VEGF receptor 2/-3 heterodimers detected in situ by proximity ligation on angiogenic sprouts

Peer reviewe

Fast Multigrid Solution Method for Nested Edge-Based Finite Element Meshes

In this paper a fast multigrid solution method for edge-based finite element magnetostatic field computation with nested meshes in introduced and its efficiency is investigated. Special prolongation and restriction matrices were constructed according to the nature of the edge based field approximation

Rapid Identification of Bio-Molecules Applied for Detection of Biosecurity Agents Using Rolling Circle Amplification

Detection and identification of pathogens in environmental samples for biosecurity applications are challenging due to the strict requirements on specificity, sensitivity and time. We have developed a concept for quick, specific and sensitive pathogen identification in environmental samples. Target identification is realized by padlock- and proximity probing, and reacted probes are amplified by RCA (rolling-circle amplification). The individual RCA products are labeled by fluorescence and enumerated by an instrument, developed for sensitive and rapid digital analysis. The concept is demonstrated by identification of simili biowarfare agents for bacteria (Escherichia coli and Pantoea agglomerans) and spores (Bacillus atrophaeus) released in field

Visualization of Protein Activity Status in situ Using Proximity Ligation Assays

In 2001 the human proteome organization (HUPO) was created with the ambition to identify and characterize all proteins encoded in the human genome according to several criteria; their expression levels in different tissues and under different conditions; the sub-cellular localization; post-translational modifications; interactions, and if possible also the relationship between their structure and function.When the knowledge of different proteins and their potential interactions increases, so does the need for methods able to unravel the nature of molecular processes in cells and organized tissues, and ultimately for clinical use in samples obtained from patients. The in situ proximity ligation assay (in situ PLA) was developed to provide localized detection of proteins, post-translational modifications and protein-protein interactions in fixed cells and tissues. Dual recognition of the target or interacting targets is a prerequisite for the creation of a circular reporter DNA molecule, which subsequently is locally amplified for visualization of individual protein molecules in single cells. These features offer the high sensitivity and selectivity required for detection of even rare target molecules. Herein in situ PLA was first established and then employed as a tool for detection of both interactions and post-translational modifications in cultured cells and tissue samples. In situ PLA was also adapted to high content screening (HCS) for therapeutic effects, where it was applied for cell-based drug screening of inhibitors influencing post-translational modifications. This was performed using primary cells, paving the way for evaluation of drug effects on cells from patient as a diagnostic tool in personalized medicine. In conclusion, this thesis describes the development and applications of in situ PLA as a tool to study proteins, post-translational modifications and protein-protein interactions in genetically unmodified cells and tissues, and for clinical interactomics.

COMBImage : a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies

Background: Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements. Results: COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer.This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use. Conclusions: COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument

Phenotypic screening platform identifies statins as enhancers of immune cell-induced cancer cell death

Background: High-throughput screening (HTS) of small molecule drug libraries has greatly facilitated the discovery of new cancer drugs. However, most phenotypic screening platforms used in the field of oncology are based solely on cancer cell populations and do not allow for the identification of immunomodulatory agents. Methods: We developed a phenotypic screening platform based on a miniaturized co-culture system with human colorectal cancer- and immune cells, providing a model that recapitulates part of the tumor immune microenvironment (TIME) complexity while simultaneously being compatible with a simple image-based readout. Using this platform, we screened 1,280 small molecule drugs, all approved by the Food and Drug Administration (FDA), and identified statins as enhancers of immune cell-induced cancer cell death. Results: The lipophilic statin pitavastatin had the most potent anti-cancer effect. Further analysis demonstrated that pitavastatin treatment induced a pro-inflammatory cytokine profile as well as an overall pro-inflammatory gene expression profile in our tumor-immune model. Conclusion: Our study provides an in vitro phenotypic screening approach for the identification of immunomodulatory agents and thus addresses a critical gap in the field of immuno-oncology. Our pilot screen identified statins, a drug family gaining increasing interest as repurposing candidates for cancer treatment, as enhancers of immune cell-induced cancer cell death. We speculate that the clinical benefits described for cancer patients receiving statins are not simply caused by a direct effect on the cancer cells but rather are dependent on the combined effect exerted on both cancer and immune cells

COMBImage2 : a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining

Background: Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated. Results: Motivated by this, we developed COMBImage2, a parallel computational framework for higher-order drug combination analysis. COMBImage2 goes far beyond its predecessor COMBImage in many different ways. In particular, it offers automated 384-well plate design, as well as quality control that involves resampling statistics and inter-plate analyses. Moreover, it is equipped with a generic matched filter based object counting method that is currently designed for apoptotic-like cells. Furthermore, apart from higher-order synergy analyses, COMBImage2 introduces a novel data mining approach for identifying interesting temporal response patterns and disentangling higher- from lower- and single-drug effects.COMBImage2 was employed in the context of a small pilot study focused on the CUSP9v4 protocol, which is currently used in the clinic for treatment of recurrent glioblastoma. For the first time, all 246 possible combinations of order 4 or lower of the 9 single drugs consisting the CUSP9v4 cocktail, were evaluated on an in vitro clonal culture of glioma initiating cells. Conclusions: COMBImage2 is able to automatically design and robustly analyze exhaustive and in general higher-order drug combination experiments. Such a versatile video microscopy oriented framework is likely to enable, guide and accelerate systematic large-scale drug combination studies not only for cancer but also other diseases

Single-cell transcriptional pharmacodynamics of trifluridine in a tumor-immune model

Understanding the immunological effects of chemotherapy is of great importance, especially now that we have entered an era where ever-increasing pre-clinical and clinical efforts are put into combining chemotherapy and immunotherapy to combat cancer. Single-cell RNA sequencing (scRNA-seq) has proved to be a powerful technique with a broad range of applications, studies evaluating drug effects in co-cultures of tumor and immune cells are however scarce. We treated a co-culture comprised of human colorectal cancer (CRC) cells and peripheral blood mononuclear cells (PBMCs) with the nucleoside analogue trifluridine (FTD) and used scRNA-seq to analyze posttreatment gene expression profiles in thousands of individual cancer and immune cells concurrently. ScRNA-seq recapitulated major mechanisms of action previously described for FTD and provided new insight into possible treatment-induced effects on T-cell mediated antitumor responses

The method developer's guide to oligonucleotide design

Introduction Development of new methods is essential to make great leaps in science, opening up new avenues for research, but the process behind method development is seldom described. Areas covered Over the last twenty years we have been developing several new methods, such as in situ PLA, proxHCR, and MolBoolean, using oligonucleotide-conjugated antibodies to visualize protein-protein interactions. Herein, we describe the rationale behind the oligonucleotide systems of these methods. The main objective of this paper is to provide researchers with a description on how we thought when we designed those methods. We also describe in detail how the methods work and how one should interpret results. Expert opinion Understanding how the methods work is important in selecting an appropriate method for your experiments. We also hope that this paper may be an inspiration for young researchers to enter the field of method development. Seeing a problem is a motivation to develop a solution

Immuno-oncological effects of standard anticancer agents and commonly used concomitant drugs : an in vitro assessment

Background It has become evident in the field of oncology that the outcome of medical treatment is influenced by the combined effect exerted on both cancer- and immune cells. Therefore, we evaluated potential immunological effects of 46 standard anticancer agents and 22 commonly administered concomitant non-cancer drugs. Methods We utilized a miniaturized in vitro model system comprised of fluorescently labeled human colon and lung cancer cell lines grown as monocultures and co-cultured with activated peripheral blood mononuclear cells (PBMCs). The Bliss Independence Model was then applied to detect antagonism and synergy between the drugs and activated immune cells. Results Among the standard anticancer agents, tyrosine kinase inhibitors (TKIs) stood out as the top inducers of both antagonism and synergy. Ruxolitinib and dasatinib emerged as the most notably antagonistic substances, exhibiting the lowest Bliss scores, whereas sorafenib was shown to synergize with activated PBMCs. Most concomitant drugs did not induce neither antagonism nor synergy. However, the statins mevastatin and simvastatin were uniquely shown to synergize with activated PBMC at all tested drug concentrations in the colon cancer model. Conclusion We utilized a miniaturized tumor-immune model to enable time and cost-effective evaluation of a broad panel of drugs in an immuno-oncology setting in vitro. Using this approach, immunomodulatory effects exerted by TKIs and statins were identified