Mr. Andrej Cedilnik Profile

Andrej Cedilnik

Senior System Software Engineer at TiVo Inc

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Proceedings Article | 28 January 2008 Paper

Integration of information and volume visualization for analysis of cell lineage and gene expression during embryogenesis

Andrej Cedilnik, Jeffrey Baumes, Luis Ibanez, Sean Megason, Brian Wylie

Proceedings Volume 6809, 68090J (2008) https://doi.org/10.1117/12.768014

KEYWORDS: Visualization, Organisms, Information visualization, Confocal microscopy, Volume visualization, Image segmentation, Visual analytics, Microscopy, Data processing, Volume rendering

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Dramatic technological advances in the field of genomics have made it possible to sequence the complete genomes of many different organisms. With this overwhelming amount of data at hand, biologists are now confronted with the challenge of understanding the function of the many different elements of the genome. One of the best places to start gaining insight on the mechanisms by which the genome controls an organism is the study of embryogenesis. There are multiple and inter-related layers of information that must be established in order to understand how the genome controls the formation of an organism. One is cell lineage which describes how patterns of cell division give rise to different parts of an organism. Another is gene expression which describes when and where different genes are turned on. Both of these data types can now be acquired using fluorescent laser-scanning (confocal or 2-photon) microscopy of embryos tagged with fluorescent proteins to generate 3D movies of developing embryos. However, analyzing the wealth of resulting images requires tools capable of interactively visualizing several different types of information as well as being scalable to terabytes of data. This paper describes how the combination of existing large data volume visualization and the new Titan information visualization framework of the Visualization Toolkit (VTK) can be applied to the problem of studying the cell lineage of an organism. In particular, by linking the visualization of spatial and temporal gene expression data with novel ways of visualizing cell lineage data, users can study how the genome regulates different aspects of embryonic development.

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