Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Since 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, for the first time, 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 important practical conclusions for users and developers.

SummaryThe guideline writing group was selected to be representative of UK-based medical experts. MEDLINE was systematically searched for publications in English up to the Summer of 2010 using key words platelet, platelet function testing and platelet aggregometry. Relevant references generated from initial papers and published guidelines/reviews were also examined. Meeting abstracts were not included. The writing group produced the draft guideline, which was subsequently revised and agreed by consensus. Further comment was made by members of the Haemostasis and Thrombosis Task Force of the British Committee for Standards in Haematology. The guideline was then reviewed by a sounding board of approximately 40 UK haematologists, the British Committee for Standards in Haematology (BCSH) and the British Society for Haematology

Previous studies on the pattern of joint bleeding in patients with haemophilia have reported that the knee joint is most frequently affected. Home treatment data reporting bleeding frequency and location collected from 100 patients registered at six haemophilia centres in the UK have been analysed to determine current patterns of bleeding. Bleeding frequency has markedly decreased although bleeding into joints remains the main characteristic of haemophilia. However, the ankle joint has replaced the knee joint as the most common joint affected. Furthermore, it seems that the frequency of knee joint bleeding is also less than the elbow joint suggesting that the traditional pattern of joint bleeding in haemophilia has now changed significantly.

The Rab family of small GTPases function as molecular switches regulating membrane and protein trafficking. Individual Rab isoforms define and are required for specific endosomal compartments. To facilitate in vivo investigation of specific Rab proteins, and endosome biology in general, we have generated transgenic zebrafish lines to mark and manipulate Rab proteins. We also developed software to track and quantify endosome dynamics within time-lapse movies. The established transgenic lines ubiquitously express EGFP fusions of Rab5c (early endosomes), Rab11a (recycling endosomes), and Rab7 (late endosomes) to study localization and dynamics during development. Additionally, we generated UAS-based transgenic lines expressing constitutive active (CA) and dominant negative (DN) versions for each of these Rab proteins. Predicted localization and functional consequences for each line were verified through a variety of assays, including lipophilic dye uptake and Crumbs2a localization. In summary, we have established a toolset for in vivo analyses of endosome dynamics and functions.

This protocol and the accompanying software program called LEVER enable quantitative automated analysis of phase contrast time-lapse images of cultured neural stem cells. Images are captured at 5 min. intervals over a period of 5 to 15 days as the cells proliferate and differentiate. LEVER automatically segments, tracks and generates lineage trees of the stem cells from the image sequence. In addition to generating lineage trees capturing the population dynamics of clonal development, LEVER extracts quantitative phenotypic measurements of cell location, shape, movement, and size. When available, the system can include biomolecular markers imaged using fluorescence. It then displays the results to the user for highly efficient inspection and editing to correct any errors in the segmentation, tracking or lineaging. In order to enable high-throughput inspection, LEVER incorporates features for rapid identification of errors, and learning from user-supplied corrections to automatically identify and correct related errors.