Abstract
Daphnia are keystone species of freshwater habitats used as model organisms in ecology and evolutionary biology. Their small size, wide geographic distribution, and sensitivity to chemicals make them useful as environmental sentinels in regulatory toxicology and chemical risk assessment. Biomolecular (-omic) assessments of responses to chemical toxicity, which reveal detailed molecular signatures, become more powerful when correlated with other phenotypic outcomes (such as behavioral, physiological, or histopathological) for comparative validation and regulatory relevance. However, the lack of histopathology or tissue phenotype characterization of this species presently limits our ability to access cellular mechanisms of toxicity. Here, we address the central concept that interpreting aberrant tissue phenotypes requires a basic understanding of species normal microanatomy. We introduce the female and male Daphnia Histology Reference Atlas (DaHRA) for the baseline knowledge of Daphnia magna microanatomy. Additionally, we also included developmental stages of female Daphnia in this current atlas. This interactive web-based resource of adult Daphnia features overlaid vectorized demarcation of anatomical structures whose labels comply with an anatomical ontology created for this atlas. We demonstrate the potential utility of DaHRA for toxicological investigations by presenting aberrant phenotypes of acetaminophen-exposed D. magna. We envision DaHRA to facilitate the effort of integrating molecular and phenotypic data from the scientific community as we seek to understand how genes, chemicals, and environment interactions determine organismal phenotype.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version of manuscript has been revised to include the updates on the atlas: 1. images featuring various developmental stages of parthenogenetic female D. magna 2. images of a D. magna clone (UOB_LRV0_1) to demonstrate how this atlas can be expanded for the addition of new image datasets