Automated prior knowledge-based quantification of neuronal patterns in the spinal cord of zebrafish

摘要

Motivation: To reliably assess the effects of unknown chemicals on the development of fluorescently labeled sensory-, moto-and interneuron populations in the spinal cord of zebrafish, automated data analysis is essential. Results: For the evaluation of a high-throughput screen of a large chemical library, we developed a new method for the automated extraction of quantitative information from green fluorescent protein (eGFP) and red fluorescent protein (RFP) labeled spinal cord neurons in double-transgenic zebrafish embryos. The methodology comprises region of interest detection, intensity profiling with reference comparison and neuron distribution histograms. All methods were validated on a manually evaluated pilot study using a Notch inhibitor dose-response experiment. The automated evaluation showed superior performance to manual investigation regarding time consumption, information detail and reproducibility.