Petr Matula

Recruitment and activation of a lipid kinase by hepatitis C virus NS5A is essential for integrity of the membranous replication compartment

Hepatitis C virus (HCV) is a major causative agent of chronic liver disease in humans. To gain insight into host factor requirements for HCV replication, we performed a siRNA screen of the human kinome and identified 13 different kinases, including phosphatidylinositol-4 kinase III alpha (PI4KIIIα), as being required for HCV replication. Consistent with elevated levels of the PI4KIIIα product phosphatidylinositol-4-phosphate (PI4P) detected in HCV-infected cultured hepatocytes and liver tissue from chronic hepatitis C patients, the enzymatic activity of PI4KIIIα was critical for HCV replication. Viral nonstructural protein 5A (NS5A) was found to interact with PI4KIIIα and stimulate its kinase activity. The absence of PI4KIIIα activity induced a dramatic change in the ultrastructural morphology of the membranous HCV replication complex. Our analysis suggests that the direct activation of a lipid kinase by HCV NS5A contributes critically to the integrity of the membranous viral replication complex.

A Benchmark for Comparison of Cell Tracking Algorithms

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge. Contact: codesolorzano@unav.es Supplementary information: Supplementary data are available at Bioinformatics online.

An efficient algorithm for measurement and correction of chromatic aberrations in fluorescence microscopy

Even the best optical microscopes available on the market exhibit chromatic aberrations to some extent. In some types of study, chromatic aberrations of current optics cannot be neglected and a software correction is highly desirable. This paper describes a novel method of chromatic aberration measurement and software correction using sub‐resolution bead imaging and computer image analysis. The method is quick, precise and enables the determination of both longitudinal and lateral chromatic aberrations. Correction function can be computed in about half an hour, including image acquisition. Using this approach, chromatic aberrations can be reduced to 10–20 nm laterally and 10–60 nm axially depending on the type of optical set‐up. The method is especially suitable for fluorescence microscopy, where a limited number of wavelengths are observed.

Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection.

The identification of eukaryotic genes involved in virus entry and replication is important for understanding viral infection. Our goal is to develop a siRNA‐based screening system using cell arrays and high‐throughput (HT) fluorescence microscopy. A central issue is efficient, robust, and automated single‐cell‐based analysis of massive image datasets. We have developed an image analysis approach that comprises (i) a novel, gradient‐based thresholding scheme for cell nuclei segmentation which does not require subsequent postprocessing steps for separation of clustered nuclei, (ii) quantification of the virus signal in the neighborhood of cell nuclei, (iii) localization of regions with transfected cells by combining model‐based circle fitting and grid fitting, (iv) cell classification as infected or noninfected, and (v) image quality control (e.g., identification of out‐of‐focus images). We compared the results of our nucleus segmentation approach with a previously developed scheme of adaptive thresholding with subsequent separation of nuclear clusters. Our approach, which does not require a postprocessing step for the separation of nuclear clusters, correctly segmented 97.1% of the nuclei, whereas the previous scheme achieved 95.8%. Using our algorithm for the detection of out‐of‐focus images, we obtained a high discrimination power of 99.4%. Our overall approach has been applied to more than 55,000 images of cells infected by either hepatitis C or dengue virus. Reduced infection rates were correctly detected in positive siRNA controls, as well as for siRNAs targeting, for example, cellular genes involved in viral infection. Our image analysis approach allows for the automatic and accurate determination of changes in viral infection based on high‐throughput single‐cell‐based siRNA cell array imaging experiments.

Fast point-based 3-D alignment of live cells

Typical time intervals between acquisitions of three-dimensional (3-D) images of the same cell in live cell imaging are in the orders of minutes. In the meantime, the live cell can move in a water basin on the stage. This movement can hamper the studies of intranuclear processes. We propose a fast point-based image registration method for the suppression of the movement of a cell as a whole in the image data. First, centroids of certain intracellular objects are computed for each image in a time-lapse series. Then, a matching between the centroids, which have the maximal number of pairs, is sought between consecutive point sets by a 3-D extension of a two-dimensional fast point pattern matching method, which is invariant to rotation, translation, local distortion, and extra/missing points. The proposed 3-D extension assumes rotations only around the z axis to retain the complexity of the original method. The final step involves computing the optimal fully 3-D transformation between images from corresponding points in the least-squares manner. The robustness of the method was evaluated on generated data. The results of the simulations show that the method is very precise and its correctness can be estimated. This article also presents two practical application examples, namely the registration of images of HP1 domains and the registration of images of telomeres. More than 97% of time-consecutive images were successfully registered. The results show that the method is very well suited to live cell imaging.

Precise 3D image alignment in micro-axial tomography

Micro (µ‐) axial tomography is a challenging technique in microscopy which improves quantitative imaging especially in cytogenetic applications by means of defined sample rotation under the microscope objective. The advantage of µ‐axial tomography is an effective improvement of the precision of distance measurements between point‐like objects. Under certain circumstances, the effective (3D) resolution can be improved by optimized acquisition depending on subsequent, multi‐perspective image recording of the same objects followed by reconstruction methods. This requires, however, a very precise alignment of the tilted views. We present a novel feature‐based image alignment method with a precision better than the full width at half maximum of the point spread function. The features are the positions (centres of gravity) of all fluorescent objects observed in the images (e.g. cell nuclei, fluorescent signals inside cell nuclei, fluorescent beads, etc.). Thus, real alignment precision depends on the localization precision of these objects. The method automatically determines the corresponding objects in subsequently tilted perspectives using a weighted bipartite graph. The optimum transformation function is computed in a least squares manner based on the coordinates of the centres of gravity of the matched objects. The theoretically feasible precision of the method was calculated using computer‐generated data and confirmed by tests on real image series obtained from data sets of 200 nm fluorescent nano‐particles. The advantages of the proposed algorithm are its speed and accuracy, which means that if enough objects are included, the real alignment precision is better than the axial localization precision of a single object. The alignment precision can be assessed directly from the algorithms output. Thus, the method can be applied not only for image alignment and object matching in tilted view series in order to reconstruct (3D) images, but also to validate the experimental performance (e.g. mechanical precision of the tilting). In practice, the key application of the method is an improvement of the effective spatial (3D) resolution, because the well‐known spatial anisotropy in light microscopy can be overcome. This allows more precise distance measurements between point‐like objects.

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays todays state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

Fibroblast growth factor and canonical WNT/β-catenin signaling cooperate in suppression of chondrocyte differentiation in experimental models of FGFR signaling in cartilage

Aberrant fibroblast growth factor (FGF) signaling disturbs chondrocyte differentiation in skeletal dysplasia, but the mechanisms underlying this process remain unclear. Recently, FGF was found to activate canonical WNT/β-catenin pathway in chondrocytes via Erk MAP kinase-mediated phosphorylation of WNT co-receptor Lrp6. Here, we explore the cellular consequences of such a signaling interaction. WNT enhanced the FGF-mediated suppression of chondrocyte differentiation in mouse limb bud micromass and limb organ cultures, leading to inhibition of cartilage nodule formation in micromass cultures, and suppression of growth in cultured limbs. Simultaneous activation of the FGF and WNT/β-catenin pathways resulted in loss of chondrocyte extracellular matrix, expression of genes typical for mineralized tissues and alteration of cellular shape. WNT enhanced the FGF-mediated downregulation of chondrocyte proteoglycan and collagen extracellular matrix via inhibition of matrix synthesis and induction of proteinases involved in matrix degradation. Expression of genes regulating RhoA GTPase pathway was induced by FGF in cooperation with WNT, and inhibition of the RhoA signaling rescued the FGF/WNT-mediated changes in chondrocyte cellular shape. Our results suggest that aberrant FGF signaling cooperates with WNT/β-catenin in suppression of chondrocyte differentiation.

miR-17-5p regulates endocytic trafficking through targeting TBC1D2/Armus.

miRNA cluster miR-17-92 is known as oncomir-1 due to its potent oncogenic function. miR-17-92 is a polycistronic cluster that encodes 6 miRNAs, and can both facilitate and inhibit cell proliferation. Known targets of miRNAs encoded by this cluster are largely regulators of cell cycle progression and apoptosis. Here, we show that miRNAs encoded by this cluster and sharing the seed sequence of miR-17 exert their influence on one of the most essential cellular processes – endocytic trafficking. By mRNA expression analysis we identified that regulation of endocytic trafficking by miR-17 can potentially be achieved by targeting of a number of trafficking regulators. We have thoroughly validated TBC1D2/Armus, a GAP of Rab7 GTPase, as a novel target of miR-17. Our study reveals regulation of endocytic trafficking as a novel function of miR-17, which might act cooperatively with other functions of miR-17 and related miRNAs in health and disease.

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

BackgroundHigh-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 cells 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.ResultsWe present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cells 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.ConclusionsUsing 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.