Toponomics: visualizing cellular protein networks in health and disease –‘A single picture is worth more than a thousand words!’ (2008)

With rapidly advancing technology, we can easily be overwhelmed by the complexity of new computer-interfaced and robotic techniques directed at deciphering molecular networks. However, without an understanding of emerging technology, there is a risk of failing to adequately critique a study that utilizes such techniques. At first glance, we may be skeptical and regard toponomics as the ivory tower of academics, but as esoteric as this subject initially seems, there may be practical applications within therapeutic and diagnostic medicine, as the authors will discuss below. Due to the complexity of this subject, this discussion will be separated into two parts. Part I will briefly discuss the concept and technique of toponomics. In Part II (next month’s issue), the authors will focus on the applications and limitations of toponomics relevant to dermatopathology. –ar. In the post-human genome era, the disciplines of genomics and proteomics together with advances in computational sciences (bioinformatics) have lead to significant advances in our understanding about health and disease. Briefly, genomics is the study of large-scale genetic patterns across the genome for a given species using techniques such as mapping and sequencing genes. Proteomics is the study of the structure and function of the complete set of expressed proteins (proteome) produced by a species through the use of technologies such as large-scale protein separation and identification.1,2 The proteome is directly dependent on the genome via the transcriptome, which is the complete set of RNA transcripts produced by the genome at any one-time. However, proteomics is much more complicated than genomics. While the genome is a rather constant entity, the proteome is a dynamic network constantly modulated in response to various genetic and environmental signals that are translated into specific cellular functions and adaptations. To illustrate this relationship between the genome and the proteome, if one compares the genome to the alphabet, then the proteome represents the words created by the genome to generate the vocabulary, and post-translational modifications of proteins results in the addition of modifiers, prefixes, suffixes or punctuation marks to the words in a given cell as a snapshot in time. Understanding the various words and their derivatives, and how they are dynamically combined to give meaning within the cell, remains a challenge. Progress has been made in the past several years in generating large-scale data sets for protein-protein interactions, organelle composition, protein activity patterns and protein profiles.1,3,4 This progress has provided important information on (a) whether a given gene is actually translated, (b) the relative abundance of expressed proteins and (c) whether the proteins undergo post-translational modification. However, there are important limitations. Large-scale protein profiling techniques require that proteins be extracted from a large number of cells and tissue homogenates. Although the derived data provides information about proteins regulated under defined conditions in a large number of cells and tissues, the data provide only a partial view of the functionalities in a proteome. Going back to the language analogy, we are only able to identify the specific words which have been ‘shouted’ by the majority of cells under defined conditions, while the equally important more softly spoken words and the order in which they are spoken by individual cells are scrambled by the homogenization procedures. Consequently, further technological improvements are needed in proteomics in order to understand the spatial and temporal protein networks within a single cell and how these contribute to diverse cellular functions. In other words, we need to have the ability to listen to all the individual cells as they speak together in order to identify unique words and the order in which they are spoken if we really wish to understand their roles in health and disease. New techniques, pioneered by Schubert et al., have begun to allow us to see specific spatial relations between cellular elements and better understand the lexicon of cells. This has resulted in a new field of study called toponomics or topological proteomics.5–9 A: The toponome represents the complete topological organization of the major molecules including proteins, lipids, carbohydrates and nucleic acids within a cell by simultaneous identification and mapping of these molecules at the single cell level. It allows visualization of signatures/patterns of molecular networks in a cell and reveals hierarchical features of protein organization, which would otherwise be lost in traditional proteomics. This topological mapping is possible through the technique of Multi-Epitope Ligand Cartography (MELC). MELC combines proteomics and biomathematical tools in order to visualize and analyze cellular protein networks in situ. The information obtained can be analyzed and extrapolated to decipher cellular functions.6–9 A: Briefly, a tissue section (or cell/blood smear) is subjected to an iterative automated cycle of (i) incubation with a fluorescent-labeled antibody/marker, (ii) signal detection by fluorescence microscopy and (iii) soft photo bleaching to abolish the fluorescence prior to re-use of the slide for subsequent re-staining. This cycle can be repeated multiple times with a computer-automated microscope and digital camera capturing the fluorescence signals after each cycle of antibody/marker staining. The repeated cycles of staining, signal detection and photo bleaching permit the investigator to directly trace out the relative locations of many different proteins and other molecules in thousands of cells simultaneously (Fig. 1A). In this way, the slide can now function as a biochip and can be subdivided into topographic micro-units (TMUs). A computer-directed microscope and a digital camera capture the fluorescence signals after each antibody/marker staining. The signals are binarized (0-absent, 1-present) and point-precise overlays of each image are generated. The result is a visual representation of positive and negative spatial co-expressions of multiple epitopes termed ‘combinatorial molecular phenotypes’ (CMPs; Fig. 1A). Unlike traditional large-scale expression profiles based upon tissue homogenates, toponomics is able to capture the subtle but important differences in the patterns of protein expression that become evident when multiple proteins or networks are visualized. A) Multiple Epitope Ligand Cartography (MELC) is used to analyze the highly complex information contained in the toponome of fixed tissue by visualizing at high resolution the in situ proteomic networks within single cells (topographical proteomics = toponomics). A tissue or skin section is subdivided into TMUs (shown as a single red box). The automated MELC process of sequential and iterative incubations with fluorescent markers, binarization and fluorescence image overlay enables a combinatorial colocalization vector to be defined for each TMU. This vector stores the binary information regarding the presence (1) or absence (0) of an expressed molecular determinant as a combinatorial molecular phenotype or CMP. B) Inset, CMPs analyzed across the entire tissue can reveal unique patterns of protein networks or CMP motifs and lead protein(s) between two different conditions (0-always absent, 1-always present, *-variably present). Refer to Bonnekoh et al.10,11 and Schubert for additional information.8 Within these patterns of protein expression, important CMP motifs can be identified which represent specific functional protein network signatures of tissues, cell types, cell states and diseases.7 Briefly, CMP motifs result from the analysis of multiple sets of CMPs within the tissue which then can be represented as a motif (1, 0, *) of the most dominant CMP pattern (1, invariably present proteins which are defined as lead protein(s); 0, absent proteins and *, variably present proteins; Fig. 1B). The article published in Nature Biotechnology by Schubert et al. is recommended to readers who wish to further explore the technology of toponomics.7 This article also has excellent images, including supplementary online videos. In Part II, we will discuss the practical applications of toponomics, as it has been used to identify proteins that may play important roles in various pathologic processes. Specifically, we will focus on the initial studies of toponomics as it relates to both inflammatory and neoplastic skin diseases. We will also discuss the potential uses of toponomics in identifying therapeutic targets for treatment.