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Author

Dennis Trede

Other affiliations: PerkinElmer
Bio: Dennis Trede is an academic researcher from University of Bremen. The author has contributed to research in topics: MALDI imaging & Mass spectrometry imaging. The author has an hindex of 19, co-authored 43 publications receiving 1113 citations. Previous affiliations of Dennis Trede include PerkinElmer.

Papers
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Journal ArticleDOI
TL;DR: This Letter suggests the use of a sparsity-promoting prior, verified in many inline holography applications, and presents a simple iterative algorithm for 3D object reconstruction under sparsity and positivity constraints.
Abstract: Inline digital holograms are classically reconstructed using linear operators to model diffraction. It has long been recognized that such reconstruction operators do not invert the hologram formation operator. Classical linear reconstructions yield images with artifacts such as distortions near the field-of-view boundaries or twin images. When objects located at different depths are reconstructed from a hologram, in-focus and out-of-focus images of all objects superimpose upon each other. Additional processing, such as maximum-of-focus detection, is thus unavoidable for any successful use of the reconstructed volume. In this Letter, we consider inverting the hologram formation model in a Bayesian framework. We suggest the use of a sparsity-promoting prior, verified in many inline holography applications, and present a simple iterative algorithm for 3D object reconstruction under sparsity and positivity constraints. Preliminary results with both simulated and experimental holograms are highly promising.

163 citations

Journal ArticleDOI
TL;DR: A new pipeline of efficient computational methods is presented, which enables analysis and interpretation of a 3D MALDI-IMS data set and reveals the 3D kidney anatomical structure based on mass spectrometry data only.
Abstract: 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 a...

123 citations

Journal ArticleDOI
TL;DR: To understand the mechanism by which MB metastasis occurs, three-dimensional matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) experiments were performed on whole brains from a mouse model of human medulloblastoma, leading to the detection of low abundance, spatially-heterogeneous lipids associated with tumor development.
Abstract: Treatment for medulloblastoma (MB) — the most common malignant pediatric brain tumor — includes prophylactic radiation administered to the entire brain and spine due to the high incidence of metastasis to the central nervous system. However, the majority of long-term survivors are left with permanent and debilitating neurocognitive impairments as a result of this therapy, while the remaining 30–40% of patients relapse with terminal metastatic disease. Development of more effective targeted therapies has been hindered by our lack of understanding of the underlying mechanisms regulating the metastatic process in this disease. To understand the mechanism by which MB metastasis occurs, three-dimensional matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) experiments were performed on whole brains from a mouse model of human medulloblastoma. Analyzing the tumor and surrounding normal brain in its entirety enabled the detection of low abundance, spatially-heterogeneous lipids associated with tumor development. Boundaries of metastasizing and non-metastasizing primary tumors were readily defined, leading to the identification of lipids associated with medulloblastoma metastasis, including phosphatidic acids, phosphatidylethanolamines, phosphatidylserines, and phosphoinositides. These lipids provide a greater insight into the metastatic process and may ultimately lead to the discovery of biomarkers and novel targets for the diagnosis and treatment of metastasizing MB in humans.

97 citations

Journal ArticleDOI
TL;DR: The orthogonal matching pursuit (OMP) algorithm as discussed by the authors is an algorithm to solve sparse approximation problems and it has been applied to solve ill-posed inverse problems in general and in particular for two deconvolution examples from mass spectrometry and digital holography.
Abstract: The orthogonal matching pursuit (OMP) is an algorithm to solve sparse approximation problems. Sufficient conditions for exact recovery are known with and without noise. In this paper we investigate the applicability of the OMP for the solution of ill-posed inverse problems in general and in particular for two deconvolution examples from mass spectrometry and digital holography respectively. In sparse approximation problems one often has to deal with the problem of redundancy of a dictionary, i.e. the atoms are not linearly independent. However, one expects them to be approximatively orthogonal and this is quantified by the so-called incoherence. This idea cannot be transfered to ill-posed inverse problems since here the atoms are typically far from orthogonal: The ill-posedness of the operator causes that the correlation of two distinct atoms probably gets huge, i.e. that two atoms can look much alike. Therefore one needs conditions which take the structure of the problem into account and work without the concept of coherence. In this paper we develop results for exact recovery of the support of noisy signals. In the two examples in mass spectrometry and digital holography we show that our results lead to practically relevant estimates such that one may check a priori if the experimental setup guarantees exact deconvolution with OMP. Especially in the example from digital holography our analysis may be regarded as a first step to calculate the resolution power of droplet holography.

93 citations

Journal ArticleDOI
TL;DR: A complete and robust 3D MALDI-MSI pipeline is established combined with efficient computational data analysis methods for 3D edge preserving image denoising, 3D spatial segmentation as well as finding colocalized m/z values, which will be reviewed here in detail.

81 citations


Cited by
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Journal ArticleDOI
TL;DR: An imaging method, termed Fourier ptychographic microscopy (FPM), which iteratively stitches together a number of variably illuminated, low-resolution intensity images in Fourier space to produce a wide-field, high-resolution complex sample image, which can also correct for aberrations and digitally extend a microscope's depth-of-focus beyond the physical limitations of its optics.
Abstract: We report an imaging method, termed Fourier ptychographic microscopy (FPM), which iteratively stitches together a number of variably illuminated, low-resolution intensity images in Fourier space to produce a wide-field, high-resolution complex sample image. By adopting a wavefront correction strategy, the FPM method can also correct for aberrations and digitally extend a microscope’s depth of focus beyond the physical limitations of its optics. As a demonstration, we built a microscope prototype with a resolution of 0.78 µm, a field of view of ∼120 mm^2 and a resolution-invariant depth of focus of 0.3 mm (characterized at 632 nm). Gigapixel colour images of histology slides verify successful FPM operation. The reported imaging procedure transforms the general challenge of high-throughput, high-resolution microscopy from one that is coupled to the physical limitations of the system’s optics to one that is solvable through computation.

1,363 citations

30 Jul 2010
TL;DR: An analysis of the validity of Histological grade as a prognostic factor and a consensus view on the significance of histological grade and its role in breast cancer classification and staging systems in this era of emerging clinical use of molecular classifiers are presented.
Abstract: Breast cancer is a heterogeneous disease with varied morphological appearances, molecular features, behavior, and response to therapy. Current routine clinical management of breast cancer relies on the availability of robust clinical and pathological prognostic and predictive factors to support clinical and patient decision making in which potentially suitable treatment options are increasingly available. One of the best-established prognostic factors in breast cancer is histological grade, which represents the morphological assessment of tumor biological characteristics and has been shown to be able to generate important information related to the clinical behavior of breast cancers. Genome-wide microarray-based expression profiling studies have unraveled several characteristics of breast cancer biology and have provided further evidence that the biological features captured by histological grade are important in determining tumor behavior. Also, expression profiling studies have generated clinically useful data that have significantly improved our understanding of the biology of breast cancer, and these studies are undergoing evaluation as improved prognostic and predictive tools in clinical practice. Clinical acceptance of these molecular assays will require them to be more than expensive surrogates of established traditional factors such as histological grade. It is essential that they provide additional prognostic or predictive information above and beyond that offered by current parameters. Here, we present an analysis of the validity of histological grade as a prognostic factor and a consensus view on the significance of histological grade and its role in breast cancer classification and staging systems in this era of emerging clinical use of molecular classifiers.

642 citations

Journal ArticleDOI
TL;DR: This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in $d=2$), and it is proved that one can find low rank (almost) best approximations in a hierarchical format ($\mathcal{H}$-Tucker) which requires only $\ mathcal{O}((d-1)k^3+dnk)$ parameters.
Abstract: We define the hierarchical singular value decomposition (SVD) for tensors of order $d\geq2$. This hierarchical SVD has properties like the matrix SVD (and collapses to the SVD in $d=2$), and we prove these. In particular, one can find low rank (almost) best approximations in a hierarchical format ($\mathcal{H}$-Tucker) which requires only $\mathcal{O}((d-1)k^3+dnk)$ parameters, where $d$ is the order of the tensor, $n$ the size of the modes, and $k$ the (hierarchical) rank. The $\mathcal{H}$-Tucker format is a specialization of the Tucker format and it contains as a special case all (canonical) rank $k$ tensors. Based on this new concept of a hierarchical SVD we present algorithms for hierarchical tensor calculations allowing for a rigorous error analysis. The complexity of the truncation (finding lower rank approximations to hierarchical rank $k$ tensors) is in $\mathcal{O}((d-1)k^4+dnk^2)$ and the attainable accuracy is just 2-3 digits less than machine precision.

602 citations

Journal ArticleDOI
TL;DR: IMS is a technology that makes regiospecific molecular measurements directly from biological specimens, allowing it to make significant contributions to the authors' understanding of biological molecules, and mass spectrometry is unique among analytical technologies in its ability to directly measure individual molecular species in complex samples.
Abstract: Human beings are adept at discerning relevant information from complex systems by processing visual information. Similarly, as scientists labor to understand the fundamental nature of complex biological systems, they have continued to rely on visual information in the form of images to characterize and classify natural phenomena. New technologies designed to produce images of biological specimens have played a key role in the development of our modern understanding of biology. One of the earliest technological examples, the application of light microscopy to the analysis of biological tissue in the 17th century, ultimately led to the discovery of the cell as a key component of biology.1 Fortunately, the ways in which scientists now visualize biological systems have significantly matured. Currently, the methods for imaging biological specimens encompass an extraordinarily large range of technologies, capitalizing on many different measurable physical phenomena to produce images that provide insight into the underlying biology within the specimen. During the previous century, many imaging technologies including microscopy, radiography, ultrasonography, and magnetic resonance imaging have contributed greatly to the visualization of biological processes and to the practice of medicine.2 Each imaging modality has unique advantages and disadvantages that enable them to make contributions to research and clinical practice. One key aspect of imaging that remains a challenge is the effective integration of molecularly specific information as part of the image. Many of the commonly used in vivo imaging technologies produce high quality images, but these cannot be expressed as individual molecular images. Although immunostaining can be used to localize specific molecules within a biological sample, this method depends upon the use of a surrogate marker of the molecule such as an antibody or other specialized reagent and is usually performed on one or at most only a few molecules of interest in a single experiment. Mass spectrometry (MS) is unique among analytical technologies in its ability to directly measure individual molecular species in complex samples, allowing it to make significant contributions to our understanding of biological molecules. Indeed, the fundamental basis of the dynamic state of living systems was discovered by Rittenberg and Schoenheimer in the 1930's and 1940's through the use of MS and stable isotope tracers.3–5 With the introduction of ionization techniques such as electrospray ionization (ESI)6 and matrix-assisted laser desorption/ionization (MALDI),7 the field of mass spectrometry has grown exponentially in the past 20 years due to the application of MS to biological molecules. These capabilities ushered in a new era of biological research wherein a systems approach can be used to analyze the molecules in living systems in the wake of information provided by the Human Genome Project.8 With the drive to discover new biology has come a concomitant drive for the development of new mass spectrometry instrumentation. The primary benefit of this technology innovation is the ability to measure specific molecular compounds at high structural fidelity with high speed of acquisition, making it possible to perform experiments on biological systems that have not been possible before. Even single experiments have shown near comprehensive coverage of entire proteomes of simple organisms.9–10 Imaging Mass Spectrometry (IMS) is a technology that makes regiospecific molecular measurements directly from biological specimens.11–15 This method of imaging capitalizes on all the advantages of modern mass spectrometers, including high sensitivity, high throughput, and molecular specificity, to produce images that visually represent tissue biology on the basis of specific molecules (e.g. peptides, proteins, lipids, drugs and metabolites). The capabilities of mass spectrometry are unique in the imaging world, providing unique insights into biological systems. The distinguishing principle of imaging mass spectrometry from other mass spectrometric techniques is that the preparation of the sample and the acquisition of the MS data must be performed in a manner that preserves the spatial integrity of the sample within the limits of the spatial resolution of the measurement. Therefore, IMS of a biological sample, such as a tissue section, requires that the mass spectral data be registered to specific spatial locations in order to correlate the molecular information to specific cells or groups of cells commonly visualized by microscopy. Images are reconstructed by plotting the intensities of a given ion on a coordinate system that represents the relative position of the mass spectral acquisition from the biological sample. The resulting images create a visual representation of the sample based on the specific molecular information measured from the sample itself. IMS has a number of advantages relative to other imaging techniques currently used for biological and clinical studies. First, MS can be used to detect analytes without the need for labeling or otherwise structurally modifying the native compound. This distinction is important for many reasons, but primarily this avoids potential problems if the tagging reagent affects or changes the physical, chemical, or biological function of the molecules of interest or if the reagent has multiple molecular affinities. Second, MS has the capability of monitoring thousands of molecules in a single experiment. From a systems biology perspective, the advantage of the concurrent measurement of whole pathways or components in multiple pathways is crucial to understanding the function of intact cells. Among the several mass spectrometry ionization techniques that can be used to directly analyze tissues, MALDI has led the way in the development of biological and clinical applications for IMS.16–17 This report describes the essential considerations for performing MALDI IMS experiments on tissue, reviews some of the recent applications to the analysis of clinical specimens, highlights specific contributions of MALDI IMS to our understanding of biology and medicine, and discusses specific advantages and limitations of the technology. This review is not intended to be comprehensive with respect to all aspects of imaging mass spectrometry; rather it focuses on the themes that are essential to the analysis of biological and clinical tissue samples using MALDI IMS. There are excellent reviews that extensively cover both the ionization techniques used in IMS as well as the various mass analyzers that have been adapted for use in IMS and the reader is referred to these for further information.12,18–21

549 citations

Journal ArticleDOI
TL;DR: The combination of information gained from mass spectrometry (MS) and visualization of spatial distributions in thin sample sections makes this a valuable chemical analysis tool useful for biological specimen characterization.
Abstract: Mass spectrometry imaging (MSI) is a powerful tool that enables untargeted investigations into the spatial distribution of molecular species in a variety of samples. It has the capability to image thousands of molecules, such as metabolites, lipids, peptides, proteins, and glycans, in a single experiment without labeling. The combination of information gained from mass spectrometry (MS) and visualization of spatial distributions in thin sample sections makes this a valuable chemical analysis tool useful for biological specimen characterization. After minimal but careful sample preparation, the general setup of an MSI experiment involves defining an (x, y) grid over the surface of the sample, with the grid area chosen by the user. The mass spectrometer then ionizes the molecules on the surface of the sample and collects a mass spectrum at each pixel on the section, with the resulting spatial resolution defined by the pixel size. After collecting the spectra, computational software can be used to select an ...

507 citations