Localizing the lipid products of PI3Kγ in neutrophils.

Class I phosphoinositide 3-kinases (PI3Ks) are important regulators of neutrophil migration in response to a range of chemoattractants. Their primary lipid products PtdIns(3,4,5)P3 and PtdIns(3,4)P2 preferentially accumulate near to the leading edge of migrating cells and are thought to act as an important cue organizing molecular and morphological polarization. We have investigated the distribution and accumulation of these lipids independently in mouse neutrophils using eGFP-PH reportersand electron microscopy (EM). We found that authentic mouse neutrophils rapidly polarized their Class I PI3K signalling, as read-out by eGFP-PH reporters, both at the up-gradient leading edge in response to local stimulation with fMLP as well as spontaneously and randomly in response to uniform stimulation. EM studies revealed these events occurred at the plasma membrane, were dominated by accumulation of PtdIns(3,4,5)P3, but not PtdIns(3,4)P2, and were dependent on PI3Kγ and its upstream activation by both Ras and Gßγs.

3D time series analysis of cell shape using Laplacian approaches.

BACKGROUND: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. RESULTS: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. CONCLUSIONS: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations.

Automated motion estimation of root responses to sucrose in two Arabidopsis thaliana genotypes using confocal microscopy.

Root growth is a highly dynamic process influenced by genetic background and environment. This paper reports the development of R scripts that enable root growth kinematic analysis that complements a new motion analysis tool: PlantVis. Root growth of Arabidopsis thaliana expressing a plasma membrane targeted GFP (C24 and Columbia 35S:LTI6b-EGFP) was imaged using time-lapse confocal laser scanning microscopy. Displacement of individual pixels in the time-lapse sequences was estimated automatically by PlantVis, producing dense motion vector fields. R scripts were developed to extract kinematic growth parameters and report displacement to ± 0.1 pixel. In contrast to other currently available tools, Plantvis-R delivered root velocity profiles without interpolation or averaging across the root surface and also estimated the uncertainty associated with tracking each pixel. The PlantVis-R analysis tool has a range of potential applications in root physiology and gene expression studies, including linking motion to specific cell boundaries and analysis of curvature. The potential for quantifying genotype × environment interactions was examined by applying PlantVis-R in a kinematic analysis of root growth of C24 and Columbia, under contrasting carbon supply. Large genotype-dependent effects of sucrose were recorded. C24 exhibited negligible differences in elongation zone length and elongation rate but doubled the density of lateral roots in the presence of sucrose. Columbia, in contrast, increased its elongation zone length and doubled its elongation rate and the density of lateral roots.

Interactive segmentation of clustered cells via geodesic commute distance and constrained density weighted Nyström method.

An interactive method is proposed for complex cell segmentation, in particular of clustered cells. This article has two main contributions: First, we explore a hybrid combination of the random walk and the geodesic graph based methods for image segmentation and propose the novel concept of geodesic commute distance to classify pixels. The computation of geodesic commute distance requires an eigenvector decomposition of the weighted Laplacian matrix of a graph constructed from the image to be segmented. Second, by incorporating pairwise constraints from seeds into the algorithm, we present a novel method for eigenvector decomposition, namely a constrained density weighted Nyström method. Both visual and quantitative comparison with other semiautomatic algorithms including Voronoi-based segmentation, grow cut, graph cuts, random walk, and geodesic method are given to evaluate the performance of the proposed method, which is a powerful tool for quantitative analysis of clustered cell images in live cell imaging.

Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi.

An automatic method for estimating the content of intramuscular fat (IMF) in beef M. longissimus dorsi (LD) was developed using a sequence of image processing algorithm. To extract IMF particles within the LD muscle from structural features of intermuscular fat surrounding the muscle, three steps of image processing algorithm were developed, i.e. bilateral filter for noise removal, kernel fuzzy c-means clustering (KFCM) for segmentation, and vector confidence connected and flood fill for IMF extraction. The technique of bilateral filtering was firstly applied to reduce the noise and enhance the contrast of the beef image. KFCM was then used to segment the filtered beef image into lean, fat, and background. The IMF was finally extracted from the original beef image by using the techniques of vector confidence connected and flood filling. The performance of the algorithm developed was verified by correlation analysis between the IMF characteristics and the percentage of chemically extractable IMF content (P<0.05). Five IMF features are very significantly correlated with the fat content (P<0.001), including count densities of middle (CDMiddle) and large (CDLarge) fat particles, area densities of middle and large fat particles, and total fat area per unit LD area. The highest coefficient is 0.852 for CDLarge.

Prediction of beef eating quality from colour, marbling and wavelet texture features.

Beef longissimus dorsi colour, marbling fat and surface texture are long established properties that are used in some countries by expert graders to classify beef carcasses, with subjective and inconsistent decision. As a computer vision system can deliver objective and consistent decisions rapidly and is capable of handling a greater variety of image features, attempts have been made to develop computerised predictions of eating quality based on these and other properties but have failed to adequately model the variation in eating quality. Therefore, in this study, examination of the ribeye at high magnification and consideration of a broad range of colour and marbling fat features was used to attempt to provide better information on beef eating quality. Wavelets were used to describe the image texture of the beef surface at high magnification rather than classical methods such as run lengths, difference histograms and co-occurrence matrices. Sensory panel and Instron analyses were performed on duplicate steaks to measure the quality of the beef. Using the classical statistical method of partial least squares regression (PLSR) it was possible to model a very high proportion of the variation in eating quality (r(2)=0.88 for sensory overall acceptability and r(2)=0.85 for 7-day WBS). Addition of non-linear texture terms to the models gave some improvements.

Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture.

Pores formed in pork ham have a significant effect on its quality. However, they are mostly characterised using manual methods with special devices. In this paper, an automatic method for pore characterisation of pork ham was developed using computer vision. To segment pores from images of pork ham, three stages of image processing algorithm were developed, i.e., ham extraction, image enhancement, and pore segmentation. From the segmented pores, the porosity, number of pores, pore size, and size distribution were measured. The statistical analysis showed that 79.81% of pores have area sizes between 6.73×10(-3) and 2.02×10(-1)mm(2). Furthermore, it was found that the total number of pore (TNP) and porosity highly negatively related to the water content of pork ham (P<0.05), and had negative correlations with the cooking and cooling time. However, for texture analysis, positive correlations were found between the pore characterisations and WBS, hardness, cohesion, and chewiness, respectively, while springiness and gumminess were negatively related to TNP and porosity.