Automated classification of estrous stage in rodents using deep learning.

The rodent estrous cycle modulates a range of biological functions, from gene expression to behavior. The cycle is typically divided into four stages, each characterized by distinct hormone concentration profiles. Given the difficulty of repeatedly sampling plasma steroid hormones from rodents, the primary method for classifying estrous stage is by identifying vaginal epithelial cell types. However, manual classification of epithelial cell samples is time-intensive and variable, even amongst expert investigators. Here, we use a deep learning approach to achieve classification accuracy at expert level. Due to the heterogeneity and breadth of our input dataset, our deep learning approach ("EstrousNet") is highly generalizable across rodent species, stains, and subjects. The EstrousNet algorithm exploits the temporal dimension of the hormonal cycle by fitting classifications to an archetypal cycle, highlighting possible misclassifications and flagging anestrus phases (e.g., pseudopregnancy). EstrousNet allows for rapid estrous cycle staging, improving the ability of investigators to consider endocrine state in their rodent studies.

Long-term transverse imaging of the hippocampus with glass microperiscopes.

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

High-fat food biases hypothalamic and mesolimbic expression of consummatory drives.

Maintaining healthy body weight is increasingly difficult in our obesogenic environment. Dieting efforts are often overpowered by the internal drive to consume energy-dense foods. Although the selection of calorically rich substrates over healthier options is identifiable across species, the mechanisms behind this choice remain poorly understood. Using a passive devaluation paradigm, we found that exposure to high-fat diet (HFD) suppresses the intake of nutritionally balanced standard chow diet (SD) irrespective of age, sex, body mass accrual and functional leptin or melanocortin-4 receptor signaling. Longitudinal recordings revealed that this SD devaluation and subsequent shift toward HFD consumption is encoded at the level of hypothalamic agouti-related peptide neurons and mesolimbic dopamine signaling. Prior HFD consumption vastly diminished the capacity of SD to alleviate the negative valence associated with hunger and the rewarding properties of food discovery even after periods of HFD abstinence. These data reveal a neural basis behind the hardships of dieting.

Bringing immersive science to undergraduate laboratory courses using CRISPR gene knockouts in frogs and butterflies.

The use of CRISPR/Cas9 for gene editing offers new opportunities for biology students to perform genuine research exploring the gene-to-phenotype relationship. It is important to introduce the next generation of scientists, health practitioners and other members of society to the technical and ethical aspects of gene editing. Here, we share our experience leading hands-on undergraduate laboratory classes, where students formulate hypotheses regarding the roles of candidate genes involved in development, perform loss-of-function experiments using programmable nucleases and analyze the phenotypic effects of mosaic mutant animals. This is enabled by the use of the amphibian Xenopus laevis and the butterfly Vanessa cardui, two organisms that reliably yield hundreds of large and freshly fertilized eggs in a scalable manner. Frogs and butterflies also present opportunities to teach key biological concepts about gene regulation and development. To complement these practical aspects, we describe learning activities aimed at equipping students with a broad understanding of genome editing techniques, their application in fundamental and translational research, and the bioethical challenges they raise. Overall, our work supports the introduction of CRISPR technology into undergraduate classrooms and, when coupled with classroom undergraduate research experiences, enables hypothesis-driven research by undergraduates.