Dataset of chicken-embryo blood cells exposed to quercetin, methyl methanesulfonate, or cadmium chloride.

Toxicological analysis of the effects of natural compounds is frequently mandated to assess their safety. In addition to more simple in vitro cellular systems, more complex biological systems can be used to evaluate toxicity. This dataset is comprised of bright-field microscopy images of chicken-embryo blood cells, a complex biological model that recapitulates several features found in human organisms, including circulation in blood stream and biodistribution to different organs. In the presented collection of blood smear images, cells were exposed to the flavonoid quercetin, and the two mutagens methyl methanesulfonate (MMS) and cadmium chloride (CdCl2). In ovo models offer a unique opportunity to investigate the effects of various substances, pathogens, or cancer treatments on developing embryos, providing valuable insights into potential risks and therapeutic strategies. In toxicology, in ovo models allow for early detection of harmful compounds and their impact on embryonic development, aiding in the assessment of environmental hazards. In immunology, these models offer a controlled system to explore the developing immune responses and the interaction between pathogens and host defenses. Additionally, in ovo models are instrumental in oncology research as they enable the study of tumor development and response to therapies in a dynamic, rapidly developing environment. Thus, these versatile models play a crucial role in advancing our understanding of complex biological processes and guiding the development of safer therapeutics and interventions. The data presented here can aid in understanding the potential toxic effects of these substances on hematopoiesis and the overall health of the developing organism. Moreover, the large dataset of blood smear images can serve as a resource for training machine learning algorithms to automatically detect and classify blood cells, provided that specific optimized conditions such as image magnification and background light are maintained for comparison. This can lead to the development of automated tools for blood cell analysis, which can be useful in research. Moreover, the data is amenable to the use as teaching and learning resource for histology and developmental biology.