Analyzing proteome topology and function by automated multidimensional fluorescence microscopy

The gain in brain: novel imaging techniques and multiplexed proteomic imaging of brain tissue ultrastructure

The rapid accumulation of neuroproteomics data in recent years has prompted the emergence of novel antibody-based imaging methods that aim to understand the anatomical and functional context of the multitude of identified proteins. The pioneering field of ultrastructural multiplexed proteomic imaging now includes a number of high resolution methods, such as array tomography, stimulated emission depletion microscopy, stochastic optical reconstruction microscopy and automated transmission electron microscopy, which allow a detailed molecular characterization of individual synapses and subsynaptic structures within brain tissues for the first time. While all of these methods still face considerable limitations, a combined complementary approach building on the respective strengths of each method is possible and will enable fascinating research into the proteomic diversity of the nervous system.

BioIMAX: a Web 2.0 approach for easy exploratory and collaborative access to multivariate bioimage data

Background

Innovations in biological and biomedical imaging produce complex high-content and multivariate image data. For decision-making and generation of hypotheses, scientists need novel information technology tools that enable them to visually explore and analyze the data and to discuss and communicate results or findings with collaborating experts from various places.

Results

In this paper, we present a novel Web2.0 approach, BioIMAX, for the collaborative exploration and analysis of multivariate image data by combining the webs collaboration and distribution architecture with the interface interactivity and computation power of desktop applications, recently called rich internet application.

Conclusions

BioIMAX allows scientists to discuss and share data or results with collaborating experts and to visualize, annotate, and explore multivariate image data within one web-based platform from any location via a standard web browser requiring only a username and a password. BioIMAX can be accessed at http://ani.cebitec.uni-bielefeld.de/BioIMAX with the username "test" and the password "test1" for testing purposes.

Systems biology beyond networks: generating order from disorder through self-organization

Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated.

Fractal immunology and immune patterning: potential tools for immune protection and optimization

Fractals are self-similar geometric patterns that are inherently embedded throughout nature. Their discovery and application have produced significant benefits across a wide variety of biomedical applications. Recently, complex physiological systems (e.g., neurological, respiratory, cardiovascular) have been shown to exhibit fractal dimensions that are capable of distinguishing among physiologic function versus dysfunction and, in turn, health versus disease. Additionally, fractal data suggest that the immune system operates under similar patterned relationships, and this is in keeping with the recent findings that immune-based diseases are organized according to specific patterns. This review considers the potential benefits of using fractal analysis along with considerations of nonlinearity, scaling, and chaos as calibration tools to obtain holistic information on immune-environment interactions. The potential uses of both synthetic and artificial immune systems for improved protection of the biological immune system are also discussed. The addition of holistic measures of immune status to currently collected biomarkers of immunotoxicity has the potential to increase the effectiveness of health risk assessment. The objective of extending fractal physiology analyses to the immune system would be to promote immune optimization as a public health benefit, which would include improved: (1) immunotoxicity testing and effective health risk reduction and (2) measures of effective immune management for children, adults, and aged individuals.

Layered electrophoretic transfer - A method for pre-analytic processing of histological sections

Current technologies for measuring protein expression across a tissue section are based on MS or in situ detection such as immunohistochemistry. However, due to the inherent molecular complexity of tissue samples and the large dynamic range of protein expression in cells, current approaches are often unable to measure moderate- and low-abundant proteins. In addition, they do not provide information on the physico-chemical properties of the proteins studied. To address these problems, we are developing a new pre-analytic methodology termed layered electrophoretic transfer (LET) that selectively separates and processes proteins from an intact tissue section without compromising important two-dimensional histological information. LET offers two potential advantages over standard techniques: (i) A reduced complexity of the tissue proteome for subsequent analysis; (ii) An opportunity to assess the biochemical status of proteins as they exist in situ. As an initial proof-of-concept, we demonstrate here that the protein content from a mixture of molecular weight standards, human tissue lysates, and tissue sections can be successfully transferred and separated using LET, and further demonstrate that the method can be coupled with immunoblotting or MS for downstream measurements. LET technology represents a new pre-analytic tool for interrogating the proteome in tissue sections while preserving valuable spatial information.

The use and abuse of -omes

The diverse fields of Omics research share a common logical structure combining a cataloging effort for a particular class of molecules or interactions, the underlying -ome, and a quantitative aspect attempting to record spatiotemporal patterns of concentration, expression, or variation. Consequently, these fields also share a common set of difficulties and limitations. In spite of the great success stories of Omics projects over the last decade, much remains to be understood not only at the technological, but also at the conceptual level. Here, we focus on the dark corners of Omics research, where the problems, limitations, conceptual difficulties, and lack of knowledge are hidden.

Introduction A: recent advances in cytometry instrumentation, probes, and methods--review

Cytometric techniques are continually being improved, refined, and adapted to new applications. This chapter briefly outlines recent advances in the field of cytometry with the main focus on new instrumentations in flow and image cytometry as well as new probes suitable for multiparametric analyses. There is a remarkable trend for miniaturizing cytometers, developing label-free and fluorescence-free analytical approaches, and designing "intelligent" probes. Furthermore, new methods for analyzing complex data for extracting relevant information are reviewed.

Ct3d: tracking microglia motility in 3D using a novel cosegmentation approach

MOTIVATION

Cell tracking is an important method to quantitatively analyze time-lapse microscopy data. While numerous methods and tools exist for tracking cells in 2D time-lapse images, only few and very application-specific tracking tools are available for 3D time-lapse images, which is of high relevance in immunoimaging, in particular for studying the motility of microglia in vivo.

RESULTS

We introduce a novel algorithm for tracking cells in 3D time-lapse microscopy data, based on computing cosegmentations between component trees representing individual time frames using the so-called tree-assignments. For the first time, our method allows to track microglia in three dimensional confocal time-lapse microscopy images. We also evaluate our method on synthetically generated data, demonstrating that our algorithm is robust even in the presence of different types of inhomogeneous background noise.

AVAILABILITY

Our algorithm is implemented in the ct3d package, which is available under http://www.picb.ac.cn/patterns/Software/ct3d; supplementary videos are available from http://www.picb.ac.cn/patterns/Supplements/ct3d.

Anti-inflammatory role of microsomal prostaglandin E synthase-1 in a model of neuroinflammation

A major immunological response during neuroinflammation is the activation of microglia, which subsequently release proinflammatory mediators such as prostaglandin E(2) (PGE(2)). Besides its proinflammatory properties, cyclooxygenase-2 (COX-2)-derived PGE(2) has been shown to exhibit anti-inflammatory effects on innate immune responses. Here, we investigated the role of microsomal PGE(2) synthase-1 (mPGES-1), which is functionally coupled to COX-2, in immune responses using a model of lipopolysaccharide (LPS)-induced spinal neuroinflammation. Interestingly, we found that activation of E-prostanoid (EP)2 and EP4 receptors, but not EP1, EP3, PGI(2) receptor (IP), thromboxane A(2) receptor (TP), PGD(2) receptor (DP), and PGF(2) receptor (FP), efficiently blocked LPS-induced tumor necrosis factor α (TNFα) synthesis and COX-2 and mPGES-1 induction as well as prostaglandin synthesis in spinal cultures. In vivo, spinal EP2 receptors were up-regulated in microglia in response to intrathecally injected LPS. Accordingly, LPS priming reduced spinal synthesis of TNFα, interleukin 1β (IL-1β), and prostaglandins in response to a second intrathecal LPS injection. Importantly, this reduction was only seen in wild-type but not in mPGES-1-deficient mice. Furthermore, intrathecal application of EP2 and EP4 agonists as well as genetic deletion of EP2 significantly reduced spinal TNFα and IL-1β synthesis in mPGES-1 knock-out mice after LPS priming. These data suggest that initial inflammation prepares the spinal cord for a negative feedback regulation by mPGES-1-derived PGE(2) followed by EP2 activation, which limits the synthesis of inflammatory mediators during chronic inflammation. Thus, our data suggest a role of mPGES-1-derived PGE(2) in resolution of neuroinflammation.

Toponome imaging system: in situ protein network mapping in normal and cancerous colon from the same patient reveals more than five-thousand cancer specific protein clusters and their subcellular annotation by using a three symbol code

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.

Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR

Motivation: The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification.

Results: Here, we report a large-scale analysis of spatial, i.e. 3D, gene-expression data from an entire organ (the mouse brain) for the purpose of evaluating and ranking positional candidate genes, showing that the spatial gene-expression patterns can be successfully exploited for the prediction of gene–phenotype associations not only for mouse phenotypes, but also for human central nervous system-related Mendelian disorders. We apply our method to the case of X-linked mental retardation, compare the predictions to the results obtained from a previous large-scale resequencing study of chromosome X and discuss some promising novel candidates.

Contact:rosario.piro@unito.it

Supplementary information:Supplementary data are available at Bioinformatics online.

NorthernLights in slide-based cytometry and microscopy

In recent years, slide-based cytometry has become a key technology for polychromatic cytometric investigations, and many efforts have been made to increase the number of measurable fluorochromes for multiparametric analysis. Sequential photobleaching of fluorochromes next to very photostable dyes is one approach for this technology. As the ALEXA dyes are known to be photostable as compared to the conventional fluorochromes FITC, PE (Riggs et al., Am J Pathol 1958;34:1081-1097), and APC, a differentiation within a fluorochrome pair is possible. Here, we have analyzed the newly available NorthernLights secondary antibodies for use in slide-based cytometry and microscopy. Currently, these fluorochrome-conjugates are now available with three distinct excitation- and emission maxima (NL493, NL557, NL637). Their spectral properties are similar to the frequently used fluorochromes FITC, PE, and APC and can, therefore, be used with most common excitation sources of cytometers or microscopes. As the NorthernLights are bright, resistant to photobleaching, stable in alcohols and xylene and of affordable price, these dyes are promising candidates for use with most laser- and HBO/XBA-based fluorescence microscopy-like techniques.

Comparative Multi-Epitope-Ligand-Cartography reveals essential immunological alterations in Barrett's metaplasia and esophageal adenocarcinoma

Background

Barrett's esophagus (BE) is caused by gastroesophageal reflux with consecutive mucosal inflammation, predisposing patients to the development of esophageal adenocarcinoma (EAC). We investigated changes in T cell-related mucosal combinatorial molecular protein patterns in both diseases using the novel Multi-Epitope-Ligand-Cartography, a unique robotic whole-cell imaging technology that simultaneously visualizes dozens of proteins in structurally intact tissues and correlates cellular localization of proteins with function.

Results

Biopsies were taken during endoscopy from BE, EAC, and normal control tissue, and proteomic microscopy was performed on 32 different epitopes. When the significance level was set to p < 0.0005 and the search depth to five antibody combinations, controls and BE can be differentiated by 63, controls and EAC by 3222, and BE from EAC by 1521 distinct protein combinations.

For example, the number of activated apoptotic naïve and memory T cells was significantly increased only in BE, whereas the number of activated apoptotic helper and regulatory T cells was significantly elevated in BE and EAC. In contrast, the number of activated apoptotic cytotoxic T cells was significantly elevated only in EAC. Confirming different pathways in BE and EAC, the number of T lymphocytes with p53 expression and downregulation of bcl2 expression (CD3⁺p53⁺Bcl2^-NfkB^-) was significantly increased in EAC compared to BE and controls. Interestingly, the number of precursor T cells (CD7⁺) was significantly elevated only in EAC. These cells lack Bax and caspase-8, suggesting impaired apoptosis in the early stages of T cell differentiation.

Conclusion

Proteomic analysis showed for the first time that proteins, which are critically involved in the mucosal immune system of the esophagus, are distinctly expressed in BE and EAC, whereas others are comparably altered in both diseases, suggesting that many pathogenic events might be shared by both diseases. Topological proteomic analysis, therefore, helps us to understand the different pathogenic events in the underlying disease pathways.

Analysis of NF-kappaB signaling pathways by proteomic approaches

NF-kappaB is a transcription factor that plays important roles in the regulation of apoptosis and inflammation as well as innate and adaptive immunity. Consequently, dysregulations in the NF-kappaB activation cascade have been associated with the pathogenesis of several diseases such as cancer, atherosclerosis and rheumatoid arthritis. Although NF-kappaB signaling pathways have been extensively investigated in this context, its varying components and targets are far from being completely elucidated. There is still an urgent need for the detection of novel NF-kappaB target proteins, novel interaction partners and novel regulators in the activation cascade, in particular with regard to its role in the aforementioned diseases. Therefore, several groups have performed different proteomic approaches to further investigate NF-kappaB signal transduction pathways. Most of these studies have been carried out in the area of cancer research; however, there are also several analyses in the field of inflammatory or autoimmune diseases. Furthermore, there have been a number of basic investigations that principally examined binding partners or so far unknown target proteins of NF-kappaB-related proteins. With these approaches, a number of novel and interesting proteins have been found that interfere with NF-kappaB signal transduction and might have an impact on NF-kappaB-related diseases. The results of these studies are summarized and discussed in this review.

Visualization of image data from cells to organisms

Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.

Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns

Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in conjunction with the macromolecule of interest. Alternatively, tailored algorithms allow finding particular regions in images and quantifying the amount of fluorescence they contain. Unfortunately, this approach requires extensive hand-tuning of algorithms and is often cell type-dependent. Here we describe a machine-learning approach for estimating the amount of fluorescent signal in different subcellular compartments without hand tuning, requiring only the acquisition of separate training images of markers for each compartment. In testing on images of cells stained with mixtures of probes for different organelles, we achieved a 93% correlation between estimated and expected amounts of probes in each compartment. We also demonstrated that the method can be used to quantify drug-dependent protein translocations. The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts.

Toponomics: studying protein-protein interactions and protein networks in intact tissue

The function of a protein is determined on several levels including the genome, transcriptome, proteome, and the recently introduced toponome. The toponome describes the topology of all proteins, protein complexes and protein networks which constitute and influence the microenvironment of a given protein. It has long been known that cellular function or dysfunction of proteins strongly depends on their microenvironment and even small changes in protein arrangements can dramatically alter their activity/function. Thus, deciphering the topology of the multi-dimensional networks which control normal and disease-related pathways will give a better understanding of the mechanisms underlying disease development. While various powerful proteomic tools allow simultaneous quantification of proteins, only a limited number of techniques are available to visualize protein networks in intact cells and tissues. This review discusses a novel approach to map and decipher functional molecular networks of proteins in intact cells or tissues. Multi-epitope-ligand-cartography (MELC) is an imaging technology that identifies and quantifies protein networks at the subcellular level of morphologically-intact specimens. This immunohistochemistry-based method allows serial visualization and biomathematical analysis of up to 100 cellular components using fluorescence-labelled tags. The resulting toponome maps, simultaneously ranging from the subcellular to the supracellular scale, have the potential to provide the basis for a mathematical description of the dynamic topology of protein networks, and will complement current proteomic data to enhance the understanding of physiological and pathophysiological cell functions.

Light microscopic analysis of mitochondrial heterogeneity in cell populations and within single cells

Heterogeneity in the shapes of individual multicellular organisms is a daily experience. Likewise, even a quick glance through the ocular of a light microscope reveals the morphological heterogeneities in genetically identical cultured cells, whereas heterogeneities on the level of the organelles are much less obvious. This short review focuses on intracellular heterogeneities at the example of the mitochondria and their analysis by fluorescence microscopy. The overall mitochondrial shape as well as mitochondrial dynamics can be studied by classical (fluorescence) light microscopy. However, with an organelle diameter generally close to the resolution limit of light, the heterogeneities within mitochondria cannot be resolved with conventional light microscopy. Therefore, we briefly discuss here the potential of subdiffraction light microscopy (nanoscopy) to study inner-mitochondrial heterogeneities.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Histopathological image analysis: a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Comparative in situ topoproteome analysis reveals differences in patch test-induced eczema: cytotoxicity-dominated nickel versus pleiotrope pollen reaction

A subgroup of patients with atopic eczema develops acute eczematous reactions to type I allergy-inducing agents such as pollen that clinically resemble type IV allergies induced by haptens like metal ions. To clarify the underlying immunologic mechanisms, this study was designed to map the inflammatory in situ topoproteome of eczematous responses to grass/birch pollen and nickel by using atopy patch test (APT) and nickel patch test (NPT) as an appropriate clinical model, respectively. Biopsies from NPT (n = 6) and APT (n = 6) with positive reactions at 72 h were analysed by multiple epitope ligand cartography (MELC), which enabled to investigate coexpression of 49 different epitopes immunohistochemically in a single given tissue section. Colocalisation of IgE and FcepsilonRI was investigated by confocal microscopy. Compared with APT responses, NPT reactions were dominated by cytotoxic TIA-1 + and CD8 + T cells. In contrast, the immune response in APT reactions appeared more pleiotrope - as detected by colocalisation analysis. Multiple combinatorial molecular phenotype (CMP) motifs containing naive, early maturation and memory T cell (CD45RA, CD7, CD44, CD45R0), and general activation markers (CLA, HLA-DR, CD13, CD29, CD58, CD71, CD138) were significantly higher expressed in APT when compared with NPT reactions. APT response was confirmed to be accompanied by IgE bound to FcepsilonRI. In summary, our results demonstrate that the NPT reaction is clearly dominated by cytotoxic events, while the APT reaction to pollen grains is more heterogeneous and elicits a combined humoral and cellular immune reaction.

Toponome mapping in prostate cancer: detection of 2000 cell surface protein clusters in a single tissue section and cell type specific annotation by using a three symbol code

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.

The potential of optical proteomic technologies to individualize prognosis and guide rational treatment for cancer patients

Genomics and proteomics will improve outcome prediction in cancer and have great potential to help in the discovery of unknown mechanisms of metastasis, ripe for therapeutic exploitation. Current methods of prognosis estimation rely on clinical data, anatomical staging and histopathological features. It is hoped that translational genomic and proteomic research will discriminate more accurately than is possible at present between patients with a good prognosis and those who carry a high risk of recurrence. Rational treatments, targeted to the specific molecular pathways of an individual's high-risk tumor, are at the core of tailored therapy. The aim of targeted oncology is to select the right patient for the right drug at precisely the right point in their cancer journey. Optical proteomics uses advanced optical imaging technologies to quantify the activity states of and associations between signaling proteins by measuring energy transfer between fluorophores attached to specific proteins. Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM) assays are suitable for use in cell line models of cancer, fresh human tissues and formalin-fixed paraffin-embedded tissue (FFPE). In animal models, dynamic deep tissue FLIM/FRET imaging of cancer cells in vivo is now also feasible. Analysis of protein expression and post-translational modifications such as phosphorylation and ubiquitination can be performed in cell lines and are remarkably efficiently in cancer tissue samples using tissue microarrays (TMAs). FRET assays can be performed to quantify protein-protein interactions within FFPE tissue, far beyond the spatial resolution conventionally associated with light or confocal laser microscopy. Multivariate optical parameters can be correlated with disease relapse for individual patients. FRET-FLIM assays allow rapid screening of target modifiers using high content drug screens. Specific protein-protein interactions conferring a poor prognosis identified by high content tissue screening will be perturbed with targeted therapeutics. Future targeted drugs will be identified using high content/throughput drug screens that are based on multivariate proteomic assays. Response to therapy at a molecular level can be monitored using these assays while the patient receives treatment: utilizing re-biopsy tumor tissue samples in the neoadjuvant setting or by examining surrogate tissues. These technologies will prove to be both prognostic of risk for individuals when applied to tumor tissue at first diagnosis and predictive of response to specifically selected targeted anticancer drugs. Advanced optical assays have great potential to be translated into real-life benefit for cancer patients.

Dual spatial maps of transcript and protein abundance in the mouse brain

Integrating quantitative proteomic and transcriptomic datasets promises valuable insights in unraveling the molecular mechanisms of the brain. We concentrate on recent studies using mass spectrometry and microarray data to investigate transcript and protein abundance in normal and diseased neural tissues. Highlighted are dual spatial maps of these molecules obtained using voxelation of the mouse brain. We demonstrate that the relationship between transcript and protein levels displays a specific anatomical distribution, with greatest fidelity in midline structures and the hypothalamus. Genes are also identified that have strong correlations between mRNA and protein abundance. In addition, transcriptomic and proteomic analysis of mouse models of Parkinson's disease are discussed.

Blind source separation techniques for the decomposition of multiply labeled fluorescence images

Methods of blind source separation are used in many contexts to separate composite data sets according to their sources. Multiply labeled fluorescence microscopy images represent such sets, in which the sources are the individual labels. Their distributions are the quantities of interest and have to be extracted from the images. This is often challenging, since the recorded emission spectra of fluorescent dyes are environment- and instrument-specific. We have developed a nonnegative matrix factorization (NMF) algorithm to detect and separate spectrally distinct components of multiply labeled fluorescence images. It operates on spectrally resolved images and delivers both the emission spectra of the identified components and images of their abundance. We tested the proposed method using biological samples labeled with up to four spectrally overlapping fluorescent labels. In most cases, NMF accurately decomposed the images into contributions of individual dyes. However, the solutions are not unique when spectra overlap strongly or when images are diffuse in their structure. To arrive at satisfactory results in such cases, we extended NMF to incorporate preexisting qualitative knowledge about spectra and label distributions. We show how data acquired through excitations at two or three different wavelengths can be integrated and that multiple excitations greatly facilitate the decomposition. By allowing reliable decomposition in cases where the spectra of the individual labels are not known or are known only inaccurately, the proposed algorithms greatly extend the range of questions that can be addressed with quantitative microscopy.

A versatile platform for comprehensive chip-based explorative cytometry

Analysis of the immense complexity of the immune system is increasingly hampered by technical limitations of current methodologies, especially for multiparameter- and functional analysis of samples containing small numbers of cells. We here present a method, which is based on the stepwise functional manipulation and analysis of living immune cells that are self-immobilized within microfluidic chips using automated epifluorescence microscopy overcoming current limitations for comprehensive immunophenotyping. Crossvalidation with flow cytometry revealed a 10-fold increased sensitivity and a comparable specificity. By using small sample volumes and cell numbers (2-10 microl, down to 20,000 cells), we were able to analyze a virtually unlimited number of intracellular and surface markers even on living immune cells. We exemplify the scientific and diagnostic potential of this method by (1) identification and phenotyping of rare cells, (2) comprehensive analysis of very limited sample volume, and (3) deep immunophenotyping of human B-cells after in vitro differentiation. Finally, we propose an informatic model for annotation and comparison of cytometric data by using an ontology-based approach. The chip-based cytometry introduced here turned out to be a very useful tool to enable a stepwise exploration of precious, small cell-containing samples with an virtually unlimited number of surface- and intracellular markers.

Molecular characterization and classification of neuroblastoma

For many decades, neuroblastoma has remained a challenging disease for both clinicians and researchers. Now, techniques that efficiently specify both comprehensive genetic and gene-expression alterations of neuroblastoma tumors have provided molecular markers that indicate tumor behavior and patient outcome with very high accuracy. Once the anticipated value of these markers has been confirmed in ongoing studies, patients may profit from more accurate risk assessment by integrating these markers into clinical routine. Moreover, disclosing further tumor-initiating events, such as the recently revealed oncogenic mutations of ALK, will further promote the elucidation of the genetic etiology of the disease. Together with recent information on altered signaling pathways in aggressively growing tumors, this knowledge will help to establish therapeutic strategies specifically targeting molecular key factors of neuroblastoma tumor progression.

SIMPLE: a sequential immunoperoxidase labeling and erasing method

The ability to simultaneously visualize expression of multiple antigens in cells and tissues can provide powerful insights into cellular and organismal biology. However, standard methods are limited to the use of just two or three simultaneous probes and have not been widely adopted for routine use in paraffin-embedded tissue. We have developed a novel approach called sequential immunoperoxidase labeling and erasing (SIMPLE) that enables the simultaneous visualization of at least five markers within a single tissue section. Utilizing the alcohol-soluble peroxidase substrate 3-amino-9-ethylcarbazole, combined with a rapid non-destructive method for antibody-antigen dissociation, we demonstrate the ability to erase the results of a single immunohistochemical stain while preserving tissue antigenicity for repeated rounds of labeling. SIMPLE is greatly facilitated by the use of a whole-slide scanner, which can capture the results of each sequential stain without any information loss.

In-situ-topoproteome analysis of cutaneous lymphomas: perspectives of assistance for dermatohistologic diagnostics by Multi Epitope Ligand Cartography (MELC)

BACKGROUND

Immunophenotyping is essential for diagnostics of cutaneous lymphomas. In this regard we present a skin tissue-adapted application platform of MELC technology.

PATIENTS AND METHODS

This topoproteome analysis allows the subcellular colocalization of at least n = 100 epitopes in situ. For this purpose the specimen is processed by a Toponome Imaging Cycler for a n-fold repetition of the following cycle: 1) staining with a fluorophore-labeld antibody, 2) fluorescence-imaging, and 3) photobleaching. Overlay and binarization of fluorescence images lead to combinatorial molecular phenotypes (CMP), which relate to a pixel or microtopographic unit (450 x 450 nm2, 20x objective). Skin biopsies were derived from patients with mycosis fungoides (patch/plaque lesions), psoriasis, atopic eczema and from healthy skin donors.

RESULTS

In orientation to the WHO-EORTC-classification of cutaneous lymphomas a MELC-library of 23 markers was established. According to an inaugurative detailed procedure the CMP frequency was determined in a normalization to 100 microm horizontal skin width. By a TopoMiner strategy mycosis fungoides could be separated from the other states with a maximum of significance (p < or = 0.03) by at least 10-fold overexpression of the following tumor cell-representative CMP-motif: CD3+/CD4+/CD1a-/CD7-/CD8-/CD45R0+/CD45RA-/CD11a+.

CONCLUSIONS

The skin tissue-adapted MELC-application-platform extends substantially conventional lymphoma diagnostics by an unprecedented dimension of in-situ-analysis of marker combinatorics including its exact quantification and visualization.

Location proteomics: systematic determination of protein subcellular location

Proteomics seeks the systematic and comprehensive understanding of all aspects of proteins, and location proteomics is the relatively new subfield of proteomics concerned with the location of proteins within cells. This review provides a guide to the widening selection of methods for studying location proteomics and integrating the results into systems biology. Automated and objective methods for determining protein subcellular location have been described based on extracting numerical features from fluorescence microscope images and applying machine learning approaches to them. Systems to recognize all major protein subcellular location patterns in both two-dimensional and three-dimensional HeLa cell images with high accuracy (over 95% and 98%, respectively) have been built. The feasibility of objectively grouping proteins into subcellular location families, and in the process of discovering new subcellular patterns, has been demonstrated using cluster analysis of images from a library of randomly tagged protein clones. Generative models can be built to effectively capture and communicate the patterns in these families. While automated methods for high-resolution determination of subcellular location are now available, the task of applying these methods to all expressed proteins in many different cell types under many conditions represents a very significant challenge.

High content cell screening in a microfluidic device

A comprehensive, systems level understanding of cell signaling networks requires methods to efficiently assay multiple signaling species, at the level of single cells, responding to a variety of stimulation protocols. Here we describe a microfluidic device that enables quantitative interrogation of signaling networks in thousands of individual cells using immunofluorescence-based readouts. The device is especially useful for measuring the signaling activity of kinases, transcription factors, and/or target genes in a high throughput, high content manner. We demonstrate how the device may be used to measure detailed time courses of signaling responses to one or more soluble stimuli and/or chemical inhibitors as well as responses to a complex temporal pattern of multiple stimuli. Furthermore we show how the throughput and resolution of the device may be exploited in investigating the differences, if any, of signaling at the level of a single cell versus at the level of the population. In particular, we show that NF-kappaB activity dynamics in individual cells are not asynchronous and instead resemble the dynamics of the population average in contrast to studies of cells overexpressing p65-EGFP.

Sphingosine 1-phosphate modulates spinal nociceptive processing

Sphingosine 1-Phosphate (S1P) modulates various cellular functions such as apoptosis, cell differentiation, and migration. Although S1P is an abundant signaling molecule in the central nervous system, very little is known about its influence on neuronal functions. We found that S1P concentrations were selectively decreased in the cerebrospinal fluid of adult rats in an acute and an inflammatory pain model. Pharmacological inhibition of sphingosine kinases (SPHK) decreased basal pain thresholds and SphK2 knock-out mice, but not SphK1 knock-out mice, had a significant decrease in withdrawal latency. Intrathecal application of S1P or sphinganine 1-phosphate (dihydro-S1P) reduced the pain-related (nociceptive) behavior in the formalin assay. S1P and dihydro-S1P inhibited cyclic AMP (cAMP) synthesis, a key second messenger of spinal nociceptive processing, in spinal cord neurons. By combining fluorescence resonance energy transfer (FRET)-based cAMP measurements with Multi Epitope Ligand Cartography (MELC), we showed that S1P decreased cAMP synthesis in excitatory dorsal horn neurons. Accordingly, intrathecal application of dihydro-S1P abolished the cAMP-dependent phosphorylation of NMDA receptors in the outer laminae of the spinal cord. Taken together, the data show that S1P modulates spinal nociceptive processing through inhibition of neuronal cAMP synthesis.

Consequences of altered eicosanoid patterns for nociceptive processing in mPGES-1-deficient mice

Cyclooxygenase-2 (COX-2)-dependent prostaglandin (PG) E₂ synthesis in the spinal cord plays a major role in the development of inflammatory hyperalgesia and allodynia. Microsomal PGE₂ synthase-1 (mPGES-1) isomerizes COX-2-derived PGH₂ to PGE₂. Here, we evaluated the effect of mPGES-1-deficiency on the noci-ceptive behavior in various models of nociception that depend on PGE₂ synthesis. Surprisingly, in the COX-2-dependent zymosan-evoked hyperalgesia model, the nociceptive behavior was not reduced in mPGES-1-deficient mice despite a marked decrease of the spinal PGE₂ synthesis. Similarly, the nociceptive behavior was unaltered in mPGES-1-deficient mice in the formalin test. Importantly, spinal cords and primary spinal cord cells derived from mPGES-1-deficient mice showed a redirection of the PGE₂ synthesis to PGD₂, PGF_2α and 6-keto-PGF_1α (stable metabolite of PGI₂). Since the latter prostaglandins serve also as mediators of noci-ception they may compensate the loss of PGE₂ synthesis in mPGES-1-deficient mice.

Toponomics analysis of functional interactions of the ubiquitin ligase PAM (Protein Associated with Myc) during spinal nociceptive processing

Protein associated with Myc (PAM) is a giant E3 ubiquitin ligase of 510 kDa. Although the role of PAM during neuronal development is well established, very little is known about its function in the regulation of synaptic strength. Here we used multiepitope ligand cartography (MELC) to study protein network profiles associated with PAM during the modulation of synaptic strength. MELC is a novel imaging technology that utilizes biomathematical tools to describe protein networks after consecutive immunohistochemical visualization of up to 100 proteins on the same sample. As an in vivo model to modulate synaptic strength we used the formalin test, a common model for acute and inflammatory pain. MELC analysis was performed with 37 different antibodies or fluorescence tags on spinal cord slices and led to the identification of 1390 PAM-related motifs that distinguish untreated and formalin-treated spinal cords. The majority of these motifs related to ubiquitin-dependent processes and/or the actin cytoskeleton. We detected an intermittent colocalization of PAM and ubiquitin with TSC2, a known substrate of PAM, and the glutamate receptors mGluR5 and GLUR1. Importantly these complexes were detected exclusively in the presence of F-actin. A direct PAM/F-actin interaction was confirmed by colocalization and cosedimentation. The binding of PAM toward F-actin varied strongly between the PAM splice forms found in rat spinal cords. PAM did not ubiquitylate actin or alter actin polymerization and depolymerization. However, F-actin decreased the ubiquitin ligase activity of purified PAM. Because PAM activation is known to involve its translocation, the binding of PAM to F-actin may serve to control its subcellular localization as well as its activity. Taken together we show that defining protein network profiles by topological proteomics analysis is a useful tool to identify previously unknown protein/protein interactions that underlie synaptic processes.

[Livid polycyclic plaques of the lower extremities]

In 10%-55% of patients, leukemia cutis (LC) manifest as a symptom of acute myelomonocytic leukemia and is associated with a poor overall prognosis. Disseminated bluish-violet or red-brownish papules and plaques, nodules and also hemorrhagic ulcers may dominate the initial clinical picture. Importantly, nonspecific infiltrates (leukemids) must be differentiated from specific infiltrates of malignant cells in patients presenting with dermatoses and concomitant underlying hematopoietic neoplasms. The role of the dermatologist is the rapid clinical and dermatohistopathological diagnosis in order to allow immediate, adequate treatment of the patient's underlying systemic disease.

Interlocking transcriptomics, proteomics and toponomics technologies for brain tissue analysis in murine hippocampus

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.