Quantitative comparison of DNA detection by GFP-lac repressor tagging, fluorescence in situ hybridization and immunostaining

<p>Abstract</p> <p>Background</p> <p>GFP-fusion proteins and immunostaining are methods broadly applied to investigate the three-dimensional organization of cells and cell nuclei, the latter often studied in addition by fluorescence in situ hybridization (FISH). Direct comparisons of these detection methods are scarce, however.</p> <p>Results</p> <p>We provide a quantitative comparison of all three approaches. We make use of a cell line that contains a transgene array of lac operator repeats which are detected by GFP-lac repressor fusion proteins. Thus we can detect the same structure in individual cells by GFP fluorescence, by antibodies against GFP and by FISH with a probe against the transgene array. Anti-GFP antibody detection was repeated after FISH. Our results show that while all four signals obtained from a transgene array generally showed qualitative and quantitative similarity, they also differed in details.</p> <p>Conclusion</p> <p>Each of the tested methods revealed particular strengths and weaknesses, which should be considered when interpreting respective experimental results. Despite the required denaturation step, FISH signals in structurally preserved cells show a surprising similarity to signals generated before denaturation.</p

Visualizing fusion of pseudotyped HIV-1 particles in real time by live cell microscopy

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Background Most retroviruses enter their host cells by fusing the viral envelope with the plasma membrane. Although the protein machinery promoting fusion has been characterized extensively, the dynamics of the process are largely unknown. Results We generated human immunodeficiency virus-1 (HIV-1) particles pseudotyped with the envelope (Env) protein of ecotropic murine leukemia virus eMLV to study retrovirus entry at the plasma membrane using live-cell microscopy. This Env protein mediates highly efficient pH independent fusion at the cell surface and can be functionally tagged with a fluorescent protein. To detect fusion events, double labeled particles carrying one fluorophor in Env and the other in the matrix (MA) domain of Gag were generated and characterized. Fusion events were defined as loss of Env signal after virus-cell contact. Single particle tracking of >20,000 individual traces in two color channels recorded 28 events of color separation, where particles lost the Env protein, with the MA layer remaining stable at least for a short period. Fourty-five events were detected where both colors were lost simultaneously. Importantly, the first type of event was never observed when particles were pseudotyped with a non-fusogenic Env. Conclusion These results reveal rapid retroviral fusion at the plasma membrane and permit studies of the immediate post-fusion events.Published versio

Non-rigid Contour-Based Registration of Cell Nuclei in 2D Live Cell Microscopy Images Using a Dynamic Elasticity Model

International audienceThe analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this work, we propose a 2D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach which uses a static elasticity model to register cell images. Compared to that scheme, the new approach employs a dynamic elasticity model for forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters

Normalizing for individual cell population context in the analysis of high-content cellular screens

<p>Abstract</p> <p>Background</p> <p>High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology.</p> <p>Results</p> <p>We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach.</p> <p>Conclusions</p> <p>Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.</p

Determination of the Maximum Velocity of Filaments in the in vitro Motility Assay

The in vitro motility assay (IVMA) is a powerful tool commonly used in basic muscle research and for drug screenings with the aim to find treatment options for neuromuscular disorders. In brief, the sliding movement of fluorescence-labeled actin filaments on myosin motor proteins is recorded, and the sliding velocity is analyzed via image analysis methods. Due to low signal-to-noise ratios and large variability in the velocity signal, accurate determination of the maximum sliding velocity is challenging. We introduce a new method and software program named Actin Phase Velocity (ActiPHV). The method extracts the maximum velocity from filament tracking data. Based on simulated and real reference data we show that our method yields a higher accuracy compared to previous methods. As a result, our method enables enhancing the sensitivity of the IVMA to better exploit its full potential

Reciprocal Effects of Fibroblast Growth Factor Receptor Signaling on Dengue Virus Replication and Virion Production

International audienceDengue virus (DENV) is a human arboviral pathogen accounting for 390 million infections every year. The available vaccine has limited efficacy, and DENV-specific drugs have not been generated. To better understand DENV-host cell interaction, we employed RNA interference-based screening of the human kinome and identified fibroblast growth factor receptor 4 (FGFR4) to control the DENV replication cycle. Pharmacological inhibition of FGFR exerts a reciprocal effect by reducing DENV RNA replication and promoting the production of infectious virus particles. Addressing the latter effect, we found that the FGFR signaling pathway modulates intracellular distribution of DENV particles in a PI3K-dependent manner. Upon FGFR inhibition, virions accumulate in the trans-Golgi network compartment, where they undergo enhanced maturation cleavage of the envelope protein precursor membrane (prM), rendering virus particles more infectious. This study reveals an unexpected reciprocal role of a cellular receptor tyrosine kinase regulating DENV RNA replication and the production of infectious virions

PITX1 is a regulator of TERT expression in prostate cancer with prognostic power

Simple Summary Most prostate cancer is of an indolent form and is curable. However, some prostate cancer belongs to rather aggressive subtypes leading to metastasis and death, and immediate therapy is mandatory. However, for these, the therapeutic options are highly invasive, such as radical prostatectomy, radiation or brachytherapy. Hence, a precise diagnosis of these tumor subtypes is needed, and the thus far applied diagnostic means are insufficient for this. Besides this, for their endless cell divisions, prostate cancer cells need the enzyme telomerase to elongate their telomeres (chromatin endings). In this study, we developed a gene regulatory model based on large data from transcription profiles from prostate cancer and chromatin-immuno-precipitation studies. We identified the developmental regulator PITX1 regulating telomerase. Besides observing experimental evidence of PITX1′s functional role in telomerase regulation, we also found PITX1 serving as a prognostic marker, as concluded from an analysis of more than 15,000 prostate cancer samples. Abstract The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT , transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT , we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length

Predicting breast tumor proliferation from whole-slide images : the TUPAC16 challenge

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task