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The submission describes in the form of a communication recent experiences with a promising new therapeutic approach for amyotrophic lateral sclerosis (ALS). This approach is based on imaging cycler microscopy that led to the discovery of ALS specific cells in the blood, which invade the pyramidal system (lateral corticospinal tract) of ALS patients, where they compress motor axons. The depletion of these cells leads to remission of clinical symptoms and demonstrates the important role of these cells in ALS. The therapy will be offered to ALS patients in licensed and certified centers (in progress). © 2020 International Society for Advancement of Cytometry.

This article deals with a molecular geometry imaging platform capable of mapping the spatial protein-colocalisation and anti-colocalisation code of large molecular systems at a time. The platform called molecular unlimited systems imaging cycler (MUSIC) robotics was applied to amyotrophic lateral sclerosis (ALS). The detection of ALS specific cells with a corresponding multimolecular geometry in the blood led to therapeutic depletion of these cells and to recovery of the treated patient, obviously because this therapy interferes with pathogenic invasion of these cells into the central nervous system, where they axotomize motor axons. Large scale geometry MUSIC robotics imaging of up to 4.5 × 10481 power of combinatorial molecular resolution is key to detect these cells and to control depletion therapy for clinical success. These data and new possibilities may argue for clinical application and for a systematic research in the field of molecular geometry of diseases to discover new mathematically defined insight.

An imaging cycler microscope (ICM) is a fully automated (epi)fluorescence microscope which overcomes the spectral resolution limit resulting in parameter- and dimension-unlimited fluorescence imaging. This enables the spatial resolution of large molecular systems with their emergent topological properties (toponome) in morphologically intact cells and tissues displaying thousands of multi protein assemblies at a time. The resulting combinatorial geometry of these systems has been shown to be key for in-vivo/in-situ detection of lead proteins controlling protein network topology and (dys)function: If lead proteins are blocked or downregulated the corresponding disease protein network disassembles. Here, correct therapeutic predictions are exemplified for ALS. ICM drug target studies have discovered an 18-dimensional cell surface molecular system in ALS-PBMC with a lead drug target protein, whose therapeutic downregulation is now reported to show statistically significant effect with stop of disease progression in one third of the ALS patients. Together, this clinical and the earlier experimental validations of the ICM approach indicate that ICM readily discovers in vivo robustness nodes of disease with lead proteins controlling them. Breaking in vivo robustness nodes using drugs against their lead proteins is likely to overcome current high drug attrition rates. © 2015 The Author. Published by Wiley Periodicals, Inc, on behalf of ISAC.

Understanding biological systems at the level of their relational (emergent) molecular properties in functional protein networks relies on imaging methods, able to spatially resolve a tissue or a cell as a giant, non-random, topologically defined collection of interacting supermolecules executing myriads of subcellular mechanisms. Here, the development and findings of parameter-unlimited functional super-resolution microscopy are described—a technology based on the fluorescence imaging cycler (IC) principle capable of co-mapping thousands of distinct biomolecular assemblies at high spatial resolution and differentiation (

We present a technique to find threshold values that allows the user to separate signal from noise in fluorescence grey-level images. It can be classified as a purely comparative method based upon the amount of “Mutual Information” between two or more florescence images, and we apply it to stacks of such images produced using the newly-developed MELK technology. Our results are compared to results obtained by another research group using a quite different (completely independent and more technology-based) approach; and also to results obtained using Otsu's Thresholding Method, yet another completely distinct approach invented to separate foreground and background in a grey-level image, based on minimising “intra-class variance” [9,10]. The remarkably good agreement found suggests that our proposed comparative information based method not only accounts for the biological mechanisms governing cellular protein networks very well, but also (and probably much more importantly) shows that cells actually organise the spatial structure of their protein networks in a highly non-random fashion as might be expected – and thereby try to optimise their “mutual information content”, and thus most probably their efficiency.

In a proof of principle study, we have applied an automated fluorescence toponome imaging system (TIS) to examine whether TIS can find protein network structures, distinguishing cancerous from normal colon tissue present in a surgical sample from the same patient. By using a three symbol code and a power of combinatorial molecular discrimination (PCMD) of 2(21) per subcellular data point in one single tissue section, we demonstrate an in situ protein network structure, visualized as a mosaic of 6813 protein clusters (combinatorial molecular phenotype or CMPs), in the cancerous part of the colon. By contrast, in the histologically normal colon, TIS identifies nearly 5 times the number of protein clusters as compared to the cancerous part (32 009). By subcellular visualization procedures, we found that many cell surface membrane molecules were closely associated with the cell cytoskeleton as unique CMPs in the normal part of the colon, while the same molecules were disassembled in the cancerous part, suggesting the presence of dysfunctional cytoskeleton-membrane complexes. As expected, glandular and stromal cell signatures were found, but interestingly also found were potentially TIS signatures identifying a very restricted subset of cells expressing several putative stem cell markers, all restricted to the cancerous tissue. The detection of these signatures is based on the extreme searching depth, high degree of dimensionality, and subcellular resolution capacity of TIS. These findings provide the technological rationale for the feasibility of a complete colon cancer toponome to be established by massive parallel high throughput/high content TIS mapping.

The fluorescence imaging technology TIS enables the investigator to locate and decipher functional protein networks (the toponome) in a single cell or tissue section. TIS permits optical resolution and simultaneous detection of thousands of protein clusters in situ, composed of different protein species, and their visualization as coloured mosaic structures. Access to this level of protein organization relies on the ability of TIS to break the spectral limit of fluorescence microscopy and co-map a quasi unlimited number of different proteins by using specific tag libraries. The present review outlines the principles of the TIS technology as a fundamental approach to the internal structure, the code and the semantics of any protein system in situ. The review focusses on the discovery of basic coding rules in the toponome, indicating that cells establish functional protein networks on the cell surface by interlocking protein clusters, in which highly dissimilar proteins are topologically assembled (dissimilarity rule), and each cluster exhibits a characteristic geometry on the submicrometer to micrometer scale (geometry rule). The network is hierarchically controlled by a lead protein, whose inhibition leads to disassembly of the network and loss of function. Use of TIS on a proteome-wide scale provides a new way to medical systems biology.

We have recently described the MELC/TIS fluorescence robot technology that is capable of colocalizing at least a hundred different molecular cell components in one cell. The technology reveals new hierarchical properties of protein network organisation, referred to as the toponome, in which topologically confined protein clusters are interlocked within the structural framework of the cell. In this study we have applied MELC/TIS to construct a three-dimensional toponome map of the cell nucleus of a single human hepatocyte undergoing apoptosis. The map reveals six different spatially separated toponome domains in the nuclear interior of one apoptotic cell. In the drive to decipher the apoptosis-specific molecular network on the single cell level, the present toponome map is a first milestone towards the construction of much larger maps addressing hundreds of molecular cell components across the stages of apoptosis.

We have correlated transcriptomics, proteomics and toponomics analyses of hippocampus tissue of inbred C57BL/6 mice to analyse the interrelationship of expressed genes and proteins at different levels of organization. We find that transcriptome and proteome levels of function as well as the topological organization of synaptic protein clusters, detected by toponomics at physiological sites of hippocampus CA3 region, are all largely conserved between different mice. While the number of different synaptic states, characterized by distinct synaptic protein clusters, is enormous (>155,000), these states together form synaptic networks defining distinct and mutually exclusive territories in the hippocampus tissue. The findings provide insight in the systems biology of gene expression on transcriptome, proteome and toponome levels of function in the same brain subregion. The approach will lay the ground for designing studies of neurodegeneration in mouse models and human brains.

Background: A major challenge in the post genomic era is to map and decipher the functional molecular networks of proteins directly in a cell or a tissue. This task requires technologies for the colocalization of random numbers of different molecular components (e.g. proteins) in one sample in one experiment. Methods: Multi-epitope-ligand-“kartographie” (MELK) was developed as a microscopic imaging technology running cycles of iterative fluorescence tagging, imaging, and bleaching, to colocalize a large number of proteins in one sample (morphologically intact routinely fixed cells or tissue). Results: In the present study, 18 different cell surface proteins were colocalized by MELK in cells and tissue sections in different compartments of the human immune system. From the resulting sets of multidimensional binary vectors the most prominent groups of protein–epitope arrangements were extracted and imaged as protein “toponome” maps providing direct insight in the higher order topological organization of immune compartments uncovering new tissue domains. The data sets suggest that protein networks, topologically organized in proteomes in situ, obey a unique protein-colocation and -anticolocation code describable by three symbols. Conclusion: The technology has the potential to colocalize hundreds of proteins and other molecular components in one sample and may offer many applications in biology and medicine. © 2007 International Society for Analytical Cytology.

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.

Given a family of k + 1 real-valued functions \(f_0 , \ldots ,f_k \) defined on the set \(\{ 1, \ldots ,n\} \) and measuring the intensity of certain signals, we want to investigate whether these functions are ‘dependent’ or ‘independent’ by checking whether, for some given family of threshold values \(T_0 , \ldots ,T_k ,\) the size a of the collection of numbers \(j \in \{ 1, \ldots ,n\} \) whose signals \(f_0 (j), \ldots ,f_k (j)\) exceed the corresponding threshold values \(T_0 , \ldots ,T_k \) simultaneously for all \(0, \ldots ,k\) is surprisingly large (or small) in comparison to the family of cardinalities $$ a_i : = \# \{ j \in \{ 1, \ldots ,n\} |f_i (j) > T_i \} \;(i = 0, \ldots ,k) $$ of those numbers \(1 \leq j \leq n\) whose signals fi(j) individually exceed, for a given index i, the corresponding threshold value Ti. Such problems turn presently up in topological proteomics, a new direction of protein-interaction research that has become feasible due to new techniques developed in fluorescence microscopy called Multi-Epitope Ligand Cartography (or, for short, MELK = Multi-Epitop Liganden Kartographie). The above problem has led us to study the numbers \(A_{n|a_0 , \ldots ,a_k } (a)\) of families of subsets \(A_0 ,A_1 , \ldots ,A_k \) of \(\{ 1, \ldots ,n\} \) with \(\# A_i = a_i \) for all \(i = 0, \ldots ,k\) and \(\# \bigcap\nolimits_{i = 0, \ldots k} {A_i = a,} \) and to investigate their asymptotic behaviour. In this note, we show that the associated probability distributions $$ p_{n|a_0 , \ldots ,a_k } = \left( {p_{n|a_0 , \ldots ,a_k } (a)} \right)_{a \in {\text{N}}_0 } $$ defined on the set \({\mathbf{N}}_0 \) of non-negative integers by $$ p_{n|a_0 , \ldots ,a_k } (a): = \frac{{A_{n|a_0 , \ldots ,a_k } (a)}} {{\prod\nolimits_{i = 0}^k {\left( {\begin{array}{*{20}c} n \\ {a_i } \\ \end{array} } \right)} }}\quad (a \in {\text{N}}_0 ) $$ converge, with n → ∞, towards the Poisson distribution $$ {\text{poiss}}_\alpha = ({\text{poiss}}_\alpha (a))_{a \in {\mathbf{N}}_0 } $$ for some fixed \(\alpha \in {\mathbf{R}}_{ > 0} \) provided the numbers \(a_i \,(i = 0, \ldots ,k)\) are assumed to converge with n to infinity in such a way that the conditions $$ a_i \leq n\quad {\text{and}}\quad \mathop {\lim }\limits_{n \to \infty } \frac{{\prod\nolimits_{i = 0}^k {a_i } }} {{n^k }} = \alpha $$ are satisfied. Remarkably, it is the alternating signs in the expressions for \(A_{n|a_0 , \ldots ,a_k } (a)\) resulting from the standard exclusion-inclusion principle that correspond to the alternating signs in the power series expression for exp(−α) when n turns to infinity.

A model of cell population dynamics based on molecular adhesion is explained and discussed in this paper. We consider cancer cells experiencing interactions due to adhesion forces. In the cells' membranes there are proteins directly involved in adhesion. These proteins in the membrane are assembled in complex patterns called Combinatorial Protein Patterns (CPP). The goal of this work is to understand the mechanisms governing the adhesion process — in particular distinguishing CPPs involved in interactions. On the basis of experimental observation we have constructed an asynchronous cellular automaton (CA) model that simulates protein network dynamics in a population of cells.

An abundance of literature addresses the appropriate marketability discount to be applied to a block of publicly traded securities that cannot be immediately liquidated. Typically, the discounts are based upon inferences drawn from measuring discounts indirectly. Here, we take a different approach. We ask the specific question: What is the direct cost of providing full marketability to a less than liquid financial asset? Our findings indicate that marketability discounts for restricted stock and stock option grants, as well as large blocks of publicly traded securities that can be converted to publicly traded securities at some future date, can be determined with reasonable accuracy. This direct approach is superior to that of relying on observed discounts of restricted stock prices in private placements relative to their publicly traded counterparts. Liquidity or marketability is comprised of two components. The elimination of price risk is the first component. Price risk represents the risk that one may not receive the current market value of the underlying freely traded asset due to price fluctuations in the marketplace. The second component of marketability is the ability to convert an asset to cash in a timely fashion. Restricted stock units received as part of a compensation package may not convert to a freely traded stock for periods ranging from a few months to several years. The cost of restoring liquidity to an illiquid asset represents the appropriate marketability discount. We show that an at-the-money equity collar, engineered by buying and selling options on the underlying publicly traded stock creates a perfect hedge that removes all price risk at a point in time. In addition, borrowing against this hedged position will provide funds and restore full liquidity. In the absence of transaction costs, the return on the collar portfolio will be equal to the risk free rate. In practice this return is reduced by transactions costs and interest costs that are incurred. Therefore, the net transaction cost and net interest cost in creating the equity collar represents an appropriate marketability discount. This direct cost approach is the best tool available for calculating the size of the marketability discount. We demonstrate the application of this strategy to blockage discounts and discounts applied to restricted stock units. This approach cannot be directly applied to situations where an equity collar cannot be engineered. However, even where a perfect hedge cannot be directly created we can develop a sense of the appropriate marketability discount that should be applied in a given situation. We begin by looking at previous work dealing with the measurement of liquidity discounts for blockage and restricted stock. Next we apply the collar strategy to determine the appropriate discount to both restricted stock and stock subject to blockage discounts. Blockage Discount Literature Moore (1992) presents a checklist of 22 factors to consider when determining the appropriate blockage discount. The weighting of the factors is based upon the judgment of the valuation professional. Several of the factors he suggests are useful for determining whether a blockage discount is warranted. For example, The share size of the block – especially as compared to the number of shares outstanding of that

A discount for the lack of marketability is the implicit cost of quickly monetizing a non-marketable asset at its current value. These discounts are used in many venues to determine the fair market value of a non-marketable asset such as a privately-held business. There has been much written on the quantification of the discount for the lack of the marketability which is briefly summarized in this article. Marketability refers to monetizing the non-marketable asset at its cash equivalent current value. Current practice often uses the cost of a put option as a proxy for the discount. A put option insures that the investor will receive no less than the current value of the underlying asset. However, the use of a put also allows the investor to maintain the asset's upside potential. Therefore, the cost of a put overstates the discount for the lack of marketability. We show that the cost of monetizing a non-marketable asset at its current value through a loan, secured by an at-the-money equity collar, more effectively captures the true cost of marketability. When puts and calls cannot be employed to secure the current value on the underlying asset, a portfolio consisting of the non-marketable asset and a stock index, where puts and calls can be written on the index can be constructed. The effectiveness of the portfolio in creating a risk free outcome depends upon the correlation and volatility of the stock index and the non-marketable asset. We demonstrate that, relative to current practice, the use of an equity collar with a loan greatly reduces the implied discount for the lack of marketability.

A system for the automatic segmentation of fluorescence micrographs is presented. In the first step, positions of fluorescent cells are detected by a fast learning neural network, which acquires the visual knowledge from a set of training cell-image patches selected by the user. Guided by the detected cell positions the system extracts in the second step the contours of the cells. For contour extraction, a recurrent neural network model is used to approximate the cell shapes. Even though the micrographs are noisy and the fluorescent cells vary in shape and size, the system detects at minimum 95% of the cells. (C) 2002 Elsevier Science B.V. All rights reserved.

The study examines the effect of weathering on mechanical and chemical properties of two 2K (twopack, with and without light stabilisation additives) and one 1K (onepack) clearcoats, concentrating on scratch and mar resistance and acid etch resistance. The clearcoats were investigated before and in the course of accelerated weathering. For determining the mar resistance two different instruments were used: - AMTEC laboratory car wash equipment (rotating wet brush) - Crockmeter Explanations for the changes in clearcoat properties caused by weathering are given by Dynamic-Mechanical Analysis, FT-IR-Spectroscopy, Universal-hardness and surface tension measurements. The clearcoat systems investigated show a clear deterioration of their properties after short periods of accelerated weathering. This effect is more expressed for the clearcoat samples without light stabilizers.

Polymyositis is an inflammatory myopathy characterized by muscle invasion of T-cells penetrating the basal lamina and displacing the plasma membrane of normal muscle fibers. This investigation presents a technology for the direct mapping of protein networks involved in T-cell invasionin situ. Simultaneous localization of 17 adhesive cell surface receptors reveals 18 different combinatorial expression patterns (CEP), which are unique for the T-cell invasion process in muscle tissue. Each invasion step can be assigned to specific CEP on the surface of individual T-cells. This indicates, that the T-cell invasion is enciphered combinatorially in the T-cells' adhesive cell surface proteome fraction. Given 217possible combinations, the T-cell appears to have at its disposal a highly non-random restricted repertoire to specify migratory pathways at the cell surface. These higher-level order functions in the cellular proteome cannot be detected by large-scale protein profiling techniques from tissue homogenates. High-throughput whole cell mapping machines working on structurally intact tissues, as shown here, will allow to measure how cells of different origin (immune cells, tumor cells) combine cell surface receptors to encipher specificity and selectivity for interactions.

Abeta peptides are major components of the amyloid plaques that characterize Alzheimer's disease. These peptides are proteolytic cleavage products of the amyloid precursor protein (APP) and are generated by beta- and gamma-secretases. Here we show by multiparameter immunofluorescence imaging in muscle cells that localization of the Abeta40 and Abeta42 cleavage products reveals different myocyte types in a three-dimensional culture system. These myocyte types are heterogeneous by selective intracellular concentration of either Abeta40 or Abeta42 in vesicular structures, whilst only the Abeta40 peptide is secreted as indicated by Western blot analysis. This cellular pattern of APP proteolysis and Abeta peptide secretion correlates with lack of L-APP mRNA splice isoforms. Differential secretion and intracellular accumulation of Abeta peptides is characteristic for the early myocyte development and might be related to cell fusion.

SUMMARYPancreatic cancer is characterized by an increasing incidence and an extremely poor prognosis. It is resistant to most of the conventional treatment modalities. Histomorphologically, it presents with a strong desmoplastic reaction around cancer cells, and lymphocytes are typically localized as aggregates in the fibrotic interstitial tissue. Using the method of multi-epitope imaging with fluorochrome-tagged specific MoAbs which allows the simultaneous localization and characterization of T cells in tissues, we studied phenotypes and distribution of tumour-infiltrating lymphocytes (TIL) in pancreatic cancer. CD3+ T cells comprised up to 90% of the tumour-infiltrating cells which were either CD4+ or CD8+, most of them being memory cells (CD45RO+). In decreasing order of frequency, T lymphocytes carried the markers for CD45RO, CD18, CD103 and TCR γδ. Very few natural killer cells (CD56+) were observed. Twenty percent of CD8+were labelled with CD103. These CD8+ CD103+T cells, analogous to the gut intraepithelial lymphocytes (IEL), were found in the fibrous interstitial tissue. Furthermore, an inverse correlation was found between the expression of CD18, the β2-integrin, which mediates adhesion of activated lymphocytes, and CD45RO in the CD8+subset of TIL (P = 0.046). In conclusion, phenotyping of T lymphocytes in pancreatic cancer raises the possibility that pancreatic cancer cells develop several strategies to escape the T cell-induced cytolysis by (i) the aggregation of cytotoxic CD8+ CD103+ T cells in the fibrous tissue distant from the tumour cells, and (ii) the presence of CD18-bearing cells which lack the expression of the activation marker CD45RO.

BACKGROUND T lymphocytes play a central role in the immune response to cancer, with specific T-cell reactivity provided by the T-cell receptor (TCR) alphabeta-chain heterodimer. Whereas human pancreatic adenocarcinoma is characterized by a massive infiltration of T lymphocytes, to date no analysis of the TCR Valpha-gene expression of the tumor-infiltrating lymphocytes in pancreatic cancer has been performed. METHODS Using reverse transcriptase polymerase chain reaction (RT-PCR) followed by dot blot hybridization, we determined the TCR alpha-chain repertoire at the mRNA level in pancreatic carcinoma and compared our findings with the TCR Valpha repertoire in the normal pancreas and chronic pancreatitis. RESULTS A heterogeneous lowly restricted TCR Valpha repertoire was observed in pancreatic carcinomas, different from the TCR Valpha repertoire in chronic pancreatitis. CONCLUSIONS Pancreas-infiltrating T cells show a distinct TCR Valpha gene expression profile in normal pancreas, chronic pancreatitis, and pancreatic cancer.

A powerful way to coatings with low volatile content are waterborne systems. They are an important alternative to high solid- and powder coatings. It is essential, that waterborne coatings should not have a lower property level compared with conventional systems. A novel type of emulsifier allows the preparation of non-ionic stabilized aqueous emulsions. The main advantage of this new type of emulsifier consists in improving the chemical resistance of the resulting crosslinked films, especially against acids. There upon they show no discoloring even at high staving temperatures and a very good emulsifying behavior. The base type can be easily functionalized with a multitude of chemical functionalities like -OH, -COOH or vinyl groups. That means the emulsifier can be incorporated in the film during the crosslinking reaction. A migration to the coating surface can be avoided under these conditions. The different synthesis routes will be described in detail. A comparison of three non-ionic stabilized waterborne automotive coating systems with different crosslinking reactions (polyurethane, transetherifying and nucleophilic addition) with the corresponding ionic stabilized systems shows the extraordinary quality of the new tones of emulsifiers.

By using quantitative multiparameter microscopic imaging we demonstrate concentration of two peripheral mononuclear blood leukocyte types expressing the Fc gamma RIII receptor for immunoglobulin G in a clinical subgroup of amyotrophic lateral sclerosis (ALS, n = 9) showing bulbar palsy (ALSBP) and/or predominant involvement of the upper motor neuron (ALSC). Triple fluorescence staining and overlay with phase contrast images (4 parameters) reveals that cell type 1 co-expresses Fc gamma RIII (CD16), CD8 and CD57 surface antigens (ALSC 50 +/- 33.6 cells/microliters, P = 0.0012; ALSBP 16.5 +/- 32.4, P = 0.029). This cell type is not observed in healthy individuals (n = 8) and is only insignificantly increased (P0.05) in neurological disease controls (stroke, n = 3, 2.1 +/- 3.7; polymyositis, n = 6, 1.5 +/- 4.0 cells/microliters) and in ALS cases with peripheral symptoms (ALSP n = 12: 7.6 +/- 8.7). Cell type 2 co-expresses Fc gamma RIII (CD16) and CD8, but is negative for CD57 (ALSC 60.1 +/- 19.3; ALSBP 24.2 +/- 28.0 cells/microliters). These findings are consistent with previous reports on IgG isotype changes and immune-cell invasion of the motor system in ALS.

A review of accounting textbooks indicates that most have not addressed the compelling arguments for including stock compensation plans in the calculation of compensation expense. This article relies on accounting theory to evaluate the recent FASB exposure draft dealing with stock-based compensation. Evaluating current coverage of ESOs in intermediate accounting texts supports the need for supplementary materials in this area that can be used as a teaching resource in accounting theory courses. This additional coverage, we argue, should include generating estimated values for ESOs and reinforcing the importance of statistical measures in valuation issues faced by accountants today. To enhance this understanding, a teaching pedagogy is developed which supports the measurement of economic value of ESOs reported in financial statements and attempts to articulate the need for a more theoretical discussion of these valuation issues in accounting theory classes.

In Toponomics, the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction. The most advanced imaging technique is robot-driven multi-parameter fluorescence microscopy. This technique is capable of co-mapping hundreds of proteins and their distribution and assembly in protein clusters across a cell or tissue sample by running cycles of fluorescence tagging with monoclonal antibodies or other affinity reagents, imaging, and bleaching in situ. The imaging results in complex multi-parameter data composed of one slice or a 3D volume per affinity reagent. Biologists are particularly interested in the localization of co-occurring proteins, the frequency of co-occurrence and the distribution of co-occurring proteins across the cell. We present an interactive visual analysis approach for the evaluation of multi-parameter fluorescence microscopy data in toponomics. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The feature specification result is linked to all views establishing a focus+context visualization in 3D. In a new attribute view, we integrate techniques from graph visualization. Each node in the graph represents an affinity reagent while each edge represents two co-occurring affinity reagent bindings. The graph visualization is enhanced by glyphs which encode specific properties of the binding. The graph view is equipped with brushing facilities. By brushing in the spatial and attribute domain, the biologist achieves a better understanding of the function protein patterns of a cell. Furthermore, an interactive table view is integrated which summarizes unique fluorescence patterns. We discuss our approach with respect to a cell probe containing lymphocytes and a prostate tissue section.

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.

A new CH-acidic enamine crosslinking system is described. These crosslinking resins are prepared by the reaction of acetoacetate polymers with amines to increase the CH acidity; thus it is possible to cure α,β-unsaturated resins via Michael addition at room temperature, even without strong base catalysis. Furthermore, the high reactivity of the crosslinkers allows curing in water as the solvent. Extensive curing studies via IR spectroscopy are described to investigate shelflife, and the dependence the enamine/ketimine equilibrium on curing speed, which is influenced by the polarity of the solvents. Although the enamine function can be hydrolyzed in acidic media, the crosslinked films are resistant to a multitude of both organic and inorganic compounds.

The automated detection and quantification of fluorescently labeled synapses in the brain is a fundamental challenge in neurobiology. Here we have applied a framework, based on machine learning, to detect and quantify synapses in murine hippocampus tissue sections, fluorescently labeled for synaptophysin using a direct and indirect labeling method with FITC as fluorescent dye. In a pixel-wise application of the classifier, small neighborhoods around the image pixels are mapped to confidence values. Synapse positions are computed from these confidence values by evaluating the local confidence profiles and comparing the values with a chosen minimum confidence value, the so called confidence threshold. To avoid time-consuming hand-tuning of the confidence threshold we describe a protocol for deriving the threshold from a small set of images, in which an expert has marked punctuate synaptic fluorescence signals. We can show that it works with high accuracy for fully automated synapse detection in new sample images. The resulting patch-by-patch synapse screening system, referred to as i3S (intelligent synapse screening system), is able to detect several thousand synapses in an area of 768 x 512 pixels in approx. 20s. The software approach presented in this study provides a reliable basis for high throughput quantification of synapses in neural tissue. (C) 2010 Elsevier B.V. All rights reserved.

In this note, we present a new method that allows us to determine threshold values for separating presence and absence of proteins in a stack of fluorescence images describing a spatial distribution of proteins across a biological object (like a slice of nervous tissue, a sample of blood cells, etc.). We apply this method to stacks of fluorescence images and find that the resulting threshold values are almost identical with threshold values found using completely independent methods based on technological and biological aspects of the images in question.

The toponome imaging technology MELC/TIS was applied to analyze prostate cancer tissue. By cyclical imaging procedures, we detected 2100 cell surface protein clusters in a single tissue section. This study provides the whole data set, a new kind of high dimensional data space, solely based on the structure-bound architecture of an in situ protein network, a putative fraction of the tissue code of prostate cancer. It is visualized as a colored mosaic composed of distinct protein clusters, together forming a motif expressed exclusively on the cell surface of neoplastic cells in prostate acini. Cell type specific expression of this motif, found in this preliminary study, suggests that high-throughput toponome analyses of a larger number of cases will provide insight into disease specific protein networks.

In this note, we present a new method that allows us to determine threshold values for separating presence and absence of proteins in a stack of fluorescence images describing a spatial distribution of proteins across a biological object (like a slice of nervous tissue, a sample of blood cells etc.). This method is based on the so-called Multi-Information Function which is closely related to the Mutual-Information Function and the Kullback-Leibler distance. We apply this method to stacks of fluorescence images and find that the resulting threshold values are almost identical with threshold values found using completely independent methods based on technological and biological aspects of the images in question.

At the cellular level, Alzheimer's disease (AD) must be the result of neuronal dysfunction and degeneration leading to a reduction in synaptic density. Filamentous deposits of amyloid, which define the disease at the molecular level, occur within perikarya, axons, dendrites, and terminals of neurons as neurofibrillary tangles (NFT), in the extracellular neuropil as amyloid plaques (APC), and around blood vessels as amyloid congophilic angiopathy (ACA). These fibrillar amyloid protein aggregates are also found in the brain of all individuals with Down's syndrome after the age of 30 years. The amyloid deposits apparently occur in the terminal zones of neurons that develop NFT. It is suggested that amyloid deposition is of fundamental significance in AD and that a thorough understanding of amyloid formation will eventually lead to successful therapeutic intervention in AD. As elucidation of the reasons behind amyloid deposition must shed some light on the pathogenesis of AD, we review the current state of knowledge on the nature of the AD amyloid protein, its origin, and its formation. Although there is yet no agreement about the chemical nature of the amyloid protein of NFT, the major constituent of both APC and ACA has been shown to be a 4.5-kD amyloid protein originally termed "beta-protein" or "amyloid A4" which we now denote as "beta A4." Amyloid beta A4 protein is proteolytically derived from a transmembrane protein termed amyloid precursor protein (APP) which is encoded by a widely expressed gene on chromosome 21. Our present results are consistent with the possibility that amyloid formation requires membrane damage or APP molecules that are not or are incorrectly integrated into membranes. To allow the generation of the C-terminus of beta A4, one proteolytic cleavage step has to occur in the sequence that normally forms the transmembrane domain of the APP proteins. This cleavage is crucial for amyloid formation because we could show that the ability of synthetic beta A4 to form amyloid depositions is mainly based on hydrophobic parts of the sequence that have to interact with each other and build up large aggregates under physiologic conditions. Membrane association of APP is expected to interfere with this cleavage and the process of aggregation.