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The zebrafish has emerged as one of the most commonly used organisms in scientific research. They are genetically malleable, have transparent embryonic bodies, develop rapidly and are the most complex vertebrate that can be used for large-scale screening –a combination that makes zebrafish a relatively easy-to-screen model for human disease. has upgraded the zebrafish model’s status to really-fast-and-easy-to-screen.

A study, led by , an Associate Member at the Ó³»­´«Ã½, published in the July 18 issue of describes a pipeline that rapidly screens live zebrafish for phenotypic traits. Using fluidics, the pipeline pumps live larvae from a reservoir through a detection area to an imaging platform, where animals are positioned and rotated in three-dimensions for subsequent fluorescent and confocal microscopy.

By automating zebrafish imaging, human labor and error is effectively removed from the screening process. The researchers showed that each zebrafish could be detected, placed and imaged in less than 20 seconds, whereas similar assays performed manually would require approximately 10 minutes per larvae.

The pipeline specifically and accurately detected abnormal neural growth in the zebrafish, demonstrating the platform’s ability to image phenotypes at the single-cell level. Yanik’s group showed that the pipeline could accurately track the regeneration of the lateral-neurons without harming the animals traveling through the system, effectively demonstrating that time-course experiments can be completed with their pipeline.

This technology can be used to rapidly study many complex processes in vivo including organ regeneration, cardiovascular, immune, endocrine and nervous system functions, pathogenesis, cancer development and neuronal degeneration.  Prior to zebrafish, Yanik’s group developed a system that rapidly screens and sorts Caenorhabditis elegans –a nematode often used in molecular and developmental biology. Yanik's imaging pipelines will likely enhance our ability to screen chemicals for desired effects, test compounds for toxicity, and understand the inherit function of gene products.

With the growing number of studies associating gene variants to human diseases, the biomedical community will need to examine the function of these genes, thereby establishing a causal relationship between sub-cellular pathways and disease. By cataloguing and coupling the genes identified as disease associated in human studies with functions identified using Yanik’s pipeline, scientists may understand the basis of common human diseases faster than previously realized.

Development of the new technology was partially funded by a National Institutes of Health Director’s Innovator Award, the Packard Award in Science and Engineering, an Alfred Sloan Award in Neuroscience, and a SPARC grant from the Ó³»­´«Ã½.  Mehmet Fatih Yanik's primary appointment is as an Associate Professor in MIT’s Electrical Engineering and Computer Science Department.