Life-Saving Microscopy Method for Amyotrophic Lateral Sclerosis Patients.

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.

Large molecular systems landscape uncovers T cell trapping in human skin cancer.

Immune surveillance of tumour cells is an important function of CD8 T lymphocytes, which has failed in cancer for reasons still unknown in many respect but mainly related to cellular processes in the tumour microenvironment. Applying imaging cycler microscopy to analyse the immune contexture in a human skin cancer we could identify and map 7,000 distinct cell surface-associated multi-protein assemblies. The resulting combinatorial geometry-based high-functional resolution led to discovery of a mechanism of T cell trapping in the epidermis, which involves SPIKE, a network of suprabasal keratinocyte projections piercing and interconnecting CD8 T cells. It appears initiated by clusters of infrabasal T and dendritic cells connected via cell projections across a fractured basal lamina to suprabasal keratinocytes and T lymphocytes.

Advances in toponomics drug discovery: Imaging cycler microscopy correctly predicts a therapy method of amyotrophic lateral sclerosis.

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.

Systematic, spatial imaging of large multimolecular assemblies and the emerging principles of supramolecular order in biological systems.

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 (<40 nm distances). It is shown that the subcellular and transcellular features of such supermolecules can be described at the compositional and constitutional levels; that the spatial connection, relational stoichiometry, and topology of supermolecules generate hitherto unrecognized functional self-segmentation of biological tissues; that hierarchical features, common to thousands of simultaneously imaged supermolecules, can be identified; and how the resulting supramolecular order relates to spatial coding of cellular functionalities in biological systems. A large body of observations with IC molecular systems microscopy collected over 20 years have disclosed principles governed by a law of supramolecular segregation of cellular functionalities. This pervades phenomena, such as exceptional orderliness, functional selectivity, combinatorial and spatial periodicity, and hierarchical organization of large molecular systems, across all species investigated so far. This insight is based on the high degree of specificity, selectivity, and sensitivity of molecular recognition processes for fluorescence imaging beyond the spectral resolution limit, using probe libraries controlled by ICs.

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

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.

Interactive, graph-based visual analysis of high-dimensional, multi-parameter fluorescence microscopy data in toponomics.

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.

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.

On the origin of cell functions encoded in the toponome.

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.

Automated detection and quantification of fluorescently labeled synapses in murine brain tissue sections for high throughput applications.

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 768×512 pixels in approx. 20s. The software approach presented in this study provides a reliable basis for high throughput quantification of synapses in neural tissue.

An information theoretic thresholding method for detecting protein colocalizations in stacks of fluorescence images.

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.

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.

Functional architecture of the cell nucleus: towards comprehensive toponome reference maps of apoptosis.

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.

Toponomics and neurotoponomics: a new way to medical systems biology.

The fluorescence robot imaging technology multi-epitope-ligand-cartography/toponome imaging system has revolutionized the field of proteomics/functional genomics, because it enables the investigator to locate and decipher functional protein networks, the toponome, consisting of hundreds of different proteins in a single cell or tissue section. The technology has been proven to solve key problems in biology and therapy research. It has uncovered a new cellular transdifferentiation mechanism of vascular cells giving rise to myogenic cells in situ and in vivo; a finding that has led to efficient cell therapy models of muscle disorders, and discovered a new target protein in sporadic amyotrophic lateral sclerosis by hierarchical protein network analysis, a finding that has been confirmed by a mouse knockout model. A lead target protein in tumor cells that controls cell polarization as a mechanism that is fundamental for migration and metastasis formation has also been uncovered, and new functional territories in the CNS defined by high-dimensional synaptic protein clusters have been unveiled. The technology can be effectively interlocked with genomics and proteomics to optimize time-to-market and the overall attrition rate of new drugs. This review outlines major proofs of principle with an emphasis on neurotoponomics.

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.

Fluorescence detection of protein clusters in individual cells and tissue sections by using toponome imaging system: sample preparation and measuring procedures.

This protocol details sample preparation and measurement procedures for a fluorescence technology capable of colocalizing hundreds of different proteins in a cell or tissue section. The procedure relies on fixation of samples and on the use of dye-conjugated tag libraries. To colocalize proteins, a sample is placed on the microscope stage of an imaging system (toponome imaging system (TIS)) performing sequential cycles of tag-dye incubation, imaging and bleaching to generate images for each localization cycle. TIS overcomes the spectral limitations of traditional fluorescence microscopy. Image processing reveals toponome maps, uncovering the coexistence of proteins at a location (protein clusters). The approach provides direct insight into the topological organization of proteins on a proteomic scale for the first time. If, for example, two dyes are used per cycle, 18 proteins in 4 visual fields can be colocalized in 21 h. Parallel TIS procedures using more than two dyes per cycle enhance the throughput.

A three-symbol code for organized proteomes based on cyclical imaging of protein locations.

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.

Analyzing proteome topology and function by automated multidimensional fluorescence microscopy.

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

Exploring molecular networks directly in the cell.

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