Fluoreszenzindikatoren für intrazelluläre Natriumkonzentrationsmessungen

Mithilfe von Fluoreszenzionenindikatoren können intrazelluläre Ionenkonzentrationen verlässlich und für die Zelle schonend bestimmt werden. Für Natrium gibt es allerdings nur wenige kommerziell erhältliche Ionenindikatoren, mit denen man Natrium selektiv messen kann. Sodium Green und CoroNa Green sind zwei der wenigen Fluoreszenzindikatoren für Natriummessungen in der Zelle, die mithilfe eines in konventionellen Laserscanningmikroskopen enthaltenen Argon-Lasers, bei 488 nm angeregt werden können. In dieser Arbeit wurden verschiedene Ansätze verfolgt, mithilfe dieser Indikatoren Natriumkonzentrationen verlässlich zu messen. Natriummessungen mit dem nichtratiometrischen Natriumindikator Sodium Green und CoroNa Green, bei denen die Indikatorkonzentration oder die Anregungsintensität nicht konstant sind, sind sehr fehlerbehaftet. Sinkt beispielsweise die Anregungsintensität oder die Indikatorkonzentration, nimmt auch die Fluoreszenzintensität ab. Dies könnte dann fälschlicherweise als Abfall der Natriumkonzentration interpretiert werden. Bei Fluoreszenzlebensdauermessungen werden diese Fehlerquellen ausgeschaltet. Die Fluoreszenzlebensdauer ist eine Stoffkonstante und deshalb unabhängig von der Indikatorkonzentration und der Anregungsintensität. Auch die Fluoreszenzlebensdauer der Indikatoren Sodium Green un CoroNa Green zeigt in einer Pufferlösung definierter Zusammensetzung eine deutliche Abhängigkeit von der Natriumkonzentration und eignet sich deshalb als Signalparameter für Natriumkonzentrationsmessungen In Gegenwart von 5% BSA (w/v) nimmt die Fluroreszenzintensität des Indikators Sodium Green um den Faktor 2,7 zu und das Fluoreszenzmaximum von Sodium Green verlagert sich um 14 nm in den roten Wellenlängenbereich. Dies ist auf die Interaktion des Indikators mit Proteinen zurückzuführen. Deshalb wurden Polyacrylamid-Nanopartikel (20nm) synthetisiert, die den Farbstoff Sodium Green enthielten. Der Indikator konnte so vor der Interaktion mit Proteinen geschützt werden

Phage display selection of HIV specific conserved mimotopes with IgG from long-term non-progressors

Poster presentation Background The aim of this study is to identify conserved epitopes of HIV-1 neutralizing antibodies in polyclonal plasma from LTNP to finally derive vaccine candidates. Materials and methods The presence of neutralizing antibodies in 9 LTNP sera was proved by in vitro neutralization assays. Phage displayed peptide libraries were screened with LTNP IgG. HIV-specific mimotopes were analyzed for homology to the gp120 structure by a software (3DEX) especially developed for this purpose. Mice were immunized with interesting phages and their sera were analyzed for neutralizing activities against HIV-1. Results After biopannings, between 19% and 75% HIV-specific phage clones were identified by ELISA. Mimotope sequences were identified and could be aligned by 3DEX to linear or conformational epitopes on gp120. A peptide specific immune response was detected in sera of immunized mice. The first mice sera analyzed showed neutralizing activities against HIV-1. Conclusion Mimotopes could be selected from LTNP sera that represent conformational epitopes on gp120. Those ones inducing neutralizing antibodies upon immunization potentially are suited to derive vaccine candidates

Drug-perturbation-based stratification of blood cancer

As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non-BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care

Biofilm Formation Induces C3a Release and Protects Staphylococcus epidermidis from IgG and Complement Deposition and from Neutrophil-Dependent Killing

BackgroundBiofilm formation is considered to be an important virulence factor of the opportunistic pathogen Staphylococcus epidermidis. We hypothesized that biofilm formation could interfere with the deposition of immunoglobulins and complement on the bacterial surface, leading to diminished activation of the complement system and protection from killing by human phagocytes MethodsThe killing of biofilm-encased and planktonically grown wild-type (wt) S. epidermidis and the killing of an isogenic biofilm-negative ica mutant (ica−) by human polymorphonuclear neutrophils (PMNs) were compared. C3a induction and deposition of C3b and immunoglobulin G (IgG) on the bacteria after opsonization with human serum were assessed by enzyme-linked immunosorbent assay, flow cytometry, and electron microscopy. The virulence of the bacterial strains was compared in a mouse model of catheter-associated infection ResultsBiofilm-embedded wt S. epidermidis was killed less well by human PMNs and induced more C3a than planktonically grown wt and ica− S. epidermidis. However, the deposition of C3b and IgG on the bacterial surface was diminished in biofilm-encased staphylococci. wt S. epidermidis was more virulent in implant-associated infections and was killed more slowly than ica− in ex vivo assays of killing by PMNs ConclusionsThe results indicate that prevention of C3b and IgG deposition on the bacterial surface contributes to the biofilm-mediated protection of S. epidermidis from killing by PMN

3D Image Based Structural Analysis of Leather for Macroscopic Structure- Property Simulation

Content: The intrinsic structure significantly influences the mechanical properties of leather. In consequence, knowledge of leather’s hierarchical structure is essential in order to find the most suited leather for specific application. Leather structure based parameters are of major importance for both manufacturing and leather processing industries. In this respect, intensive structure investigations have been subjected in continuous research work. Quantitative image analysis combined with stochastic micro-structure modelling and numerical simulation of macroscopic properties is a promising approach to gain a deeper understanding of complex relations between material’s micro-structure geometry and macroscopic properties. Key ingredient is a reliable geometric description provided by the quantitative analysis of 3D images of the material micro-structures. For leather, both imaging and image analysis are particularly challenging, due to the multi-scale nature of the leather’s micro-structure. Scales in leather are not well separated. Previously, high resolution computed tomography allowed 3D imaging of purely vegetable tanned leather samples at micro- and submicro- scale. Segmentation of leather structure as well as of typical structural elements in resulting image data is however hampered by a strong heterogeneity caused by lower scale structural information. The first method for automatic segmentation of typical structural elements at varying scales combined morphological smoothing with defining and iteratively coarsening regions using the waterfall algorithm on local orientations. It yields a hierarchical segmentation of the leather into coarse and fine structural elements that can be used to analyze and compare the structure of leather samples. Size and shape of the structural elements as well as their sub-structure yield information, e. g. on undulation, branching, thickness, cross-sectional shape, and preferred directions. In order to compare the micro-structure of leather samples from various body parts or even species, the segmentation has to be applicable without extensive pre-processing and parameter tuning. Robustness can be gained by applying smoothing methods that are adapted to the goal of defining image regions by similar local orientation. The challenge is that the space of fiber orientations in 3D is not equipped with an order. Motivated by a recent approach for nevertheless defining erosion and dilation on the sphere, we suggest new definitions for these morphological base transformations on the space of directions in 3D. We present segmentation results for 3D images of leather samples derived by these new morphological smoothing methods. Take-Away: The intrinsic structure significantly influences the mechanical properties of leather. Leather’s hierarchical structure can be analyzed by quantitative 3D image analysis combined with stochastic micro-structure modelling. Segmentation results for 3D images of leather samples derived by new morphological smoothing methods

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation