Aayush Udupi
Research Mentor(s): Xiaoqian Wang
Department or Program: Chemical Engineering
Authors: Aayush Udupi, Xiaoqian Wang, Sasha Cai Lesher-Perez, PhD
Session: Session 2: 1:00pm-1:50pm
Poster: 53
Abstract
Cancer cells, characterized by uncontrolled proliferation, are often linked with mutations in genes that regulate the cell cycle. Due to these mutations, it is unclear whether cancer cells’ cell cycle respond to circadian (24 hour) rhythms of biomolecules, i.e., hormones and metabolites, which cycle in the blood and orchestrate healthy cells, tissues, and organ function in our body. This project aims to evaluate if we can use dynamic fluidic environments to apply a circadian rhythm of entraining biomolecules and synchronize the cell cycle of various cancer cell lines. To monitor the cell cycle, we have used lentiviral transduction of cancer cells with Fluorescent Ubiquitination-based Cell Cycle Indicators (PIP-FUCCI), which labels cells in different phases of the cell cycle, with different colors of fluorescent proteins, allowing for real-time visualization of cell cycle progression. Lentiviral transduction involves infecting cells with FUCCI lentivirus, verifying transduction efficiency through fluorescent microscopy and then optimizing the protocol to yield the highest efficiency of transduced cells. Thus far we have successfully transduced HT1080 cancer cells to express the PIP-FUCCI system and are actively applying our protocol to transduce other cell lines such as MDA-MB-231 and U2OS. Once reporter cell lines are prepared, the project seeks to evaluate cellular responses to different biomolecules rhythmic stimulation. Our initial target will be applying circadian rhythms of dexamethasone to assess if we can entrain the cell cycle of these reporter cells. Our hypothesis is that cyclic exposure to dexamethasone, a known cell cycle entrainment molecule, will induce cell cycle synchronization in cancer cells . This research will validate a fundamental protocol for entraining cancer cells in vitro and will provide baseline data for future studies we plan to develop which involve more complex dynamic fluidic environments within microfluidic devices. The insights gained through this work could enhance our understanding of how dynamic fluidic environments influence cancer’s response to specific entraining cues, in this case dexamethasone.
