Abstract and Presentation for International Work-Conference on Bioinformatics and Biomedical Engineering
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README.md

High-throughput, Scalable, Quantitative, Cellular Phenotyping using X-Ray Tomographic Microscopy

Authors

Kevin Mader (4Quant and ETH Zurich), Leah-Rae Donahue (The Jackson Laboratory), Ralph Müller (ETH Zurich), Marco Stampanoni (ETH Zurich, Paul Scherrer Institut)

Biography

Kevin Mader is a lecturer in the X-ray Microscopy Group within the Department for Information Technology and Electrical Engineering at ETH Zurich. His research focuses on turning big hairy 3D images into simple, robust, reproducible numbers without resorting to black boxes or magic. In particular, as part of several collaborations, he is currently working on automatically segmenting full animal zebrafish images, characterizing rheology in 3D flows, and measuring viral infection dynamics in cell lines.

Abstract

With improvements in rate and quality of deep sequencing, the bottleneck for many genetic studies has become phenotyping. The complexity of many biological systems makes even developing these phenotypes a challenging task. In particular cortical bone can contain 10s of thousands of osteocyte cells interconnected in a complicated network. Easily measurable ensemble phenotypes like average size and density describe only a small portion of the variation in the system. We demonstrate a new approach to high-throughput phenotyping using Synchrotron-based X-ray Tomographic Microscopy (SRXTM) combined with our custom 3D image processing pipeline known as TIPL. The cluster-based evaluation tool enables high-speed data exploration and hypothesis testing over millions of structures. With these tools, we compare different strains of mice and look for trends in millions of cells. The flexible infrastructure offers a full spectrum of shape, distribution, and connectivity metrics for cellular networks and can be adapted to a wide variety of new studies requiring high sample counts such as the drug-gene interactions. Full Text

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Imaging