Development of an Undergraduate Cell Biology Laboratory to Assess Pigmentation and Cell Size in a Zebrafish Model of Uveal Melanoma.
Andrea M HenlePublished in: Zebrafish (2024)
This study outlines a 2-week laboratory module for an authentic cell biology undergraduate research experience that uses zebrafish ( Danio rerio ), a popular model organism for research. Previous research has indicated that course-based undergraduate research experiences such as this one increase student confidence, active learning, and retention. During this research experience, students investigate variations in pigmentation in the caudal fins of wild type (WT) and transgenic fish [ Tg(mitfa:GNAQ Q209L )]. The transgenic fish express a hyperactive Gα protein, GNAQ Q209L , under the melanocyte-specific mitfa promoter, offering insights into uveal melanoma, a common eye cancer. Students specifically analyze the black pigmented cells, melanophores, within the caudal fin. We determined that the transgenic zebrafish have increased pigmentation in their caudal fins, but smaller melanophores. These results suggest there are more melanophores in the Tg(mitfa:GNAQ Q209L ) fish compared to the WT. Future undergraduate research could investigate these cellular differences. This research experience imparts microscopy and image analysis skills and instills the ability to grapple with large datasets, statistical tests, and data interpretation in alignment with biology education principles. Post-laboratory surveys reveal students attain confidence in the above skills and in handling animals, along with a deeper appreciation for model organism research and its relevance to cancer cell biology.
Keyphrases
- medical students
- single cell
- medical education
- nursing students
- cell therapy
- high school
- healthcare
- wild type
- dna methylation
- induced apoptosis
- stem cells
- randomized controlled trial
- gene expression
- squamous cell carcinoma
- clinical trial
- quality improvement
- electronic health record
- genome wide
- signaling pathway
- big data
- endoplasmic reticulum stress
- deep learning
- pi k akt