An incremental approach to automated protein localisation

Pomegranate: 2D segmentation and 3D reconstruction for fission yeast and other radially symmetric cells

Three-dimensional (3D) segmentation of cells in microscopy images is crucial to accurately capture signals that extend across optical sections. Using brightfield images for segmentation has the advantage of being minimally phototoxic and leaving all other channels available for signals of interest. However, brightfield images only readily provide information for two-dimensional (2D) segmentation. In radially symmetric cells, such as fission yeast and many bacteria, this 2D segmentation can be computationally extruded into the third dimension. However, current methods typically make the simplifying assumption that cells are straight rods. Here, we report Pomegranate, a pipeline that performs the extrusion into 3D using spheres placed along the topological skeletons of the 2D-segmented regions. The diameter of these spheres adapts to the cell diameter at each position. Thus, Pomegranate accurately represents radially symmetric cells in 3D even if cell diameter varies and regardless of whether a cell is straight, bent or curved. We have tested Pomegranate on fission yeast and demonstrate its ability to 3D segment wild-type cells as well as classical size and shape mutants. The pipeline is available as a macro for the open-source image analysis software Fiji/ImageJ. 2D segmentations created within or outside Pomegranate can serve as input, thus making this a valuable extension to the image analysis portfolio already available for fission yeast and other radially symmetric cell types.

In vivo selective imaging and inhibition of leukemia stem-like cells using the fluorescent carbocyanine derivative, DiOC5(3)

Elimination of leukemia stem cells (LSCs) is necessary for the destruction of malignant cell populations. Owing to the very small number of LSCs in leukemia cells, xenotransplantation studies are difficult in terms of functionally and pathophysiologically replicating clinical conditions of cell culture experiments. There is currently a limited number of lead compounds that target LSCs. Using the LSC-xenograft zebrafish screening method we previously developed, we found that the fluorescent compound 3,3'-dipentyloxacarbocyanine iodide (DiOC5(3)) selectively marked LSCs and suppressed their proliferation in vivo and in vitro. DiOC5(3) had no obvious toxicity to human umbilical cord blood CD34+ progenitor cells and normal zebrafish. It accumulated in mitochondria through organic anion transporter polypeptides that are overexpressed in the plasma membrane of LSCs, and induced apoptosis via ROS overproduction. DiOC5(3) also inhibited the nuclear translocation of NF-κB through the downregulation of LSC-selective pathways, as indicated from DNA microarray analysis. In summary, DiOC5(3) is a new type of anti-LSC compound available for diagnostic imaging and therapeutics that has the advantage of being a single fluorescent chemical.

Counting unstained, confluent cells by modified bright-field microscopy

We present a very simple procedure yielding high-contrast images of adherent, confluent cells such as human neuroblastoma (SH-EP) cells by ordinary bright-field microscopy. Cells are illuminated through a color filter and a pinhole aperture placed between the condenser and the cell culture surface. Refraction by each cell body generates a sharp, bright spot when the image is defocused. The technique allows robust, automatic cell counting from a single bright-field image in a wide range of focal positions using free, readily available image-analysis tools. Contrast may be enhanced by swelling cell bodies with a brief incubation in PBS. The procedure was benchmarked against manual and automated counting of fluorescently labeled cell nuclei. Counts from day-old and freshly seeded plates were compared in a range of densities, from sparse to densely overgrown. On average, bright-field images produced the same counts as fluorescence images, with less than 5% error. This method will allow routine cell counting using a plain bright-field microscope without cell-line modification or cell staining.

A hierarchical ART network for the stable incremental learning of topological structures and associations from noisy data

In this article, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology-learning neural networks is presented. It enables stable on-line clustering of stationary and non-stationary input data by learning their inherent topology. Here, two network components representing two different levels of detail are trained simultaneously. By virtue of several filtering mechanisms, the sensitivity to noise is diminished, which renders the proposed network suitable for the application to real-world problems. Furthermore, we demonstrate that this network constitutes an excellent basis to learn and recall associations between real-world associative keys. Its incremental nature ensures that the capacity of the corresponding associative memory fits the amount of knowledge to be learnt. Moreover, the formed clusters efficiently represent the relations between the keys, even if noisy data is used for training. In addition, we present an iterative recall mechanism to retrieve stored information based on one of the associative keys used for training. As different levels of detail are learnt, the recall can be performed with different degrees of accuracy.