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Walter Schubert

Publications and source records attributed to Walter Schubert.

5 recordsLinked to original sources
PubMed2006

Analyzing proteome topology and function by automated multidimensional fluorescence microscopy.

Temporal and spatial regulation of proteins contributes to function. We describe a multidimensional microscopic robot technology for high-throughput protein colocalization studies that runs cycles of fluorescence tagging, imaging and bleaching in situ. This technology combines three advances: a fluorescence technique capable of mapping hundreds of different proteins in one tissue section or cell sample; a method selecting the most prominent combinatorial molecular patterns by representing the data as binary vectors; and a system for imaging the distribution of these protein clusters in a so-called toponome map. By analyzing many cell and tissue types, we show that this approach reveals rules of hierarchical protein network organization, in which the frequency distribution of different protein clusters obeys Zipf's law, and state-specific lead proteins appear to control protein network topology and function. The technology may facilitate the development of diagnostics and targeted therapies.

Dermatitis, Atopic↗
PubMed2006

Exploring molecular networks directly in the cell.

The hierarchy of cell function comprises at least four distinct functional levels: genome, transcriptome, proteome, and toponome. The toponome is the entirety of all protein networks traced out directly as patterns on the single cell level in the natural environment of cells in situ (e.g. tissues). In this work a photonic microscopic robot technology (MELK) capable of tagging and imaging hundreds (and possibly thousands) of different molecular components (e.g. proteins) of morphologically-intact fixed cells and tissue have been developed. MELK data sets represent multidimensional vectors of the topologically determined arrangements of proteins within the cell. The data, assembled in a toponome dictionary of the cell, give rise to a new concept for target and drug lead discovery.

Eukaryotic Cells↗
PubMed2003

Topological proteomics, toponomics, MELK-technology.

MELK is an ultrasensitive topological proteomics technology analysing proteins on the single cell level (Multi-Epitope-Ligand-'Kartographie'). It can trace out large scale protein patterns with subcellular resolution, mapping the topological position of many proteins simultaneously in a cell. Thereby, it addresses higher level order in a proteome, referred to as the toponome, coding cell functions by topologically and timely determined webs of interacting proteins. The resulting cellular protein maps provide new structures in the proteome: single combinatorial protein patterns (s-CPP), and combinatorial protein pattern motifs (CPP-motifs), bound to superior units. They are images of functional protein networks, which are specific signatures of tissues, cell types, cell states and diseases. The technology unravels hierarchies of proteins related to particular cell functions or dysfunctions, thus identifying and prioritising key proteins within cell and tissue protein networks. Interlocking MELK with the drug screening machinery provides new clues related to the selection of target proteins, and functionally relevant hits and drug leads. The present chapter summarizes the steps that have contributed to the establishment of the technology.

Humans↗
PubMed2003

Human vs machine: evaluation of fluorescence micrographs.

To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs. As a part of this, we focus on the question of how to measure the accuracy of microscope image interpretation, by human experts or a computer system. Following standard practice we use methods motivated by receiver operator characteristics to discuss the accuracies of human experts and of neural network-based algorithms. For images of good quality the algorithms achieve the accuracy of the medium-skilled experts. In images with increased noise, the classifiers are outperformed by some of the experts. Furthermore, the neural network-based cell detection is much faster than the human experts.

Algorithms↗