Calendar

Please join Dr. Mia Markey as she moderates a panel with three current BME graduate students. Research presentations by Christian Jennings (Dr. Sapun Parekh), Tyer Jost (Dr. Amy Brock), and Hung-Yun Lu (Dr. Samantha Santacruz) will be followed by a Q&A session.

Abstracts

Chrisitan Jennings:

Eliminating refractory cancer cells that survive a multifaceted treatment regimen is a significant challenge that necessitates novel tools to identify cellular phenotypes that confer survival and greater metastatic potential. To address this need, we are developing novel instrumentation to measure system-level biophysical phenotypes of individual cancer cells. Broadband coherent anti-Stokes Raman scattering (BCARS) spectroscopy is a high-speed, label-free method for chemical fingerprinting of cells. Raman spectroscopy can identify cancer cells' metastatic capability based on the relative quantities of macromolecular classes (e.g., nucleic acids, lipids, and proteins). Additionally, real-time deformability cytometry (RT-DC) is a high-throughput method of measuring cellular mechanics. Therefore, we expect an instrument that combines the BCARS and RT-DC approaches in a microfluidic system to enable fast screening and sorting based on intrinsic cellular mechanical properties and biochemical composition. In this talk, I will briefly outline the development of a low-cost deformability cytometer. Furthermore, I will discuss the challenges associated with morphological and learned image-processing approaches to extract the deformability of individual cells. 

Tyler Jost:

Resistance to treatment in cancer remains a significant hurdle. This resistance can be attributed to the heterogeneity among tumor cells. While multiple therapies are often administered, little is known about how the ordering of treatments affects cellular response or the heterogeneity of cells within the tumor. Using cellular barcoding, we show that resistance to therapy comes from pre-existing resistant cells. We found that when barcoded BT474 cells were treated with varying orders of treatment that the abundance of each lineage was ­­largely conserved across replicates, particularly in the most abundant lineages. This was observed across each treatment type, suggesting that resistant cells were pre-existing and not induced by treatment. Single-cell RNA sequencing was also performed after each treatment to characterize the transcriptomic response of individual cells. Both clonal origin and RNA expression level were used to find clusters of similar lineages, which revealed four distinct clusters of sensitivity and resistance. Using this new modality of clustering gives a biologically rooted foundation for clustering while also allowing for future experiments to focus on lineages of interest. For the most drug-tolerant group of lineages, differential expression analysis identified multiple targetable. These initial findings suggest that these pre-existing resistant cells come from clusters of transcriptomically similar lineages. In contrast to treating entire populations, focusing on specific lineages could reduce heterogeneity to improve future treatments.