set. Construction of a MALDI-IMS data set was done according to the state-of-the-art protocols and involved sample preparation, spectra acquisition, spectra preprocessing, and registration of serial sections. For analysis and interpretation of 3D MALDI-IMS data, we applied the spatial segmentation approach which is well-accepted in analysis of two-dimensional (2D) MALDI-IMS data. In line with 2D data analysis, we used edge-preserving 3D image denoising prior to segmentation to reduce strong and chaotic spectrum-to-spectrum variation. For segmentation, we used an efficient clustering method, called bisecting k-means, which is optimized for hierarchical clustering of a large 3D MALDI-IMS data set. Using the proposed pipeline, we analyzed a central part of a mouse kidney using 33 serial sections of 3.5 μm thickness after the PAXgene tissue fixation and paraffin embedding. For each serial section, a 2D MALDI-IMS data set was acquired following the standard protocols with the high spatial resolution of 50 μm. Altogether, 512 495 mass spectra were acquired that corresponds to approximately 50 gigabytes of data. After registration of serial sections into a 3D data set, our computational pipeline allowed us to reveal the 3D kidney anatomical structure based on mass spectrometry data only. Finally, automated analysis discovered molecular masses colocalized with major anatomical regions. In the same way, the proposed pipeline can be used for analysis and interpretation of any 3D MALDI-IMS data set in particular of pathological cases. © 2012 American Chemical Society.


Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center for Scientific Computing in Life science | Becker M.,Bruker | Guntinas-Lichius O.,University Hospital Jena | And 2 more authors.
Journal of Cancer Research and Clinical Oncology | Year: 2013

Purpose: For several decades, conventional histological staining and immunohistochemistry (IHC) have been the main tools to visualize and understand tissue morphology and structure. IHC visualizes the spatial distribution of individual protein species directly in tissue. However, a specific antibody is required for each protein, and multiplexing capabilities are extremely limited, rarely visualizing more than two proteins simultaneously. With the recent emergence of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-imaging), it is becoming possible to study more complex proteomic patterns directly in tissue. However, the analysis and interpretation of large and complex MALDI-imaging data requires advanced computational methods. In this paper, we show how the recently introduced method of spatial segmentation can be applied to analysis and interpretation of a larynx carcinoma section and compare the spatial segmentation with the histological annotation of the same tissue section. Methods: Matrix-assisted laser desorption/ionization imaging is a label-free spatially resolved analytical technique, which allows detection and visualization of hundreds of proteins at once. Spatial segmentation of the MALDI-imaging data by clustering of spectra by their similarity was performed, automatically generating spatial a segmentation map of the tissue section, where regions of similar proteomic patterns were highlighted. The tissue was stained with the hematoxylin and eosin (H&E), histopathologically analyzed and annotated. The segmentation map was interpreted after its overlay with the H&E microscopy image. Results: The automatically generated segmentation map exhibits high correspondence to the detailed histological annotation of the larynx carcinoma tissue section. By superimposing, the segmentation map based on the proteomic profiles with H&E-stained microscopic images, we demonstrate precise localization of complex and histopathologically relevant tissue features in an automated way. Conclusions: The combination of MALDI-imaging and automatic spatial segmentation is a useful approach in analyzing carcinoma tissue and provides a deeper insight into the functional proteomic organization of the respective tissue. © 2012 Springer-Verlag.


Crecelius A.C.,Friedrich - Schiller University of Jena | Crecelius A.C.,Dutch Polymer Institute | Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center for Scientific Computing in Life science | And 2 more authors.
Rapid Communications in Mass Spectrometry | Year: 2011

This study presents the application of matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) to monitor changes occurring at polymer surfaces. As an example, a poly(styrene) (PS) film was irradiated with ultraviolet (UV) light at 254nm for different time intervals, while areas of the film were protected from UV light by covering it with an aluminum mask. After the UV treatment, the polymer surface was analyzed by MALDI-MSI. Time-dependent photo-induced cross-linking of the polymer film was observed, and a correlation curve between UV radiation time and area of cross-linking was constructed. This represents the first step towards the surface analysis of polymer components of photoresists and top coatings of cars, and it will also enable a new characterization strategy for combinatorial material research. Copyright © 2011 John Wiley & Sons, Ltd.


Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center for Scientific Computing in Life science | Kobarg J.H.,University of Bremen
Bioinformatics | Year: 2011

Motivation: Imaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability. Methods: We segment pixels by clustering their mass spectra. Importantly, we incorporate spatial relations between pixels into clustering, so that pixels are clustered together with their neighbors. We propose two methods. One is non-adaptive, where pixel neighborhoods are selected in the same manner for all pixels. The second one respects the structure observable in the data. For a pixel, its neighborhood is defined taking into account similarity of its spectrum to the spectra of adjacent pixels. Both methods have the linear complexity and require linear memory space (in the number of spectra). Results: The proposed segmentation methods are evaluated on two IMS datasets: a rat brain section and a section of a neuroendocrine tumor. They discover anatomical structure, discriminate the tumor region and highlight functionally similar regions. Moreover, our methods provide segmentation maps of similar or better quality if compared to the other state-of-the-art methods, but outperform them in runtime and/or required memory. © The Author(s) 2011. Published by Oxford University Press.


PubMed | Steinbeis Innovation Center for Scientific Computing in Life science
Type: Journal Article | Journal: Analytical chemistry | Year: 2012

Three-dimensional (3D) imaging has a significant impact on many challenges of life sciences. Three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging label-free bioanalytical technique capturing the spatial distribution of hundreds of molecular compounds in 3D by providing a MALDI mass spectrum for each spatial point of a 3D sample. Currently, 3D MALDI-IMS cannot tap its full potential due to the lack efficient computational methods for constructing, processing, and visualizing large and complex 3D MALDI-IMS data. We present a new pipeline of efficient computational methods, which enables analysis and interpretation of a 3D MALDI-IMS data set. Construction of a MALDI-IMS data set was done according to the state-of-the-art protocols and involved sample preparation, spectra acquisition, spectra preprocessing, and registration of serial sections. For analysis and interpretation of 3D MALDI-IMS data, we applied the spatial segmentation approach which is well-accepted in analysis of two-dimensional (2D) MALDI-IMS data. In line with 2D data analysis, we used edge-preserving 3D image denoising prior to segmentation to reduce strong and chaotic spectrum-to-spectrum variation. For segmentation, we used an efficient clustering method, called bisecting k-means, which is optimized for hierarchical clustering of a large 3D MALDI-IMS data set. Using the proposed pipeline, we analyzed a central part of a mouse kidney using 33 serial sections of 3.5 m thickness after the PAXgene tissue fixation and paraffin embedding. For each serial section, a 2D MALDI-IMS data set was acquired following the standard protocols with the high spatial resolution of 50 m. Altogether, 512495 mass spectra were acquired that corresponds to approximately 50 gigabytes of data. After registration of serial sections into a 3D data set, our computational pipeline allowed us to reveal the 3D kidney anatomical structure based on mass spectrometry data only. Finally, automated analysis discovered molecular masses colocalized with major anatomical regions. In the same way, the proposed pipeline can be used for analysis and interpretation of any 3D MALDI-IMS data set in particular of pathological cases.

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