Arnaud Ogier

Lipocalin-2 Induces Cardiomyocyte Apoptosis by Increasing Intracellular Iron Accumulation

Background: The proinflammatory adipokine lipocalin-2 is associated with obesity-related complications, such as heart failure. Results: Lipocalin-2 induces cardiomyocyte apoptosis via elevating intracellular iron levels and mediates detrimental effects on cardiac function. Conclusion: Lipocalin-2 is an important mediator of cardiac remodeling. Significance: Regulation of cardiomyocyte apoptosis by lipocalin-2, and the mechanistic role of changes in intracellular iron, may contribute to the pathogenesis of obesity-related heart failure. Our objective was to determine whether lipocalin-2 (Lcn2) regulates cardiomyocyte apoptosis, the mechanisms involved, and the functional significance. Emerging evidence suggests that Lcn2 is a proinflammatory adipokine associated with insulin resistance and obesity-related complications, such as heart failure. Here, we used both primary neonatal rat cardiomyocytes and H9c2 cells and demonstrated for the first time that Lcn2 directly induced cardiomyocyte apoptosis, an important component of cardiac remodeling leading to heart failure. This was shown by detection of DNA fragmentation using TUNEL assay, phosphatidylserine exposure using flow cytometry to detect annexin V-positive cells, caspase-3 activity using enzymatic assay and immunofluorescence, and Western blotting for the detection of cleaved caspase-3. We also observed that Lcn2 caused translocation of the proapoptotic protein Bax to mitochondria and disruption of mitochondrial membrane potential. Using transient transfection of GFP-Bax, we confirmed that Lcn2 induced co-localization of Bax with MitoTracker® dye. Importantly, we used the fluorescent probe Phen Green SK to demonstrate an increase in intracellular iron in response to Lcn2, and depleting intracellular iron using an iron chelator prevented Lcn2-induced cardiomyocyte apoptosis. Administration of recombinant Lcn2 to mice for 14 days increased cardiomyocyte apoptosis as well as an acute inflammatory response with compensatory changes in cardiac functional parameters. In conclusion, Lcn2-induced cardiomyocyte apoptosis is of physiological significance and occurs via a mechanism involving elevated intracellular iron levels and Bax translocation.

Dimerization, Oligomerization, and Aggregation of Human Amyotrophic Lateral Sclerosis Copper/Zinc Superoxide Dismutase 1 Protein Mutant Forms in Live Cells

Background: Copper/zinc superoxide dismutase (SOD1) genetic mutants are associated with familial amyotrophic lateral sclerosis (ALS). Mutant proteins form abnormal aggregates. Results: We used imaging of live cells to observe SOD1 proteins harboring mutations associated with ALS. Conclusion: SOD1 mutations impair its dimerization, leading to subsequent aggregation. Significance: Analysis of the SOD1 quaternary structure in living human cells correlates with previous biochemical data. More than 100 copper/zinc superoxide dismutase 1 (SOD1) genetic mutations have been characterized. These mutations lead to the death of motor neurons in ALS. In its native form, the SOD1 protein is expressed as a homodimer in the cytosol. In vitro studies have shown that SOD1 mutations impair the dimerization kinetics of the protein, and in vivo studies have shown that SOD1 forms aggregates in patients with familial forms of ALS. In this study, we analyzed WT SOD1 and 9 mutant (mt) forms of the protein by non-invasive fluorescence techniques. Using microscopic techniques such as fluorescence resonance energy transfer, fluorescence complementation, image-based quantification, and fluorescence correlation spectroscopy, we studied SOD1 dimerization, oligomerization, and aggregation. Our results indicate that SOD1 mutations lead to an impairment in SOD1 dimerization and, subsequently, affect protein aggregation. We also show that SOD1 WT and mt proteins can dimerize. However, aggregates are predominantly composed of SOD1 mt proteins.

A novel specific edge effect correction method for RNA interference screenings

MOTIVATION High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV-host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step.

HCS-Analyzer

MOTIVATION High-throughput screening is a powerful technology principally used by pharmaceutical industries allowing the identification of molecules of interest within large libraries. Originally target based, cellular assays provide a way to test compounds (or other biological material such as small interfering RNA) in a more physiologically realistic in vitro environment. High-content screening (HCS) platforms are now available at lower cost, giving the opportunity for universities or research institutes to access those technologies for research purposes. However, the amount of information extracted from each experiment is multiplexed and hence difficult to handle. In such context, there is an important need for an easy-to-use, but still powerful software able to manage multidimensional screening data by performing adapted quality control and classification. HCS-analyzer includes: a user-friendly interface specifically dedicated to HCS readouts, an automated approach to identify systematic errors potentially occurring during screening and a set of tools to classify, cluster and identify phenotypes of interest among large and multivariate data. AVAILABILITY The application, the C# .Net source code, as well as detailed documentation, are freely available at the following URL: http://hcs-analyzer.ip-korea.org.

Contextual Automated 3D Analysis of Subcellular Organelles Adapted to High-Content Screening

Advances in automated imaging microscopy allow fast acquisitions of multidimensional biological samples. Those microscopes open new possibilities for analyzing subcellular structures and spatial cellular arrangements. In this article, the authors describe a 3D image analysis framework adapted to medium-throughput screening. Upon adaptive and regularized segmentation, followed by precise 3D reconstruction, they achieve automatic quantification of numerous relevant 3D descriptors related to the shape, texture, and fluorescence intensity of multiple stained subcellular structures. A global analysis of the 3D reconstructed scene shows additional possibilities to quantify the relative position of organelles. Implementing this methodology, the authors analyzed the subcellular reorganization of the nucleus, the Golgi apparatus, and the centrioles occurring during the cell cycle. In addition, they quantified the effect of a genetic mutation associated with the early onset primary dystonia on the redistribution of torsinA from the bulk endoplasmic reticulum to the perinuclear space of the nuclear envelope. They show that their method enables the classification of various translocation levels of torsinA and opens the possibility for compound-based screening campaigns restoring the normal torsinA phenotype.

Biased Image Correction Based on Empirical Mode Decomposition

The automated analysis of images is an active field of research in image processing and pattern recognition. In many applications, the first issue is to face illuminations artifacts that can appear due to bad imaging conditions. These artifacts often have direct consequences on the efficiency of the image analysis algorithms but also on the quantitative measures. This paper presents a fully automated nonuniformity correction based on empirical mode decomposition. The performances are outlined using both synthetic and real data.

BIAS FREE FEATURES DETECTION FOR HIGH CONTENT SCREENING

Recent automated confocal microscopes used in high content screening (HCS) platforms require fully automated quantitative analysis due to the large amount of images they produce. Tuning the imaging process can not be manually performed on each image, therefore these automated acquisitions may come along with strong illumination artifacts due to poor physical imaging conditions. Such artifacts obviously have direct consequences on the efficiency of the image analysis algorithms but also on the quantitative measures. In this paper, we propose a method robust to illumination artifacts to extract any kind of small and isotropic objects within cells. To do so, we include a pre-processing step where a bias correction algorithm first attempts to retrieve original images from corrupted observations. We validate our framework with two different and independent statistical criteria

3D spatial drift correction using Kalman filtering for fluorescence based imaging

In this paper we present a framework for correcting the spatial drift that can occur in 3D optical fluorescence microscopy images. These shifts happen during long time acquisition and can corrupt further analysis. This artifact has to be taken into account especially if the application requires an high spatial detection accuracy. Our correction method is based on the use of a microsphere located within the biological assay. As the bead does not provide the same correction quality for each Z-slice, we propose here, to include a level of confidence depending on the depth in a Kalman filtering process. This framework allows then to extend the motion compensation along the complete 3D images. This method is validated on real data and provides an easy and accurate way to correct 3D images corrupted along Z by a XY motion.

Inhomogeneous deconvolution in a biological images context

In this paper we present a new framework for correcting partially out-of-focus biological images. To evaluate depth variation, our method extracts similar objects of interest over the image. The depth is based on a defocus value computation for each salient point. We use an iterative algorithm to convert each defocus value into a Gaussian standard deviation. These measures are then interpolated over the whole image using thin plate spline transform, providing a regularized map. This array provides, for every spatial position, an evaluation of the standard deviation used in the final step for an inhomogeneous deconvolution based on the Richardson-Lucy algorithm. Experiments on real and simulated images validate the accuracy of such a method compare to traditional shift invariant deconvolution approaches.

Bias image correction via stationarity maximization

Automated acquisitions in microscopy may come along with strong illumination artifacts due to poor physical imaging conditions. Such artifacts obviously have direct consequences on the efficiency of an image analysis algorithm and on the quantitative measures. In this paper, we propose a method to correct illumination artifacts on biological images. This correction is based on orthogonal polynomial modeling, combined with stationary maximization criteria. To validate the proposed method we show that we improve particle detection algorithm.