An optimized zebrafish obesogenic test protocol with an artificial intelligence-based analysis software for screening obesogens and anti-obesogens.

Obesity is defined as a disease in which abnormal excessive body fat accumulation causes adverse effects on health. One proposed contributing factor to the rise in obesity is the exposure to endocrine disruptors acting as obesogens. Semitransparent zebrafish larvae, with their well-developed white adipose tissue (WAT), offer a unique opportunity for studying the effects of toxicant chemicals and pharmaceuticals on adipocyte dynamics and whole-organism adiposity in a vertebrate model. The work presented here is a detailed optimized zebrafish obesogenic test (ZOT) protocol. The method allows to assess the effects of diet composition, drugs and environmental contaminants, acting as obesogens or anti-obesogens, alone or in combination, on WAT levels in zebrafish larvae. Zootechnical parameter guidelines, including larvae rearing conditions, feeding, and selection of larvae to be enrolled are provided. An optimized procedure for in vivo staining of adipocyte lipid droplets with Nile Red before and after exposure to compounds is provided to enhance reproducibility. Using suitable subcutaneous WAT locations, a rationally defined guide for wide-field fluorescence microscopy signal acquisition is proposed. The ZOT analysis software was developed to enable automated and efficient image data processing by using custom-trained supervised deep-learning models. The present ZOT protocol distinguishes intrinsic variability of the test method from the biological effect measured. It is the basis of a specific, sensitive, and robust quantitative in vivo assay for high-throughput screening of compounds and food content that influence adipocyte hyper/hypotrophy. As such, it provides relevant information for environmental as well as human risk and benefit assessments.