• 1gaming

    October 10, 2014

    RPI grad and NSCI scientists use gaming software to develop 5D movies for stem cell analysis

    RENSSELAER N.Y. –RPI graduate and Associate Professor of electrical and computer engineering at Drexel University, Dr. Andrew Cohen is leading a group of researchers including NSCI’s Scientific Director, Dr. Sally Temple, Manager of Research Susan Goderie, scientists, Chris Bjornsson and Yue Wang and former NSCI scientist Erzsebet Kokovay in developing revolutionary software, called  and hardware that better enables biologists to study cells.

    “It’s like Photoshop for cell biologists,” Cohen said. “The software outlines cells and blood vessels, keeping track of them as they’re dividing and moving around one another. This provides a wealth of information on the patterns of cell shape, motion and division. Visualization of the 3-D microscopy data together with the analysis results is a key step to measure and ultimately understand what drives these cells.” (See movies here: )

    “LEVER 3-D is amazing, it opens new vistas for understanding the stem cell niche,” said Dr. Sally Temple. Dr. Temple has been using Cohen’s software, through the course of its development, as part of her stem cell research since 2005.

    The software allows users to see perspectives that are limited by the traditional microscope. Cohen’s goal is to make the software open-source and readily available to any scientists who can use it for their research.



    Movies: FROM Computational Image Sequence Analysis

    The movies that we work with are time lapse image sequences of live organelles, cells and tissue. Here are a few examples. For more details, the publications page has publicly accessible links to most of the papers that are referenced. For best results – play the movies at 1080p in full screen mode.

    This first movie is actually not a time lapse image sequence. It is a static image with no time component. This movie shows a montage composed of 74 overlapping 3-D image stacks. The image resolution is 10,173 x 3,858 x 74 voxels! Each image stack contains 5 channels. We zoom in from full resolution, cutting the field of view in half with each zoom, until we reach the final unscaled resolution. The channels are: blood vessels (red), cell nuclei (dark blue), neural stem cells and astrocytes (green), oligodendrocytes (yellow), and migrating neuroblasts (cyan).

    E. Wait, M. Winter, C. Bjornsson, E. Kokovay, Y. Wang, S. Goderie, S. Temple and A. R. Cohen, Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences. BMC Bioinformatics, 15:328.. October 3, 2014.

    This movie is a “5-D” image sequence. The first three dimensions are (x,y,z). Time is dimension number four. The fifth dimension is the microscope imaging channel, with e.g. blood vessels and ependyma on one channel and neural stem cells on another. Here we can see the relationship between the stem cells and the blood vessels that form their all-important “niche”. The cleavage plane drawn during mitosis (complete with Matrix spin effect) quantifies the “polarity” of the division, or the orientation of the daughter cells relative to the nearest vasculature.

    E. Wait, M. Winter, C. Bjornsson, E. Kokovay, Y. Wang, S. Goderie, S. Temple and A. R. Cohen, Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences. BMC Bioinformatics, 15:328.. October 3, 2014.

    This movie shows mouse adult neural stem cells (NSCs) in the top panel, with embryonic NSCs in the bottom panel. The scale bars indicate 25µm. The lineage trees (right panel) show the development of the clone (family tree) from a single cell through terminal differentiation. Note the difference in size, motility and population dynamics.

    Winter, M., E. Wait, B. Roysam, S. Goderie, E. Kokovay, S. Temple, and A.R. Cohen,Vertebrate Neural Stem Cell Segmentation, Tracking and Lineaging with Validation and Editing. Nature Protocols, 2011. 6(12): p. 1942-52. ()

    This movie shows organelle transport along the axon (right panel) together with a kymograph (left panel). We are tracking the organelles directly on the image, and showing results on the kymograph. Notice how the kymograph, like the lineage tree above, is an effective dimensionality reduction technique for the image sequence data. Our goal here is to measure changes in organelle transport in neurodegenerative vs. wild type populations.

    Winter, M., C. Fang, G. Banker, B. Roysam, and A. R. Cohen, Axonal Transport Analysis Using Multitemporal Association Tracking. International Journal of Computational Biology and Drug Design 2012;5(1):35-48. ()

    In this final movie, cells 11 and 300 are rat retinal progenitor cells. Cell 300 will produce 1 photoreceptor neuron and 1 amacrine cell; cell 11 will produce 2 photoreceptor neurons. We have developed new segmentation (blue outlines) and tracking (red numbers) algorithms to extract the patterns of motion, morphology and inter-cellular assocation. Analyzing the segmentation and tracking results, our computational tools discovered behavioral differences among retinal progenitors that can accurately predict the type of daughter cells a retinal progenitor will produce, while the progenitor is alive inside the microscope.

    A.R. Cohen, F. Gomes, B. Roysam, and M. Cayouette, Computational prediction of neural progenitor cell fates. Nature Methods, 2010. 7(3): p. 213 – 218.


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