Karl Rohr

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.

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Radial basis functions with compact support for elastic registration of medical images

Abstract Common elastic registration schemes based on landmarks and radial basis functions (RBFs) such as thin-plate splines or multiquadrics are global. Here, we introduce radial basis functions with compact support for elastic registration of medical images which have an improved locality, i.e. which allow to constrain elastic deformations to image parts where required. We give the theoretical background of these basis functions and compare them with other basis functions w.r.t. locality, solvability, and efficiency. A detailed comparison with the Gaussian as well as conditions for preserving topology is given. An important property of the used RBFs (Wendlands ψ-functions) is that they are positive definite. Therefore, in comparison to the use of the truncated Gaussian, the solvability of the resulting system of equations is always guaranteed. We demonstrate the applicability of our approach for synthetic as well as for 2D and 3D tomographic images.

Incremental recognition of pedestrians from image sequences

An approach that uses a volume model consisting of cylinders for model-based recognition of pedestrians in real-world images is presented. The human body is represented by a volume model, and medical motion data are used for simulating the movement of walking. This knowledge is exploited to determine the 3-D position, as well as the posture of an observed person. By applying a Kalman filter, the model parameters in consecutive images are incrementally estimated. The approach is tested on real image data.<<ETX>>

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.

Dynamics of HIV-1 assembly and release

Assembly and release of human immunodeficiency virus (HIV) occur at the plasma membrane of infected cells and are driven by the Gag polyprotein. Previous studies analyzed viral morphogenesis using biochemical methods and static images, while dynamic and kinetic information has been lacking until very recently. Using a combination of wide-field and total internal reflection fluorescence microscopy, we have investigated the assembly and release of fluorescently labeled HIV-1 at the plasma membrane of living cells with high time resolution. Gag assembled into discrete clusters corresponding to single virions. Formation of multiple particles from the same site was rarely observed. Using a photoconvertible fluorescent protein fused to Gag, we determined that assembly was nucleated preferentially by Gag molecules that had recently attached to the plasma membrane or arrived directly from the cytosol. Both membrane-bound and cytosol derived Gag polyproteins contributed to the growing bud. After their initial appearance, assembly sites accumulated at the plasma membrane of individual cells over 1–2 hours. Assembly kinetics were rapid: the number of Gag molecules at a budding site increased, following a saturating exponential with a rate constant of ∼5×10−3 s−1, corresponding to 8–9 min for 90% completion of assembly for a single virion. Release of extracellular particles was observed at ∼1,500±700 s after the onset of assembly. The ability of the virus to recruit components of the cellular ESCRT machinery or to undergo proteolytic maturation, or the absence of Vpu did not significantly alter the assembly kinetics.

Recognizing corners by fitting parametric models

The parametric model of a certain class of characteristic intensity variations in Rohr (1990, 1992), which is the superposition of elementary model functions, is employed to identify corners in images. Estimates of the searched model parameters characterizing completely single grey-value structures are determined by a least-squares fit of the model to the observed image intensities applying the minimization method of Levenberg-Marquardt. In particular, we develop an analytical approximation of our model in such a way that function values can be calculated without numerical integration. Assuming the blur of the imaging system to be describable by Gaussian convolution our approach permits subpixel localization of the corner position of the unblurred grey-value structures, that is, to reverse the blur of the imaging system. By fitting our model to the original as well as to the smoothed original-image cues can be obtained for finding out whether the underlying model is an adequate description or not. Results are shown for real image data.

Point-Based Elastic Registration of Medical Image Data Using Approximating Thin-Plate Splines

We consider elastic registration of medical image data based on thin-plate splines using a set of corresponding anatomical point landmarks. Previous work on this topic has concentrated on using interpolation schemes. Such schemes force the corresponding landmarks to exactly match each other and assume that the landmark positions are known exactly. However, in real applications the localization of landmarks is always prone to some error. Therefore, to take into account these localization errors, we have investigated the application of an approximation scheme which is based on regularization theory. This approach generally leads to a more accurate and robust registration result. In particular, outliers do not disturb the registration result as much as is the case with an interpolation scheme. Also, it is possible to individually weight the landmarks according to their localization uncertainty. In addition to this study, we report on investigations into semi-automatic extraction of anatomical point landmarks.

On 3D differential operators for detecting point landmarks

Point-based registration of volume image data heavily relies on the selection of suitable landmarks. In this contribution, we study the extraction of 3D point landmarks from images. Point landmarks have a unique position which can be deduced from the intensity variations in a sufficiently large neighborhood around the prominent point. We propose four 3D differential operators which are generalizations of existing 2D operators for detecting points of high intensity variations. In comparison to previous approaches, our operators have the advantage that only low order partial derivatives of the image function are necessary. Therefore, these operators are computationally efficient and do not suffer from instabilities of computing high order partial derivatives. We also describe how the 3D operators can be generalized to be used on images of arbitrary dimension. First experimental results will be presented on medical imagery.

Dynamic Oscillation of Translation and Stress Granule Formation Mark the Cellular Response to Virus Infection

Virus infection-induced global protein synthesis suppression is linked to assembly of stress granules (SGs), cytosolic aggregates of stalled translation preinitiation complexes. To study long-term stress responses, we developed an imaging approach for extended observation and analysis of SG dynamics during persistent hepatitis C virus (HCV) infection. In combination with type 1 interferon, HCV infection induces highly dynamic assembly/disassembly of cytoplasmic SGs, concomitant with phases of active and stalled translation, delayed cell division, and prolonged cell survival. Double-stranded RNA (dsRNA), independent of viral replication, is sufficient to trigger these oscillations. Translation initiation factor eIF2α phosphorylation by protein kinase R mediates SG formation and translation arrest. This is antagonized by the upregulation of GADD34, the regulatory subunit of protein phosphatase 1 dephosphorylating eIF2α. Stress response oscillation is a general mechanism to prevent long-lasting translation repression and a conserved host cell reaction to multiple RNA viruses, which HCV may exploit to establish persistence.