Shayenne
Shayenne
PROMISE College and Career Academy
Cambridge, MA
Mentors:
Anthony Fazio
PRISM Lab
Shayenne spent her summer working in high-throughput oncology, where automation allows scientists to test how large numbers of cancer models respond to potential drugs. Her project focused on improving one of the foundational steps in PRISM drug screening: generating the compound source plates used throughout an experiment.
Working with Stephanie, Shayenne investigated whether PRISM could transition from a workflow involving both the Hamilton STAR and Tecan D300e to a more consolidated process using the Hamilton STAR liquid-handling robot. The Hamilton offers important advantages for large-scale screening, including barcode scanning, compound tracking, error logging, and multiple available instruments for backup.
To validate the new approach, the students compared source plates produced manually, with the existing Tecan method, and with the Hamilton STAR. Those plates were copied using an Echo acoustic liquid handler, cancer cells were added, and the cells were allowed to respond to the compounds for five days. CellTiter-Glo then provided a luminescent measurement of cell viability, allowing the team to construct and compare drug dose-response curves.
Their results identified variability between source-plate generation methods, demonstrating that the Hamilton method is promising but not yet ready for production without further optimization.
Shayenne learned how an upstream technical choice in an automated workflow can influence downstream biological results and why new methods must be rigorously validated before they are used at scale.
Shayenne's work provided firsthand experience with the scale and precision required for modern cancer drug discovery. Just as importantly, the project showed how scientists validate new technology: a faster or more automated method is only an improvement if researchers can demonstrate that it produces dependable biological results.
Beyond learning a new automated screening workflow, Shayenne said BSSP taught her “to be bold and sure of myself in the things I know.” That confidence was especially relevant in a project where interpreting variability, questioning a new method, and recognizing when a workflow still needs optimization were all essential parts of doing the science well.
