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N Biotechnol. 2012 Jun 15;29(5):599-610. doi: 10.1016/j.nbt.2011.12.004. Epub 2011 Dec 21.

Next-generation biomarkers based on 100-parameter functional super-resolution microscopy TIS.

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  • 1Molecular Pattern Recognition Research Group, Medical Faculty, Otto-von-Guericke-University Magdeburg, Germany. walter.schubert@med.ovgu.de

Abstract

Functional super-resolution (fSR) microscopy is based on the automated toponome imaging system (TIS). fSR-TIS provides insight into the myriad of different cellular functionalities by direct imaging of large subcellular protein networks in morphologically intact cells and tissues, referred to as the toponome. By cyclical fluorescence imaging of at least 100 molecular cell components, fSR-TIS overcomes the spectral limitations of fluorescence microscopy, which is the essential condition for the detection of protein network structures in situ/in vivo. The resulting data sets precisely discriminate between cell types, subcellular structures, cell states and diseases (fSR). With up to 16 bits per protein, the power of combinatorial molecular discrimination (PCMD) is at least 2(100) per subcellular data point. It provides the dimensionality necessary to uncover thousands of distinct protein clusters including their subcellular hierarchies controlling protein network topology and function in the one cell or tissue section. Here we review the technology and findings showing that functional protein networks of the cell surface in different cancers encompass the same hierarchical and spatial coding principle, but express cancer-specific toponome codes within that scheme (referred to as TIS codes). Findings suggest that TIS codes, extracted from large-scale toponome data, have the potential to be next-generation biomarkers because of their cell type and disease specificity. This is functionally substantiated by the observation that blocking toponome-specific lead proteins results in disassembly of molecular networks and loss of function.

Copyright © 2011 Elsevier B.V. All rights reserved.

PMID:
22209707
[PubMed - indexed for MEDLINE]
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